Distributed shared memory shyam soni
Download as ODP, PDF6 likes9,002 views
Distributed shared memory (DSM) allows nodes in a cluster to access shared memory across the cluster in addition to each node's private memory. DSM uses a software memory manager on each node to map local memory into a virtual shared memory space. It consists of nodes connected by high-speed communication and each node contains components associated with the DSM system. Algorithms for implementing DSM deal with distributing shared data across nodes to minimize access latency while maintaining data coherence with minimal overhead.
1 of 10
Downloaded 323 times
Ad
Recommended
Distributed Shared Memory
Distributed Shared MemoryPrakhar Rastogi This document discusses distributed shared memory (DSM) systems. It defines DSM as providing a logical abstraction of shared memory across interconnected nodes with distributed physical memories. It describes different types of DSM implementations, advantages of DSM, and various hardware architectures like on-chip memory, bus-based multiprocessors, and switched multiprocessors. It also covers DSM design issues such as granularity of sharing, data structures, consistency models like strict, sequential, and weak consistency, and coherence protocols.
File models and file accessing models
File models and file accessing modelsishmecse13 This document discusses different file models and methods for accessing files. It describes unstructured and structured file models, as well as mutable and immutable files. It also covers remote file access using remote service and data caching models. Finally, it discusses different units of data transfer for file access, including file-level, block-level, byte-level, and record-level transfer models.
Distributed shred memory architecture
Distributed shred memory architectureMaulik Togadiya Distributed shared memory (DSM) is a memory architecture where physically separate memories can be addressed as a single logical address space. In a DSM system, data moves between nodes' main and secondary memories when a process accesses shared data. Each node has a memory mapping manager that maps the shared virtual memory to local physical memory. DSM provides advantages like shielding programmers from message passing, lower cost than multiprocessors, and large virtual address spaces, but disadvantages include potential performance penalties from remote data access and lack of programmer control over messaging.
6.distributed shared memory
6.distributed shared memoryGd Goenka University A Distributed Shared Memory (DSM) system provides a logical abstraction of shared memory built using interconnected nodes with distributed physical memories. There are hardware, software, and hybrid DSM approaches. DSM offers simple abstraction, improved portability, potential performance gains, large unified memory space, and better performance than message passing in some applications. Consistency protocols ensure shared data coherency across distributed memories according to the memory consistency model.
Distributed Transactions(flat and nested) and Atomic Commit Protocols
Distributed Transactions(flat and nested) and Atomic Commit ProtocolsSachin Chauhan Its a Distributed Transaction in distributed system with atomic commit protocol.as well as flat and nested transaction.
Introduction to Distributed System
Introduction to Distributed SystemSunita Sahu The document provides an introduction to distributed systems, defining them as a collection of independent computers that communicate over a network to act as a single coherent system. It discusses the motivation for and characteristics of distributed systems, including concurrency, lack of a global clock, and independence of failures. Architectural categories of distributed systems include tightly coupled and loosely coupled, with examples given of different types of distributed systems such as database management systems, ATM networks, and the internet.
8. mutual exclusion in Distributed Operating Systems
8. mutual exclusion in Distributed Operating SystemsDr Sandeep Kumar Poonia The document discusses various algorithms for achieving distributed mutual exclusion and process synchronization in distributed systems. It covers centralized, token ring, Ricart-Agrawala, Lamport, and decentralized algorithms. It also discusses election algorithms for selecting a coordinator process, including the Bully algorithm. The key techniques discussed are using logical clocks, message passing, and quorums to achieve mutual exclusion without a single point of failure.
Market oriented Cloud Computing
Market oriented Cloud ComputingJithin Parakka This document discusses the vision, hype and reality of delivering IT services as computing utilities. It outlines the need for a market oriented cloud architecture to regulate supply and demand of cloud resources. Emerging cloud platforms like Amazon EC2, Google App Engine, Microsoft Live Mesh and Sun Grid are presented. The limitations of present cloud service providers are discussed. Finally, the concept of a Global Cloud Exchange is proposed to address these limitations through features like a market directory, banking system, brokers and price setting mechanisms.
Chap 4
Chap 4suks_87 The document discusses distributed shared memory (DSM) in three paragraphs:
1) DSM provides a virtual shared address space across loosely coupled nodes through software memory mapping. Each node caches blocks of shared memory locally to reduce network latency.
2) When a process accesses a non-local memory block, the memory manager handles the request by migrating the block over the network if needed. Cached blocks avoid repeated network transfers.
3) Key design issues include block granularity, consistency models, data location protocols, and handling replication and migration of blocks. Different protocols are used depending on whether blocks are replicated, migrating, or both.
distributed shared memory
distributed shared memoryAshish Kumar Distributed shared memory (DSM) provides processes with a shared address space across distributed memory systems. DSM exists only virtually through primitives like read and write operations. It gives the illusion of physically shared memory while allowing loosely coupled distributed systems to share memory. DSM refers to applying this shared memory paradigm using distributed memory systems connected by a communication network. Each node has CPUs, memory, and blocks of shared memory can be cached locally but migrated on demand between nodes to maintain consistency.
Google App Engine
Google App EngineSoftware Park Thailand Google App Engine
by
Assoc.Prof. Dr.Thanachart Numnonda
Asst.Prof. Thanisa Kruawaisayawan
Mini Master of Java Technology
KMITL
July 2012
Fault tolerance in distributed systems
Fault tolerance in distributed systemssumitjain2013 Fault tolerance is important for distributed systems to continue functioning in the event of partial failures. There are several phases to achieving fault tolerance: fault detection, diagnosis, evidence generation, assessment, and recovery. Common techniques include replication, where multiple copies of data are stored at different sites to increase availability if one site fails, and check pointing, where a system's state is periodically saved to stable storage so the system can be restored to a previous consistent state if a failure occurs. Both techniques have limitations around managing consistency with replication and overhead from checkpointing communications and storage requirements.
Terminologies Used In Big data Environments,G.Sumithra,II-M.sc(computer scien...
Terminologies Used In Big data Environments,G.Sumithra,II-M.sc(computer scien...sumithragunasekaran Terminologies and its types
In-Memory Analytics
In-Database processing
Symmetric Multiprocessor system(SMP)
Massively Parallel Processing
Difference Between Parallel and Distributed Systems
Shared Nothing Architecture
Advantages of a “ shared nothing Architecture”
CAP Theorem Explained
CAP Theorem
VTU 6th Sem Elective CSE - Module 3 cloud computing
VTU 6th Sem Elective CSE - Module 3 cloud computingSachin Gowda The document discusses parallel and distributed computing concepts in Aneka including multiprocessing, multithreading, task-based programming, and parameter sweep applications. It describes key aspects of implementing parallel applications in Aneka such as defining tasks, managing task execution, file handling, and tools for developing parameter sweep jobs. The document also provides an overview of how workflow managers can interface with Aneka.
Foult Tolerence In Distributed System
Foult Tolerence In Distributed SystemRajan Kumar Distributed systems use multiple autonomous computers that communicate via messages to improve processing throughput, allow for CPU specialization, and provide fault tolerance. Faults in distributed systems can include data corruption, hanging processes, misleading return values, hardware/software/network outages, and resource overcommitment. To provide fault tolerance, processes are replicated across multiple computers so the system can continue functioning even if some processes fail. There are different types of faults like crash faults, omission faults, and Byzantine faults. Recovery from failures can use backward or forward recovery approaches.
Grid protocol architecture
Grid protocol architecturePooja Dixit The document discusses the five layers of the grid protocol architecture: 1) the fabric layer which provides access to different resource types, 2) the connectivity layer which defines core communication and authentication protocols, 3) the resource layer which defines protocols for publishing, discovering, and accessing individual resources, 4) the collective layer which captures interactions across collections of resources through directory services, and 5) the application layer which comprises user applications built on top of the lower layers and operate in virtual organization environments.
Distributed shared memory ch 5
Distributed shared memory ch 5Alagappa Government Arts College, Karaikudi The document discusses distributed shared memory (DSM) which provides a shared memory abstraction for loosely coupled distributed systems. DSM allows processes on different nodes in a distributed system to access shared memory as if it were a single logical memory. It implements this shared memory space through caching blocks of data in local memories and migrating blocks between nodes when needed to service memory requests. Key aspects of DSM systems covered include memory coherence protocols to ensure data consistency and techniques for managing caching and data migration between distributed nodes.
Timestamp based protocol
Timestamp based protocolVincent Chu The document discusses timestamp-based protocols for ensuring serializability in concurrent transactions. Each transaction is assigned a unique timestamp. Transactions are executed in timestamp order to prevent conflicts between read and write operations. For each data item, the protocol maintains a write-timestamp of the most recent write and a read-timestamp of the most recent read. Transactions with later timestamps cannot read or write values overwritten by earlier transactions. While this guarantees serializability, it can lead to cascading rollbacks if transactions abort.
The CAP Theorem 
The CAP Theorem Aleksandar Bradic The CAP Theorem : Brewer's Conjecture and the Feasibility of Consistent, Available, Partition-Tolerant Web Services
CLOUD COMPUTING UNIT - 3.pptx
CLOUD COMPUTING UNIT - 3.pptxVivekKumar898803 This document discusses cloud computing architecture and management. It covers cloud architecture including the 4 layers - user/client layer, network layer, cloud management layer, and hardware resource layer. It also discusses anatomy of the cloud including the 5 core components - application, platform, infrastructure, virtualization, and physical hardware. Additionally, it covers network connectivity in cloud computing including public cloud access networking and the need for secure and efficient routing. Managing the cloud infrastructure, applications, and migrating applications to the cloud are also summarized.
Distributed design alternatives
Distributed design alternativesPooja Dixit The document discusses different distribution design alternatives for tables in a distributed database management system (DDBMS), including non-replicated and non-fragmented, fully replicated, partially replicated, fragmented, and mixed. It describes each alternative and discusses when each would be most suitable. The document also covers data replication, advantages and disadvantages of replication, and different replication techniques. Finally, it discusses fragmentation, the different types of fragmentation, and advantages and disadvantages of fragmentation.
Distributed Mutual Exclusion and Distributed Deadlock Detection
Distributed Mutual Exclusion and Distributed Deadlock DetectionSHIKHA GAUTAM This document summarizes key concepts related to distributed mutual exclusion and distributed deadlock detection. It discusses classification of distributed mutual exclusion algorithms into token-based and non-token-based approaches. For distributed mutual exclusion, it describes Lamport's algorithm, Ricart-Agrawala algorithm, Maekawa's quorum-based algorithm, and Suzuki-Kasami's token-based broadcast algorithm. It also discusses requirements for mutual exclusion such as freedom from deadlock and starvation. For distributed deadlock detection, it mentions the system model and types of deadlocks as well as approaches for prevention, avoidance, detection, and resolution of deadlocks.
Seven step model of migration into the cloud
Seven step model of migration into the cloudRaj Raj The document describes a seven-step model for migrating applications to the cloud: 1) conduct assessments, 2) isolate dependencies, 3) map messaging and environment, 4) re-architect lost functionalities, 5) leverage cloud features, 6) test the migration, and 7) iterate and optimize. The model involves assessing costs and benefits, isolating on-premise dependencies, mapping components, redesigning for the cloud, leveraging cloud features, extensive testing, and iterating to optimize and ensure a robust migration. Key risks are identified in testing and addressed through optimization iterations.
Distributed Shared Memory Systems
Distributed Shared Memory SystemsArush Nagpal Distributed shared memory (DSM) systems implement shared memory in distributed systems without physical shared memory. DSM provides a virtual shared address space across nodes. This overcomes high communication costs. Memory mapping managers map local memory to the shared space. DSM aims to build larger, less expensive parallel machines and simplify programming. It supports shared memory models like page, object, and variable-based. Issues include memory coherence, data location, replacement strategy, and thrashing. Algorithms like central server, migration, read replication, and full replication manage data distribution and consistency.
Naming in Distributed System
Naming in Distributed SystemMNM Jain Engineering College The document discusses naming in distributed systems. It covers desirable features of naming systems like location transparency and location independence. It differentiates between human-oriented and system-oriented names. It also discusses name spaces, name servers, name resolution including recursive and iterative approaches, and name caching.
management of distributed transactions
management of distributed transactionsNilu Desai 1. Distributed transaction managers ensure transactions have ACID properties through implementing the 2-phase commit protocol for reliability, 2-phase locking for concurrency control, and timeouts for deadlock detection on top of local transaction managers.
2. The 2-phase commit protocol guarantees subtransactions of the same transaction will all commit or abort despite failures, while 2-phase locking requires subtransactions acquire locks in a growing phase and release in a shrinking phase.
3. Timeouts are used to detect and abort transactions potentially experiencing a distributed deadlock.
Distributed Systems Naming
Distributed Systems NamingAhmed Magdy Ezzeldin, MSc. This document discusses structured naming in distributed systems. It describes name spaces as labeled, directed graphs with leaf nodes representing named entities and directory nodes linking to other nodes. Name resolution starts at the root node and follows the directory tables at each node until reaching the target node. Name spaces can be hierarchical trees or directed acyclic graphs. The Domain Name System (DNS) implements a global, hierarchical name space as a rooted tree with domain names representing subtrees.
Current Trends in HPC
Current Trends in HPCPutchong Uthayopas This document discusses current trends in high performance computing. It begins with an introduction to high performance computing and its applications in science, engineering, business analysis, and more. It then discusses why high performance computing is needed due to changes in scientific discovery, the need to solve larger problems, and modern business needs. The document also discusses the top 500 supercomputers in the world and provides examples of some of the most powerful systems. It then covers performance development trends and challenges in increasing processor speeds. The rest of the document discusses parallel computing approaches using multi-core and many-core architectures, as well as cluster, grid, and cloud computing models for high performance.
Symmetric Multi Processor Multiprocessors
Symmetric Multi Processor MultiprocessorsSaad Tanvir Table of contents:
1 Symmetric Multi Processing (SMP) 3
1.1 Multiprocessing and Multiprocessor 3
1.2 Definition: 3
1.3 Characteristics of SMP 3
2 Advantage of SMP vs. Single Processor 4
3 Issues in Operating System of SMP 4
3.1 Simultaneous concurrent processes or threads 5
3.2 Scheduling 5
3.3 Synchronization 5
3.4 Memory management 5
3.5 Reliability and fault tolerance 5
process management
process managementAshish Kumar The document discusses process management in distributed operating systems, focusing on process migration which involves moving a running process from one node to another to optimize resource utilization, and covering key aspects like process freezing, address space transfer, message forwarding, and maintaining communication between related processes migrated to different nodes. Efficient process migration requires mechanisms that provide transparency, minimize interference and overhead, and handle heterogeneity between system nodes.
Ad
More Related Content
What's hot (20)
Chap 4
Chap 4suks_87 The document discusses distributed shared memory (DSM) in three paragraphs:
1) DSM provides a virtual shared address space across loosely coupled nodes through software memory mapping. Each node caches blocks of shared memory locally to reduce network latency.
2) When a process accesses a non-local memory block, the memory manager handles the request by migrating the block over the network if needed. Cached blocks avoid repeated network transfers.
3) Key design issues include block granularity, consistency models, data location protocols, and handling replication and migration of blocks. Different protocols are used depending on whether blocks are replicated, migrating, or both.
distributed shared memory
distributed shared memoryAshish Kumar Distributed shared memory (DSM) provides processes with a shared address space across distributed memory systems. DSM exists only virtually through primitives like read and write operations. It gives the illusion of physically shared memory while allowing loosely coupled distributed systems to share memory. DSM refers to applying this shared memory paradigm using distributed memory systems connected by a communication network. Each node has CPUs, memory, and blocks of shared memory can be cached locally but migrated on demand between nodes to maintain consistency.
Google App Engine
Google App EngineSoftware Park Thailand Google App Engine
by
Assoc.Prof. Dr.Thanachart Numnonda
Asst.Prof. Thanisa Kruawaisayawan
Mini Master of Java Technology
KMITL
July 2012
Fault tolerance in distributed systems
Fault tolerance in distributed systemssumitjain2013 Fault tolerance is important for distributed systems to continue functioning in the event of partial failures. There are several phases to achieving fault tolerance: fault detection, diagnosis, evidence generation, assessment, and recovery. Common techniques include replication, where multiple copies of data are stored at different sites to increase availability if one site fails, and check pointing, where a system's state is periodically saved to stable storage so the system can be restored to a previous consistent state if a failure occurs. Both techniques have limitations around managing consistency with replication and overhead from checkpointing communications and storage requirements.
Terminologies Used In Big data Environments,G.Sumithra,II-M.sc(computer scien...
Terminologies Used In Big data Environments,G.Sumithra,II-M.sc(computer scien...sumithragunasekaran Terminologies and its types
In-Memory Analytics
In-Database processing
Symmetric Multiprocessor system(SMP)
Massively Parallel Processing
Difference Between Parallel and Distributed Systems
Shared Nothing Architecture
Advantages of a “ shared nothing Architecture”
CAP Theorem Explained
CAP Theorem
VTU 6th Sem Elective CSE - Module 3 cloud computing
VTU 6th Sem Elective CSE - Module 3 cloud computingSachin Gowda The document discusses parallel and distributed computing concepts in Aneka including multiprocessing, multithreading, task-based programming, and parameter sweep applications. It describes key aspects of implementing parallel applications in Aneka such as defining tasks, managing task execution, file handling, and tools for developing parameter sweep jobs. The document also provides an overview of how workflow managers can interface with Aneka.
Foult Tolerence In Distributed System
Foult Tolerence In Distributed SystemRajan Kumar Distributed systems use multiple autonomous computers that communicate via messages to improve processing throughput, allow for CPU specialization, and provide fault tolerance. Faults in distributed systems can include data corruption, hanging processes, misleading return values, hardware/software/network outages, and resource overcommitment. To provide fault tolerance, processes are replicated across multiple computers so the system can continue functioning even if some processes fail. There are different types of faults like crash faults, omission faults, and Byzantine faults. Recovery from failures can use backward or forward recovery approaches.
Grid protocol architecture
Grid protocol architecturePooja Dixit The document discusses the five layers of the grid protocol architecture: 1) the fabric layer which provides access to different resource types, 2) the connectivity layer which defines core communication and authentication protocols, 3) the resource layer which defines protocols for publishing, discovering, and accessing individual resources, 4) the collective layer which captures interactions across collections of resources through directory services, and 5) the application layer which comprises user applications built on top of the lower layers and operate in virtual organization environments.
Distributed shared memory ch 5
Distributed shared memory ch 5Alagappa Government Arts College, Karaikudi The document discusses distributed shared memory (DSM) which provides a shared memory abstraction for loosely coupled distributed systems. DSM allows processes on different nodes in a distributed system to access shared memory as if it were a single logical memory. It implements this shared memory space through caching blocks of data in local memories and migrating blocks between nodes when needed to service memory requests. Key aspects of DSM systems covered include memory coherence protocols to ensure data consistency and techniques for managing caching and data migration between distributed nodes.
Timestamp based protocol
Timestamp based protocolVincent Chu The document discusses timestamp-based protocols for ensuring serializability in concurrent transactions. Each transaction is assigned a unique timestamp. Transactions are executed in timestamp order to prevent conflicts between read and write operations. For each data item, the protocol maintains a write-timestamp of the most recent write and a read-timestamp of the most recent read. Transactions with later timestamps cannot read or write values overwritten by earlier transactions. While this guarantees serializability, it can lead to cascading rollbacks if transactions abort.
The CAP Theorem
The CAP Theorem Aleksandar Bradic The CAP Theorem : Brewer's Conjecture and the Feasibility of Consistent, Available, Partition-Tolerant Web Services
CLOUD COMPUTING UNIT - 3.pptx
CLOUD COMPUTING UNIT - 3.pptxVivekKumar898803 This document discusses cloud computing architecture and management. It covers cloud architecture including the 4 layers - user/client layer, network layer, cloud management layer, and hardware resource layer. It also discusses anatomy of the cloud including the 5 core components - application, platform, infrastructure, virtualization, and physical hardware. Additionally, it covers network connectivity in cloud computing including public cloud access networking and the need for secure and efficient routing. Managing the cloud infrastructure, applications, and migrating applications to the cloud are also summarized.
Distributed design alternatives
Distributed design alternativesPooja Dixit The document discusses different distribution design alternatives for tables in a distributed database management system (DDBMS), including non-replicated and non-fragmented, fully replicated, partially replicated, fragmented, and mixed. It describes each alternative and discusses when each would be most suitable. The document also covers data replication, advantages and disadvantages of replication, and different replication techniques. Finally, it discusses fragmentation, the different types of fragmentation, and advantages and disadvantages of fragmentation.
Distributed Mutual Exclusion and Distributed Deadlock Detection
Distributed Mutual Exclusion and Distributed Deadlock DetectionSHIKHA GAUTAM This document summarizes key concepts related to distributed mutual exclusion and distributed deadlock detection. It discusses classification of distributed mutual exclusion algorithms into token-based and non-token-based approaches. For distributed mutual exclusion, it describes Lamport's algorithm, Ricart-Agrawala algorithm, Maekawa's quorum-based algorithm, and Suzuki-Kasami's token-based broadcast algorithm. It also discusses requirements for mutual exclusion such as freedom from deadlock and starvation. For distributed deadlock detection, it mentions the system model and types of deadlocks as well as approaches for prevention, avoidance, detection, and resolution of deadlocks.
Seven step model of migration into the cloud
Seven step model of migration into the cloudRaj Raj The document describes a seven-step model for migrating applications to the cloud: 1) conduct assessments, 2) isolate dependencies, 3) map messaging and environment, 4) re-architect lost functionalities, 5) leverage cloud features, 6) test the migration, and 7) iterate and optimize. The model involves assessing costs and benefits, isolating on-premise dependencies, mapping components, redesigning for the cloud, leveraging cloud features, extensive testing, and iterating to optimize and ensure a robust migration. Key risks are identified in testing and addressed through optimization iterations.
Distributed Shared Memory Systems
Distributed Shared Memory SystemsArush Nagpal Distributed shared memory (DSM) systems implement shared memory in distributed systems without physical shared memory. DSM provides a virtual shared address space across nodes. This overcomes high communication costs. Memory mapping managers map local memory to the shared space. DSM aims to build larger, less expensive parallel machines and simplify programming. It supports shared memory models like page, object, and variable-based. Issues include memory coherence, data location, replacement strategy, and thrashing. Algorithms like central server, migration, read replication, and full replication manage data distribution and consistency.
Naming in Distributed System
Naming in Distributed SystemMNM Jain Engineering College The document discusses naming in distributed systems. It covers desirable features of naming systems like location transparency and location independence. It differentiates between human-oriented and system-oriented names. It also discusses name spaces, name servers, name resolution including recursive and iterative approaches, and name caching.
management of distributed transactions
management of distributed transactionsNilu Desai 1. Distributed transaction managers ensure transactions have ACID properties through implementing the 2-phase commit protocol for reliability, 2-phase locking for concurrency control, and timeouts for deadlock detection on top of local transaction managers.
2. The 2-phase commit protocol guarantees subtransactions of the same transaction will all commit or abort despite failures, while 2-phase locking requires subtransactions acquire locks in a growing phase and release in a shrinking phase.
3. Timeouts are used to detect and abort transactions potentially experiencing a distributed deadlock.
Distributed Systems Naming
Distributed Systems NamingAhmed Magdy Ezzeldin, MSc. This document discusses structured naming in distributed systems. It describes name spaces as labeled, directed graphs with leaf nodes representing named entities and directory nodes linking to other nodes. Name resolution starts at the root node and follows the directory tables at each node until reaching the target node. Name spaces can be hierarchical trees or directed acyclic graphs. The Domain Name System (DNS) implements a global, hierarchical name space as a rooted tree with domain names representing subtrees.
Current Trends in HPC
Current Trends in HPCPutchong Uthayopas This document discusses current trends in high performance computing. It begins with an introduction to high performance computing and its applications in science, engineering, business analysis, and more. It then discusses why high performance computing is needed due to changes in scientific discovery, the need to solve larger problems, and modern business needs. The document also discusses the top 500 supercomputers in the world and provides examples of some of the most powerful systems. It then covers performance development trends and challenges in increasing processor speeds. The rest of the document discusses parallel computing approaches using multi-core and many-core architectures, as well as cluster, grid, and cloud computing models for high performance.
Viewers also liked (18)
Symmetric Multi Processor Multiprocessors
Symmetric Multi Processor MultiprocessorsSaad Tanvir Table of contents:
1 Symmetric Multi Processing (SMP) 3
1.1 Multiprocessing and Multiprocessor 3
1.2 Definition: 3
1.3 Characteristics of SMP 3
2 Advantage of SMP vs. Single Processor 4
3 Issues in Operating System of SMP 4
3.1 Simultaneous concurrent processes or threads 5
3.2 Scheduling 5
3.3 Synchronization 5
3.4 Memory management 5
3.5 Reliability and fault tolerance 5
process management
process managementAshish Kumar The document discusses process management in distributed operating systems, focusing on process migration which involves moving a running process from one node to another to optimize resource utilization, and covering key aspects like process freezing, address space transfer, message forwarding, and maintaining communication between related processes migrated to different nodes. Efficient process migration requires mechanisms that provide transparency, minimize interference and overhead, and handle heterogeneity between system nodes.
The elements of scale
The elements of scaleFastly Camille Fournier Former CTO, Rent the Runway, at Fastly Altitude 2016
Building and scaling great teams isn’t just something that managers do alone, with spreadsheets and workflows and process documents. Great teams come from the participation of the group to push the team there. In this talk we will investigate how you can think about your role in scaling a high-performance team, whatever your job title. From interviewing future coworkers, to communicating with your colleagues, to dealing with failure and growing as a individual and group, we will go through some of the challenges that come with scaling a culture, and how everyone on the team plays a part in its success.
Consistency Models in New Generation Databases
Consistency Models in New Generation Databasesiammutex The document discusses transaction and consistency models in databases as the database world changes. It covers the CAP theorem and how it is impossible to guarantee availability, consistency and partition tolerance simultaneously in an asynchronous distributed system. It then discusses various consistency models including eventual, monotonic read, and read your own writes (RYOW). Examples are provided of eventually consistent systems. The document also discusses Amazon Dynamo's consistency model and how MongoDB supports different consistency levels and strategies for handling transactions and writes in distributed systems.
Consistency in Distributed Systems
Consistency in Distributed SystemsShane Johnson This document provides an overview of key concepts in distributed systems including:
1) The CAP theorem which states that a distributed system cannot simultaneously provide consistency, availability, and partition tolerance.
2) Consistency models such as strong, weak, and eventual consistency. Eventual consistency guarantees that if no new writes are made, reads will return the last updated value.
3) Consensus protocols like Paxos which allow distributed systems to agree on a value despite potential failures. Vector clocks are used to order events in a distributed system.
Distributed System Management
Distributed System ManagementIbrahim Amer This is a presentation for Chapter 7 Distributed system management
Book: DISTRIBUTED COMPUTING , Sunita Mahajan & Seema Shah
Prepared by Students of Computer Science, Ain Shams University - Cairo - Egypt
Scaling up food safety information transparency
Scaling up food safety information transparencyNikos Manouselis Extended version of slides used for talk on "Scaling up (and doing business with) food safety information transparency" at the Food@Cranfield network (http://www.som.cranfield.ac.uk/som/p19207/research/research-clubs/food-cranfield-research-network), on an event dedicated to Using Big Data. Presented the concept of using AGINFRA to facilitate and scale up food safety data. Part of the Big Data Europe (http://www.big-data-europe.eu) liaison & dissemination activities.
Scaling Scribd
Scaling ScribdTimothy Wee This document summarizes a presentation about scaling Ruby on Rails applications. It discusses using fragment caching to improve performance, building custom traffic analytics, and setting up a Rails application to use multiple databases including a slave database for running analytics queries. Key recommendations included using Memcached for fragment caching, writing SQL queries by hand for analytics data, and connecting to a slave database in Rails using a small amount of code.
Distributed systems and consistency
Distributed systems and consistencyseldo An overview of how recent changes in technology have changed priorities for databases to distributed systems, and how you can preserve consistency in distributed data stores like Riak.
Reactive Revealed Part 2: Scalability, Elasticity and Location Transparency i...
Reactive Revealed Part 2: Scalability, Elasticity and Location Transparency i...Legacy Typesafe (now Lightbend) Part 2: What you should know about Elasticity, Scalability and Location Transparency in Reactive systems
In the second of three webinars with live Q/A, we look into how organizations with Reactive systems are able to adaptively scale in an elastic, infrastructure-efficient way, and the role that location transparency plays in distributed Reactive systems. Reactive Streams contributor and deputy CTO at Typesafe, Inc., Viktor Klang reviews what you should know about:
How Reactive systems enable near-linear scalability in order to increase performance proportionally to the allocation of resources, avoiding the constraints of bottlenecks or synchronization points within the system
How elasticity builds upon scalability in Reactive systems to automatically adjust the throughput of varying demand when resources are added or removed proportionally and dynamically at runtime.
The role of location transparency in distributed computing (in systems running on a single node or on a cluster) and how of decoupling runtime instances from their references can embrace network constraints like partial failure, network splits, dropped messages and more.
In the third and final webinar in the series with Jonas Bonér, we go over resiliency, failures vs errors, isolation (and containment), delegation and replication in Reactive systems.
3. distributed file system requirements
3. distributed file system requirementsAbDul ThaYyal The document discusses distributed file systems (DFS), which allow files to be shared across multiple computers. DFS aims to provide a common view of a centralized file system while using a distributed implementation. This allows files to be accessed and updated from any machine on the network. The document outlines some key requirements for DFS, including transparency so users are unaware of files being distributed, concurrency controls to handle simultaneous updates, replication for fault tolerance and load balancing, and security and performance comparable to a local file system. It also provides examples of DFS like NFS, AFS, and those used in Windows.
3. challenges
3. challengesAbDul ThaYyal This document outlines 7 key challenges in designing distributed systems: heterogeneity, openness, security, scalability, failure handling, concurrency, and transparency. It discusses each challenge in detail, providing examples. Heterogeneity refers to differences in networks, hardware, operating systems, and programming languages that must be addressed. Openness means a system can be extended and implemented in various ways. Security concerns confidentiality, integrity, and availability of resources. Scalability means a system remains effective as resources and users increase significantly. Failure handling techniques include detecting, masking, tolerating, and recovering from failures. Concurrency ensures correct and high performance sharing of resources. Transparency aims to make distributed components appear as a single system regardless of location, access
Client-centric Consistency Models
Client-centric Consistency ModelsEnsar Basri Kahveci A brief presentation about client-centric consistency models and how they can be modeled on Hazelcast
message passing
message passingAshish Kumar The document discusses various design issues related to interprocess communication using message passing. It covers topics like synchronization methods, buffering strategies, process addressing schemes, reliability in message passing, and group communication. The key synchronization methods are blocking and non-blocking sends/receives. Issues addressed include blocking forever if the receiving process crashes, buffering strategies like null, single-message and finite buffers, and naming schemes like explicit and implicit addressing. Reliability is achieved using protocols like four-message, three-message and two-message. Group communication supports one-to-many, many-to-one and many-to-many communication with primitives for multicast, membership and different ordering semantics.
Transparency - The Double-Edged Sword
Transparency - The Double-Edged SwordAcando Consulting Presentation from Enterprise 2.0 SUMMIT in Frankfurt October 28th 2010 on how transparency both enables value-creation and decreases risk for organizations, but also how potential privacy, compliance and security risks need to be managed.
Distributed & parallel system
Distributed & parallel systemManish Singh The document defines distributed and parallel systems. A distributed system consists of independent computers that communicate over a network to collaborate on tasks. It has features like no common clock and increased reliability. Examples include telephone networks and the internet. Advantages are information sharing and scalability, while disadvantages include difficulty developing software and security issues. A parallel system uses multiple processors with shared memory to solve problems. Examples are supercomputers and server clusters. Advantages are concurrency and saving time, while the main disadvantage is lack of scalability between memory and CPUs.
Distributed Systems
Distributed SystemsRupsee Distributed systems allow independent computers to appear as a single coherent system by connecting them through a middleware layer. They provide advantages like increased reliability, scalability, and sharing of resources. Key goals of distributed systems include resource sharing, openness, transparency, and concurrency. Common types are distributed computing systems, distributed information systems, and distributed pervasive systems.
Unit 1 architecture of distributed systems
Unit 1 architecture of distributed systemskaran2190 The document discusses the architecture of distributed systems. It describes several models for distributed system architecture including:
1) The mini computer model which connects multiple minicomputers to share resources among users.
2) The workstation model where each user has their own workstation and resources are shared over a network.
3) The workstation-server model combines workstations with centralized servers to manage shared resources like files.
Reactive Revealed Part 2: Scalability, Elasticity and Location Transparency i...
Reactive Revealed Part 2: Scalability, Elasticity and Location Transparency i...Legacy Typesafe (now Lightbend)
Ad
Similar to Distributed shared memory shyam soni (20)
Distributed Shared Memory notes in distributed systems.pptx
Distributed Shared Memory notes in distributed systems.pptxKumbiraiLuciaKondo Distributed shared memory (DSM) is a mechanism that manages memory across multiple nodes and makes inter-process communications transparent to end-users. DSM implements a shared memory model in distributed systems without physical shared memory. Each node has its own memory and provides read/write services, while consistency protocols maintain coherence. DSM systems use techniques like caching, rings, and granularity to optimize data access and movement between nodes. Key challenges in DSM design include memory coherence, data location, replacement strategies, and avoiding thrashing.
Advance Operating Systems
Advance Operating SystemsRaghu nath This document discusses distributed systems and distributed shared memory (DSM). It defines tightly and loosely coupled systems, describing their differences. It also describes four buffering strategies used in distributed systems: no buffering, single message buffering, unbounded buffering, and finite-bound buffering. Finally, it defines DSM, discusses its advantages over message passing, and outlines four design and implementation issues for DSM: granularity, memory coherence and access synchronization, data location and access, and block replacement policy.
Dos unit3
Dos unit3JebasheelaSJ Distributed shared memory
General architecture
Design and Implementation of issues of DSM
Granularity
Factors Influencing Block size Selection
Consistency Model
Replacement strategy
Which block be replace
where to place a replace block
thrashing
heterogeneous DSM
Issues
Deadlock
Distributed Shared Memory – A Survey and Implementation Using Openshmem
Distributed Shared Memory – A Survey and Implementation Using OpenshmemIJERA Editor Parallel programs nowadays are written either in multiprocessor or multicomputer environment. Both these
concepts suffer from some problems. Distributed Shared Memory (DSM) systems is a new and attractive area of
research recently, which combines the advantages of both shared-memory parallel processors (multiprocessors)
and distributed systems (multi-computers). An overview of DSM is given in the first part of the paper. Later we
have shown how parallel programs can be implemented in DSM environment using Open SHMEM.
Distributed Shared Memory – A Survey and Implementation Using Openshmem
Distributed Shared Memory – A Survey and Implementation Using OpenshmemIJERA Editor Parallel programs nowadays are written either in multiprocessor or multicomputer environment. Both these
concepts suffer from some problems. Distributed Shared Memory (DSM) systems is a new and attractive area of
research recently, which combines the advantages of both shared-memory parallel processors (multiprocessors)
and distributed systems (multi-computers). An overview of DSM is given in the first part of the paper. Later we
have shown how parallel programs can be implemented in DSM environment using Open SHMEM.
Overview of Distributed Systems
Overview of Distributed Systemsvampugani This document provides an overview of distributed systems. It discusses tightly-coupled and loosely-coupled multiprocessor systems, with loosely-coupled systems referring to distributed systems that have independent processors, memories, and operating systems. The document outlines some key properties of distributed systems, including that they consist of independent nodes that communicate through message passing, and accessing remote resources is more expensive than local resources. It also summarizes some advantages and challenges of distributed systems.
Opetating System Memory management
Opetating System Memory managementJohan Granados Montero Memory management handles allocation of memory to processes and tracks used and free memory. It uses techniques like paging, segmentation, and dynamic allocation from a heap. Paging maps logical addresses to physical pages, avoiding external fragmentation. Segmentation divides memory into logical segments of varying sizes. Dynamic allocation fulfills requests from the heap, managing free blocks and avoiding fragmentation and memory leaks.
Chapter 9 OS
Chapter 9 OSC.U This document discusses memory management techniques in operating systems. It covers topics such as binding instructions and data to memory at different stages, logical vs physical address spaces, memory management units that map virtual to physical addresses, dynamic loading and linking of code, using overlays to only hold needed instructions and data in memory, swapping processes temporarily out of memory to secondary storage, and contiguous allocation of memory to processes.
UNIT 1 ERTS-1.pptusbce18ugxy8vsxysqvyexv
UNIT 1 ERTS-1.pptusbce18ugxy8vsxysqvyexveeerithanya This document provides an overview of embedded and real-time systems. It discusses the key components of embedded systems including processors, memory, timers, and I/O devices. It also covers memory management methods, real-time operating systems, classifications of embedded systems, and considerations for selecting processors and memory. The document aims to introduce students to the structural units and design of embedded systems.
Chapter 2 part 1
Chapter 2 part 1rohassanie 1) Virtual memory simplifies address translation by creating a virtual memory space that contains the operating system and application programs, even though it does not physically exist.
2) Virtual memory is divided into two components - the first occupies real memory and contains the operating system and page pool, while the second occupies external storage and holds application programs, with pages swapped between real memory and external storage.
3) Address translation under virtual memory uses segment tables and page tables to map virtual addresses to physical addresses.
Lecture 6
Lecture 6Mr SMAK This document discusses parallel computer memory architectures, including shared memory, distributed memory, and hybrid architectures. Shared memory architectures allow all processors to access a global address space, but lack scalability. Distributed memory assigns separate memory to each processor requiring explicit communication between tasks. Hybrid architectures combine shared memory within nodes and distributed memory between nodes for scalability.
Lecture 6
Lecture 6Mr SMAK This document discusses parallel computer memory architectures, including shared memory, distributed memory, and hybrid architectures. Shared memory architectures allow all processors to access a global address space and include uniform memory access (UMA) and non-uniform memory access (NUMA). Distributed memory architectures require a communication network since each processor has its own local memory without a global address space. Hybrid architectures combine shared and distributed memory by networking multiple shared memory multiprocessors.
Lecture 6
Lecture 6Mr SMAK This document discusses parallel computer memory architectures, including shared memory, distributed memory, and hybrid architectures. Shared memory architectures allow all processors to access a global address space, but lack scalability. Distributed memory assigns separate memory to each processor requiring explicit communication between tasks. Hybrid architectures combine shared memory within nodes and distributed memory between nodes for scalability.
Operating system
Operating systemHussain Ahmady This document discusses memory management in operating systems. It covers topics like how memory management keeps track of allocated and free memory, provides protection using base and limit registers, and different address binding schemes. It also discusses dynamic loading, dynamic linking, logical versus physical addresses, swapping, memory allocation techniques like single allocation and multiple partitions, and issues like fragmentation. Paging and segmentation techniques for managing memory are also summarized.
G.Sumithra,II-M.sc(computer science),Bon secours college for women,thanjavur.
G.Sumithra,II-M.sc(computer science),Bon secours college for women,thanjavur.SumithraG2 Terminologies and its types
In-Memory Analytics
In-Database processing
Symmetric Multiprocessor system(SMP)
Massively Parallel Processing
Difference Between Parallel and Distributed Systems
Shared Nothing Architecture
Advantages of a “ shared nothing Architecture”
CAP Theorem Explained
CAP Theorem
Memory allocation for real time operating system
Memory allocation for real time operating systemAsma'a Lafi Memory allocation strategies in real-time operating systems differ from traditional operating systems in order to improve system performance. Real-time operating systems use static and dynamic memory allocation. Static allocation assigns memory at design time while dynamic allocation occurs at runtime using memory managers. Common dynamic allocation algorithms for real-time systems include buddy allocation, indexed fit, bitmapped fit, and two-level segregated fit, which aim to minimize fragmentation and response times. The document compares various allocation algorithms and concludes that dynamic allocation presents challenges for real-time systems to optimize fragmentation, response time, and locality.
G.Sumithra,II-M.sc(computer Science),Bon Secours college for women,Thanjavur.
G.Sumithra,II-M.sc(computer Science),Bon Secours college for women,Thanjavur.SumithraG2 Terminologies and its types
In-Memory Analytics
In-Database processing
Symmetric Multiprocessor system(SMP)
Massively Parallel Processing
Difference Between Parallel and Distributed Systems
Shared Nothing Architecture
Advantages of a “ shared nothing Architecture”
CAP Theorem Explained
CAP Theorem
UNIT-2 OS.pptx
UNIT-2 OS.pptxssusera387fd1 Operating system, functions of operating system file management, device management , inut output management,resource management
NUMA
NUMAPallab Ray This document discusses implementing Non-Uniform Memory Access (NUMA) systems. It provides background on NUMA, describing how in NUMA architectures each processor has local memory that it can access directly, while still being able to access other processors' memory through interconnects. It discusses shared memory, cache coherence challenges, and strategies for managing coherence like directory-based approaches. It includes a memory architecture block diagram, circuit diagram, memory chip algorithm and example of the memory architecture in operation.
Distributed database management system
Distributed database management systemVinay D. Patel Distributed database management systems (DDBMS) allow data to be spread across multiple computer sites connected by a network. A DDBMS provides location transparency so users can access data without knowing its physical location. It also coordinates transactions that involve data stored at multiple sites. DDBMS architectures include transaction managers, data managers, and transaction coordinators to process transactions and subtransactions across distributed data.
Ad
Recently uploaded (20)
Quality Contril Analysis of Containers.pdf
Quality Contril Analysis of Containers.pdfDr. Bindiya Chauhan Quality control test for containers, rubber closures and secondary packing materials.
apa-style-referencing-visual-guide-2025.pdf
apa-style-referencing-visual-guide-2025.pdfIshika Ghosh Title: A Quick and Illustrated Guide to APA Style Referencing (7th Edition)
This visual and beginner-friendly guide simplifies the APA referencing style (7th edition) for academic writing. Designed especially for commerce students and research beginners, it includes:
✅ Real examples from original research papers
✅ Color-coded diagrams for clarity
✅ Key rules for in-text citation and reference list formatting
✅ Free citation tools like Mendeley & Zotero explained
Whether you're writing a college assignment, dissertation, or academic article, this guide will help you cite your sources correctly, confidently, and consistent.
Created by: Prof. Ishika Ghosh,
Faculty.
📩 For queries or feedback: [email protected]
Biophysics Chapter 3 Methods of Studying Macromolecules.pdf
Biophysics Chapter 3 Methods of Studying Macromolecules.pdfPKLI-Institute of Nursing and Allied Health Sciences Lahore , Pakistan. This chapter provides an in-depth overview of the viscosity of macromolecules, an essential concept in biophysics and medical sciences, especially in understanding fluid behavior like blood flow in the human body.
Key concepts covered include:
✅ Definition and Types of Viscosity: Dynamic vs. Kinematic viscosity, cohesion, and adhesion.
⚙️ Methods of Measuring Viscosity:
Rotary Viscometer
Vibrational Viscometer
Falling Object Method
Capillary Viscometer
🌡️ Factors Affecting Viscosity: Temperature, composition, flow rate.
🩺 Clinical Relevance: Impact of blood viscosity in cardiovascular health.
🌊 Fluid Dynamics: Laminar vs. turbulent flow, Reynolds number.
🔬 Extension Techniques:
Chromatography (adsorption, partition, TLC, etc.)
Electrophoresis (protein/DNA separation)
Sedimentation and Centrifugation methods.
pulse ppt.pptx Types of pulse , characteristics of pulse , Alteration of pulse
pulse ppt.pptx Types of pulse , characteristics of pulse , Alteration of pulsesushreesangita003 what is pulse ?
Purpose
physiology and Regulation of pulse
Characteristics of pulse
factors affecting pulse
Sites of pulse
Alteration of pulse
for BSC Nursing 1st semester
for Gnm Nursing 1st year
Students .
vitalsign
Niamh Lucey, Mary Dunne. Health Sciences Libraries Group (LAI). Lighting the ...
Niamh Lucey, Mary Dunne. Health Sciences Libraries Group (LAI). Lighting the ...Library Association of Ireland
To study the nervous system of insect.pptx
To study the nervous system of insect.pptxArshad Shaikh The *nervous system of insects* is a complex network of nerve cells (neurons) and supporting cells that process and transmit information. Here's an overview:
Structure
1. *Brain*: The insect brain is a complex structure that processes sensory information, controls behavior, and integrates information.
2. *Ventral nerve cord*: A chain of ganglia (nerve clusters) that runs along the insect's body, controlling movement and sensory processing.
3. *Peripheral nervous system*: Nerves that connect the central nervous system to sensory organs and muscles.
Functions
1. *Sensory processing*: Insects can detect and respond to various stimuli, such as light, sound, touch, taste, and smell.
2. *Motor control*: The nervous system controls movement, including walking, flying, and feeding.
3. *Behavioral responThe *nervous system of insects* is a complex network of nerve cells (neurons) and supporting cells that process and transmit information. Here's an overview:
Structure
1. *Brain*: The insect brain is a complex structure that processes sensory information, controls behavior, and integrates information.
2. *Ventral nerve cord*: A chain of ganglia (nerve clusters) that runs along the insect's body, controlling movement and sensory processing.
3. *Peripheral nervous system*: Nerves that connect the central nervous system to sensory organs and muscles.
Functions
1. *Sensory processing*: Insects can detect and respond to various stimuli, such as light, sound, touch, taste, and smell.
2. *Motor control*: The nervous system controls movement, including walking, flying, and feeding.
3. *Behavioral responses*: Insects can exhibit complex behaviors, such as mating, foraging, and social interactions.
Characteristics
1. *Decentralized*: Insect nervous systems have some autonomy in different body parts.
2. *Specialized*: Different parts of the nervous system are specialized for specific functions.
3. *Efficient*: Insect nervous systems are highly efficient, allowing for rapid processing and response to stimuli.
The insect nervous system is a remarkable example of evolutionary adaptation, enabling insects to thrive in diverse environments.
The insect nervous system is a remarkable example of evolutionary adaptation, enabling insects to thrive
Operations Management (Dr. Abdulfatah Salem).pdf
Operations Management (Dr. Abdulfatah Salem).pdfArab Academy for Science, Technology and Maritime Transport This version of the lectures is provided free of charge to graduate students studying the Operations Management course at the MBA level.
Presentation of the MIPLM subject matter expert Erdem Kaya
Presentation of the MIPLM subject matter expert Erdem KayaMIPLM Presentation of the MIPLM subject matter expert Erdem Kaya
How to Manage Opening & Closing Controls in Odoo 17 POS
How to Manage Opening & Closing Controls in Odoo 17 POSCeline George In Odoo 17 Point of Sale, the opening and closing controls are key for cash management. At the start of a shift, cashiers log in and enter the starting cash amount, marking the beginning of financial tracking. Throughout the shift, every transaction is recorded, creating an audit trail.
Michelle Rumley & Mairéad Mooney, Boole Library, University College Cork. Tra...
Michelle Rumley & Mairéad Mooney, Boole Library, University College Cork. Tra...Library Association of Ireland
Unit 6_Introduction_Phishing_Password Cracking.pdf
Unit 6_Introduction_Phishing_Password Cracking.pdfKanchanPatil34 Initial stages of attacks, Phishing, Password Cracking, guidelines to prevent password attacks, Strong password, weak password,
Presentation on Tourism Product Development By Md Shaifullar Rabbi
Presentation on Tourism Product Development By Md Shaifullar RabbiMd Shaifullar Rabbi Presentation on Tourism Product Development By Md Shaifullar Rabbi, Assistant Manager- SABRE Bangladesh.
Understanding P–N Junction Semiconductors: A Beginner’s Guide
Understanding P–N Junction Semiconductors: A Beginner’s GuideGS Virdi Dive into the fundamentals of P–N junctions, the heart of every diode and semiconductor device. In this concise presentation, Dr. G.S. Virdi (Former Chief Scientist, CSIR-CEERI Pilani) covers:
What Is a P–N Junction? Learn how P-type and N-type materials join to create a diode.
Depletion Region & Biasing: See how forward and reverse bias shape the voltage–current behavior.
V–I Characteristics: Understand the curve that defines diode operation.
Real-World Uses: Discover common applications in rectifiers, signal clipping, and more.
Ideal for electronics students, hobbyists, and engineers seeking a clear, practical introduction to P–N junction semiconductors.
Handling Multiple Choice Responses: Fortune Effiong.pptx
Handling Multiple Choice Responses: Fortune Effiong.pptxAuthorAIDNationalRes INTRO TO STATISTICS
INTRO TO SPSS INTERFACE
CLEANING MULTIPLE CHOICE RESPONSE DATA WITH EXCEL
ANALYZING MULTIPLE CHOICE RESPONSE DATA
INTERPRETATION
Q & A SESSION
PRACTICAL HANDS-ON ACTIVITY
How to Subscribe Newsletter From Odoo 18 Website
How to Subscribe Newsletter From Odoo 18 WebsiteCeline George Newsletter is a powerful tool that effectively manage the email marketing . It allows us to send professional looking HTML formatted emails. Under the Mailing Lists in Email Marketing we can find all the Newsletter.
Social Problem-Unemployment .pptx notes for Physiotherapy Students
Social Problem-Unemployment .pptx notes for Physiotherapy StudentsDrNidhiAgarwal Unemployment is a major social problem, by which not only rural population have suffered but also urban population are suffered while they are literate having good qualification.The evil consequences like poverty, frustration, revolution
result in crimes and social disorganization. Therefore, it is
necessary that all efforts be made to have maximum.
employment facilities. The Government of India has already
announced that the question of payment of unemployment
allowance cannot be considered in India
Metamorphosis: Life's Transformative Journey
Metamorphosis: Life's Transformative JourneyArshad Shaikh *Metamorphosis* is a biological process where an animal undergoes a dramatic transformation from a juvenile or larval stage to a adult stage, often involving significant changes in form and structure. This process is commonly seen in insects, amphibians, and some other animals.
Phoenix – A Collaborative Renewal of Children’s and Young People’s Services C...
Phoenix – A Collaborative Renewal of Children’s and Young People’s Services C...Library Association of Ireland
Biophysics Chapter 3 Methods of Studying Macromolecules.pdf
Biophysics Chapter 3 Methods of Studying Macromolecules.pdfPKLI-Institute of Nursing and Allied Health Sciences Lahore , Pakistan.
Niamh Lucey, Mary Dunne. Health Sciences Libraries Group (LAI). Lighting the ...
Niamh Lucey, Mary Dunne. Health Sciences Libraries Group (LAI). Lighting the ...Library Association of Ireland
Operations Management (Dr. Abdulfatah Salem).pdf
Operations Management (Dr. Abdulfatah Salem).pdfArab Academy for Science, Technology and Maritime Transport
Michelle Rumley & Mairéad Mooney, Boole Library, University College Cork. Tra...
Michelle Rumley & Mairéad Mooney, Boole Library, University College Cork. Tra...Library Association of Ireland
Phoenix – A Collaborative Renewal of Children’s and Young People’s Services C...
Phoenix – A Collaborative Renewal of Children’s and Young People’s Services C...Library Association of Ireland
Distributed shared memory shyam soni
- 2. INTRODUCTION ● DSM is a term in computer science that refers to a wide class of software and hardware implementations, in which each node of a cluster has access to shared memory in addition to each node's non-shared private memory. ●Distributed Shared Memory (DSM), also known as a distributed global address space (DGAS). ●The processors communicate with one another through various communication networks,such as high-speed buses or telephone lines. ●A distributed system is a collection of loosely coupled processors interconnected by a communication network.
- 3. Advantage There are four major advantage of distributed system 1. Resource Sharing 2. Computation Speedup 3. Reliability 4. Communication
- 5. General Architecture of Distributed Shared Memory (DSM) System : ●It consists of number of nodes or machines or computer each of which is connected to each other through high speed communication channel. Each node has several components associated with it. ●A software memory mapping manager of each node maps its local memory into the memory mapping into the virtual shared memory. We call it the Distributed Shared Memory
- 6. Algorithims of DSM The algorithims for implementing DSM deal with two basic problems ➔ Static and Dynamic distribution of shared data across the system, to minimize access latency, and ➔Preserving a coherent view of shared data ,while minimizing coherence-management overhead.
- 7. Classifications of DSM Algorithims ●Single reader/Single writer algorithims ●Multiple reader/Single writer algorithims ●Multiple reader/Multiple writer algorithims
- 8. Design and Implementation Issues Thee are various factors that have to be kept In mind while designing and implementing the DSM systems. They are as follows: 1.Block Size: As we know, transfer of the memory blocks is the major operation in the DSM systems. Therefore block size matters a lot here. Size of the block depends on various factors like, paging overhead, thrashing, false sharing, and directory size.
- 9. 3.Replacement Strategy: It may happen that one node might be accessing for a memory block from DSM when its own local memory is completely full. In such a case, when the memory block migrating from remote node reaches, it finds no space to get placed. Thus a replacement strategy of major concern in the design and implementation of the DSM systems. Certain block must be removed so as to place the new blocks in such a situation. Several techniques are used for the replacement of old blocks such as removal of Least Recently Used memory blocks. 2.Structure of Shared Memory Space How the shared memory space is organized with data determines the structure of the shared memory space. It refers to the layout of shared data. It depends upon the type of application the DSM is going to handle.