Managing elasticity across Multi-cloud providers
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This document discusses managing elasticity across multiple cloud providers. It introduces challenges in guaranteeing high availability, automating elasticity, and providing transparency across clouds. The contribution is a Multi-Cloud-PaaS architecture that can manage elasticity problems by providing necessary resources as needed. It is implemented using FraSCAti and deployed on ten IaaS/PaaS providers. Validation shows negligible overhead from load balancing and satisfactory performance of the system.
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Introduction & Motivation
Cloud provider What happened & why What impactWhen
Amazon [8] The Amazon Elastic
Load Balancing (ELB)
Service down in US-East
region affected the
applications using the
ELB.
21 April 2011 Offline for more
than 10 hours.
Companies
affected:
reddit, Quora, Hoot
Suite.
Windows Azure [9] A networking problem
during a routine software
update interfered with
hosted project
deployment.
13 March 2009 Offline for 22
hours.](https://image.slidesharecdn.com/multi-cloud-140423000641-phpapp01/85/Managing-elasticity-across-Multi-cloud-providers-5-320.jpg)
![6
Introduction & Motivation
Cloud provider Electricity provider
Unavailability: 7.5 hours average per year Unavailability: 15 minutes average per year
Total of 585 hours cost > 71.7 million [1]
[1] International Working Group on Cloud Computing Resiliency. http://www.iwgcr.org](https://image.slidesharecdn.com/multi-cloud-140423000641-phpapp01/85/Managing-elasticity-across-Multi-cloud-providers-6-320.jpg)
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PresentationJaspreet1192 This document summarizes a thesis submitted to Punjabi University for a Master of Technology degree in Computer Sciences and Engineering. The thesis, written by Kirandeep Kaur and supervised by Dr. Rajesh K. Bawa, proposes a novel flexible resource scheduling model for public clouds to avoid resource starvation. It introduces the problem of resource starvation in cloud computing environments and reviews existing resource scheduling mechanisms. The proposed model aims to simulate a public cloud environment using CloudSim and demonstrate that no user request will starve without proper resource allocation through a flexible scheduling approach that considers server load.
Muves3 Elastic Grid Java One2009 Final
Muves3 Elastic Grid Java One2009 FinalElastic Grid, LLC. The Army Research Laboratory is developing a next-generation ballistic vulnerability and lethality modeling system called MUVES 3 using cloud computing. MUVES 3 has a service-oriented architecture built on Java and deployed using the NetBeans IDE. Testing MUVES 3 on Amazon EC2 through Elastic Grid's cloud management platform allows scaling to hundreds of computers for integration testing while avoiding security issues of a public cloud. Further steps include expanding metrics collection and using NetBeans as a client for cloud visualization.
IRJET- Implementation of Cloud Energy Saving System using Virtual Machine...
IRJET- Implementation of Cloud Energy Saving System using Virtual Machine...IRJET Journal This document proposes a cloud energy saving system using virtual machine dynamic resource allocation on the OpenNebula platform. It aims to address the problems of high energy consumption and wasted idle resources in cloud computing. The system uses two methods: 1) autoscaling, which reallocates load between nodes based on process requirements to reduce response time and maximize throughput, and 2) live migration of VMs between hosts to power down idle machines. Two experiments are conducted to evaluate autoscaling performance and energy savings when adding and removing VMs on OpenNebula. The goal is to significantly reduce power consumption and wasted resources compared to traditional methods.
Cloud computing - Terena 2011
Cloud computing - Terena 2011Jisc The document discusses a presentation on cloud computing for research and education. It covers current usage of cloud services, barriers to adoption, and the different types of cloud services. It also discusses the financial benefits of cloud computing, using cloud services for collaboration and capacity needs, and scenarios that are well-suited for cloud deployment. Building a private research cloud is presented as a way to address trust issues. The role of national research and education networks and potential federation of national clouds in Europe is also mentioned.
Cloud scalability considerations
Cloud scalability considerationsIJCSES Journal Cloud computing is a technique that has a great capabilities and benefits for users. Cloud characteristics
encourage many organizations to move to this technology. But many consideration faces transmission
process. This paper outline some of these considerations and considerable efforts solved cloud scalability
issues.
Multi-Cloud Load Balancing and Application Services
Multi-Cloud Load Balancing and Application ServicesAvi Networks Multi-cloud load balancing presents challenges around separate control points, limited features, lack of automation and elasticity, and inability to ensure consistency across clouds. Avi Networks addresses these challenges with a software-defined load balancing architecture that provides consistent application services across multiple cloud fabrics. It features built-in integrations with leading cloud platforms, a centralized controller, and software load balancers for elasticity. Avi aims to improve operational efficiency, consistency, and time-to-market for applications in multi-cloud environments.
Why Cloud Management Makes Sense
Why Cloud Management Makes SenseRightScale RightScale Webinar: Cloud is the most nebulous and abused term in information technology today. It describes multiple, disparate service models and has been retroactively applied to countless legacy technologies in attempts to keep them current. In this webinar, we'll discuss the cloud technology landscape and where RightScale fits in to drive agility, cost, and time savings above cloud infrastructure. RightScale has been investing heavily for the past four years to make cloud infrastructure easy to leverage. This webinar will clarify elements that are straightforward, what continues to be difficult, and the impact on your schedule and budget.
UberCloud HPC Experiment Introduction for Beginners
UberCloud HPC Experiment Introduction for Beginnershpcexperiment 1. The document describes an HPC experiment that aims to make HPC resources available on demand for small and medium enterprises (SMEs) and their engineering applications.
2. It details how the experiment works by matching SMEs with software/hardware providers and experts to test running jobs on remote HPC clouds.
3. One example team tested running Abaqus simulations on the SGI Cyclone supercomputer and remotely visualizing results using NICE DCV software to assess the feasibility of using cloud HPC resources.
92 494
92 494Subramanian Senthamarai Kannan This document discusses improving fault tolerance in virtual machine-based cloud infrastructures. It proposes a model to analyze how systems tolerate faults by making decisions based on the reliability of processing nodes (virtual machines). If a virtual machine produces the correct result within the time limit, its reliability increases, and if it fails or is incorrect, reliability decreases. If reliability does not meet a minimum threshold, the system performs recovery actions. The technique executes diverse variants on multiple VMs and assigns reliability scores to results. Related work on fault tolerance techniques for clouds is also reviewed.
Above the clouds
Above the cloudsrussel_uk 1. Cloud computing refers to software and services provided over the internet (SaaS) and the hardware/software in datacenters that provide those services (Clouds).
2. Large-scale datacenters located in low-cost areas enabled cloud computing by reducing costs 5-7x through economies of scale.
3. Cloud computing provides the illusion of infinite computing resources available on demand, eliminating the need for upfront planning or commitment by users. Users pay for only the resources they use.
Cloud Computing Berkley
Cloud Computing Berkleykrmartin_dal 1. Cloud computing refers to software and data services provided over the internet, as well as the hardware and systems that power them.
2. Large-scale data centers enable cloud computing by reducing costs through economies of scale and statistical multiplexing of resources.
3. Different cloud platforms offer varying levels of control over software stacks and automation of scaling and failover, with tradeoffs between flexibility and managed services.
Working with Hybrid Clouds and Data Architectures
Working with Hybrid Clouds and Data ArchitecturesDave McAllister Discussion on the needs and impact of data movement on hybrid clouds (where public and private clouds, along with legacy datacenters) collide.
cloud computing 
cloud computing Anwar Arshad - CISSP | ITILV3 | COBIT | CCNP | JNCIA | MCITP Public, private, and hybrid clouds have different characteristics in terms of technology leverage and ownership, management of provisioned resources, workload distribution methods, security and data privacy enforcement, and example platforms. Infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS) are the three main service models for cloud computing, with each offering a different level of service and control over the infrastructure. Ensuring security across these cloud service models presents challenges that providers aim to address through techniques like trust overlays, data watermarking, and access control measures.
Conference Paper: Simulating High Availability Scenarios in Cloud Data Center...
Conference Paper: Simulating High Availability Scenarios in Cloud Data Center...Ericsson Cloud tenants require certain levels of performance in aspects like high availability and service rate and deployment options. On the other hand, Cloud providers are in constant pursuit of a system that satisfies client demands for resources, maximizes availability, minimizes power consumption and, in turn, minimizes the cloud providers’ cost. A main challenge cloud providers face here is ensuring high availability (HA).
High availability includes the combined reliability of components of all categories including network, computational, hardware and software components of all layers.
We introduce a framework to amend cloud simulators with critical HA features. We take GreenCloud, a major simulator with a direct focus on green computing, and implement these features as an additional measurement layer. We demonstrate these added features by simulating their impact on a phased communication application (PCA).
Authors
Mohamed Abu Sharkh, Abdallah Shami; Western University, London, Canada; Peter Öhlen, Abdelkader Ouda, Ali Kanso, Ericsson Research.
Presented Nov 30, 2015, at First IEEE Workshop of Quality of Service Assurance in the Cloud (QAC’2015), co-located with IEEE CloudCom.
© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Architecting and Tuning IIB/eXtreme Scale for Maximum Performance and Reliabi...
Architecting and Tuning IIB/eXtreme Scale for Maximum Performance and Reliabi...Prolifics Abstract: Recent projects have stressed the "need for speed" while handling large amounts of data, with near zero downtime. An analysis of multiple environments has identified optimizations and architectures that improve both performance and reliability. The session covers data gathering and analysis, discussing everything from the network (multiple NICs, nearby catalogs, high speed Ethernet), to the latest features of extreme scale. Performance analysis helps pinpoint where time is spent (bottlenecks) and we discuss optimization techniques (MQ tuning, IIB performance best practices) as well as helpful IBM support pacs. Log Analysis pinpoints system stress points (e.g. CPU starvation) and steps on the path to near zero downtime.
Gomez Blazing Fast Cloud Best Practices 
Gomez Blazing Fast Cloud Best Practices Compuware APM Are you planning to deploy Web applications in the cloud? Will their performance be acceptable? What will you do to make sure?
There are a lot of good reasons to deploy applications in a cloud environment — but they are all forgotten if your application is slow or has poor availability. Poor performance results in unhappy, lost customers. Traditional data center techniques for monitoring, measuring, and optimizing Web application performance won’t work in the cloud. There are a new set of best practices that you need to learn to optimize the performance of your cloud-based Web applications.
Tenant-based resource allocation model for cost-effective scaling Software-as...
Tenant-based resource allocation model for cost-effective scaling Software-as...Javier Mijail Espadas Pech Computing resources are being transformed into a model consisting of services that are delivered in a similar way to traditional utilities such as water or electricity. One of the computing paradigms that have promised to deliver this utility computing vision is known as Cloud Computing. Cloud computing provides on-demand access to computational resources which together with pay-per-use business models, enables application providers seamlessly scaling their services. With cloud computing definition comes the term of elasticity which is the ability to create a variable number of virtual machine instances depending on the applications demands. Virtualization technology is widely adopted as an enabler of cloud computing because it provides benefits such as security, performance isolation, ease of management and flexibility of running within a user-customized environment. In the other hand, the cloud applications themselves have long been known to as Software as a Service (SaaS). SaaS is a software delivery paradigm where the software is hosted off-premises, developed by service providers and delivered via Internet and the payment mode follows a subscription model. For SaaS providers, having the power to scale up or down an application to only consume and pay for the resources that are required at certain point in time is an attractive capability and if done correctly it will be less expensive than running on traditional hosting. However, cost-effective scalability is not achieved just by deploying large-scale applications over pay-per-use cloud infrastructures, and idle processes and not used resources are wasted but charged to application providers. Over and under provisioning of cloud resources are still unsolved issues. Even if peak loads can be successfully predicted, without an effective elasticity model, costly resources are wasted during nonpeak times (underutilization) or revenues from potential customers are lost after experiencing poor service (saturation). In this sense, SaaS applications give an opportunity to improve this scenario due their multi-tenancy nature, which is the ability to offer one single application instance for several clients/providers (tenants). Each tenant can interact with the application as if it were an unique user and cannot access or view the data of another tenant. Consequently, with the use of cloud computing approaches such as on-demand virtual machine creation, it is possible to efficiently create a mechanism for SaaS applications in order to allocate, consume and charge only the required cloud computing resources by each tenant. This doctoral dissertation establishes formal measurements for under and over provisioning of virtualized resources in cloud infrastructures, specifically for SaaS platforms deployments and it proposes a resource allocation model to deploy SaaS applications over cloud computing platforms by taking into account their multi-tenancy thus creating a cost-effective scalable environment.
Tenant-based resource allocation model for cost-effective scaling Software-as...
Tenant-based resource allocation model for cost-effective scaling Software-as...Javier Mijail Espadas Pech
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Introduction to cloud computingFawaz Fernand PARAISO Cloud computing allows users to access computing resources like servers, storage, databases, networking, software and more over the internet. It provides several services like IaaS, PaaS and SaaS. There are different deployment models including public, private and hybrid clouds. Virtualization is a key technology that allows multiple operating systems and applications to run on a single server. Cloud computing can help drive economic growth in Africa by providing affordable access to computing resources for businesses and individuals.
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Moteur CEPFawaz Fernand PARAISO Complex Event Processing (CEP) is a technique that analyzes events to detect complex patterns. CEP engines receive events from producers, filter and transform them, detect patterns across events, and send derived events to consumers. CEP with Canopsis allows for real-time event analysis, flexible monitoring of services, and better scaling of infrastructure by processing events quickly in memory.
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Managing elasticity across Multi-cloud providers
- 1. Managing Elasticity Accross Multi-Cloud Providers 1 Fawaz Paraïso, Philippe Merle, Lionel Seinturier 1st International workshop on multi-cloud applications and federated clouds (2013) University Lille1 & Inria Lille – Nord Europe (France)
- 3. 3 Introduction & Motivation Elasticity is the capability to rapidly provision, in some cases automatically, to quickly scale out, and rapidly release resources APP APP APP APP APP APP APP APP APP
- 5. 5 Introduction & Motivation Cloud provider What happened & why What impactWhen Amazon [8] The Amazon Elastic Load Balancing (ELB) Service down in US-East region affected the applications using the ELB. 21 April 2011 Offline for more than 10 hours. Companies affected: reddit, Quora, Hoot Suite. Windows Azure [9] A networking problem during a routine software update interfered with hosted project deployment. 13 March 2009 Offline for 22 hours.
- 6. 6 Introduction & Motivation Cloud provider Electricity provider Unavailability: 7.5 hours average per year Unavailability: 15 minutes average per year Total of 585 hours cost > 71.7 million [1] [1] International Working Group on Cloud Computing Resiliency. http://www.iwgcr.org
- 7. 7 Introduction & Motivation Multiple servers #1 Data centre Cloud provider Load balancer
- 8. 8 Introduction & Motivation Multiple data centres #2 Load balancer Data centre A Data centre B Data centre C Cloud provider
- 9. 9 Usage of multiple cloud providers in a uniform way. Multi-Cloud service Cloud B Cloud CCloud A Multiple Clouds #3 Introduction & Motivation
- 11. 11 Challenges How to guarantee high availability? How to automate elasticity through multiple clouds? How to provide transparency?
- 13. 13 Contribution Manage elasticity problem in Multi-Cloud environment Provide necessary resources when the system needs Full instrumentation for monitoring workloads Unpredictible environment Why ?What ?
- 14. 14 Contribution Overview of the Multi-Cloud-PaaS Architecture
- 15. 15 Contribution Deployment of the Multi-Cloud-PaaS Architecture
- 16. 16 Contribution Independent of Cloud Flexible architecture Automation
- 18. 18 Implementation Implementation FraSCAti (SCA Model) Multi-Cloud-PaaS SCA-Based Components
- 19. 19 The Multi-Cloud-PaaS (MCP) has been deployed on ten IaaS/PaaS providers. Deployment
- 20. 20 Use case Application responsible for checking if the JPG format is correct. App First case App Second case
- 21. 21 Evaluation Overhead Implementation Avg. exec. Time LB overhead APP 13.93 sec - APP + LB 14.10 sec 1.45% There is a negligeable overhead introduced by the LB First case Second case To evaluate the overhead of the LB instance, 10,000 pictures were sent to the application. Performance We generate 134, 021 requests and continuously connects to one instance of LB Session rate Concurrency Data rate Failures Avg. Time 850 283 4560 kB/s 0 3 ms Given the low resources used by the LB, the results obtained in are satisfactory
- 22. 22 Conclusion This paper provides solution to manage elasticity across multiple cloud providers Independent of Cloud Flexible architecture We plan to Evaluate the other Multi-Cloud-PaaS architecture components Investigate for optimization opportunities of cloud application deployment