Software-Defined Networking , Survey of HotSDN 2012
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Internet Research Lab at NTU, Taiwan. SIGCOMM HotSDN 2012 is the first conference workshop focused on SDN. This presentation provides a survey of selected papers in HotSDN'12 and revisits concepts and frameworks of SDN. Example applications are also presented.
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Software-Defined Networking , Survey of HotSDN 2012
- 1. SDN Concepts and Applications Survey of SIGCOMM HotSDN 2012 Presenter: Jason, Tsung-Cheng, HOU Advisor: Wanjiun Liao Sep. 20th, 2012 1
- 2. Agenda • Background • Review of Onix, Net OS / Controller • Hierarchical Policies for SDN • State Distribution Tradeoffs • Offloading Control Applications • Verifying Network-wide Invariants • Debugger and Network Backtrace • Programming Network for Online Applications • Programmable Enterprise WLANs • Research Directions 2
- 3. Background • SDN: Software-Defined Networking – Refactoring networks by offering control platforms – A unified system-layered abstractions – Programmability, flexibility, and extensibility • Network OS / Controller: – Handling important but tedious functions Configuring net devices, channeling states – Presenting net abstractions, states and graphs – Inter-network-applications control, providing API c.f. inter-process control 3
- 4. Network Stack / Protocol 4 http://www.conniq.com/Networking_Components7.htm
- 5. SDN Abstraction / Application 5 http://onrc.stanford.edu/research_unified_control_architecture.html
- 6. SDN Research? • How do we scope research in SDN? • SIGCOMM HotSDN 2012 – First Workshop in SDN – Acceptance: 22 of 71 submissions, 31% – 5 Stanford/Berkeley, 2 UIUC, 1 Princeton/Cornell – 1 Nicira, 2 IBM, 2 HP, 1 Microsoft • Mostly, implementations of newly proposed systems, frameworks, or applications • Very few modeling or theoretical work • Better understanding of SDN 6
- 7. Agenda • Background • Review of Onix, Net OS / Controller • Hierarchical Policies for SDN • State Distribution Tradeoffs • Offloading Control Applications • Verifying Network-wide Invariants • Debugger and Network Backtrace • Programming Network for Online Applications • Programmable Enterprise WLANs • Research Directions 7
- 8. Concept of Virtualization • Decoupling HW/SW by abstraction & layering • Using, demanding, but not owning or configuring • Resource pool: flexible to slice, resize, combine, and distribute • A degree of automation by software HOST 1 HOST 2 HOST 3 HOST 4, VMs Hypervisor: Turns 1 server into many “virtual machines” (instances or VMs) (VMWare ESX, Citrix XEN Server, KVM, Etc.) 8
- 9. Tenant View vs Provider View
- 10. Network Controller / OS • DCN relies on controller for many functions: – Address mapping / mgmt / registration / reuse – Traffic load scheduling / balancing – Route computation, switch entries configuration – Logical network view ↔ physical construction • An example: Onix – Distributed system – Maintain, exchange & distribute net states • Hard static: SQL DB • Soft dynamic: DHT – Asynchronous but eventually consistent 10
- 11. Onix Functions Control Plane / Applications API Provides Abstraction Logical Forwarding Plane / Network Graph Control Logical States Provides Commands Abstractions Distributed Mapping Network Info Base System Network Hypervisor Network OS Distributes, Configures Real States OpenFlow 11
- 12. Abstraction Layer Net Control MapReduce / GFS Cluster / Tenant Applications Master Master Network Programming API Language-Policy Interpreter Network Graph Policy-Rule Compiler Network Info Base Configuration Manager Object DB Verification-Debugger Backtrace Collector 12
- 13. Agenda • Background • Review of Onix, Net OS / Controller • Hierarchical Policies for SDN • State Distribution Tradeoffs • Offloading Control Applications • Verifying Network-wide Invariants • Debugger and Network Backtrace • Programming Network for Online Applications • Programmable Enterprise WLANs • Research Directions 13
- 14. Hierarchical Policies • Useful in many contexts in which resources are shared among multiple entities. • Express delegation of authority and the resolution of conflicts • Hierarchical Flow Tables (HFT): a framework for specifying hierarchical policies in SDN • Also proposed a compiler and runtime system to realize policy trees AD Ferguson et. al., “Hierarchical policies for software defined networks”, HotSDN 2012 14
- 15. Hierarchical Policies A Policy Tree Types of Hierarchy: • Policy Priority, ex: Access Control > Routing • IP Prefix, Longest Matching, Subnets • Flow / Traffic Type, ex: Control PKT > Data 15
- 16. Hierarchical Flow Table A rigorous definition attempting to formalize network policies. ←Observe the symbols used by authors here ! Matches a pkt with a policy, node internal conflict resolution Matches a pkt with a policy tree, using cmb recursively to children, parent-child or inter-sibling conflict resolution Declare policies ↓ Construct HFT ↓ Linearize & Compile HFT Turn into flow entries on devices 16
- 17. Runtime HFT Turn policy trees into priority-indexed linear and high-level flow tables ↓ Turn high-level flow tables into device entries → 17
- 18. Abstraction Layer Net Control MapReduce / GFS Cluster / Tenant Applications Master Master Network Programming API Language-Policy Interpreter Network Graph Policy-Rule Compiler Network Info Base Configuration Manager Object DB Verification-Debugger Backtrace Collector 18
- 19. Agenda • Background • Review of Onix, Net OS / Controller • Hierarchical Policies for SDN • State Distribution Tradeoffs • Offloading Control Applications • Verifying Network-wide Invariants • Debugger and Network Backtrace • Programming Network for Online Applications • Programmable Enterprise WLANs • Research Directions 19
- 20. Logically Centralized • Logically centralized: multiple physical machines hosting distributed controllers • Net states are distributed among controllers – What are the impact of inconsistent network view? – How does distributed SDN states impact performance of a logically centralized control application? • Depict state exchange points and trade-offs • Provide a customized flow level simulator D Levin et. al., “Logically centralized?: state distribution trade-offs in software defined networks”, HotSDN 2012 20
- 21. State Exchange Points State exchange points: • App. state mgmt layer (Net. OS/Hypervisor) • State mgmt layer network devices 21
- 22. Trade-Offs • Strongly consistent – Imposes overhead and delay, and limits responsiveness, can lead to suboptimal • Eventually consistent: – Presents a temporarily inconsistent view, may cause incorrect behavior • Trade-off: – performance (optimality), vs state distribution overhead (staleness) – Application complexity, vs robustness to inconsistency in states 22
- 23. Results • Simulation on very simple topo and workload • Only discuss impacts on load-balancing app • View staleness significantly impacts optimality • Application robustness to inconsistency increases when the application logic is aware of distribution • Not a very complete discussion, scenarios are relatively simple 23
- 24. Abstraction Layer Net Control MapReduce / GFS Cluster / Tenant Applications Master Master Network Programming API Language-Policy Interpreter Network Graph Policy-Rule Compiler Network Info Base Configuration Manager Object DB Verification-Debugger Backtrace Collector 24
- 25. Agenda • Background • Review of Onix, Net OS / Controller • Hierarchical Policies for SDN • State Distribution Tradeoffs • Offloading Control Applications • Verifying Network-wide Invariants • Debugger and Network Backtrace • Programming Network for Online Applications • Programmable Enterprise WLANs • Research Directions 25
- 26. Offloading Control ? • Frequent and resource-exhaustive events: – Such as flow arrivals and network state collection – Stress the control plane and limit scalability – As discussed in DevoFlow • Current solutions: View as intrinsic, or try to address by modifying switches • How can we move control functionalities toward datapaths, without introducing new datapath mechanisms to switches? S Hassas Yeganeh et. al., “Kandoo: a framework for efficient and scalable offloading of control applications”, HotSDN 2012 26
- 27. Local Controllers DevoFlow: Devolve tasks to switches Here, perhaps switches are equipped with local controllers Or, more local, light controllers installed near datapaths What’s the difference? Programmability and delay 27
- 28. Examples • Does not need global network states • Shield from root controller • Proxies of root controller • Local controllers do not propagate an • Scale with network size event, unless root controller subscribes • Hierarchy of controllers • Consume less bandwidth 28
- 29. Agenda • Background • Review of Onix, Net OS / Controller • Hierarchical Policies for SDN • State Distribution Tradeoffs • Offloading Control Applications • Verifying Network-wide Invariants • Debugger and Network Backtrace • Programming Network for Online Applications • Programmable Enterprise WLANs • Research Directions 29
- 30. Why Online Verifying? • SDN eases development of network applications, but bugs are still problematic – Complexity of software will increase. – SDN allows multiple applications or even multiple users to program the same physical network, potential conflicting rules • Rule verification latency in seconds is not enough to ensure real-time response – Require network-wide states – Processing churn introduce scaling challenges. A Khurshid et. al., “VeriFlow: verifying network-wide invariants in real time”, HotSDN 2012 30
- 31. VeriFlow Framework • Prototype: Only (forwarding rule, reachability) verification • Equivalent classes: rules or policies affect same type of packets or flows, verify per class not per rule • Rules in equivalent class are stored as trie or prefix tree • Generate forwarding graphs for each class • Processing reachability verification by traversing forwarding graph • Incremental, only check latest changes, fast enough 31
- 32. Agenda • Background • Review of Onix, Net OS / Controller • Hierarchical Policies for SDN • State Distribution Tradeoffs • Offloading Control Applications • Verifying Network-wide Invariants • Debugger and Network Backtrace • Programming Network for Online Applications • Programmable Enterprise WLANs • Research Directions 32
- 33. Why Debugging ? • When debugging a program: – Pause execution at a breakpoint – Shows the history of function calls leading to breakpoint, a backtrace • What bugs in SDN? – Race condition: policy on-the-way, behind flows – Controller logic error – Switch implementation or policy insertion N Handigol et. al., “Where is the debugger for my Software-Defined Network?”, HotSDN 2012 33
- 34. Breakpoint and Backtrace 34
- 35. Architecture 35
- 36. Mechanisms • Proxy: modify flow entry modification msg to cmd switches producing postcards • Flow Table State Recorder (FTSR): stores flow table versions w.r.t. flow entry modifications • Breackpoint: user-specified filtering rules • Postcard: a truncated copy of the packet’s header, augmented with the matching flow entry, switch, and output port (links back to FTSR) • Keep sending postcards to controller, all along the way a packet traverses • Reconstruct backtrace when a packet or a postcard matches a filter breakpoint 36
- 37. Setting Breakpoint 37
- 38. Collecting Postcard 38
- 39. Reconstructing Backtrace 39
- 40. Abstraction Layer Net Control MapReduce / GFS Cluster / Tenant Applications Master Master Network Programming API Language-Policy Interpreter Network Graph Policy-Rule Compiler Network Info Base Configuration Manager Object DB Verification-Debugger Backtrace Collector VeriFlow vs Debugger: Policy verification vs errant event tracing 40
- 41. Agenda • Background • Review of Onix, Net OS / Controller • Hierarchical Policies for SDN • State Distribution Tradeoffs • Offloading Control Applications • Verifying Network-wide Invariants • Debugger and Network Backtrace • Programming Network for Online Applications • Programmable Enterprise WLANs • Research Directions 41
- 42. Network Sensitive Applications • Tightly integrate applications with the network to improve performance and utilization • To support cross-layer network control, the SDN controller is interfaced to the master node for each individual application, such as the Hadoop scheduler or HBase master • For big data applications, the SDN controller provides an interface that accepts traffic demand matrices from application controllers G Wang et. al., “Programming your network at run-time for big data applications”, HotSDN 2012 42
- 43. MapReduce Distributed Execution Master also deals with: • Worker status updates User • Fault-tolerance Program • I/O Scheduling fork fork • Automatic distribution fork • Automatic parallelization Master assign assign map reduce Input Data Worker write Output local Worker File 0 Split 0 read write Split 1 Worker Split 2 Output Worker File 1 Worker remote read,sort ↑↑↑↑↑ Shuffle & Sort
- 44. Job Tracker Functions 44
- 45. SDN Controller Functions 45
- 46. Masters Work Together How to coordinate configuration requests among different kinds of applications? 46
- 47. Agenda • Background • Review of Onix, Net OS / Controller • Hierarchical Policies for SDN • State Distribution Tradeoffs • Offloading Control Applications • Verifying Network-wide Invariants • Debugger and Network Backtrace • Programming Network for Online Applications • Programmable Enterprise WLANs • Research Directions 47
- 48. Programmable WLAN • In WiFi settings, clients choose what APs to associate, broadcast, hard to track devices • Interactions among APs and clients are decentralized, hard to add functionalities • Build a virtual AP abstraction to simplify client management, without any client modificatoins • Virtual AP: enables seamless mobility and load balancing L Suresh et. al., “Towards programmable enterprise WLANS with Odin”, HotSDN 2012 48
- 49. Light Virtual Access Point 49
- 50. 50
- 51. Reference • Teemu Koponen et al., “Onix: A distributed control platform for large-scale production networks”, OSDI, Oct, 2010 • AD Ferguson et. al., “Hierarchical policies for software defined networks”, HotSDN 2012 • D Levin et. al., “Logically centralized?: state distribution trade-offs in software defined networks”, HotSDN 2012 • S Hassas Yeganeh et. al., “Kandoo: a framework for efficient and scalable offloading of control applications”, HotSDN 2012 • A Khurshid et. al., “VeriFlow: verifying network-wide invariants in real time”, HotSDN 2012 • N Handigol et. al., “Where is the debugger for my Software-Defined Network?”, HotSDN 2012 • G Wang et. al., “Programming your network at run-time for big data applications”, HotSDN 2012 • L Suresh et. al., “Towards programmable enterprise WLANS with Odin”, HotSDN 2012 51
Editor's Notes
- #44: This is the distributed overview1.User run it’s program, the input data will be split into many pieces, each pieces is 64MB2. The program will copy to many machine, one of those is master, and the master will assign some worker be mapper, some workers be reducer.3. Mapper will red the content of the corresponding input split. It pass each key-value pair to the map function, and the intermediate generate by mapper will be stored in memory4. The mapper will write intermediate data to local disk periodically. 5. After all mapper finished. The reducer will read the corresponding intermediate data and sort each key-value pair by key. This make sure that the data with same key will be group together.6. The reducer run reduce function and output the result.7. When all map tasks and reduce task finish, the mapreduce job is finished.