1. A Stratified Approach for Supporting High Throughput Event Processing Applications July 2009 Geetika T. LakshmananYuri G. RabinovichOpher Etzion IBM T. J. Watson Research CenterIBM Haifa Research Lab gtlakshm@us.ibm.comyurir@il.ibm.comopher@il.ibm.com

2. 2 Outline  Motivation and Goal  Definitions  Related Work  Overview of our solution –Credit-Card Scenario –Profiling and initial assignment of nodes to strata –Stratification –Load Distribution Algorithm  Algorithm optimizations and support for dynamic changes in event processing graph  Implementation and Results  Conclusion

3. 3 Our Goal Devise a generic framework to maximize the overall input (and thus output) throughput of an event processing application which is represented as an EPN, given a specific set of resources (cluster of nodes with varying computational power) and a traffic model. The framework should be adaptive to changes either in the configuration or in the traffic model. EPN Event Producer Event Consumer Event Producer Event Producer Event Consumer Event Consumer Engine EPA Engine EPA Engine EPA Repository

4. 4 Why is this an important problem?  Quantity of events that a single application needs to process is constantly increasing (E.g. RFID events, Massive Online Multiplayer Games).  Manual partitioning is difficult (due to semantic dependencies between event processing agents) particularly when it is required to be adaptive and dynamic.

5. 5 Event Processing Agent  An event processing agent has input and output event channels.  In general it receives a collection of events as input, derives one or more events as output and emits them on one or more output channels.  The input channels are partitioned according to a context which partitions the space of events according to semantic partitions of relevance

6. 6 Related Work  Scalability in event processing –Large scale event processing applications (E.g. Astrolobe, PIER, Sienna.) –Kulkarni et al., Wu et al. –More work needs to be done.  Numerous centralized implementations arising due to interdependencies among event processing agents.  Synergy between stream processing and event processing. – Load distribution techniques for streams proposed: Shah et al. Mehta et al. Gu et al. Xing et al. Zhou et al.

7. 7 Is this a solved problem? Scalable event processing implementations (Astrolobe, PIER, Sienna) Centralized event processing Implementations Load distribution algorithms for scalable stream processing Shah et al., Mehta et al., Gu et al., Xing et al. Zhou et al., Liu et al. ……… Event-at-a-time ImplementationsSet-at-a-time Implementations Centralized stream processing Implementations

8. 8 Overview of Our Solution 1. Profiling –Used to assign agents to nodes in order to maximize throughput 2. Stratification of EPN –Splitting the EPN into strata layers –Based on semantic dependencies between agents –Distributed implementation with event proxy to relay events between strata 3. Load Distribution –Distribute load among agents dynamically during runtime and respect statistical load relationships between nodes

9. 9 Distributed Event Processing Network Architecture  Input: Specification of an Event Processing Application  Output: Stratified EPN ( event processing operations event processing agents)  Event Proxy receives input events and routes them to nodes in a stratum according to the event context.  Event proxy periodically collects performance statistics per node in a stratum.

10. 10 Stratified Event Processing Graph 1.Define the event processing application in the form of an Event Processing Network Dependency Graph  G=(V,E) (directed edges from event source to event target) 2. Overview of Stratification Algorithm  Create partitions by finding sub graphs that are independent in the dependency graph.  For each sub graph, construct a network of EPAs.  Push filters to the beginning of the network to filter out irrelevant events.  Iterate through graph and identify areas of strict interdependence. (i.e. sub graphs with no connecting edges).  For each sub graph define stratum levels.

11. 11 Credit Card Scenario Stratification algorithm Event Processing Dependency Graph Stratified Event Processing Graph

12. 12 Initial Placement of Agents  Goal is to maximize throughput.  Assume agents in a single stratum are replicated on all nodes in that stratum.  Overall strategy: 1.Profiling. Determine maximum event processing capability of available nodes. –r i : Maximum possible event processing rate (events/sec) –d i : Maximum possible derived event production rate (events/sec) 2.Assigning nodes to each stratum. Executing at a user set percentage of their capacity, these nodes can process all of the incoming events in their stratum level in parallel under peak event traffic conditions. –Compute ratio of events split between nodes –Iterative calculation starting with the first stratum.

13. 13 Assigning Nodes to Each Stratum 1.Assigning nodes to each stratum. Executing at a user set percentage of their capacity t i, these nodes can process all of the incoming events in their stratum level in parallel under peak event traffic conditions. –Compute ratio of events split between nodes –Iterative calculation starting with the first stratum. Formulas Example : Incoming event rate: 200,000/sec. Ti=0.95. Processing Capacity of node n: 36,000 events/sec. ((t i *r i )/m) i *100m i *(d i /r i ) Stratum nStratum n+1 Stratum n Stratum n+1 Percentage of event stream directed to node n i Derived event production rate of nodes in stratum n ((0.95*36,000)/200,000)*100 = 17.1% Percentage of event stream directed to node n If (d i /r i )=0.5, derived event production rate is 200,000*0.5=100,000 events/sec Thus, 6 nodes will be needed in this stratum

14. 14 Dynamic Load Distribution Strategy  Desirable qualities include: –Dynamic –Observes Semantics of Agent Dependencies –Observes Link Latency –Can perform task splitting –Observes load, average load, and load variance –Observes state

15. 15 Overview of Dynamic Load Distribution Algorithm  Event Proxy collects statistics and maintains a time series and makes the following decisions: 1.Identify most heavily loaded node in a stratum (donor node). 2.Identify a heavy context to migrate from the donor node. 3.Identify recipient node for migrated load. 4.Estimate post migration utilization of donor and recipient nodes. If post migration utilization of recipient node are unsatisfactory, go back to step 3 and identify new recipient node. If post migration utilization of donor node is unsatisfactory, go back to step 2 and identify new context to migrate. 5.Execute migration and wait for x length time interval. Go to step 1. Engine Queue AMIT Engine Queue AMIT Engine Queue AMIT Engine Queue AMIT Engine Queue AMIT Engine Queue AMIT EPProxy Stratum nStratum n+1

16. 16 Overview of Dynamic Load Distribution Algorithm  Statistics collected by event Proxy: –Number of input events processed by execution of agents in a particular context –Number of derived events produced by the execution of agents in this context –Number of different agent executions evaluated in this context –Total amount of latency to evaluate all agents executed in this context  For these statistics, event proxy maintains a time series, and computes statistics such as mean, standard deviation, covariance and correlation coefficient.  These statistics dictate the choice of donor and recipient nodes.  Definition of load is purposely generic to incorporate different application priorities.

17. 17 Post Migration Utilization Calculation  We need to determine whether this migration will lead to overload. If it triggers other migrations then the system will become unstable. Therefore compute the post migration utilization of the donor and recipient machines.  Thus the post migration utilization, U d, of the donor machine and U r of the recipient machine after migrating an task t1, and where n d and n r are the total number of tasks on the donor and recipient respectively, is:  Post migration utilization of donor must be less than preset quality threshold  Post migration utilization of recipient must be less than preset quality threshold

18. 18 Support for Dynamic Changes in EP Graph  Our algorithm supports: –Addition of a new connected sub graph to the existing EPN. –Addition of an agent to the graph in the EPN. –Deletion of agents from the graph –Failure of one or more nodes in a stratum level.  Algorithm is also amenable to agent-level optimizations (E.g. coalescing of neighboring agents).

19. 19 Implementation  Used nodes running IBM Active Middleware Technology (AMiT), a CEP engine that serves as a container for event processing agents.  Event processing scenario: credit card scenario  Node hardware characteristics: –Type 1: Dual Core AMD Opteron 280 2.4 GHz and 1GB memory. –Type 2: Intel Pentium D 3.6 Ghz and 2GB memory. –Type 3: Intel Xeon 2.6 Ghz and 2 GB memory.

20. 20 Goal of Implementation  Explore benefits of event processing on stratified vs. centralized vs. partitioned network (single stratum in which load is distributed according to context).  Explore benefit of stratified approach under heavy load (when the number of incoming events that trigger the generation of derived events increases).  Explore the effectiveness and scalability of the load distribution algorithm

21. 21 Results Input events processing rate by stratified versus partitioned event processing networks

22. 22 Results Derived events production rate by stratified versus partitioned event processing networks.

23. 23 Results Percentage of improvement in performance of the stratified network relative to a partitioned network

24. 24 Results Average input events processing rate per node in a stratified network with different configurations

25. 25 Results Throughput results for the load distribution algorithm Scalability of load distribution algorithm

26. 26 Conclusion and Future Work  Demonstrated stratified, load distribution for scalable event processing  Future Work: Investigate high availability  Future Work: Investigate other objectives in addition to scalability  Future Work: Execution of multiple strata within a single nodes cluster.  Future Work: Techniques for effective load migration between nodes.