1 Supporting Dynamic Migration in Tightly Coupled Grid Applications Liang Chen Qian Zhu Gagan Agrawal Computer Science & Engineering The Ohio State University

2 Introduction- Motivation –Grid resources vary frequently –Tightly coupled applications in Grid v.s. bag of tasks Pipelined applications and streaming applications Features: –Dependencies –Run-longing –Large volumes of data transfer between tasks (stages) –Dynamically allocating new resources and migrating applications to the new resources improve performance

3 Introduction- Challenges –Checkpointing is a classic method to support dynamic migration A snapshot of system’s running state Transmit to a remote site Restore execution context and restart processes –Pros of checkingpointing Maybe transparent to applications –Cons of checkingpointing Platform dependent Inefficient

4 Introduction- Our approach –Typical processing structure of a data streaming application:... while(true) { read_data_from_streams(); process_data(); accumulate_intermediate_results(); reset_auxiliary_structures(); }... –Our approach is based on Light-weight Summary Structure (LSS) Data structure storing summary information is Light-weight summary structure Others are Auxiliary structures

5 Introduction - Contribution Proposed the notion of LSS that enables efficient process migration Implemented application migration using LSS in the GATES middleware Designed a dynamic resource allocation algorithm for pipeline processing on streaming data Demonstrated an architecture for resource monitoring and allocation Extensively evaluated the LSS implementation using 3 data stream applications

6 Middleware System Architecture Features of data steam –Data arrive continuously –Enormous volume and must be processed online –Need to be processed in real-time –Data sources could be distributed The needs for processing distributed data streams –A middleware running in Grid –Allocate Grid resources –Provide self-adaptation function

7 Middleware System Architecture –GATES (Grid-Based AdapTive Execution on Streams) middleware Use Globus Toolkit 3.0, built on OGSA Allows users to specify their algorithms implemented in Java Take care of plugging user-defined algorithms into the system and running them in Grid. Applications need be broken down into a number of pipelined stages

8 ABC Stage A Stage BStage C :GATES services :Stages of an application :Queues between Grid services :Buffers for applications Middleware System Architecture Application Stage A Stage B Stage C

9 Public class Second-Stage implements StreamProcessing { … void work(buffer in, buffer out) { … while(true) { DATA = GATES.getFromInputBuffer(in); Inter-Results = Processing(Data); GATES.putToOutputBuffer (out, Inter-Results); } System Architecture and Design (GATES API Functions)

10 Roadmap Introduction ‒ Motivation for tight-coupled applications in Grid ‒ Challenge and our approach Middleware System Overview ‒ Introduce the system architecture and design Implementing Dynamic Migration Using LSS ‒ Light-weight summary structure (LSS) and its example ‒ Advantages of utilizing LSS ‒ LSS Implementation Detail ‒ Architecture of dynamic resource allocation scheme Evaluation ‒ Three distributed data stream applications ‒ Memory usage of LSS ‒ Efficient migration by using LSS ‒ Processing accuracy and LSS migration Related work Conclusion

11 Light-weight Summary Structure (LSS) & its Example LSS is a data structure that stores summary information of processing Auxiliary structures An application calculates the average value of all integer numbers in a stream –Two stage: the first is data source the second calculates the sum and counts the number of integers, ave=sum/count – LSS would be the sum and the count – Auxiliary structures would be loop index and other temporary variables

12 Advantages of using LSS Efficient, only LSS is migrated –Only “sum” and “count” migrate Not impact the accuracy of processing Support migration across heterogeneous platforms –“sum” and “count” are logic structures Reduce application developers’ efforts on making application capable of migration

13 An Example of LSS LSS can be used to support dynamic migration –GAETS provides an API function to allocate memory to be LSS –An application stores summary information to LSS –transmit only LSS at the end of the loop to a new node –Restore the LSS at the new node

14 Public class Second-Stage implements StreamProcessing { … void work(buffer in, buffer out) { … while(true) { DATA = GATES.getFromInputBuffer(in); Inter-Results = Processing(Data); GATES.putToOutputBuffer (out, Inter-Results); } Application using LSS LSS = Get a LSS from GATES Accumulate Inter-Results to LSS Reset all Auxiliary structures Inform GATES migration could be executed

15 Implementation Detail

16 Architecture of dynamic resource allocation scheme Using Information Service to collect resource information Apply dynamic resource allocation algorithm Advise and assist GATES services to migrate

17 Roadmap Introduction ‒ Motivation for tight-coupled applications in Grid ‒ Challenge and our approach Middleware System Overview ‒ Introduce the system architecture and design Implementing Dynamic Migration Using LSS ‒ Light-weight summary structure (LSS) and its example ‒ How applications utilize LSS ‒ LSS Implementation Detail ‒ Architecture of dynamic resource allocation scheme Evaluation ‒ Three distributed data stream applications ‒ Memory usage of LSS ‒ Efficient migration by using LSS ‒ Processing accuracy and LSS migration Related work Conclusion

18 Experimental Evaluation Evaluation –Three applications Counting sample –LSS stores intermediate top M frequently occurring numbers Clustream, clustering data points in streams –LSS stores micro-clusters computed at the second stage Dist-Freq-Counting, finding frequent itemsets in distributed streams. –LSS stores unprocessed itemsets

19 Experimental Evaluation Memory usage of LSS

20 Experimental Evaluation Memory usage of LSS

21 Experimental Evaluation Migration using LSS is efficient

22 Experimental Evaluation Migration using LSS is efficient

23 Experimental Evaluation Migration using LSS is efficient

24 Experimental Evaluation Migration using LSS is efficient

25 Experimental Evaluation Benefits of migration in a dynamic environment

26 Experimental Evaluation Benefits of migration in a dynamic environment

27 Experimental Evaluation LSS migration does not impact processing accuracy –The counting sample application was used –Compared the average accuracy of the processing results from the non- migration and the migration versions, they are 97.28% and 97.51% accurate

28 Related work  Condor  XCATS  Charm++  User Level Processes (ULP)  Migratable PVM  Piranha

29 Related Work Middleware for data stream processing –Data cutter, Stampede –Differences: in a cluster, no self-adaptation, no specifically for real-time processing Continuous query systems –STREAM, dQUOB, TelegraphCQ, NiagraCQ –Differences: centralized, no adaptation supports Distributed continuous query systems –Aurora*, Medusa, Borealis –Differences: continuous queries, not in Grid environment In- Network aggregation in sensor network Stream-based overlay networks

30 Conclusion  LSS enables efficient migration for distributed data stream applications  The main observations from our experiments –Enables efficient process migration; the size of process state reduced by times –Introduces a very small overhead –Significantly improve the performance of long- running applications. –Our migration scheme does not impact the accuracy of the processing.

31 Questions?

32 Implementing Dynamic Migration Using LSS

33 Architecture of Dynamic Resource Allocation Scheme