The presentation I did, when presenting my work at UCC 2014 in London on the 8th of December, 2014. http://kkpradeeban.blogspot.com/2014/09/ucc-2014-adaptive-distributed-simulator.html

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IEEE/ACM 7th International Conference on Utility and Cloud Computing –
UCC 2014. Dec 8th – 11th, 2014.
Pradeeban Kathiravelu
Luis Veiga
INESC-ID Lisboa
Instituto Superior Técnico,
Universidade de Lisboa
PPoowweerrppooiinntt TTeemmppllaatteess 1

2. Agenda
•Introduction
•Background
•Solution Architecture
•Implementation
•Evaluation
•Conclusion
Powerpoint Templates 2

3. Introduction
•Computing systems becoming
increasingly larger.
•Simulations empower researches.
•Cloud simulators are mostly
sequential and executed from a
single computer.
–CloudSim (Calheiros et al. 2009; Buyya et al. 2009; Calheiros et al. 2011)
–SimGrid (Casanova 2001; Legrand et al. 2003; Casanova et al. 2008)
–GreenCloud (Kliazovich et al. 2012)
Powerpoint Templates 3

4. Motivation
•Large and complex simulations.
•Distributed Execution Frameworks.
– Illusion of a single large system.
•Clusters in the research labs.
Powerpoint Templates 4
•What if..?

5. Goals
•An adaptive distributed cloud and
MapReduce simulator.
•Extending CloudSim Cloud Simulator
– Leveraging in-memory data grids.
• Hazelcast (Johns 2013)
• Infinispan (Marchioni 2012)
• ...
Powerpoint Templates 5

6. Contributions
•An adaptive distributed architecture
– for cloud and MapReduce simulations.
•A generic adaptive scaling algorithm.
•A scalable middleware platform
– elastic
– multi-tenanted
•Evaluation of MapReduce
implementations.
–Hazelcast vs Infinispan.
Powerpoint Templates 6

7. Major Features of the Work
•Simulations → Actual Technology.
•Loosely coupled.
•Fault-Tolerant.
•Internal cycle-sharing.
•Deployable over real clouds.
Powerpoint Templates 7

8. Cloud2Sim
Powerpoint Templates 8

9. Design and Deployment
Storage, Execution, and Data Locality
• Simulator–Initiator based Approach
• Simulator–SimulatorSub based Approach
•Multiple Simulator Instances Approach
Powerpoint Templates 9

10. Cloud2Sim
Execution
Flow
Powerpoint Templates 10

11. 1. Objects
Initialization
& Scheduling
Powerpoint Templates 11

12. 2. Final Execution
Powerpoint Templates 12

13. Cloud2Sim
Execution
Flow
Powerpoint Templates 13

14. Powerpoint Templates 14
Cloud2Sim
Software
Architecture

15. Algorithms:
Dynamic Scaling and Elasticity
Powerpoint Templates 15

16. Algorithms:
Dynamic Scaling and Elasticity
•Auto Scaling
•Adaptive Scaling
Powerpoint Templates 16

17. Auto Scaling
Powerpoint Templates 17

18. Adaptive Scaling
Powerpoint Templates 18

19. IntelligentAdaptiveScaler
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20. Subscribing for Scaling
Powerpoint Templates 20

21. High Load
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22. Updating the flag
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23. Open Access
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24. Scaling Out
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25. Spawning an Initiator Instance
Powerpoint Templates 25

26. Waiting Period..
Powerpoint Templates 26

27. Waiting Period..
Powerpoint Templates 27

28. Monitor for Scale Ins Too..
Powerpoint Templates 28

29. After some time..
Powerpoint Templates 29

30. Scale Out Again..
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31. One more Initiator..
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32. After more scalings..
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33. Scale In..
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34. Shut down an Initiator Instance
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35. Finally..
Powerpoint Templates 35

36. Parallel Simulations
Powerpoint Templates 36

37. Multi-tenanted Deployments
Powerpoint Templates 37

38. MapReduce
Executions
Powerpoint Templates 38

39. Implementation
•CloudSim trunk forked
•Hazelcast version 3.2 and Infinispan
version 6.0.2.
•Dependencies abstracted away.
Powerpoint Templates 39

40. Evaluation
•Setup: Cluster with 6 identical nodes
–Intel® Core™ i7-2600K CPU @
3.40GHz and 12 GB memory.
•Varying number of parameters
–Cloudlets: 100 → 400.
–VMs: 100 → 200.
–Nodes: 1 → 6.
Powerpoint Templates 40

41. Simulation 1: CloudSim and Cloud2Sim
•Round robin application scheduling
with 200 VMs and 400 cloudlets.
Execution Time
Powerpoint Templates 41

42. Varying number of Cloudlets
Powerpoint Templates 42

43. With Adaptive Scaling
Powerpoint Templates 43

44. Simulation 2: Matchmaking-based
Application Scheduling
Execution Time
Powerpoint Templates 44

45. Speed up
Powerpoint Templates 45

46. Simulation 3: MapReduce
Implementations
Powerpoint Templates 46

47. Scalability
Powerpoint Templates 47
Hazelcast
Implementation
Map() invocations = 3
Infinispan
Implementation
Reduce() invocations = 159,069

48. Conclusion
•Summary
– Distribution strategies and algorithms for
cloud and MapReduce simulations.
– Implementation of an Elastic Middleware
platform.
– Scale and perform with multiple nodes and
larger simulations.
Powerpoint Templates 48

49. Conclusion
•Conclusions
–Distributed architecture facilitates larger
simulations.
– Faster execution of time-consuming
applications.
Powerpoint Templates 49

50. Conclusion
Powerpoint Templates 50
• Conclusions
– Distributed architecture facilitates larger
simulations.
– Faster execution of time-consuming
applications.
• Future Work
– State-aware Adaptive Scaling
– Infinispan based Cloud Simulations.
– Lighter objects.
– Generic Elastic Middleware Platform-as-a-
Service.

51. Conclusion
Powerpoint Templates 51
• Conclusions
– Distributed architecture facilitates larger
simulations.
– Faster execution of time-consuming
applications.
• Future Work
– State-aware Adaptive Scaling
– Infinispan based Cloud Simulations.
– Lighter objects.
– Generic Elastic Middleware Platform-as-a-
Service.
TThhaannkk yyoouu!! QQuueessttiioonnss??

52. References
 Buyya, R., R. Ranjan, & R. N. Calheiros (2009). Modeling and simulation of scalable cloud computing
environments and the cloudsim toolkit: Challenges and opportunities. In High Performance Computing
& Simulation, 2009. HPCS’09. International Conference on, pp. 1–11. IEEE.
 Calheiros, R. N., R. Ranjan, C. A. De Rose, & R. Buyya (2009). Cloudsim: A novel framework for
modeling and simulation of cloud computing infrastructures and services. arXiv preprint
arXiv:0903.2525
 Calheiros, R. N., R. Ranjan, A. Beloglazov, C. A. De Rose, & R. Buyya (2011). Cloudsim: a toolkit for
modeling and simulation of cloud computing environments and evaluation of resource provisioning
algorithms. Software: Practice and Experience 41 (1), 23–50.
 Casanova, H. (2001). Simgrid: A toolkit for the simulation of application scheduling. In Cluster
Computing and the Grid, 2001. Proceedings. First IEEE/ACM International Symposium on, pp. 430–437.
IEEE.
 Casanova, H., A. Legrand, & M. Quinson (2008). Simgrid: A generic framework for large-scale
distributed experiments. In Computer Modeling and Simulation, 2008. UKSIM 2008. Tenth International
Conference on, pp. 126–131. IEEE.
 Johns, M. (2013). Getting Started with Hazelcast. Packt Publishing Ltd.
 Kliazovich, D., P. Bouvry, & S. U. Khan (2012). Greencloud: a packet-level simulator of energy-aware
cloud computing data centers. The Journal of Supercomputing 62 (3), 1263–1283.
 Legrand, A., L. Marchal, & H. Casanova (2003). Scheduling distributed applications: the simgrid
simulation framework. In Cluster Computing and the Grid, 2003. Proceedings. CCGrid 2003. 3rd
IEEE/ACM International Symposium on, pp. 138–145. IEEE.
 Marchioni, F. (2012). Infinispan Data Grid Platform. Packt Publishing Ltd.
Powerpoint Templates 52