This document presents an elastic middleware platform for concurrent and distributed cloud and MapReduce simulations called Cloud2Sim. Cloud2Sim extends the CloudSim cloud simulator to enable distributed execution across multiple computing nodes for larger simulations. It implements an adaptive scaling algorithm to dynamically scale simulations across nodes based on workload. An evaluation shows Cloud2Sim can speed up simulations and scale well with increasing problem sizes and nodes compared to the standard CloudSim.

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Supervised by:
Prof. Luis Veiga
INESC-ID / Instituto Superior Técnico,
Universidade de Lisboa
Pradeeban Kathiravelu
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2. Agenda
•Introduction
•Background
•Solution Architecture
•Implementation
•Evaluation
•Conclusion
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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)
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4. Motivation
•Large and complex simulations.
•Distributed Execution Frameworks.
– Illusion of a single large system.
•Clusters in the research labs.
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•What if..?

5. Thesis Goals
•A concurrent and distributed cloud
and MapReduce simulator.
•Extending CloudSim Cloud Simulator
– Leveraging in-memory data grids.
• Hazelcast (Johns 2013)
• Infinispan (Marchioni 2012)
• ...
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6. Contributions
•Concurrent & 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.
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7. Major Features of the Work
•Simulations → Actual Technology.
•Loosely coupled.
•Fault-Tolerant.
•Internal cycle-sharing.
•Deployable over real clouds.
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8. Cloud2Sim
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9. Design and Deployment
Storage, Execution, and Data Locality
• Simulator–Initiator based Approach
• Simulator–SimulatorSub based Approach
•Multiple Simulator Instances Approach
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10. Cloud2Sim
Execution
Flow
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11. 1. Objects
Initialization
& Scheduling
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12. 2. Final Execution
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13. Cloud2Sim
Execution
Flow
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14. Powerpoint Templates 14
Cloud2Sim
Software
Architecture

15. Algorithms:
Dynamic Scaling and Elasticity
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16. Algorithms:
Dynamic Scaling and Elasticity
•Auto Scaling
•Adaptive Scaling
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17. Auto Scaling
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18. Adaptive Scaling
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19. IntelligentAdaptiveScaler
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20. Subscribing for Scaling
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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
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26. Waiting Period..
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27. Waiting Period..
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28. Monitor for Scale Ins Too..
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29. After some time..
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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..
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36. Parallel Simulations
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37. Multi-tenanted Deployments
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38. MapReduce
Executions
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39. Implementation
•CloudSim trunk forked
•Hazelcast version 3.2 and Infinispan
version 6.0.2.
•Dependencies abstracted away.
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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.
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41. Simulation 1: CloudSim and Cloud2Sim
•Round robin application scheduling
with 200 VMs and 400 cloudlets.
Execution Time
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42. Varying number of Cloudlets
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43. With Adaptive Scaling
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44. Simulation 2: Matchmaking-based
Application Scheduling
Execution Time
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45. Speed up
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46. Simulation 3: MapReduce
Implementations
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47. Scalability
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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.
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49. Conclusion
• Conclusions
–Distributed architecture facilitates larger
simulations.
– Faster execution of time-consuming
applications.
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50. Conclusion
• 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-
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Service.

51. Publications
• Kathiravelu, P. & L. Veiga (2014).
Concurrent and Distributed CClloouuddSSiimm SSiimmuullaattiioonnss..
In IEEE 22nd International Symposium on Modeling, Analysis
and Simulation of Computer and Telecommunication
Systems (MASCOTS'14), pp. 490–493 (work–in–progress).
IEEE Computer Society.
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52. Publications
• Kathiravelu, P. & L. Veiga (2014).
Concurrent and Distributed CClloouuddSSiimm SSiimmuullaattiioonnss..
In IEEE 22nd International Symposium on Modeling, Analysis
and Simulation of Computer and Telecommunication
Systems (MASCOTS'14), pp. 490–493 (work–in–progress).
IEEE Computer Society.
• KKaatthhiirraavveelluu,, PP.. && LL.. VVeeiiggaa ((22001144))..
AAnn AAddaappttiivvee DDiissttrriibbuutteedd SSiimmuullaattoorr ffoorr CClloouudd aanndd
MMaappRReedduuccee AAllggoorriitthhmmss aanndd AArrcchhiitteeccttuurreess..
IInn IIEEEEEE//AACCMM 77tthh IInntteerrnnaattiioonnaall CCoonnffeerreennccee oonn UUttiilliittyy aanndd CClloouudd
CCoommppuuttiinngg ((UUCCCC 22001144)).. IIEEEEEE CCoommppuutteerr SSoocciieettyy.. ((aacccceepptteedd))..
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53. 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.
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