This document discusses Cloud2Sim, a new concurrent and distributed cloud simulation tool that extends CloudSim. Cloud2Sim leverages distributed execution and storage capabilities of in-memory data grids to allow cloud simulations to run in a distributed manner across multiple nodes. This improves upon existing cloud simulators that typically run sequentially on a single computer. The document describes Cloud2Sim's design, implementation, evaluations showing its ability to reduce simulation time, and outlines future work such as incorporating search capabilities and optimizing object sizes.

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IEEE 22nd International Symposium on Modeling Analysis and
Simulation of Computer and Telecommunication Systems –
MASCOTS 2014. Sep 9th – 11th, 2014.
Pradeeban Kathiravelu
Luis Veiga
INESC-ID
Instituto Superior Técnico,
Universidade de Lisboa
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2. Introduction
•Researches are empowered by
Simulations.
•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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3. Motivation
•More and more, larger, complex
simulations.
•Clusters in research labs can be
leveraged for simulations in a cycle
sharing fashion.
•Distributed Execution Frameworks.
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4. Motivation
•More and more, larger, complex
simulations.
•Clusters in research labs can be
leveraged for simulations in a cycle
sharing fashion.
•Distributed Execution Frameworks.
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•What if..?

5. Cloud2Sim
•A concurrent and distributed cloud
simulator.
• Extending CloudSim Cloud Simulator
–Leveraging in-memory data grids.
• Hazelcast
• Infinispan
• ...
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6. Overall Use-Case View
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7. Design and Deployment
•Distributed Storage, Execution, and
Data Locality
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8. Execution
Flow
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9. Software Architecture
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10. Implementation Details
•CloudSim trunk forked
•Hazelcast v. 3.2 and Infinispan v.
6.0.2.
•Dependencies on Hazelcast and
Infinispan are abstracted away.
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11. Evaluations
•A cluster with 6 identical nodes
–Intel® Core™ i7-2600K CPU @
3.40GHz and 12 GB memory.
•Varying number of parameters such
as cloudlets (from 100 - 400) and
VMs (from 100 – 200), on 1 to 6
nodes.
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12. Simulation 1. CloudSim and Cloud2Sim
• Round robin application (cloudlet)
scheduling with 200 VMs and 400
cloudlets.
Execution Time
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13. Simulation 2. Fair matchmaking-based
cloudlet scheduling with varying
number of cloudlets
Execution Time
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14. Speedup
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15. Cloud2Sim Features
• Scalable and Fault-Tolerant.
• Uniformly distributed Storage and
Execution.
• Multi-tenanted Deployments.
• Elasticity based on Adaptive Scaling.
•Cycle-sharing across the cluster.
•Deployable over cloud environments.
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16. Conclusion and Future Work
•Conclusion
– Cloud2Sim leverages the
distributed execution and storage
provided by in-memory data grids.
•While exploiting CloudSim as the
core Simulation module.
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17. Conclusion and Future Work
•Conclusion
– Cloud2Sim leverages the
distributed execution and storage
provided by in-memory data grids.
•While exploiting CloudSim as the
core Simulation module.
• Future Work
– Infinispan/Hibernate Search based
CloudSim Simulations and
Application Scheduling.
– Lighter objects.
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18. Conclusion and Future Work
• Conclusion
– Cloud2Sim leverages the distributed
execution and storage provided by in-memory
data grids.
• While exploiting CloudSim as the core
Simulation module.
• Future Work
– Infinispan/Hibernate Search based
CloudSim Simulations and Application
Scheduling.
– Lighter objects.
–Thank you!
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19. 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.
 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.
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