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.
An Elastic Middleware Platform for Concurrent and Distributed Cloud and MapReduce Simulations
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Supervised by:
Prof. Luis Veiga
INESC-ID / Instituto Superior Técnico,
Universidade de Lisboa
Pradeeban Kathiravelu
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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..?
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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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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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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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
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53. References
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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
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Casanova, H., A. Legrand, & M. Quinson (2008). Simgrid: A generic framework for large-scale
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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
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Marchioni, F. (2012). Infinispan Data Grid Platform. Packt Publishing Ltd.
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