An adaptive distributed simulator for cloud andmap reduce algorithms and architectures
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An Adaptive Distributed Simulator for An Adaptive Distributed Simulator for Cloud and MapReduce Cloud and MapReduce Algorithms and ArchitecturesAlgorithms and Architectures
Pradeeban KathiraveluLuis VeigaINESC-ID Lisboa Instituto Superior Técnico, Universidade de Lisboa
IEEE/ACM 7th International Conference on Utility and Cloud Computing – UCC 2014. Dec 8th – 11th, 2014.
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Agenda
•Introduction•Background•Solution Architecture•Implementation•Evaluation•Conclusion
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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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Motivation
•Large and complex simulations.•Distributed Execution Frameworks.– Illusion of a single large system.
•Clusters in the research labs.
•What if..?
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Goals
•An adaptive distributed cloud and MapReduce simulator.
•Extending CloudSim Cloud Simulator –Leveraging in-memory data grids.•Hazelcast (Johns 2013)
• Infinispan (Marchioni 2012)
• ...
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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.
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Major Features of the Work•Simulations → Actual Technology.•Loosely coupled.•Fault-Tolerant.• Internal cycle-sharing.•Deployable over real clouds.
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Design and DeploymentStorage, Execution, and Data Locality
• Simulator–Initiator based Approach
• Simulator–SimulatorSub based Approach
• Multiple Simulator Instances Approach
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Algorithms:Dynamic Scaling and Elasticity
•Auto Scaling•Adaptive Scaling
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Implementation
•CloudSim trunk forked•Hazelcast version 3.2 and Infinispan
version 6.0.2.•Dependencies abstracted away.
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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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Simulation 1: CloudSim and Cloud2Sim
•Round robin application scheduling with 200 VMs and 400 cloudlets.
Execution Time
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Simulation 2: Matchmaking-based Application Scheduling
Execution Time
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Scalability
Hazelcast Implementation
Map() invocations = 3
Infinispan Implementation
Reduce() invocations = 159,069
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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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Conclusion• Conclusions–Distributed architecture facilitates larger
simulations.– Faster execution of time-consuming
applications.
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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-
Service.
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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-
Service.
Thank you! Questions?Thank you! Questions?
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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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