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Rapid Testing of IaaS Resource Management Algorithms via Cloud Middleware Simulation
Vise andre og tillknytning
2018 (engelsk)Inngår i: Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering, ACM Digital Library, 2018, s. 184-191Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Infrastructure as a Service (IaaS) Cloud services allow users to deploy distributed applications in a virtualized environment without having to customize their applications to a specific Platform as a Service (PaaS) stack. It is common practice to host multiple Virtual Machines (VMs) on the same server to save resources. Traditionally, IaaS data center management required manual effort for optimization, e.g. by consolidating VM placement based on changes in usage patterns. Many resource management algorithms and frameworks have been developed to automate this process. Resource management algorithms are typically tested via experimentation or using simulation. The main drawback of both approaches is the high effort required to conduct the testing. Existing Cloud or IaaS simulators require the algorithm engineer to reimplement their algorithm against the simulator's API. Furthermore, the engineer manually needs to define the workload model used for algorithm testing. We propose an approach for the simulative analysis of IaaS Cloud infrastructure that allows algorithm engineers and data center operators to evaluate optimization algorithms without investing additional effort to reimplement them in a simulation environment. By leveraging runtime monitoring data, we automatically construct the simulation models used to test the algorithms. Our validation shows that algorithm tests conducted using our IaaS Cloud simulator match the measured behavior on actual hardware.

sted, utgiver, år, opplag, sider
ACM Digital Library, 2018. s. 184-191
Emneord [en]
IaaS middleware simulation, cloud simulation, performance model extraction, performance simulation, power consumption prediction, simulation-based testing of resource management algorithms
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Identifikatorer
URN: urn:nbn:se:umu:diva-146655DOI: 10.1145/3184407.3184428OAI: oai:DiVA.org:umu-146655DiVA, id: diva2:1197958
Konferanse
9th ACM/SPEC International Conference on Performance Engineering (ICPE 2018), Berlin, Germany, April 9–13, 2018
Tilgjengelig fra: 2018-04-16 Laget: 2018-04-16 Sist oppdatert: 2018-06-09bibliografisk kontrollert

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