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CoLocateMe: Aggregation-based, Energy, Performance and Cost Aware VM Placement and Consolidation in Heterogeneous IaaS Clouds
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  • Muhammad Zakarya ,
  • Lee Gillam ,
  • Khaled Salah ,
  • Omer F. Rana ,
  • Santosh Tirunagari ,
  • RAJKUMAR BUYYA
Muhammad Zakarya
Abdul Wali Khan University Mardan

Corresponding Author:[email protected]

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Lee Gillam
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Khaled Salah
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Omer F. Rana
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Santosh Tirunagari
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RAJKUMAR BUYYA
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Abstract

In many production clouds, with the notable exception of Google, aggregation-based VM placement policies are used to provision datacenter resources energy and performance efficiently. However, if VMs with similar workloads are placed onto the same machines, they might suffer from contention, particularly, if they are competing for similar resources. High levels of resource contention may degrade VMs performance, and, therefore, could potentially increase users’ costs and infrastructure’s energy consumption. Furthermore, segregation-based methods result in stranded resources and, therefore, less economics. The recent industrial interest in segregating workloads opens new directions for research. In this paper, we demonstrate how aggregation and segregation-based VM placement policies lead to variabilities in energy efficiency, workload performance, and users’ costs. We, then, propose various approaches to aggregation-based placement and migration. We investigate through a number of experiments, using Microsoft Azure and Google’s workload traces for more than twelve thousand hosts and a million VMs, the impact of placement decisions on energy, performance, and costs. Our extensive simulations and empirical evaluation demonstrate that, for certain workloads, aggregation-based allocation and consolidation is ~9.61% more energy and ~20.0% more performance efficient than segregation-based policies. Moreover, various aggregation metrics, such as runtimes and workload types, offer variations in energy consumption and performance, therefore, users’ costs.
01 Mar 2023Published in IEEE Transactions on Services Computing volume 16 issue 2 on pages 1023-1038. 10.1109/TSC.2022.3181375