Memory and CPU utilization-aware Energy-Efficient VM Placement and Consolidation in Cloud Data Centers

Choudhury, Alok; Nath, Kamaljyoti; Ghose, Manojit; Thakran, Yogita

doi:10.1109/gcon58516.2023.10183444

2023 IEEE Guwahati Subsection Conference (GCON) 2023

DOI: 10.1109/gcon58516.2023.10183444

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Memory and CPU utilization-aware Energy-Efficient VM Placement and Consolidation in Cloud Data Centers

Alok Choudhury

Kamaljyoti Nath

Manojit Ghose

et al.

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2024

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Cited by 2 publications

References 21 publications

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Optimizing Scheduled Virtual Machine Requests Placement in Cloud Environments: A Tabu Search Approach

Koubàa,

Karar,

Bahloul

2024

Computers

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This paper introduces a novel model for virtual machine (VM) requests with predefined start and end times, referred to as scheduled virtual machine demands (SVMs). In cloud computing environments, SVMs represent anticipated resource requirements derived from historical data, usage trends, and predictive analytics, allowing cloud providers to optimize resource allocation for maximum efficiency. Unlike traditional VMs, SVMs are not active concurrently. This allows providers to reuse physical resources such as CPU, RAM, and storage for time-disjoint requests, opening new avenues for optimizing resource distribution in data centers. To leverage this opportunity, we propose an advanced VM placement algorithm designed to maximize the number of hosted SVMs in cloud data centers. We formulate the SVM placement problem (SVMPP) as a combinatorial optimization challenge and introduce a tailored Tabu Search (TS) meta-heuristic to provide an effective solution. Our algorithm demonstrates significant improvements over existing placement methods, achieving up to a 15% increase in resource efficiency compared to baseline approaches. This advancement highlights the TS algorithm’s potential to deliver substantial scalability and optimization benefits, particularly for high-demand scenarios, albeit with a necessary consideration for computational cost.

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Optimizing Scheduled Virtual Machine Requests Placement in Cloud Environments: A Tabu Search Approach

Koubàa,

Karar,

Bahloul

2024

Computers

View full text Add to dashboard Cite

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A Multi-Objective Approach for Optimizing Virtual Machine Placement Using ILP and Tabu Search

Koubàa,

Regaieg,

Karar

et al. 2024

Telecom

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Efficient Virtual Machine (VM) placement is a critical challenge in optimizing resource utilization in cloud data centers. This paper explores both exact and approximate methods to address this problem. We begin by presenting an exact solution based on a Multi-Objective Integer Linear Programming (MOILP) model, which provides an optimal VM Placement (VMP) strategy. Given the NP-completeness of the MOILP model when handling large-scale problems, we then propose an approximate solution using a Tabu Search (TS) algorithm. The TS algorithm is designed as a practical alternative for addressing these complex scenarios. A key innovation of our approach is the simultaneous optimization of three performance metrics: the number of accepted VMs, resource wastage, and power consumption. To the best of our knowledge, this is the first application of a TS algorithm in the context of VMP. Furthermore, these three performance metrics are jointly optimized to ensure operational efficiency (OPEF) and minimal operational expenditure (OPEX). We rigorously evaluate the performance of the TS algorithm through extensive simulation scenarios and compare its results with those of the MOILP model, enabling us to assess the quality of the approximate solution relative to the optimal one. Additionally, we benchmark our approach against existing methods in the literature to emphasize its advantages. Our findings demonstrate that the TS algorithm strikes an effective balance between efficiency and practicality, making it a robust solution for VMP in cloud environments. The TS algorithm outperforms the other algorithms considered in the simulations, achieving a gain of 2% to 32% in OPEF, with a worst-case increase of up to 6% in OPEX.

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Memory and CPU utilization-aware Energy-Efficient VM Placement and Consolidation in Cloud Data Centers

Cited by 2 publications

References 21 publications

Optimizing Scheduled Virtual Machine Requests Placement in Cloud Environments: A Tabu Search Approach

Optimizing Scheduled Virtual Machine Requests Placement in Cloud Environments: A Tabu Search Approach

A Multi-Objective Approach for Optimizing Virtual Machine Placement Using ILP and Tabu Search

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