Putting the Next 500 VM Placement Algorithms to the Acid Test: The Infrastructure Provider Viewpoint

Lèbre, Adrien; Pastor, Jonathan; Simonet, Anthony; Südholt, Mario

doi:10.1109/tpds.2018.2855158

Cited by 18 publications

(8 citation statements)

References 27 publications

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“…From the perspective of the infrastructure supplier, such policies obstruct the best deployment of resources. More dynamic and frequently more efficient strategies, based on consolidation and load balancing approaches, have been constructed during the past 10 years 68 …”

Section: Resultsmentioning

confidence: 99%

Nature‐inspired virtual machine placement mechanisms: A systematic review

Kong

He²,

Abnoosian

2022

Concurrency and Computation

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Cloud data centers do not completely use their resources, resulting in resource underutilization. Cloud computing companies primarily leverage virtualization technologies to supply cost-effective service provision. In order to optimize cloud performance, virtual machines (VMs) must be placed among physical machines (PMs). When it comes to concentrating on the issues in the cloud computing environment, effective VM placement (VMP) is one of the primary difficulties that might cost suppliers money. VMP may be applied in a variety of ways in cloud computing. In terms of lowering related processing overhead, consolidating the cloud environment to become a highly on-demand method, balancing the load among PMs, power usage, and refining performance, VMP techniques still require improvement in the computing environment. This study aims to provide a comprehensive overview of VMP approaches. This article provides an up-to-date survey of the most related VMP literature to highlight study possibilities in cloud settings utilizing nature-inspired metaheuristic algorithms. The findings suggest that placing VMs in the most efficient place saves power usage substantially. The key problem is to minimize data center energy usage without compromising performance or breaking service level agreements. Finally, we will discuss and look at what further may be accomplished in this line of science.

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Section: Resultsmentioning

confidence: 99%

Nature‐inspired virtual machine placement mechanisms: A systematic review

Kong

He²,

Abnoosian

2022

Concurrency and Computation

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“…Similarly to what has been proposed for the Cloud Computing paradigm [13], we claim that a dedicated simulator toolkit to help researchers investigate Edge scheduling strategies should be released soon. Indeed, we claim that using placement simulators for Cloud Computing is not appropriate to study Edge challenges.…”

Section: Introductionmentioning

confidence: 95%

Evaluating Computation and Data Placements in Edge Infrastructures through a Common Simulator

SILVA

Mommessin

Neyron

et al. 2020

2020 IEEE 32nd International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

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Scheduling computational jobs with data-sets dependencies is an important challenge of edge computing infrastructures. Although several strategies have been proposed, they have been evaluated through ad-hoc simulator extensions that are, when available, usually not maintained. This is a critical problem because it prevents researchers to -easily-perform fair comparisons between different proposals.In this paper, we propose to address this limitation by presenting a simulation engine dedicated to the evaluation and comparison of scheduling and data movement policies for edge computing use-cases. Built upon the Batsim/SimGrid toolkit, our tool includes an injector that allows the simulator to replay a series of events captured in real infrastructures. It also includes a controller that supervises storage entities and data transfers during the simulation, and a plug-in system that allows researchers to add new models to cope with the diversity of edge computing devices.We demonstrate the relevance of such a simulation toolkit by studying two scheduling strategies with four data movement policies on top of a simulated version of the Qarnot Computing platform, a production edge infrastructure based on smart heaters. We chose this use-case as it illustrates the heterogeneity as well as the uncertainties of edge infrastructures.Our ultimate goal is to gather industry and academics around a common simulator so that efforts made by one group can be factorised by others.

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“…However, aggregation and segregation based VMs placement and consolidation techniques are not explored. Lebre et al [36] have also discussed various VM placement and consolidation techniques in terms of three different schedulers: centralised, hierarchical and distributed. Tchana et al [37] suggested software or application migration to achieve energy efficiency in datacenters.…”

Section: Related Workmentioning

confidence: 99%

CoLocateMe: Aggregation-based, Energy, Performance and Cost Aware VM Placement and Consolidation in Heterogeneous IaaS Clouds

Zakarya¹,

Gillam²,

Rana³

et al. 2021

Preprint

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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.<br>

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Putting the Next 500 VM Placement Algorithms to the Acid Test: The Infrastructure Provider Viewpoint

Cited by 18 publications

References 27 publications

Nature‐inspired virtual machine placement mechanisms: A systematic review

Nature‐inspired virtual machine placement mechanisms: A systematic review

Evaluating Computation and Data Placements in Edge Infrastructures through a Common Simulator

CoLocateMe: Aggregation-based, Energy, Performance and Cost Aware VM Placement and Consolidation in Heterogeneous IaaS Clouds

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