Workload Prediction of Virtual Machines for Harnessing Data Center Resources

Qazi, Kashifuddin; Li, Yang; Sohn, Andrew

doi:10.1109/cloud.2014.76

Cited by 22 publications

(8 citation statements)

References 20 publications

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“…In our work, we use the publicly available real-world workload traces collected from Google's production compute clusters. The traces are reliable and widely used to evaluate the performance of resource provisioning in many the existing works (Zhang et al 2014;Qazi, Li, and Sohn 2014). We present the publicly available real-world workload traces from Google's production compute clusters in Figure 2.…”

Section: Performance Evaluationmentioning

confidence: 99%

Cost-aware request routing in multi-geography cloud data centres using software-defined networking

Yuan

et al. 2015

Enterprise Information Systems

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Current geographically distributed cloud data centres (CDCs) require gigantic energy and bandwidth costs to provide multiple cloud applications to users around the world. Previous studies only focus on energy cost minimisation in distributed CDCs. However, a CDC provider needs to deliver gigantic data between users and distributed CDCs through internet service providers (ISPs). Geographical diversity of bandwidth and energy costs brings a highly challenging problem of how to minimise the total cost of a CDC provider. With the recently emerging software-defined networking, we study the total cost minimisation problem for a CDC provider by exploiting geographical diversity of energy and bandwidth costs. We formulate the total cost minimisation problem as a mixed integer non-linear programming (MINLP). Then, we develop heuristic algorithms to solve the problem and to provide a cost-aware request routing for joint optimisation of the selection of ISPs and the number of servers in distributed CDCs. Besides, to tackle the dynamic workload in distributed CDCs, this article proposes a regression-based workload prediction method to obtain future incoming workload. Finally, this work evaluates the cost-aware request routing by trace-driven simulation and compares it with the existing approaches to demonstrate its effectiveness.

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Section: Performance Evaluationmentioning

confidence: 99%

Cost-aware request routing in multi-geography cloud data centres using software-defined networking

Yuan

et al. 2015

Enterprise Information Systems

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“…Previous studies on resource consolidation strategies are divided into three main categories: static strategies [22–23], dynamic scheduling [24–26] and decision-making on the prediction [27–29]. The traditional static approach can be implemented to meet a varying demand, but it generates more overheads Halder et al [30] presented a static consolidating algorithm that considered CPU utilization and SLA violations.…”

Section: Related Workmentioning

confidence: 99%

A multiobjective migration algorithm as a resource consolidation strategy in cloud computing

Feng

Zuo

et al. 2019

PLoS ONE

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To flexibly meet users’ demands in cloud computing, it is essential for providers to establish the efficient virtual mapping in datacenters. Accordingly, virtualization has become a key aspect of cloud computing. It is possible to consolidate resources based on the single objective of reducing energy consumption. However, it is challenging for the provider to consolidate resources efficiently based on a multiobjective optimization strategy. In this paper, we present a novel migration algorithm to consolidate resources adaptively using a two-level scheduling algorithm. First, we propose the grey relational analysis (GRA) and technique for order preference by similarity to the ideal solution (TOPSIS) policy to simultaneously determine the hotspots by the main selected factors, including the CPU and the memory. Second, a two-level hybrid heuristic algorithm is designed to consolidate resources in order to reduce costs and energy consumption, mainly depending on the PSO and ACO algorithms. The improved PSO can determine the migrating VMs quickly, and the proposed ACO can locate the positions. Extensive experiments demonstrate that the two-level scheduling algorithm performs the consolidation strategy efficiently during the dynamic allocation process.

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“…In order to optimize the utilization of the applications' components, it is not only necessary to place the hosting virtual machines (or containers) in a meaningful way onto the available physical hosts, but also to select the best-suited virtual machine flavor for each specific component. Most of the literature considers the former, leading to a waste of resources and/or inappropriate application performance [29]. There are works which also consider the workload pattern while taking reconfiguration actions (such as scaling or (re)placement), of special importance in the edge computing deployment scenario, as per by Zhang et al [30].…”

Section: Related Workmentioning

confidence: 99%

Reliable capacity provisioning for distributed cloud/edge/fog computing applications

Östberg

Byrne

Casari

et al. 2017

2017 European Conference on Networks and Communications (EuCNC)

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The REliable CApacity Provisioning and enhanced remediation for distributed cloud applications (RECAP) project aims to advance cloud and edge computing technology, to develop mechanisms for reliable capacity provisioning, and to make application placement, infrastructure management, and capacity provisioning autonomous, predictable and optimized. This paper presents the RECAP vision for an integrated edge-cloud architecture, discusses the scientific foundation of the project, and outlines plans for toolsets for continuous data collection, application performance modeling, application and component auto-scaling and remediation, and deployment optimization. The paper also presents four use cases from complementing fields that will be used to showcase the advancements of RECAP.

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Workload Prediction of Virtual Machines for Harnessing Data Center Resources

Cited by 22 publications

References 20 publications

Cost-aware request routing in multi-geography cloud data centres using software-defined networking

Cost-aware request routing in multi-geography cloud data centres using software-defined networking

A multiobjective migration algorithm as a resource consolidation strategy in cloud computing

Reliable capacity provisioning for distributed cloud/edge/fog computing applications

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