Optimization of a Cloud Resource Management Problem from a Consumer Perspective

Coutinho, Rafaelli; Drummond, Lúcia Maria de A.; Frota, Yuri

doi:10.1007/978-3-642-54420-0_22

Cited by 16 publications

(18 citation statements)

References 21 publications

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“…In doing so, we have tested it over a set of problem instances based on real cloud market offerings proposed in Coutinho et al [13]. These instances include different requirement sets on Gflop, memory, and hard disk capacity of five real applications [13]: a branch-and-bound algorithm for solving the Quadratic Assignment Problem (QAP) [20], three algorithms that tackle the manipulation of a biological sequence problem (RAXML [21], ModelGenerator [22] and Segemehl [23]), and a typical user analysis job for the CMS experiment [24]. Each instance further includes a maximum allowed execution time and payment cost, defined by the consumer.…”

Section: Computational Resultsmentioning

confidence: 99%

“…The CRMP is proposed by Coutinho et al [13] as a cloud computing optimization problem oriented towards consumers and cloud brokers. That is, in the CRPM, we specifically focus on IaaS enabling consumers to select and utilize resizable computational resources to run their applications, after the consumer has decided which CSP will be used (for example, Amazon Web Services, Google Cloud Platform, etc.).…”

Section: Cloud Resource Management Problemmentioning

confidence: 99%

“…In the following, with the aim of making this paper self-contained, we include the mathematical formulation of the CRMP as presented in Coutinho et al [13]. For a consumer, we define both, a set of application requirements and a set of consumer requirements.…”

Section: Mathematical Formulationmentioning

confidence: 99%

“…Recently, Coutinho et al [13] proposed the Cloud Resource Management Problem (CRMP), which is a novel multi-criteria optimization model taking into account both, cost and performance preferences of consumers. The authors present an integer programming (IP) formulation, referred to as CC-IP, which considers resource demands of applications, budget limits, and various resource packages that describe the physical capabilities of associated virtual machine (VM) instances.…”

Section: Introductionmentioning

confidence: 99%

“…It should be noted that fast algorithms may be desirable from the consumer viewpoint, as such an algorithm could be executed on a standard user computer without requiring expensive high performance computing. For evaluating its performance, we undertake an extensive experimentation over the benchmark suite proposed for this problem in Coutinho et al [13]. In a nutshell, our evolutionary approach noticeably outperforms recent approaches within small computation times in the millisecond range, on average.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Biased Random-Key Genetic Algorithm for the Cloud Resource Management Problem

Heilig

Lalla-Ruiz

Voß

2015

Evolutionary Computation in Combinatorial Optimization

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Flexible use options and associated cost savings of cloud computing are increasingly attracting the interest from both researchers and practitioners. Since cloud providers offer various cloud services in different forms, there is a large potential of optimizing the selection of those services from the consumer perspective. In this paper, we address the Cloud Resource Management Problem that is a recent optimization problem aimed at reducing the payment cost and the execution time of consumer applications. In the related literature, there is one approach that successfully addresses this problem based on a Greedy Randomized Adaptive Search Procedure. Due to the fact that consumers require fast and high-quality solutions to economically automate cloud resource management and deployment processes, we propose an efficient Biased Random-Key Genetic Algorithm. The computational experiments over a benchmark suite generated based on real cloud market offerings indicate that the performance of our approach outperforms the approaches proposed in the literature.

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Section: Computational Resultsmentioning

confidence: 99%

Section: Cloud Resource Management Problemmentioning

confidence: 99%

Section: Mathematical Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Biased Random-Key Genetic Algorithm for the Cloud Resource Management Problem

Heilig

Lalla-Ruiz

Voß

2015

Evolutionary Computation in Combinatorial Optimization

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Extensive review of cloud resource management techniques in industry 4.0: Issue and challenges

et al. 2020

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Resource provisioning in the cloud is the most popular business model for any service provider due to profit that is based on how resources are distributed among users in Industry 4.0. Moreover, much research carried out to provide a better resource provisioning system to service providers; however, efficient resource provisioning may save the environments too. This study presents an extensive analysis of different resource provisioning systems based on concert parameters. More than 250 relative research articles have been considered for this study, out of these 125 articles has been processed for comparative analysis on the basis of different performance metrics. This study highlights the classifications of resource management techniques, objective functions, and open research challenges and issues while analyzing resource management techniques. It also provides the depth knowledge of uses of performance metrics utilization based on its classifications.

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