A method for the optimum selection of datacenters in geographically distributed clouds

Ziafat, Hassan; Babamir, Seyed Morteza

doi:10.1007/s11227-017-1999-5

Cited by 13 publications

(6 citation statements)

References 30 publications

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“…However, in none of these studies, the influence of the datacenter location on the cost of ser-vices in the Cloud is verified. Ziafat and Babamir [22,43] used different multiobjective optimization algorithms for the selection of datacenters considering qualitative aspects such as the distance between datacenter and user, indices of reliability and availability, response time, and lower cost. However, when considering a large number of conflicting objectives, the selection of a datacenter that satisfies all the objectives, despite being considered optimal by the optimization algorithm, tends not to satisfy user needs effectively, since it is not possible to prioritize an objective in relation to another.…”

Section: Related Workmentioning

confidence: 99%

Selecting services in the cloud: a decision support methodology focused on infrastructure-as-a-service context

et al. 2022

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Growing demand for reduced local hardware infrastructure is driving the adoption of Cloud Computing. In the Infrastructure-as-a-Service model, service providers offer virtualized computational resources in the form of virtual machine instances. The existence of a large variety of providers and instances makes the decision-making process a difficult task for users, especially as factors such as the datacenter location -where the virtual machine is hosted -have a direct influence on the price of instances. The same instance may present price differences when hosted in dif-ferent geographically distributed datacenters and, because of that, the datacenter location needs to be taken into account through the decision-making process. Given this problem, we propose the D-AHP, a methodology to aid decision-making based on Pareto Dominance and Analytic Hierarchy Process (AHP). In the D-AHP, the dominance concept is applied to reduce the number of instances to be compared; the instances selection is based on a set of objectives, while AHP ranks the selected ones from a set of criteria and sub-criteria, among them the datacenter location. The results from case studies show that differences may arise in the results, regarding which instance is more suitable for the user, when considering the datacenter loca-tion as a criterion to choose an instance. This fact highlights the need to consider this factor during the process of migrating applications to the Cloud. In addition, Pareto Dominance applied early over the set of total instances has proved to be efficient, once it significantly reduces the number of instances to be compared and ordered by the AHP by excluding instances with less computational resources and higher cost in the decision-making process, mainly for larger application workloads.

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Section: Related Workmentioning

confidence: 99%

Selecting services in the cloud: a decision support methodology focused on infrastructure-as-a-service context

et al. 2022

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“…Reliability is an indicator of the successful running of a task by the virtual machine in a datacenter and is expressed by Equation as follows:

Reliability ((), RE) V_{k} = \frac{{italicC}_{italick}}{{italicA}_{italick}},

…”

Section: Performance Evaluationmentioning

confidence: 99%

A moth‐flame optimization algorithm for web service composition in cloud computing: Simulation and verification

et al. 2018

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In recent years, users are becoming increasingly accustomed to using the Internet to gain software resources in the form of web services provided by information technology organizations. Cloud computing is a service delivery paradigm that shares services and resources to access the web services to the end users over the Internet. In the cloud environment, based on the user's needs, various types of services with similar functionalities but different quality-of-service (QoS) criteria can be delivered, which often must be combined to meet the users' requests. The optimal selection and composition of these services are realized as an interesting issue. In this paper, we propose a moth-flame optimization (MFO) algorithm, which is a novel nature-inspired metaheuristic paradigm for the web service composition (WSC) problem called "MFO-WSC," to improve the QoS criteria in the distributed cloud environment. Also, formal modeling is presented for the QoS-aware MFO-WSC algorithm with the model checking approach that receives the particular benefits to collaborate the correctness of the proposed algorithm. The correctness of the proposed behavior model is examined using some logical problems such as deadlock-free, fairness, and reachability conditions in the new symbolic model verifier model checker. The experimental results indicate the effectiveness of the proposed algorithm in comparison with similar related works.

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“…In the work of Ziafat and Babamir, inspired by the nondominated sorting genetic algorithm II (NSGA‐II) metaheuristic algorithm, we proposed a method to select optimal data centers in geo‐distributed clouds according to SLA rules where conflicting objectives, ie, maximizing availability and reliability and minimizing response time and cost, were considered. The results were compared with those that use another metaheuristic algorithm called multiobjective particle swarm optimization.…”

Section: Related Workmentioning

confidence: 99%

“…In this section, we compare results of our proposed method to those of Random, Greedy, NSGA‐II, and Approx on the basis of the two workloads. The Approx method was explained in Section 2.4 and NSGA‐II was introduced in Section 2.3.…”

Section: Effectiveness Evaluationmentioning

confidence: 99%

Optimal selection of VMs for resource task scheduling in geographically distributed clouds using fuzzy c‐mean and MOLP

Ziafat

Babamir

2018

Softw Pract Exp

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Because of widespread distribution of resources in the geographically distributed cloud environment, optimal selection of virtual machines (VMs) is one of the most important challenges for the structure of the network. This is due to the high number of data centers and VMs with different qualities of service parameters. Because of redundancy in the VMs and the high number of service parameters, optimal selection of VMs is an NP-hard problem. Therefore, a method is required, which can suggest the best VMs on the basis of the user's request and on the service-level agreements (SLAs). This study focuses on four important factors in SLAs: cost, response time, availability, and reliability.In this paper, we propose a four-tier structure, Observe, Orient, Decide, and Act, where (1) Observe is responsible for continuous monitoring users' requests and characteristics of data centers and VMs, (2) Orient is responsible for clustering data centers using fuzzy c-means and based of the four quality of services (SLA's factors) and then the selection of the most suitable data center cluster for the VM selection, (3) Decide is responsible for making decision on the most suitable VMs using multiobjective linear programming, and (4) Act is responsible for the execution of the decision. The proposed structure was implemented, and its effectiveness was evaluated through considering the number of SLA violations. KEYWORDSclustering, fuzzy c-means (FCM) algorithm, geographically distributed data centers, multiobjective linear programming (MOLP), service-level agreement (SLA), virtual machine 1820

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A method for the optimum selection of datacenters in geographically distributed clouds

Cited by 13 publications

References 30 publications

Selecting services in the cloud: a decision support methodology focused on infrastructure-as-a-service context

Selecting services in the cloud: a decision support methodology focused on infrastructure-as-a-service context

A moth‐flame optimization algorithm for web service composition in cloud computing: Simulation and verification

Optimal selection of VMs for resource task scheduling in geographically distributed clouds using fuzzy c‐mean and MOLP

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