A multiswarm for composite SaaS placement optimization based on PSO

Chainbi, Walid; Sassi, E.

doi:10.1002/spe.2600

Cited by 6 publications

(5 citation statements)

References 23 publications

(36 reference statements)

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“…Solutions before expanded permutation [1,2,4,5,6,8,9,11] Solutions after expanded permutation [1,3,4,7,6,8,9,11,2,5] Compressed permutation involves two or more task used. It combines two solutions s 1 and s 2 into a new one s. For example, let s 1 be [1,2,3,4,6,8], s 2 be [5,7,9,11,12,13,14].The numbers 1, 2, and three are randomly selected from the cloud users' task s 1 and then delivered to the matching locations in s. The left-behind numbers, 11, 12, 13, are selected from s 2 is put in the next location.…”

Section: F I G U R E 7 Flow Chart Of Permutation Function In Ssgomentioning

confidence: 99%

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An efficient load balancing using seven stone game optimization in cloud computing

Karthikeyan

2021

Softw Pract Exp

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Cloud computing offers massive processing power to cloud client to solve the scientific, financial forecasting, and weather forecasting applications. The process of distributing to the load to the different cloud service providers is a complex problem. Cloud service providers have different types of virtual machines with different computing power types in multi-layered architectures. Various optimization works have been proposed to tackle the load balancing problem in cloud service providers. Improving performance in load balancing is a cumbersome task. Seven stone game optimization (SSGO) is designed based on the south Indian seven stone game workflow. The proposed method's foremost ambition is to reduce makespan time and maximize cloud service providers' utilization. The proposed method was simulated, and results demonstrate that minimizes the makespan time and maximizes the resource utilization than the particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), and Tabu search (TS). The experimental results show that the SSGO provides 4% more resource utilization than PSO, 5% more than GA, and 7% more than SA and 10% more than TS.

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Section: F I G U R E 7 Flow Chart Of Permutation Function In Ssgomentioning

confidence: 99%

“…Solutions before straight permutation [1,2,4,5,6,8,9,11] Solutions after straight permutation [2,4,6,8,1,5,9,11]…”

Section: F I G U R E 7 Flow Chart Of Permutation Function In Ssgomentioning

confidence: 99%

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An efficient load balancing using seven stone game optimization in cloud computing

Karthikeyan

2021

Softw Pract Exp

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“…The article entitled “A multi‐swarm for composite SaaS placement optimization based on PSO” by Chainbi and Sassi tackled the dynamic SaaS placement problem occurring in cloud environments. This problem seeks to determine how SaaS components should be placed into the cloud considering operational cloud server requirements and nonoperational ones given by predefined criteria like cost, energy, time, etc.…”

Section: Summary Of the Contributionsmentioning

confidence: 99%

“…First, methods for aiding the selection of VMs for task scheduling in geographically distributed clouds are presented . The subsequent two articles focus on composite software‐as‐a‐service (SaaS) placement optimization and web service composition . Finally, the last article of this special issue addresses an industrial IoT application by proposing metaheuristic‐based methods for optimizing the placement of software‐defined networking controllers and sink nodes in cloud environments .…”

Section: Introductionmentioning

confidence: 99%

Metaheuristics in cloud computing

et al. 2018

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The cloud computing paradigm is increasingly becoming mainstream and a growing number of companies and research organizations seek to gain value from its unique characteristics, service models, and deployment forms. According to recent estimates of Gartner 1 and IDC, 2 the public cloud services market is projected to grow at a compound annual growth rate of 16.5% and 21.5%, respectively, from 2015-2020. Gartner expects the highest growth rates in spendings on public cloud infrastructure services. This implies not only a high relevance of approaches supporting the management of cloud infrastructures, but also the need for approaches facilitating and improving the use of cloud infrastructures from the consumer's point of view. The realization of the computing utility vision, commoditized and delivered in a manner similar to traditional utilities like water and electricity, 3 however, currently fails because of the huge complexity in cloud environments where many complex decisions have to be made, as recently reported in one of the largest global IT leadership surveys. 4 In addition, due to the environmental impact of cloud computing and the relation between energy consumption and costs, a sustainable use of resources is of considerable importance and requires the development of new policies and techniques. As a result, the success of cloud computing adoption and provision is highly dependent on efficient and intelligent decision making.This gives rise to many complex combinatorial optimization problems both from the consumers' and providers' perspective. 5 The implications of decisions need to be quantifiable in terms of different dimensions (eg, economics, sustainability, performance) to allow a comparison of alternatives and to decide on the right course of action. Cloud service providers aim to reduce operational expenses and improve economies of scale to maximize profits and provide competitive prices in a highly competitive market. Therefore, cloud service providers are particularly concerned with a cost-effective, energy-efficient, fault-tolerant utilization and management of physical and virtual computing resources, whereby the quality-of-service (QoS) requirements of consumers have to be satisfied. From the consumers' point of view, particularly an efficient selection and utilization of cloud providers and cloud services, as well as the management thereof, is of interest. Several requirements in terms of costs, service quality, and legal constraints need to be taken into account when placing applications into the cloud (for extensive overviews, the interested reader is referred to Heilig et al 6,7 ). This also involves specific characteristics of cloud computing, such as the options of scalability, load balancing, and replication. Moreover, cloud-related application areas and research directions have been expanded to novel paradigms in recent years including fog computing, serverless computing, and internet of things (IoT), leading to new open issues and optimization challenges regarding resource management...

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A DFA‐based approach for the deployment of BPaaS fragments in the cloud

Hedhli¹,

Mezni

2018

Concurrency and Computation

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SummaryCloud computing is an emerging technology that is largely adopted by the current computing industry. With the growing number of Cloud services, Cloud providers' main focus is how to best offer efficient services (eg, SaaS, BPaaS, mobile services, etc) in order to hook the eventual customers. To meet this goal, services arrangement and placement in the cloud is becoming a serious problem because an optimal placement of these applications and their related data in accordance with the available resources can increase companies' benefits. Since there is a widespread deployment of business processes in the cloud, the hereinafter conducted research works aim to enhance the business processes' outsourcing by providing an optimized placement scheme that would attract cloud customers. In the light of these facts, the purpose of this paper is to deal with the BPaaS placement problem while optimizing both the total execution time and cloud resources' usage. To do so, we first determine the redundant BPaaS fragments using a DNA Fragment Assembly technique. We apply a variant of the Genetic Algorithm to resolve it. Then, we propose a placement algorithm, which produces an optimized placement scheme on the basis of the determined fragments relations. We follow that by an implementation of the whole placement process and a set of experimental results that have shown the feasibility and efficiency of the proposed approach.

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A multiswarm for composite SaaS placement optimization based on PSO

Cited by 6 publications

References 23 publications

An efficient load balancing using seven stone game optimization in cloud computing

An efficient load balancing using seven stone game optimization in cloud computing

Metaheuristics in cloud computing

A DFA‐based approach for the deployment of BPaaS fragments in the cloud

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