A composite particle swarm optimization approach for the composite SaaS placement in cloud environment

Hajji, Mohamed Amin; Mezni, Haithem

doi:10.1007/s00500-017-2613-8

Cited by 22 publications

(13 citation statements)

References 16 publications

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“…Recently, another PSO‐based work has been proposed . In this work, the authors adopt a variant of PSO, PSO with composite particle (PSO‐CP), where each particle is composite and represents a candidate SaaS placement solution.…”

Section: Related Workmentioning

confidence: 99%

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A multiswarm for composite SaaS placement optimization based on PSO

Chainbi

Sassi

2018

Softw Pract Exp

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Recently, the demand for software as a service (SaaS) has witnessed an increasing interest, which has raised many challenges for SaaS management. One of these challenges is to deliver a high performance composite SaaS for users while optimizing the resources used. In this paper, we focus on the problem of SaaS placement. This problem occurs in the deployment of SaaS components in Cloud. It deals with the way a composite SaaS should be placed in a Cloud by the Cloud's provider such that its performance is optimal based on its estimated execution time. Previous work, including metaheuristic methods and particle swarm optimization, focuses on resolving the problem in a static environment.Moreover, in a Cloud data center, the workloads of applications and resources capacities keep changing over time, and the environment is dynamic, so the solution found for the initial placement may need to be reconfigured to maintain the SaaS performance and to optimize the resource used. As multiswarm technique has attracted increasing attention during the last decade, in this paper, we propose a new placement solution based on such technique enhanced with a cooperative learning strategy to cope with the dynamic aspect of the Cloud. KEYWORDS Cloud, MPSO, PSO, SaaS placement INTRODUCTIONCloud computing is a distributed computing paradigm providing ubiquitous and on-demand network access to a shared pool of configurable computing resources (eg, servers, services, applications, network, storage, etc) that can rapidly provisioned and released with minimal effort or service provider interaction. 1 Stated another way, Cloud computing is the use of computing resources (software and hardware) that are delivered as a service over the Cloud on a pay-per-use basis.Cloud services are categorized into three service models. 2• Infrastructure as a service: It provides infrastructure components (eg, servers, processing, storage, network, and operating systems) to Cloud customers in the form of virtual machines (VMs). • Platform as a service: Cloud providers deliver a development platform, typically including operating system, programming language execution environment, database, and Web server. Application developers can develop and run their software solutions on a Cloud platform without the cost and complexity of buying and managing the underlying hardware and software layers.

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Section: Related Workmentioning

confidence: 99%

“…Finally, Hajji and Mezni have not adopted the same objective function as the related literature works. For objectivity reasons, we adopt the same objective function when comparing our approach to the related works.…”

Section: Related Workmentioning

confidence: 99%

A multiswarm for composite SaaS placement optimization based on PSO

Chainbi

Sassi

2018

Softw Pract Exp

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“…In, Hajji and Mezni a new PSO model inspired from physics, and called PSO with composite particles (PSO‐CP), was employed to solve the SPP. The authors aimed to maintain a good machine performance by keeping the capacity usage of the hosting servers at a moderate level.…”

Section: Related Workmentioning

confidence: 99%

“…In this section, we verify the efficiency and the quality of solutions produced by the multi‐swarm based SPP approach, and we evaluate the effectiveness of the cooperative learning strategy alongside with the scalability of the algorithms. MS‐SPP is then compared with 3 approaches: GA, CPSO‐SPP, and a First Fit Decreasing (FFD) heuristic algorithm that we developed to solve the SPP.…”

Section: Experimental Evaluationmentioning

confidence: 99%

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Security‐aware SaaS placement using swarm intelligence

Mezni

Sellami

Kouki³

2018

J Software Evolu Process

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Cloud computing has emerged as a new powerful service delivery model to cope with resource challenges and to offer various on-demand services (eg, software, storage, network, etc.). Software as a Service (SaaS) is one of the most popular service models. To meet the increasing demands of users, SaaS can be offered in a composite form. Although this approach offers some advantages like flexibility and reusability, it raises a question about how to manage composite SaaS in the distributed and the highly dynamic cloud environment. In this paper, we address one of the major SaaS resource management issues referred to as SaaS placement problem. As existing efforts only focus on SaaS placement problem from the perspective of resources utilization to optimize SaaS performance and minimize resource usage, in this paper, we also incorporate security concerns in SaaS placement strategy. In fact, security risk is one of the major factors influencing the efficiency of the composite SaaS. We adopt a multi-swarm variant of particle swarm optimization to propose a security-aware SaaS placement method. Also, a cooperative learning strategy is hybridized to the placement algorithm, which makes information of best candidate servers be used more effectively to generate better placement plan. Experiments show that our solution outperforms existing SaaS placement approaches.

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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 composite particle swarm optimization approach for the composite SaaS placement in cloud environment

Cited by 22 publications

References 16 publications

A multiswarm for composite SaaS placement optimization based on PSO

A multiswarm for composite SaaS placement optimization based on PSO

Security‐aware SaaS placement using swarm intelligence

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

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