MR-IDPSO: a novel algorithm for large-scale dynamic service composition

Zhang, Yanping; Jing, Zihui; Zhang, Yiwen

doi:10.1109/tst.2015.7349932

Cited by 13 publications

(12 citation statements)

References 21 publications

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“…Alrifai et al and Yu and Bouguettaya presented skyline computational approaches for service selection, which helps to prune the number of candidate services. Zhang et al developed a MapReduce‐based IDPSO for QoS‐aware large scale service composition. Xu et al presented a novel approach based on MIP for QoS‐aware BSS.…”

Section: Related Workmentioning

confidence: 99%

“…We implemented the following MapReduce‐ based optimization algorithms: MR‐PSO, MR‐ABC, MR‐GA, MR‐IDPSO, and MR‐EA/G and compared the results with our MR‐MGWO. In our experiments, the population size is fixed to 30 and each experiment was executed steadily for 30 times, and mean is considered for each run.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…The parameters settings for MR‐GA are crossover probability of 0.5, mutation probability of 0.001 using the random selection. The parameters setting for MR‐PSO and MR‐IDPSO are inertia weight varying from 0.9 to 0.7 linearly and learning factors c 1 and c 2 taken as 2. The parameters setting for MR‐ABC and MR‐EA/G are same as the parameters considered in the works of Chandrashekar et al and Wang et al The weights of QoS criteria were set to cost ( wt =3.5), response time ( wt =1.0), throughput ( wt =1.5), availability ( wt =2.5), and reliability ( wt =1.5) based on user preferences.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…The parameters settings for MR-GA are crossover probability of 0.5, mutation probability of 0.001 using the random selection. The parameters setting for MR-PSO 44 and MR-IDPSO 28 The weights of QoS criteria were set to cost (wt = 3.5), response time (wt = 1.0), throughput (wt = 1.5), availability (wt = 2.5), and reliability (wt = 1.5) based on user preferences. All these values are considered empirically after conducting multiple experiments.…”

Section: Comparison With Traditional Algorithmsmentioning

confidence: 99%

See 3 more Smart Citations

A MapReduce‐based modified Grey Wolf optimizer for QoS‐aware big service composition

Bhaskar

Jatoth

Gangadharan

et al. 2019

Concurrency and Computation

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Summary Big services are the collection of interrelated web services across virtual and physical domains, integrating service oriented computing and big data. The rapid growth of Big services that offer similar functionality with varying QoS attributes makes the process of selection and composition of these big services as highly challenging and complex. In this paper, we develop an efficient QoS‐aware Big service composition approach by applying a MapReduce based Modified Grey Wolf Optimizer (MR‐MGWO) that explores more search space, especially in a multidimensional environment. Our approach ensures an optimal balance of exploration and exploitation that enhances the convergence rate and minimizes the computational time. The empirical analysis illustrates that the performance of MR‐MGWO is superior to other similar approaches for solving Big service composition.

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Section: Related Workmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Comparison With Traditional Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

A MapReduce‐based modified Grey Wolf optimizer for QoS‐aware big service composition

Bhaskar

Jatoth

Gangadharan

et al. 2019

Concurrency and Computation

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“…The Pareto set model proposed in this paper has been theoretically proved effective first and then evaluated by a large number of experiments. In [17], a new large-scale service composition selection method, that is, the Hadoop distributed computing platform, is introduced. The discrete particle swarm optimization algorithm is combined with the Hadoop platform to select the service composition.…”

Section: Related Workmentioning

confidence: 99%

Service Composition Optimization Method Based on Parallel Particle Swarm Algorithm on Spark

Guo¹,

Chen²,

Zhang³

et al. 2017

Security and Communication Networks

Self Cite

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Web service composition is one of the core technologies of realizing service-oriented computing. Web service composition satisfies the requirements of users to form new value-added services by composing existing services. As Cloud Computing develops, the emergence of Web services with different quality yet similar functionality has brought new challenges to service composition optimization problem. How to solve large-scale service composition in the Cloud Computing environment has become an urgent problem. To tackle this issue, this paper proposes a parallel optimization approach based on Spark distributed environment. Firstly, the parallel covering algorithm is used to cluster the Web services. Next, the multiple clustering centers obtained are used as the starting point of the particles to improve the diversity of the initial population. Then, according to the parallel data coding rules of resilient distributed dataset (RDD), the large-scale combination service is generated with the proposed algorithm named Spark Particle Swarm Optimization Algorithm (SPSO). Finally, the usage of particle elite selection strategy removes the inert particles to optimize the performance of the combination of service selection. This paper adopts real data set WS-Dream to prove the validity of the proposed method with a large number of experimental results.

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QoS‐aware big service composition using distributed co‐evolutionary algorithm

Dutta

Jatoth

Gangadharan

et al. 2021

Concurrency and Computation

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Big services are collections of interrelated web services across virtual and physical domains, processing Big Data. Existing service selection and composition algorithms fail to achieve the global optimum solution in a reasonable time. In this paper, we design an efficient quality of service-aware big service composition methodology using a distributed co-evolutionary algorithm. In our proposed model, we develop a distributed NSGA-III for finding the optimal Pareto front and a distributed multi-objective Jaya algorithm for enhancing the diversity of solutions. The distributed co-evolutionary algorithm finds the near-optimal solution in a fast and scalable way. K E Y W O R D Sbig services, distributed co-evolutionary algorithm, distributed Jaya, distributed NSGA-III, quality of service, service composition INTRODUCTIONWith the onset of cloud computing, Internet of Things (IoT), and machine learning/ deep learning, manufacturing industries are undergoing a drastic change. Modern industries are incorporating these new technologies to achieve better efficiency in manufacturing products and services, with an improvement in quality control and a reduction in production costs. The Fourth Industrial Revolution (or Industry 4.0) is the ongoing automation of traditional manufacturing and industrial practices, using these modern smart technologies. Big services are collections of interrelated web services across the virtual and physical domains of Industry 4.0, processing Big Data. 1,2 Big services are integrated from various domains to develop a composite service that addresses the requirements of a customer. 3 Consider a fictitious company that manufactures electric vehicles. The company uses IoT, cloud computing, and machine learning/ deep learning to test for and predict possible failures and for tweaking or designing new engine parts, suspension units, and many more. Let us consider that the company has two divisions: design analysis unit and production unit. The design analysis unit analyzes the performances of the electric vehicles and suggests new design models. The lessons learned from the design analysis unit are incorporated in the design and manufacturing of electric vehicles by the production unit. Assume that both the design analysis unit and the production unit use a similar workflow. We can imagine two separate sets of service composition requirements: The design analysis unit requires high performance in terms of response time and throughput, high security, and with no restrictions on cost. The production unit has to keep the cost low and maintain the desirable quality of service (QoS) parameters and changes with respect to supply and demand throughout the year.In this scenario, we need to execute the service composition process repeatedly whenever there is a change in the requirements, with certain changes in the fitness function. This becomes computationally expensive as the number of services grows, being classified as NP-hard. 4,5 Co-evolutionary approaches offer promising solutions in this case and man...

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MR-IDPSO: a novel algorithm for large-scale dynamic service composition

Cited by 13 publications

References 21 publications

A MapReduce‐based modified Grey Wolf optimizer for QoS‐aware big service composition

A MapReduce‐based modified Grey Wolf optimizer for QoS‐aware big service composition

Service Composition Optimization Method Based on Parallel Particle Swarm Algorithm on Spark

QoS‐aware big service composition using distributed co‐evolutionary algorithm

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