Discrete gbest-guided artificial bee colony algorithm for cloud service composition

Hu, Yi; Zhuang, Yi; Gu, Jun; Ni, Siru; Xue, Yu

doi:10.1007/s10489-014-0617-y

Cited by 71 publications

(33 citation statements)

References 32 publications

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“…To tackle scalability issue, the swarm intelligent optimization algorithm with high efficiency and fast speed is widely used in the service composition problem field. In literature [1], the bee colony algorithm is applied. The introduction of time enhancement function establishes a trusted service composition model, thus transforming service composition problem into a nonlinear integer coding problem.…”

Section: Related Workmentioning

confidence: 99%

“…1 and 2 are random variables evenly distributed over the interval [0,1]. is the inertia weight which measures the effect of the velocity of the migration on the next movement.…”

Section: Rdd Positionmentioning

confidence: 99%

“…Quality of Service (QoS) represents nonfunctional attribute in Web services, such as Availability, Price, and Reputation. With an ever-larger number of cloud services, selecting the optimal cloud service composition solution which satisfies user's requirement has become a matter of great interest in the field of service composition [1]. The existing service selection methods which obtain best service composition solution are based on their QoS information.…”

Section: Introductionmentioning

confidence: 99%

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

Guo¹,

Chen²,

Zhang³

et al. 2017

Security and Communication Networks

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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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Section: Related Workmentioning

confidence: 99%

“…1 and 2 are random variables evenly distributed over the interval [0,1]. is the inertia weight which measures the effect of the velocity of the migration on the next movement.…”

Section: Rdd Positionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Guo¹,

Chen²,

Zhang³

et al. 2017

Security and Communication Networks

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“…A r a n g o a et al [24] found a solution to the decision-making system which is based on cloud environment in Water distribution systems. Huo et al [26] tackled the problem of facing a large number of cloud application services. S u g u n a and S u h a s i n i [27] analyzed the cloud efficient disaster recovery process.…”

Section: O G H a D D A M R M O G H A D D A M And C H E R I E T [22]mentioning

confidence: 99%

“…M a m u n et al [23] developed a cloud-based framework for identifying the Parkinson patients by their voice samples through their phones, thereby checking their Dysphonia over the cloud. Huo et al [26] introduced the Discrete Gbest-guided Artificial Bee Colony algorithm for optimizing the cloud service composition. S u g u n a and S u h a s i n i [27] performed the analysis of massive data storage process using the Enriched MultiObjective Optimization Model for Efficient Disaster Recovery in a cloud environment.…”

Section: Introductionmentioning

confidence: 99%

Emphasis on Cloud Optimization and Security Gaps: A Literature Review

Naidu

Bhagat²

2017

Cybernetics and Information Technologies

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A new service composition method in the cloud‐based Internet of things environment using a grey wolf optimization algorithm and MapReduce framework

Vakili,

Al‐Khafaji,

Darbandi

et al. 2024

Concurrency and Computation

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SummaryCloud computing is quickly becoming a common commercial model for software delivery and services, enabling companies to save maintenance, infrastructure, and labor expenses. Also, Internet of Things (IoT) apps are designed to ease developers' and users' access to networks of smart services, devices, and data. Although cloud services give nearly infinite resources, their reach is constrained. Designing coherent and organized apps is made possible by integrating the cloud and IoT. Expanding facilities by combining services is a critical component of this technology. Various services may be presented in this environment based on the user's demands. Considering their Quality of Service (QoS) attributes, discovering the appropriate available atomic services to construct the needed composite service with their collaboration in an orchestration model is an NP‐hard issue. This article suggests a service composition method using Grey Wolf Optimization (GWO) and MapReduce framework to compose services with optimized QoS. The simulation outcomes illustrate cost, availability, response time, and energy‐saving improvements through the suggested approach. Comparing the suggested technique to three baseline algorithms, the average gain is a 40% improvement in energy savings, a 14% decrease in response time, an 11% increase in availability, and a 24% drop in cost.

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Discrete gbest-guided artificial bee colony algorithm for cloud service composition

Cited by 71 publications

References 32 publications

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

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

Emphasis on Cloud Optimization and Security Gaps: A Literature Review

A new service composition method in the cloud‐based Internet of things environment using a grey wolf optimization algorithm and MapReduce framework

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