Cloud service and service selection algorithm research

Zeng, Wenying; Zhao, Yuhai; Zeng, Junwei

doi:10.1145/1543834.1544004

Cited by 77 publications

(31 citation statements)

References 5 publications

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“…There are several work in the literature, which deals with this subject. We can define four classes of works : (1) work focusing on the quality of services (QoS) [2] [7] [10] [8]; (2) work based on collaborative filtering [3] [11]; (3) work focusing on the performance factor [12] [13] and (4) work searching relevance services [14], [15].…”

Section: Related Workmentioning

confidence: 99%

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A Novel Cloud Services Recommendation System Based on Automatic Learning Techniques

Djiroun¹,

Guesssoum²,

Boukhalfa

et al. 2017

2017 International Conference on New Trends in Computing Sciences (ICTCS)

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Abstract-The Cloud Computing technology is evolving constantly but essence remains the same that is to offer distinct cost saving opportunities by consolidating and restructuring information technology as a service. With the continuously increasing cloud provisions, cloud consumers start to have difficulties to find the best relevant services that suit their requirements. Therefore, selecting best services by cloud users is becoming a greater challenge. In this paper, we present a framework of services' recommendation system in a Cloud environment, using automatic learning techniques. The system aims at finding the services that suit the interests and preferences of cloud consumers by combining content based and behaviour based recommendations. In this paper, we present, USTHB-CLOUD, a cloud services recommendation prototype evaluated with an experimental study.

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Section: Related Workmentioning

confidence: 99%

“…Authors proposed, in [12], a cloud service architecture and service selection algorithm in order to select the optimum service. The cloud users can access cloud services by proxy to select services with lower cost and larger gains (more rapid response, more economic, or more convenient, etc.).…”

Section: Related Workmentioning

confidence: 99%

A Novel Cloud Services Recommendation System Based on Automatic Learning Techniques

Djiroun¹,

Guesssoum²,

Boukhalfa

et al. 2017

2017 International Conference on New Trends in Computing Sciences (ICTCS)

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“…Zeng et al [8] presented a method similar to Cardoso [9] approach to compute the overall QoS in composition except for loop used in the method. The work performs a Genetic operation on the problem encoded into Genome.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Objective Optimization of Cloud Based SLA-Violation Prediction and Adaptation

Gaur¹,

Dhyani²,

Rishi³

2016

IJITCS

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Abstract-Monitoring of Cloud services is vital for both service providing organizations and consumers. The service providers need to maintain the quality of service to comply their services with the QoS parameters defined in SLA's such as response time, throughput, delay through continuous monitoring of services. The dynamic monitoring involves prediction of SLA violations and subsequent adaptation of the service compositions. The task of adaptation is in fact the task of discovering another plausible composition in the face of services recorded to have generated QoS violations. QoS-Driven Utility based service composition approach considers the individual user's priorities for QoS parameters and determines the overall utility measure of the service composition for the end user. In this work we present the problem of service composition adaptation as a multiobjective assignment optimization problem, which in turn is a NP-hard problem. The evolutionary algorithm GA with Tabu has been formulated as a Memetic and Pareto optimal approach for the adaptation problem and analyzed for efficiency in solving the problem.Index Terms-Quality of service, service level agreement, service composition, service utility.

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“…New solutions (offsprings) are generated from the randomly produced population according to their fitness values. Better solutions are expected to be produced on every subsequent search with higer fitness value [17].…”

Section: B Basic Genetic Algorithm (Bga)mentioning

confidence: 99%

A GA-Tabu Based User Centric Approach for Discovering Optimal Qos Composition

Gaur

Dhyani²,

Rishi³

2015

IJMECS

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Abstract-Cloud computing is an emerging internetbased paradigm of rendering services on pay-as -per -use basis. Increasing growth of cloud service providers and services creates the need to provide a tool for retrieval of the high-quality optimal cloud services composition with relevance to the user priorities. Quality of Service rankings provides valuable information for making optimal cloud service selection from a set of functionally equivalent service candidates. To obtain weighted user-centric Quality of Service Composition, real-world invocations on the service candidates are usually required. To avoid the time-consuming and expensive real-world service invocations, this paper proposes framework for prediction of optimal composition of services requested by the user. Taking advantage of the past service usage experiences of the consumers more cost effective results are achieved. Our proposed framework enables the end user to determine the optimal service composition based on the input weight for individual service Quality of Service. The Genetic algorithm and basic Tabu search is applied for the user-centric Quality of Service ranking prediction and the optimal service composition. The experimental results proves that our approaches outperform other competing approaches.

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Cloud service and service selection algorithm research

Cited by 77 publications

References 5 publications

A Novel Cloud Services Recommendation System Based on Automatic Learning Techniques

A Novel Cloud Services Recommendation System Based on Automatic Learning Techniques

A Multi-Objective Optimization of Cloud Based SLA-Violation Prediction and Adaptation

A GA-Tabu Based User Centric Approach for Discovering Optimal Qos Composition

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