Optimal Service Selection Based on Business for Cloud Computing

2013 ◽  
Author(s):  
Xiaochen Liu ◽  
Chunhe Xia ◽  
Zhao Wei ◽  
Xiaoning Sun ◽  
Zhiqiang Zhan
Keyword(s):  
Cloud Computing ◽  
Service Selection ◽  
Optimal Service

scholarly journals Survey on QoS metrics and ranking in cloud services

2017 ◽  
Vol 7 (1.3) ◽  
pp. 146
Author(s):  
Rajeswari P ◽  
Jayashree K
Keyword(s):  
Cloud Computing ◽  
High Speed ◽  
Service Selection ◽  
Cloud Services ◽  
Utilization Efficiency ◽  
Essential Requirement ◽  
Optimal Service ◽  
Cloud Applications ◽  
Qos Metrics

The Cloud Computing uses high speed broadband for good Quality of Service (QoS) so that Cloud based application can be used with high speed which entails the minimum response time, less latency rate and reduced amount of loss of packets. Because of the ample range within the delivered Cloud solutions, from the customer’s aspect, it's emerged as irksome to decide whose providers they need to utilize and then what's the thought of his or her option. Bestowing suitable metrics is vital in assessing practices. QoS metrics are playing an important role in selecting Cloud providers and also revamping resource utilization efficiency. To guarantee a specialized product is published, describing metrics for assessing the QoS might be an essential requirement. To obtain high quality Cloud applications, Optimal Service Selection is needed. With the increasing number of Cloud services, QoS is usually selected for describing non-functional characteristics of Cloud services. In this paper, a widespread survey on QoS metrics for service vendors and QoS Ranking in Cloud Computing is presented.

An Optimal Service Selection with Constraints Based on QoS Algorithm

2012 ◽  
Vol 487 ◽  
pp. 357-364
Author(s):  
Xin Jun Li
Keyword(s):  
Computing Time ◽  
Service Selection ◽  
Optimal Service ◽  
Availability Constraints ◽  
The One ◽  
Qos Performance ◽  
The Impact ◽  
Structural Aspects ◽  
Reliability And Availability

In the paper, we propose an allocation scheme that minimizes the response time and cost of the solution subject to reliability and availability constraints in terms of expected value. The algorithm proposed in this paper aims to discover services with high QoS performance, and reduce the execute time at the same time. First, we identify the impact of various structural aspects of the composition in terms of the performance and outcomes of the composition. Then, an algorithm is proposed which can reduce the computing time and makes sure better quality of the services selection at the same time by examining a very tiny fraction of the solution space.Finally, we proves the advantage of the new algorithm by comparing the time obtained by our proposed algorithm with the one achieved by other algorithm.

A Cloud Trusting Mechanism Based on Resource Ranking

2020 ◽  
pp. 130-155
Author(s):  
Ajai K. Daniel
Keyword(s):  
Cloud Computing ◽  
Resource Management ◽  
Service Selection ◽  
Cloud Service ◽  
Cloud Services ◽  
Storage And Retrieval ◽  
Computing Paradigm ◽  
Efficient Resource ◽  
Software And Hardware ◽  
Cloud User

The cloud-based computing paradigm helps organizations grow exponentially through means of employing an efficient resource management under the budgetary constraints. As an emerging field, cloud computing has a concept of amalgamation of database techniques, programming, network, and internet. The revolutionary advantages over conventional data computing, storage, and retrieval infrastructures result in an increase in the number of organizational services. Cloud services are feasible in all aspects such as cost, operation, infrastructure (software and hardware) and processing. The efficient resource management with cloud computing has great importance of higher scalability, significant energy saving, and cost reduction. Trustworthiness of the provider significantly influences the possible cloud user in his selection of cloud services. This chapter proposes a cloud service selection model (CSSM) for analyzing any cloud service in detail with multidimensional perspectives.

A Robust and Efficient MCDM-Based Framework for Cloud Service Selection Using Modified TOPSIS

2021 ◽  
Vol 11 (1) ◽  
pp. 21-51
Author(s):  
Rohit Kumar Tiwari ◽  
Rakesh Kumar
Keyword(s):  
Service Provider ◽  
Service Providers ◽  
Service Selection ◽  
Cloud Service ◽  
Reversal Problem ◽  
Rank Reversal ◽  
Reversal Phenomenon ◽  
Optimal Service ◽  
Selection Framework ◽  
Cloud Users

Cloud computing has become a business model and organizations like Google, Amazon, etc. are investing huge capital on it. The availability of many organizations in the cloud has posed a challenge for cloud users to choose a best cloud service. To assist the cloud users, we have proposed a MCDM-based cloud service selection framework to choose a best service provider based on QoS requirement. The cloud service selection methods based on TOPSIS suffers from rank reversal problem as it ranks optimal service provider to non-optimal on addition or removal of a service provider and deludes the cloud user. Therefore, a robust and efficient TOPSIS (RE-TOPSIS)-based novel framework has been proposed to rank the cloud service providers using QoS provided by them and cloud user's priority for each QoS. The proposed framework is robust to rank reversal problem and its effectiveness has been demonstrated through a case study performed on a real dataset. Sensitivity analysis has also been performed to show the robustness against the rank reversal phenomenon.

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