Decision making on adoption of cloud computing in e-commerce using fuzzy TOPSIS

© 2017 IEEE. Cloud computing promises enhanced scalability, flexibility, and cost-efficiency. In practice, however, there are many uncertainties about the usage of cloud computing resources in the e-commerce context. As e-commerce is dependent on a reliable and secure online store, it is important for decision makers to adopt an optimal cloud computing mode (Such as SaaS, PaaS and IaaS). This study assesses the factors associated with cloud-based e-commerce based on TOE (technological, organizational, and environmental) framework using multi-criteria decision-making technique (Fuzzy TOPSIS). The results show that Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) approach proposes software-as-a-service (SaaS) as the best choice for e-commerce business

A framework for QoS driven user-side cloud service management

This thesis presents a comprehensive framework that assists the cloud service user in making cloud service management decisions, such as service selection and migration. The proposed framework utilizes the QoS history of the available services for QoS forecasting and multi-criteria decision making. It then integrates all the inherent necessary processes, such as QoS monitoring, forecasting, service comparison and ranking to recommend the best and optimal decision to the user

Efficiency measurement of cloud service providers using network data envelopment analysis

An increasing number of organizations and businesses around the world use cloud computing services to improve their performance in the competitive marketplace. However, one of the biggest challenges in using cloud computing services is performance measurement and the selection of the best cloud service providers (CSPs) based on quality of service (QoS) requirements (Duan, 2017). To address this shortcoming in this article we propose a network data envelopment analysis (DEA) method in measuring the efficiency of CSPs. When network dimensions are taken into consideration, a more comprehensive analysis is enabled where divisional efficiency is reflected in overall efficiency estimates. This helps managers and decision makers in organizations to make accurate decisions in selecting cloud services. In the current study, variable returns to scale (VRS), the non-oriented network slacks-based measure (SBM) model and input-oriented and output-oriented SBM models are applied to measure the performance of 18 CSPs. The obtained results show the superiority of the network DEA model and they also demonstrate that the proposed model can evaluate and rank CSPs much better than compared to traditional DEA models

Towards multi-criteria cloud service selection

Cloud computing despite being in an early stage of adoption is becoming a popular choice for businesses to replace in-house IT infrastructure due to its technological advantages such as elastic computing and cost benefits resulting from pay-as-you-go pricing and economy of scale. These factors have led to a rapid increase in both the number of cloud vendors and services on offer. Given that cloud services could be characterized using multiple criteria (cost, pricing policy, performance etc.) it is important to have a methodology for selecting cloud services based on multiple criteria. Additionally, the end user requirements might map to different criteria of the cloud services. This diversity in services and the number of available options have complicated the process of service and vendor selection for prospective cloud users and there is a need for a comprehensive methodology for cloud service selection. The existing research literature in cloud service selection is mostly concerned with comparison between similar services based on cost or performance benchmarks. In this paper we discuss and formalize the issue of cloud service selection in general and propose a multi-criteria cloud service selection methodology

A Multi Criteria Recommendation Engine Model for Cloud Renderfarm Services

Cloud services that provide a complete platform for rendering the animation files using the resources in the cloud are known as cloud renderfarm services. This work proposes a multi criteria recommendation engine model for recommending these Cloud renderfarm services to animators. The services are recommended based on the functional requirements of the animation file to be rendered like the rendering software, plug-in required etc and the non functional Quality of Service (QoS) requirements like render node cost, time taken to upload animation files etc. The proposed recommendation engine model uses a domain specific ontology of renderfarm services to identify the right services that could satisfy the functional requirements of the user and ranks the identified services using the popular Multi Criteria Decision Analysis method like Simple Additive Weighting (SAW). The ranked list of services is provided as recommended services to the animators in the ranking order. The Recommendation model was tested to rank and recommend the cloud renderfarm services in multi criteria requirements by assigning different QoS criteria weight for each scenario. The ranking based recommendations were generated for six different scenarios and the results were analyzed. The results show that the services recommended for each scenario were different and were highly dependent on the weights assigned to each criterion

Selecting cloud computing service provider with fuzzy ahp

With the growing demand for outsourcing the ICT section of enterprises, Cloud Computing service providers increased their popularity. Selecting the most appropriate provider for a demanding enterprise depends on many criteria that are based on the strategies, requirements, and resources of the enterprise. Since this problem is a kind of decision problem and depends on criteria of decision-maker, it can be modeled as Multi-criteria Decision Making (MCDM) problem. In this research, a pilot case study is conducted in which the Cloud Computing service provider selection problem is modeled as a MCDM problem. For selecting the most appropriate provider, Fuzzy Extend Analysis is implemented in the case study

A service broker for Intercloud computing

This thesis aims at assisting users in finding the most suitable Cloud resources taking into account their functional and non-functional SLA requirements. A key feature of the work is a Cloud service broker acting as mediator between consumers and Clouds. The research involves the implementation and evaluation of two SLA-aware match-making algorithms by use of a simulation environment. The work investigates also the optimal deployment of Multi-Cloud workflows on Intercloud environments

Cloud Service Provider Evaluation System using Fuzzy Rough Set Technique

Cloud Service Providers (CSPs) offer a wide variety of scalable, flexible, and cost-efficient services to cloud users on demand and pay-per-utilization basis. However, vast diversity in available cloud service providers leads to numerous challenges for users to determine and select the best suitable service. Also, sometimes users need to hire the required services from multiple CSPs which introduce difficulties in managing interfaces, accounts, security, supports, and Service Level Agreements (SLAs). To circumvent such problems having a Cloud Service Broker (CSB) be aware of service offerings and users Quality of Service (QoS) requirements will benefit both the CSPs as well as users. In this work, we proposed a Fuzzy Rough Set based Cloud Service Brokerage Architecture, which is responsible for ranking and selecting services based on users QoS requirements, and finally monitor the service execution. We have used the fuzzy rough set technique for dimension reduction. Used weighted Euclidean distance to rank the CSPs. To prioritize user QoS request, we intended to use user assign weights, also incorporated system assigned weights to give the relative importance to QoS attributes. We compared the proposed ranking technique with an existing method based on the system response time. The case study experiment results show that the proposed approach is scalable, resilience, and produce better results with less searching time.Comment: 12 pages, 7 figures, and 8 table