London university signs up for cloud measurement body
The decision making framework for criteria weights is based on the hybrid DANP method — a combination of Decision-Making Trial and Evaluation Laboratory (DEMATEL) method and Analytical Network Process (ANP) method. With the rapidly growing number of available Cloud services, to fulfill the need for ordinary users to select accurate services has become a significant challenge. However, as a Cloud service environment encompasses many uncertainties that may hinder users to make sound decisions, it is highly desirable to handle fuzzy information when choosing a suitable service in an uncertain environment. In this paper, we present a novel fuzzy decision-making framework that improves the existing Cloud service selection techniques. In particular, we build a fuzzy ontology to model uncertain relationships between objects in databases for service matching, and present a novel analytic hierarchy process approach to calculate the semantic similarity between concepts.
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Many cloud service providers, with almost similar functionality, pose a selection problem to the cloud users. To assist the users in the best service selection, as per its requirement, a framework has been developed in which users list their quality of service (QoS) expectation, while service providers express their offerings. Experience of the existing cloud users is also taken into account in order to select the best cloud service provider.
The proposed C-RCE process may be used as a guideline and reference model for constructing, operating, and managing actual CSBs. Purpose The purpose of this paper is to compare and evaluate the personal cloud storage csmic products (PCSPs) in China and find the gap among them for promoting their service level. There are five representative products including Baidu cloud, Tencent cloud, Qihoo 360-cloud, Kingsoft cloud and Huawei DBank.
This work identifies some new QoS metrics, besides few existing ones, and defines it in a way that eases both the user and the provider to express their expectations and offers, respectively, in a quantified manner. Further, a dynamic and flexible model, using a variant of ranked voting method, is proposed that considers users’ requirement and suggests the best cloud service provider.
Outstanding Research, Academic, Government, and Industry Leadership Support Efforts To Develop Standardized Cloud Services Metrics
However, with a vast diversity in the cloud service, selection of a suitable cloud service is a very challenging task for a customer under an unpredictable environment. This study introduces a computational https://cryptolisting.org/coin/pac framework for determining the most suitable candidate cloud service by integrating the analytical hierarchical process (AHP) and Technique for order preference by similarity to ideal solution (TOPSIS).
The selection of the appropriate cloud services and Cloud providers (CPs) according to the cloud users’ requirements is a complex problem, given the increased number of CPs. In the paper is proposed a new decision making framework for obtaining the criteria weights in the process of selection of a CP.
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Furthermore, we conduct extensive experiments to evaluate the performance of the fuzzy ontology-based similarity matching. With the rapid growth of cloud services in recent years, it is very difficult to choose the suitable cloud services among those services that provide similar functionality. The non-functional quality of services is considered the most significant factor for appropriate service selection and user satisfaction in cloud computing.
- As the rendering process is both a computationally intensive and a time consuming task, the cloud services based rendering in cloud render farms is gaining popularity among the animators.
- The cloud services based renderfarms are ranked and selected services based on multi criteria QoS requirements.
- Analytical Hierarchical Process (AHP), the popular Multi Criteria Decision Making (MCDM) method is used for ranking and selecting the cloud services based renderfarms.
- In this paper we propose a Cloud Service Broker (CSB) framework called the RenderSelect that helps in the dynamic ranking, selection, negotiation and monitoring of the cloud based render farm services.
- In the 3D studios the animation scene files undergo a process called as rendering, where the 3D wire frame models are converted into 3D photorealistic images.
- It could be verified that AHP method ranks the cloud services effectively with less time and complexity.
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A CSB requires intermediation technologies with service recommendation, contract management, and cloud service usage assistance (such as evaluation) capabilities. These intermediation technologies enable CSBs to increase the quality of cloud service usage.
Cloud computing is an upcoming and promising solution for utility computing that provides resources on demand. As it has grown into a csmic business model, a large number of cloud service providers exist today in the cloud market, which further is expanding exponentially.
Findings Among them, Qihoo 360-cloud gets the highest evaluation score contributed by large space, file editing and fast transmission speed. These storage products are all want of the addition or improvement of the online editing service similar as Google Docs. Research limitations/implications AHP method is subjective, some of the data is incomplete, and some accidental error and systematic error exist in the actual testing process. Practical implications The findings can assist users in selecting more suitable products and offer cloud storage providers (CSPs) a general direction of improving their product performance.
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The cloud services based renderfarms are ranked and selected services based on multi criteria QoS requirements. Analytical Hierarchical Process (AHP), the popular Multi Criteria Decision Making (MCDM) method is used for ranking and selecting the cloud services based renderfarms. It could be verified that AHP method ranks the vlr lion password cloud services effectively with less time and complexity. The advent of cloud computing has led to the emergence of various cloud services and providers. Cloud service brokers (CSBs) were introduced to serve as intermediaries between cloud service providers and cloud users who wish to select an appropriate cloud service.
Though service providers use SLAs for quality standards, it is difficult for customers to compare multiple services themselves. For our proposed model, we choose service assessment indicators from SMI model by csmic and apply AHP method to measure service scores. And we also show process through a case study how our proposed model assesses cloud services. With the vast emergence of cloud computing in the recent year, numerous cloud service provider has started to provide similar functionality to the cloud customer. From a customer point of view, it has become very challenging task to select the suitable cloud services.
In the 3D studios the animation scene files undergo a process called as rendering, where the 3D wire frame models are converted into 3D photorealistic images. As the rendering process is both a computationally intensive and a time consuming task, the cloud services based rendering hexcoin in cloud render farms is gaining popularity among the animators. In this paper we propose a Cloud Service Broker (CSB) framework called the RenderSelect that helps in the dynamic ranking, selection, negotiation and monitoring of the cloud based render farm services.
In addition, many open research problems remain in the technologies and approaches underpinning CSB intermediation technologies. This paper proposes Cloud Service—Recommendation, Contract, and Evaluation (C-RCE), which supports CSB processes, including the management and operation of each proposed process. We implement a prototype of the proposed C-RCE process in a CSB to evaluate its performance and confirm that it is superior to existing CSBs.
Social implications Contributing to improve the overall level of the cloud storage services in China. Originality/value The study perfects the evaluation index system of the PCSP and fills the research gap in studying PCSPs in China, and expands the application field of the multiple criteria decision-making problems. This evaluation process and results have implied that CSPs in China should provide good services of large capacity, cooperation and security with the good internet environment of economical, high and stable speed by institutions and internet access providers. Cloud service have strength that can increase the satisfaction of customers’ various requirements. In that environment, customers should be able to choose the service that best suits their requirements through quality verification.
The Quality of Service (QoS) is considered as the most significant factor for appropriate service selection and user satisfaction in cloud computing. Due to the multidimensional attribute of QoS and an interconnected relationship between them, the cloud service selection problem treated as a complex decision problem for a cloud customer. This study introduces a methodology for determining the appropriate cloud https://cryptolisting.org/ service by integrating the entropy weight method with TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. By using entropy method, we calculate the objective weight of evaluation criteria and reduce the subjective factor in the cloud service selection problem. Thereafter, TOPSIS method is utilized to evaluate the final rank of cloud service alternative based on overall performance.