Cloud Service Selection Based on the Aggregation of User Feedback and Quantitative Performance Assessment

This article describes how with the rapid growth of cloud services in recent years, it is very difficult to choose the most suitable cloud services among those services that provide similar functionality. The quality of services (QoS) is considered the most significant factor for appropriate service selection and user satisfaction in cloud computing. However, with a vast diversity in the cloud services, selection of a suitable cloud service is a very challenging task for a customer under an unpredictable environment. Due to the multidimensional attributes of QoS, cloud service selection problems are treated as a multiple criteria decision-making (MCDM) problem. This study introduces a methodology for determining the appropriate cloud service by integrating the AHP weighing method with TOPSIS method. Using AHP, the authors define the architecture for selection process of cloud services and compute the criteria weights using pairwise comparison. Thereafter, with the TOPSIS method, the authors obtain the final ranking of the cloud service based on overall performance. A real-time cloud case study affirms the potential of our proposed methodology, when compared to other MCDM methods. Finally, a sensitivity analysis testifies the effectiveness and the robustness of our proposed methodology.

Choosing the right cloud service provider and cloud services according to cloud user requirements is a complex task, due to the increase in the number of cloud providers. Multi-criteria decision making (MCDM) is used where selecting, comparing and ranking of different Criteria of multiple alternatives is a requirement. Cloud providers present all kinds of cloud services, but they vary in quality, costs, customer experience, and service delivery. One of the problems with current cloud computing is the commitment of cloud service providers to the Service Level Agreement (SLA) with cloud providers users, but there are little verification frameworks to verify that cloud service providers provide cloud services according to their commitment. In the current literature, there is a lack of assessment models that provide the true status of cloud providers to cloud users. Therefore, this paper focuses on designing a new technique for selecting cloud service in the fuzzy environment by relying on Fuzzy AHP-TOPSIS by integrating the fuzzy analytical hierarchy process (AHP) concepts and the ideal solution (TOPSIS) concepts. The structure of the cloud service selection problem and the weights of the standards are determined by The Fuzzy AHP technique concepts. Fuzzy TOPSIS concepts used to arrange the final solution. The effectiveness and efficiency of the proposed technique is clarified by increasing accuracy by almost 15% through implementation and case studies.

Cloud-service recommendation systems make suggestions based on ratings provided by cloud users. These ratings may contain sparse data, which makes it difficult to speculate on suitable cloud services. Moreover, new cloud users often suffer from cold-start difficulties. Therefore, in this study, we attempt to better overcome these two challenges, i.e., cold start and data sparsity, using a hybrid approach incorporating neural matrix factorization, deep autoencoders, and suitable questionnaires. The proposed approach provides a list of the top N cloud service providers for old cloud users based on the predicted preferences using quality of service data and asymmetrically weighted cosine similarity. To address the cold start problem, we design a questionnaire to survey new user preferences and suggest personalized cloud providers accordingly. The experiments based on the Cloud Armor database demonstrate that our approach outperforms other models. The proposed approach has a precision of 85% and achieves a mean absolute error (MAE) of 0.05 and root-mean-square error (RMSE) of 0.14 for the differences between the input and predicted values. We also receive a satisfaction level of nearly 78.5% for recommendation lists provided to new cloud service customers.

Cloud providers ranking and selection using quantitative and qualitative approach

Cloud broker is an entity that manages the use, performance and delivery of cloud services and negotiates relationships between cloud providers and cloud consumers. In real life scenarios, automated cloud service brokering is often challenging because the service descriptions may involve complex constraints and require flexible semantic matching. Furthermore, cloud providers often use non-standard formats leading to semantic interoperability issues. In this paper, we formulate cloud service brokering under a service oriented framework, and propose a novel OWL-S based semantic cloud service discovery and selection system. The proposed system supports dynamic semantic matching of cloud services described with complex constraints. We consider a practical cloud service brokering scenario, and show with detailed illustration that our system is promising for real-life applications.

Cloud Computing is a business model revolution more than a technological one. It capitalized on various technologies that have proved themselves and reshaped the use of computers by replacing their local use by a centralized one where shared resources are stored and managed by a third-party in a way transparent to end-users. With this new use came new needs and one of them is the need to search through Cloud services and select the ones that meet certain requirements. To address this need, we have developed, in a previous work, the Cloud Service Research and Selection System (CSRSS) which aims to allow Cloud users to search through Cloud services in the database and find the ones that match their requirements. It is based on the Skyline and ELECTRE IS. In this paper, we improve the system by introducing 7 new dimensions related to QoS constraints. Our work's main contribution is conceiving an Agent that uses both the Skyline and an outranking method, called ELECTREIsSkyline, to determine which Cloud services meet better the users' requirements while respecting QoS properties. We programmed and tested this method for a total of 10 dimensions and for 50 000 cloud services. The first results are very promising and show the effectiveness of our approach.

Quality of cloud service (QoS) is one of the crucial factors for the success of cloud providers in mobile cloud computing. Context-awareness is a popular method for automatic awareness of the mobile environment and choosing the most suitable cloud provider. Lack of context information may harm the users’ confidence in the application rendering it useless. Thus, mobile devices need to be constantly aware of the environment and to test the performance of each cloud provider, which is inefficient and wastes energy. Crowdsourcing is a considerable technology to discover and select cloud services in order to provide intelligent, efficient, and stable discovering of services for mobile users based on group choice. This article introduces a crowdsourcing-based QoS supported mobile cloud service framework that fulfills mobile users’ satisfaction by sensing their context information and providing appropriate services to each of the users. Based on user's activity context, social context, service context, and device context, our framework dynamically adapts cloud service for the requests in different kinds of scenarios. The context-awareness based management approach efficiency achieves a reliable cloud service supported platform to supply the Quality of Service on mobile device.

This position paper summarizes the development and the present status of Department of Defense (DoD) and other government policies and guidances regarding cloud computing services. Due to the heterogeneous and growing biomedical big datasets, cloud computing services offer an opportunity to mitigate the associated storage and analysis requirements. Having on-demand network access to a shared pool of flexible computing resources creates a consolidated system that should reduce potential duplications of effort in military biomedical research. Interactive, online literature searches were performed with Google, at the Defense Technical Information Center, and at two National Institutes of Health research portfolio information sites. References cited within some of the collected documents also served as literature resources. We gathered, selected, and reviewed DoD and other government cloud computing policies and guidances published from 2009 to 2017. These policies were intended to consolidate computer resources within the government and reduce costs by decreasing the number of federal data centers and by migrating electronic data to cloud systems. Initial White House Office of Management and Budget information technology guidelines were developed for cloud usage, followed by policies and other documents from the DoD, the Defense Health Agency, and the Armed Services. Security standards from the National Institute of Standards and Technology, the Government Services Administration, the DoD, and the Army were also developed. Government Services Administration and DoD Inspectors General monitored cloud usage by the DoD. A 2016 Government Accountability Office report characterized cloud computing as being economical, flexible and fast. A congressionally mandated independent study reported that the DoD was active in offering a wide selection of commercial cloud services in addition to its milCloud system. Our findings from the Department of Health and Human Services indicated that the security infrastructure in cloud services may be more compliant with the Health Insurance Portability and Accountability Act of 1996 regulations than traditional methods. To gauge the DoD's adoption of cloud technologies proposed metrics included cost factors, ease of use, automation, availability, accessibility, security, and policy compliance. Since 2009, plans and policies were developed for the use of cloud technology to help consolidate and reduce the number of data centers which were expected to reduce costs, improve environmental factors, enhance information technology security, and maintain mission support for service members. Cloud technologies were also expected to improve employee efficiency and productivity. Federal cloud computing policies within the last decade also offered increased opportunities to advance military healthcare. It was assumed that these opportunities would benefit consumers of healthcare and health science data by allowing more access to centralized cloud computer facilities to store, analyze, search and share relevant data, to enhance standardization, and to reduce potential duplications of effort. We recommend that cloud computing be considered by DoD biomedical researchers for increasing connectivity, presumably by facilitating communications and data sharing, among the various intra- and extramural laboratories. We also recommend that policies and other guidances be updated to include developing additional metrics that will help stakeholders evaluate the above mentioned assumptions and expectations.

With the wide usage of Cloud Computing Services (CCS), finding the most suitable cloud service by matching criteria provided by different CCS providers have gained considerable importance in various commercial and scientific tasks. In this paper, we analyze cloud service provider evaluation and selection problem under fuzzy environment. To solve the problem, a novel fuzzy integrated multicriteria decision making method is proposed. In the proposed model, a hierarchical structure with six criteria which are financial, performance, security and privacy, assurance, agility and usability, and four alternatives, is designed based on comphrehensive literature review and consulting to the experts. The evaluations regarding to the cloud alternatives are expressed using Pythagorean fuzzy sets being an extension of intuitionistic fuzzy sets where the square sum of membership and non-membership degrees is at most “1”. In the analysis, the weights of the criteria are obtained using fuzzy Analytical Hierarchy Process (AHP) based on single-valued Pythagorean fuzzy sets. Then, to rank the alternatives, the calculated weights from the previous analysis are used as inputs in single-valued Pythagorean fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje, meaning of multi-criteria optimization and compromise solution (VIKOR) method. Finally, sensitivity analysis is performed to check the robustness and the applicability of the proposed model.

Cloud computing opens a new dimension and becomes a foundation technology for developing and integrating Web applications offered as cloud services. However, the number of these services is expected to grow in the future, then, it is required for providers to overcome this situation by being able to offer cloud services with several qualities. That is why the cloud services selection is becoming a big challenge. In this paper, we design an optimization approach based on a meta-heuristic by using Cuckoo Search Algorithm (CSA) with a combination with the Lévy flight behavior to solve the optimal service selection optimization problem in the cloud computing environment considering QoS constraints. The proposed algorithm consists of three phases, initialization phase of the initial population then the evaluation of this population and the last phase of the search for relevant services using Lévy flight. A simulation design demonstrates that a strong selection using the CSA can be achieved in the cloud environment.KeywordsCloud computingService selectionQoSCuckoo Search Algorithm

The growth in the number of cloud computing users has led to the availability of a variety of cloud based services provided by different vendors. This has made the task of selecting suitable set of services quite difficult. There has been a lot of research towards the development of suitable decision support system (DSS) to assist users in making an optimal selection of cloud services. However, existing decision support systems cannot address two crucial issues: firstly, the involvement of both business and technical perspectives in decision making simultaneously and, secondly, the multiple-clouds services based selection using single DSS. In this paper, we tackle these issues in the light of solving the problem of cloud service discovery. In particular, we present the following novel contributions: Firstly, we present critical analysis of the state-of-the-art in decision support systems. Based on our analysis, we identify critical shortcomings in the existent tools and develop the set of requirements which should be met by a potential DSS. Secondly, we present a new holistic framework for the development of DSS which allows a pragmatic description of user requirements. Additionally, the data gathering and analysis is studied as an integral part of the proposedDSS and therefore, we present concrete algorithms to assess the data for an optimal service discovery. Thirdly, we assess our framework for applicability to cloud service selection using an industrial case study. We also demonstrate the implementation and performance of our proposed framework using a prototype which serves as a proof of concept. Overall, this paper provides novel and holistic framework for development of a multiple cloud service discovery based decision support system.

Recent years have seen a paradigm shift from PC-centric computing to cloud computing. 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. 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. However, currently commercially available CSBs fail to satisfy user requirements. 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. The proposed C-RCE process may be used as a guideline and reference model for constructing, operating, and managing actual CSBs.

Cloud computing (CC) has recently been receiving tremendous attention from the IT industry and academic researchers. CC leverages its unique services to cloud customers in a pay-as-you-go, anytime, anywhere manner. Cloud services provide dynamically scalable services through the Internet on demand. Therefore, service provisioning plays a key role in CC. The cloud customer must be able to select appropriate services according to his or her needs. Several approaches have been proposed to solve the service selection problem, including multicriteria decision analysis (MCDA). MCDA enables the user to choose from among a number of available choices. In this paper, we analyze the application of MCDA to service selection in CC. We identify and synthesize several MCDA techniques and provide a comprehensive analysis of this technology for general readers. In addition, we present a taxonomy derived from a survey of the current literature. Finally, we highlight several state-of-the-art practical aspects of MCDA implementation in cloud computing service selection. The contributions of this study are four-fold: (a) focusing on the state-of-the-art MCDA techniques, (b) highlighting the comparative analysis and suitability of several MCDA methods, (c) presenting a taxonomy through extensive literature review, and (d) analyzing and summarizing the cloud computing service selections in different scenarios.

Cloud computing is the latest computing paradigm that delivers hardware and software resources as virtualization enabled services. Recently, cloud service selection has emerged as an important research problem due to large number of cloud providers and their diverse service configurations, multiple selection criteria, and customer's fuzzy perception of Quality of Service (QoS). In this paper, we propose a novel fuzzy logic framework for cloud service selection based on the individual QoS criteria of customers. In this model, the necessary service configuration and run-time QoS data is collected from reliable sources such as monitoring services, customers' feedbacks, and certified cloud providers' information. The obtained results from conducted case study shows validity and applicability of the proposed framework.

The increase of cloud technology solutions has made the evaluation and selection of desired cloud services, a cumbersome task for the user. In particular, the lack of standard mechanisms that allow the comparison of cloud service specifications against user requirements taking into account the implicit uncertainty and vagueness is a major hindrance during the cloud service evaluation and selection. In this paper, we discuss an alternative classification of metrics used for ranking cloud services based on their level of fuzziness and present an approach that allows cloud service evaluation based on a heterogeneous model of service characteristics. Our approach allows the multi-objective assessment of cloud services in a unified way, taking into account precise and imprecise metrics. We use fuzzy numbers to model the imprecise service characteristics and vague user preferences and we validate a fuzzy AHP approach that solves the problem of service ranking.