Cloud Service Selection Research Articles

Cloud Service Evaluation and Selection Using Fuzzy Hybrid MCDM Approach in Marketplace

Cloud services are offered independently or combining two or more services to satisfy consumer requirements. Different types of cloud service providers such as direct sellers, resellers and aggregators provide services with different level of service features and quality. The selection of best suitable services involves multi-criteria nature of services to be compared with the presence of both qualitative and quantitative factors, which make it considerably more complex. To overcome this complexity, a fuzzy hybrid multi-criteria decision making approach has been proposed, which includes both qualitative and quantitative factors. Triangular fuzzy numbers are used in all pairwise comparison matrices in the Fuzzy ANP and the criteria weights are utilized by Fuzzy TOPSIS and Fuzzy ELECTRE methods to rank the alternatives. This strategy is demonstrated with selection of cloud based collaboration tool for designers. Finally, sensitivity analysis is performed to prove the robustness of the proposed approach.

Trust Based Cloud Service Composition Framework

Cloud Computing plays an important role in academic and industry. Business process are composed and executed in the distributed loosely coupled environment. Business processes are implemented as the composite service which involves many services and these services connected by different workflow patterns. Numerous functionally similar services are available in the cloud. As it is an on-demand service, cloud service consumer needs some Quality of Service factors in services. Trust plays an import role in service selection. Selecting the trust worthy service for service composition with the cost and other constraints is a tedious task. In this paper, service trust estimation method based on Beta distribution is proposed. Trust based Cloud service selection framework for service composition is proposed and it is proved that the proposed framework provides better performance as compared to the well-known service composition methods based on the execution time and optimality value.

Towards a trust evaluation middleware for cloud service selection

With the advent of cloud computing, employing various cloud services to build highly reliable cloud applications has become increasingly popular. The trustworthiness of cloud services is a critical issue that hinders the development of cloud applications, and thus is an urgently-required research problem. Previous studies evaluate trustworthiness of services via either QoS monitoring mechanisms or user feedback ratings, while seldom they combine both of them for enhancing service trust evaluation. This paper proposes a trustworthy selection framework for cloud service selection, named TRUSS. Aiming at developing an effective trust evaluation middleware for TRUSS, we propose an integrated trust evaluation method via combining objective trust assessment and subjective trust assessment. The objective trust assessment is based on QoS monitoring, while the subjective trust assessment is based on user feedback ratings. Experiments conducted using a synthesized dataset show that our proposed method significantly outperforms the other trust and reputation methods.

A cloud service selection model using improved ranked voting method

SummaryCloud computing is an upcoming and promising solution for utility computing that provides resources on demand. As it has grown into a business model, a large number of cloud service providers exist today in the cloud market, which further is expanding exponentially. 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. 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. Case studies affirm the correctness and the effectiveness of the proposed model. Copyright © 2016 John Wiley & Sons, Ltd.

Federated selection of network and cloud services for high-performance software-defined cloud computing

The crucial role of networking in cloud service provisioning calls for federated selection of network and cloud services in order to guarantee the service performance required by diverse applications. In order to address the new challenges brought in by the software-defined cloud environment SDCE to service selection, we develop an approach for performance-based federated selection of network and cloud services in this paper. The main contributions we make in this paper include an abstract profile for network/cloud service capability that is agnostic to service implementations, a general demand profile applicable to diverse applications, an analysis technique for evaluating achievable performance of composite network-cloud services, and a scheme for federated selection of network and cloud services based on performance evaluation. The technique and method developed in this paper enable service selection with a holistic vision across the networking and computing domains, which may greatly facilitate high-performance cloud service provisioning.

A dynamic cloud service selection strategy using adaptive learning agents

Cloud service selection with multi-type cloud computing resources is a novel research and catching increasing attention. To help users efficiently select and integrate their desired cloud services, the agent-based computing paradigm has emerged. In this work, we propose an intermediary service agent model called ISAM which lets each intermediary service agent manage some clustered cloud services of the same type. And based on this model, a dynamic cloud service selection strategy named DCS is given. The core of DCS uses an adaptive learning mechanism consisting of the incentive and forgetting functions, which is devised to dynamically perform the optimising service selections and to return integrated solutions to users. A set of dynamic cloud service selection algorithms are also presented to implement our mechanism. The results of the simulation experiments show that our strategy has better overall performance and efficiency in obtaining a high quality services solution.

PQsel: combining privacy with quality of service in cloud service selection

Big data analytics and management often need to exploit the processing and storage power of cloud computing. It is obviously important to choose a cloud service with good quality. Meanwhile, users fear data leakage and loss of privacy if their sensitive data is processed in the cloud. In this paper, we propose a novel cloud service selection method, called PQsel, where a cloud service is estimated by colligating its capability of privacy protection (CoPP) and quality of service (QoS). Using a fuzzy comprehensive evaluation technique and AHP-based approach, we calculate the CoPP of each cloud service based on the CoPPs of security mechanisms covering the entire data life-cycle. Furthermore, QoS-driven approaches are introduced to support the privacy-aware cloud selection. An example analysis is given to prove the reasonableness of the proposed method. Comprehensive experiments have been conducted, which demonstrate the effectiveness of the proposed method.

Evaluation of Parameters Importance in Cloud Service Selection Using Rough Sets

With the rapid development of the cloud computing technology, it has matured enough for a lot of individuals and organizations to move their work into the cloud. Correspondingly, a variety of cloud services are emerging. It is a key issue to assess the cloud services in order to help the cloud users select the most suitable cloud service and the cloud providers offer this service with the highest quality. The criteria parameters defining the cloud services are complex which lead to cloud service deviation. In this paper, we propose an assessment method of parameters importance in cloud services using rough set theory. The method can effectively compute the importance of cloud services parameters and sort them. On the one hand, the calculation can be used as the credible reference when users choose their appropriate cloud services. On the other hand, it can help cloud service providers to meet user requirements and enhance the user experience. The simulation results show the effectiveness of the method and its relevance in the cloud context.

Using Service Clustering and Self-Adaptive MOPSO-CD for QoS-Aware Cloud Service Selection

A promising way to effectively manage the composition of services in a heterogeneous and dynamic environment is to make workflow management able to self-adapt at runtime to react to changes in its environment by autonomously reconfiguring itself. Most of the proposed methodologies address this issue as a QoS-aware service selection problem in which a web service broker can dynamically select the “right” service that takes part in the composition, and adaptively change the bound service when the delivered QoS has changed.In this paper, we propose a self-organizing framework that uses a service clustering based discovery approach to effectively and efficiently support the selection of services in which runtime changes in the QoS of the services are taken into account. Two bio-inspired algorithms are designed to support a QoS-aware dynamic service selection mechanism. An ant-based clustering algorithm enhanced with a template mechanism that guides the artificial ants to move data items to construct and maintain a specific topology is adopted as a method for efficient service discovery. As a consequence, services can dynamically be discovered in a shorter time and with lower network traffic. To select the actual concrete services that best meet the user QoS requirements a Self-Adaptive Multi-Objective Particle Swarm Optimization Algorithm using crowding distance technique (MOPSO-CD) is executed using the topological map generated by the ant algorithm. In the end, simulation results show the effectiveness of the method proposed.

Application based brokering algorithm for optimal resource provisioning in multiple heterogeneous clouds

In recent years, adoption of cloud computing for computational needs is growing significantly due to various factors such as no upfront cost and access to latest service. In general, cloud infrastructure providers offer a wide range of services with different pricing models, instance types and a host of value-added features. Efficient selection of cloud services constitutes significant management challenges for cloud consumer, which is tedious and involves large information processing. To overcome this, the cloud brokers provide resource provisioning options that ease the task of choosing the best services based on consumers requirements and also provide a uniform management interface to access cloud services. This paper proposes a novel cloud brokering architecture that provides an optimal deployment plan for placement of virtual resources in multiple clouds. The objective of the deployment plan is to select the best cloud services with optimal cost, taking into account various attributes defined in service measurement index (SMI) with additional physical and logical constraints. The proposed cloud brokering architecture has been modeled using mixed integer programming formulation and Benders decomposition algorithm to solve efficiently. Efficacy of the proposed algorithm has been verified by extensive numerical studies and sensitivity analysis.

Cloud-FuSeR: Fuzzy ontology and MCDM based cloud service selection

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. We also present a multi-criteria decision-making technique to rank Cloud services. Furthermore, we conduct extensive experiments to evaluate the performance of the fuzzy ontology-based similarity matching. The experimental results show the efficiency of the proposed method.

A New Performance Assessment Modeling and Development of a Performance Assessment System for a Cloud Service

Cloud service is the next-generation core service in the field of green IT for maximizing the efficiency of IT resource operation and management, and energy consumption. Cloud service market size is expected to rapidly grow in the future due to easy usefulness, efficiency in operation and management, and relatively high industrial ripple effect by investment. Nevertheless, the market has not been considerably activated due to ICT users' concerns on security and cost, loss of control, and uncertainty on reliability and performance when adopting and using cloud service. This study suggests a modeling for assessing the performance of cloud service and the performance assessment system based on the modeling in order to provide the standard for cloud service users to rely on and select cloud services which are suitable for their needs by providing objective and quantitative comparison and assessment results. In particular, this study defines the range for performance evaluation of the virtual machine (VM) targeting Infrastructure-as-a-Service among various cloud services, and deducts the items of performance assessment by analyzing requirements. The suggested system modulates and composes the target of performance assessment into five items of system performance, network performance, service performance, availability, and security performance. In addition, we aim to develop a system for performance assessment of VM based on those items, and to help use the system to manage the performance of cloud infrastructure services.

Trust-Enhanced Cloud Service Selection Model Based on QoS Analysis.

Cloud computing technology plays a very important role in many areas, such as in the construction and development of the smart city. Meanwhile, numerous cloud services appear on the cloud-based platform. Therefore how to how to select trustworthy cloud services remains a significant problem in such platforms, and extensively investigated owing to the ever-growing needs of users. However, trust relationship in social network has not been taken into account in existing methods of cloud service selection and recommendation. In this paper, we propose a cloud service selection model based on the trust-enhanced similarity. Firstly, the direct, indirect, and hybrid trust degrees are measured based on the interaction frequencies among users. Secondly, we estimate the overall similarity by combining the experience usability measured based on Jaccard’s Coefficient and the numerical distance computed by Pearson Correlation Coefficient. Then through using the trust degree to modify the basic similarity, we obtain a trust-enhanced similarity. Finally, we utilize the trust-enhanced similarity to find similar trusted neighbors and predict the missing QoS values as the basis of cloud service selection and recommendation. The experimental results show that our approach is able to obtain optimal results via adjusting parameters and exhibits high effectiveness. The cloud services ranking by our model also have better QoS properties than other methods in the comparison experiments.

Personalized QOS based Ranking Approach for Cloud Service Selection

Cloud computing is an Internet-based computing model. This model enables access to resources and services on demand. Cloud computing users have applications with different Quality of Service requirements. On the other hand, there are different cloud service providers offering services with different qualitative characteristics. Determining the best cloud computing service for a specific application is a significant research problem. Ranking of cloud service providers compares different services offered by different providers based on quality of service, in order to select the most suitable cloud service provider. QoS parameters provide valuable information for making optimal cloud service selection from a set of functionally equivalent service candidates. To obtain QoS values, realworld invocations on the service candidates are usually required. This project proposes a QoS ranking prediction framework for cloud services that eliminates delay and expenses involved in real-world service invocations. It makes use of the past service usage experiences of other users. This framework does not require any additional invocations of cloud services while making QoS ranking prediction. The algorithm is implemented by considering both cost and benefit parameters such as Response time and throughput respectively using a database containing response time and throughput values of 300 users for 10 different cloud providers. Also, Sensitivity analysis is done by varying weights of individual QoS parameters to verify the correctness of the algorithm. It is observed from the results that the proposed cloud service selection algorithm is able to appropriately choose the best cloud service provider depending on the weights of the respective QoS parameters. General Terms Algorithms

Cloud Service ranking and Selection using Linear Programming

Cloud computing is basically an internet based computing, whereby shared configurable resources are provided to cloud service consumers as services on demand. As an increasing growth of cloud computing, many enterprises provide different cloud services to cloud service consumers. From cloud service consumer's perspective, it is difficult to choose an appropriate cloud service that satisfies their QoS requirements. As requirements of one cloud service consumer will vary from another, dynamic ranking has to be used to satisfy the requirements of different cloud service consumers. A simple model is needed to address the dynamic ranking of cloud services. The dynamic ranking and selection of cloud services is solved using Linear Programming(LP) model. This project considers quantifiable attributes such as processor speed, cost, etc. and some non-quantifiable attributes to rank various cloud services according to the requirements of cloud service consumer using Linear Programming(LP) technique.

A Cloud Service Selection Model based on Analytic Network Process

A cloud computing service is the next-generation core service of green ICT for operational management of ICT resources and maximization of energy consumption efficiency. Cloud service is expected to rapidly increase in its market size in the future through its convenience in usage, efficiency of operational management and economic effects and so on. In users’ perspectives, the choice of the appropriative cloud service is very important to meet their business goals and strategies. A cloud service selection problem is a multiple-criteria decision analysis with feedback. An Analytical Network Process (ANP) is a popular decision analysis technique. This study proposes a cloud service selection model based on ANP. According to the proposed model, the criteria and sub-factors for cloud service selection are identified and then determined their weights. In this study, ANP based the multiple criteria decision-making model is applied to choose the best cloud services.

PuLSaR: preference-based cloud service selection for cloud service brokers

Over the last few years, the vast increase of cloud service offerings that are available from heterogeneous cloud vendors, has made the evaluation and selection of desired cloud services, a cumbersome task for service consumers. In that respect, there is an increasing need for user guidance and intermediation during the service selection process but also during the cloud service consumption that should always refer to the best possible choice based on user preferences. In this paper, we discuss the Preference-based cLoud Service Recommender (PuLSaR) that uses a holistic multi-criteria decision making (MCDM) approach for offering optimisation as a brokerage service. The specification and implementation details of this proposed software mechanism are thoroughly discussed while the background method used is summarised. Both method and brokerage service allow for the multi-objective assessment of cloud services in a unified way, taking into account precise and imprecise metrics and dealing with their fuzziness. We cope with the fuzziness of imprecise metrics in the sense that this approach deals with linguistically expressed preferences and cloud service characteristics that lack a fixed or precise value and entail a level of vagueness which can only be captured using the Zadeh’s Fuzzy Set Theory. Furthermore, this paper reports on a number of experiments that were conducted in order to measure PuLSaR’s performance and scalability.

A Hybrid Multi-Criteria Decision-Making Model for a Cloud Service Selection Problem Using BSC, Fuzzy Delphi Method and Fuzzy AHP

In rapid changing the global business environment, information communication technology (ICT) is essential for the survival of a firm, and the functions of ICT is becoming increasingly important. The emergence of cloud computing represents a fundamental change of ICT services and cloud services continue to grow rapidly with increasing functionality and more users. As a result of this growth, it is a critical issue to select a suitable cloud service which meets all the business strategies and the objectives of firms. This paper proposes a hybrid multi-criteria decision-making model for a cloud service selection problem using balanced scorecard (BSC), fuzzy Delphi method (FDM) and fuzzy analytical hierarchy process (FAHP). We focus on selecting an IaaS among cloud services for firms' users. The BSC concept is applied to define the hierarchy with four major perspectives (i.e. financial, customer, internal business process, and learning and growth), and to derive decision-making criteria and decision-making factors are selected for each BSC perspective. FDM is used to select the list of important decision-making factors within each BSC perspective based on the decision makers' opinion. A FAHP approach is then proposed in order to compares the decision-making criteria and factors and determine the importance of them. It is also used to select the best cloud service among the cloud service alternatives based on the predetermined weights of decision-making criteria and factors. In this study, the BSC and FAHP as the hybrid multi-criteria decision-making technique are used to select the best cloud service. Our findings can be utilized as bases to apply systematic decision-making processes for the best cloud service selection and for providing guidance to IT department managers or CTO regarding performance evaluation and strategies to improve companies' performance and capability.

A QoS Guaranteed Selection of Efficient Cloud Services

Objective: The main objective of this work is to select the optimal cloud services by considering price and Quality-ofService (QoS) concession in the cloud computing environment. Methods: In this manuscript an innovative technique is introduced that is called Optimal Selection of Cloud Service with Price for QoS Concession (OSCSPQC). In this technique the more sophisticated constraints like accountability, agility, assurance of service, security and privacy, and usability are included for improving VM placement strategies. Moreover, if the cloud services are reserved by customers, price and QoS concession mechanism are used to facilitate both providers and customers indicate their preferences for price and QoS metrics and search for equally acceptable prices and QoS metrics. Results: The OSCSPQC method shows high performance in terms of user satisfaction, response time and total utility when compared to the existing A Novel Customer Hints Scheduling (ANCHS) method. The proposed OSCSPQC method considers the customer hints and QoS requirements for selecting the cloud service. If the cloud load is 1, the user satisfaction in OSCSPQC is 97.2%, the total utility is 89% and response time is 9500 ms. The comparison results shows that the proposed method achieves better performance when compared to the existing system. Conclusion: The findings demonstrate that the OSCSPQC method is presented and suggested that this method high performance.Keywords: Cloud Computing, Cloud Negotiation, IaaS Cloud, Service Measurement, Virtual Machine Scheduling

CCCloud: Context-Aware and Credible Cloud Service Selection Based on Subjective Assessment and Objective Assessment

Due to the diversity and dynamic nature of cloud services, it is usually hard for potential cloud consumers to select the most suitable cloud service. This paper proposes CCCloud: a context-aware and credible cloud service selection model based on the comparison and aggregation of subjective assessments extracted from ordinary cloud consumers and objective assessments from quantitative performance testing parties. We propose a novel approach to evaluate cloud users' credibility, which not only can accurately evaluate how truthfully they assess cloud services, but also resist user collusion. In addition, in our model, objective assessments are used as benchmarks to filter out potentially biased subjective assessments, and then objective assessments and subjective assessments are aggregated to evaluate the overall performance of a cloud service. Furthermore, our model takes the contexts of objective assessments and subjective assessments into account. By calculating the similarity between different contexts, the benchmark level of objective assessments is dynamically adjusted according to context similarity, and the aggregated final scores of alternative cloud services are weighted by the similarity between the contexts of a potential cloud consumer and every testing party. This makes our cloud service selection model reflect potential cloud consumers' customized requirements more effectively. Finally, our proposed model is evaluated through the experiments conducted under different conditions. The experimental results demonstrate that our model significantly outperforms the existing work, especially in the resistance of user collusion.