How to obtain personalized quality of cloud/IoT services and assist users selecting the appropriate service has become a hot issue with the explosion of services on the Internet. Collaborative QoS ...prediction is proposed to address this issue by borrowing ideas from recommender systems. However, there is still a challenging problem as how to incorporate contextual factors into existing algorithms to realize context-aware QoS prediction as contextual factors play a crucial role in QoS assessment. In this paper, we propose a general context-sensitive matrix-factorization approach (CSMF) to make collaborative QoS prediction. By considering the complexity of service invocations, CSMF models the interactions of users-to-services and environment-to-environment simultaneously, and make full use of implicit and explicit contextual factors in the QoS data. Experimental results show that CSMF significantly outperforms the-state-of-art methods in metric of prediction accuracy. Particularly, when the QoS data is very sparse, CSMF is more effective and robust.
•This paper presents a general context-sensitive approach for collaborative QoS prediction.•Interactions of users-to-services and environment-to-environment are considered simultaneously as contextual factors in the QoS data.•Our method takes advantages of both implicit and explicit factors entailed in the QoS data through exploiting contextual information.•Experimental results reflect that this study offers an efficient global optimization, enabling robust and accurate prediction results.
In cloud computing environment, resources can be dynamically provisioned on deman for cloud services The amount of the resources to be provisioned is determined during runtime according to the ...workload changes. Deciding the right amount of resources required to run the cloud services is not trivial, and it depends on the current workload of the cloud services. Therefore, it is necessary to predict the future demands to automatically provision resources in order to deal with fluctuating demands of the cloud services. In this paper, we propose a hybrid resource provisioning approach for cloud services that is based on a combination of the concept of the autonomic computing and the reinforcement learning (RL). Also, we present a framework for autonomic resource provisioning which is inspired by the cloud layer model. Finally, we evaluate the effectiveness of our approach under two real world workload traces. The experimental results show that the proposed approach reduces the total cost by up to 50%, and increases the resource utilization by up to 12% compared with the other approaches.
•We designed a framework for autonomic resource provisioning to cloud services.•We customized an autonomic resource provisioning approach based on the control MAPE loop.•We enhanced the performance of the planning phase by using the RL-based agent.•We conducted a series of experiments under real-world workload traces for different metrics.
Many organizations are currently seeking to contract services from cloud computing rather than owing the possessions to supply those services. Due to the fast expansion of cloud computing, many cloud ...services have been developed. Any organization that tries to achieve the best flexibility and quick response to market requests, they have the options to use cloud services. Due to the diversity of cloud service providers, it is a very significant defy for organizations to select the appropriate cloud services which can fulfill their requirements, as numerous criteria should be counted in the selection process of cloud services. Therefore, the selection process of cloud services can be considered as a type of multi-criteria decision analysis problems. In this research paper, we present how to aid a decision maker to estimate different cloud services by providing a neutrosophic multi-criteria decision analysis (NMCDA) approach for estimating the quality of cloud services. Triangular neutrosophic numbers are used to deal with ambiguous and incompatible information which exist usually in the performance estimation process. An efficacious model is evolved depending on neutrosophic analytic hierarchy process (NAHP). The aim is to solve the performance estimation problem and improve the quality of services by creating a strong competition between cloud providers. To demonstrate the pertinence of the proposed model for disbanding the multi-criteria decision analysis, a case study is presented.
•Cloud services considered as a type of multi-criteria decision analysis problem.•A new approach (NMCDA) for estimating the quality of cloud services is proposed.•Ambiguous and incompatible information handling by Triangular neutrosophic numbers.•To demonstrate the performance of the proposed model a case study is presented.
In recent years, several cloud services have proliferated that conspicuously result in providing similar services having same functionality by multiple service providers, but varying in Quality of ...Service (QoS) properties. Thus, providing a cloud service composition with optimal QoS values that satisfy the requirements of an user becomes complex and challenging in a cloud environment. Several metaheuristics proposed in solving this problem. However, many of them fail to maintain a suitable balance between exploration and exploitation. We propose a novel Eagle Strategy with Whale Optimization Algorithm (ESWOA) that ensures the proper balance between exploration and exploitation.
The emerging serverless computing paradigm has attracted attention from both academia and industry. This paradigm brings benefits such as less operational complexity, a pay-as-you-go pricing model, ...and an auto-scaling feature. The paradigm opens up new opportunities and challenges for cloud application developers. In this article, we present a comprehensive overview of the past development as well as the recent advances in research areas related to serverless computing. First, we survey serverless applications introduced in the literature. We categorize applications in eight domains and separately discuss the objectives and the viability of the serverless paradigm along with challenges in each of those domains. We then classify those challenges into nine topics and survey the proposed solutions. Finally, we present the areas that need further attention from the research community and identify open problems.
Cloud computing has become the most popular concept for on-demand delivery of Cloud computing services. Due to its high flexibility, many Cloud computing services are designed and implemented to meet ...the users’ needs and expectations. As a result, new challenges have emerged in the search for relevant Cloud services. In fact, the description, discovery, and recommendation of Cloud services are the main challenges of Cloud computing. It stems from the lack of standardization for the Cloud services description and publication, as well as the exponential growth in the number and functionality of Cloud services. Our objective in this paper is to present a comparative study of the different approaches that address the Cloud services description, discovery, and recommendation issues. This comparison study’s findings are separated into three parts. First, the approaches to Cloud service description have several flaws, such as the lack of a unified description that encompasses all types of Cloud services, the lack of a definition for several properties (such as Cloud characteristics, actors, and pricing model), and the failure to consider several critical SLA elements (for example, QoS guarantee, compensation, monitoring, notification, and termination). Second, we determined that web-based approaches, also known as crawling approaches, are the most likely to be adopted in the field of Cloud service discovery, in order to keep up with the ever-changing nature of Cloud computing. Hence, the crawling approach’s main purpose is to update the Cloud service registry with new services that are available on the internet. Existing crawling methods, on the other hand, have a set of shortcomings, including the types and categories of discovered Cloud services, the constant increase in web-published Cloud services, and the automatic updating of Cloud vocabulary. Finally, major Cloud service recommendation issues such as cold start, data sparsity, attack resistance, and diversity remain unaddressed.
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 trustworthy cloud service selection framework is proposed.•An objective trust measure based on QoS monitoring is proposed.•A subjective trust measure based on user feedback ratings is proposed.•Integrating objective and subjective trust assessment improves the performance.
The ever-increasing number of cloud services has led to the service’s identification problem. It has become difficult to provide users with cloud services that meet their functional and ...non-functional requirements, especially as many cloud services offer the same or similar functionality but with different execution constraints (cloud characteristics, QoS, price, and so on). Service recommendation systems can solve the service’s identification problem by helping users to retrieve the right cloud services according to their desired needs. However, the majority of service recommendation systems rely on user feedback to locate the user’s neighbors, predict missing ratings, and rank the recommended services. As a result, users’ rating histories might cause three major problems: cold start, data sparsity, and malicious attack. In order to deal with these issues, we propose in this paper a hybrid recommendation approach, called “HRPCS”, that provides a list of personalized cloud services to the active user. This approach is based on user and service clustering. In this approach, cloud services are recommended based on the user’s needs (functional and non-functional) and QoS preferences. Then, the services are ranked according to their prices and credibility. Further, the proposed approach returns a list of diversified cloud services. The experimental results confirmed our expectations and proved the effectiveness of our approach.
•Editorial of the special issue on digital transformation of manufacturing through cloud services and resource virtualization.•The context of this special issue is relevant to the federative concept ...of Industry 4.0.•A framework characterizing research activities led in the field is suggested.•This framework is used to present and position the 12 papers composing the special issue.•Perspectives are introduced as a guideline for future works.
This editorial introduces the special issue in the Elsevier journal Computers in Industry that analyses how the digital transformation of manufacturing is speeded up by two important drivers: cloud services and resource virtualization, which are vital for implementing the main building blocks - Cyber Physical Production Systems and Industrial Internet of Things - in the “Industry of the future” framework. The context of this special issue is firstly presented, with a specific focus on the federative concept of Industry 4.0. A framework characterizing research activities led in the field of the digital transformation of manufacturing processes and systems is then introduced. This framework is used to present and position the 12 papers composing the special issue. Perspectives are finally introduced as a guideline for future work in the digital transformation of manufacturing through cloud services and resource virtualization.
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