Towards an Intelligent Framework for Cloud Service Discovery

The variety of cloud services (CSs) that are described, their non-uniform naming conventions, and their heterogeneous types and features make cloud service discovery a difficult problem. Therefore, an intelligent cloud service discovery framework (CSDF) is needed for discovering the appropriate services that meet the user's requirements. This study proposes a CSDF for extracting cloud service attributes (CSAs) based on classification, ontology, and agents. Multiple-phase classification with topic modeling has been implemented using different machine learning techniques to increase the efficiency of CSA extraction. CSAs that are represented in different formats have been extracted and represented in a comprehensive ontology to enhance the efficiency and effectiveness of the framework. The experimental results showed that the multiple-phase classification methods with topic modeling for CSs using a support vector machine (SVM) obtained a high accuracy (87.90%) compared to other methods. In addition, the results of extracting CSAs showed high values for precision, recall, and f-measure of 99.24%, 99.24%, and 99.24%, respectively, for Java script object notation(JSON) format, followed by 99.05%, 97.20%, and 98.11% for table formats, and with lower accuracy for text format (90.63%, 86.57%, and 88.55%).

Service discovery mechanisms in cloud computing: a comprehensive and systematic literature review

PurposeThe main goal of this paper is to study the cloud service discovery mechanisms. In this paper, the discovery mechanisms are ranked in three major classes: centralized, decentralized, and hybrid. Moreover, in this classification, the peer-to-peer (P2P) and agent-based mechanisms are considered the parts of the decentralized mechanism. This paper investigates the main improvements in these three main categories and outlines new challenges. Moreover, the other goals are analyzing the current challenges in a range of problem areas related to cloud discovery mechanisms and summarizing the discussed service discovery techniques.Design/methodology/approachSystematic literature review (SLR) is utilized to detect, evaluate and combine findings from related investigations. The SLR consists of two key stages in this paper: question formalization and article selection processes. The latter includes three steps: automated search, article selection and analysis of publication. These investigations solved one or more service discovery research issues and performed a general study of an experimental examination on cloud service discovery challenges.FindingsIn this paper, a parametric comparison of the discovery methods is suggested. It also demonstrates future directions and research opportunities for cloud service discovery. This survey will help researchers understand the advances made in cloud service discovery directly. Furthermore, the performed evaluations have shown that some criteria such as security, robustness and reliability attained low attention in the previous studies. The results also showed that the number of cloud service discovery–related articles rose significantly in 2020.Research limitations/implicationsThis research aimed to be comprehensive, but there were some constraints. The limitations that the authors have faced in this article are divided into three parts. Articles in which service discovery was not the primary purpose and their title did not include the related terms to cloud service discovery were also removed. Also, non-English articles and conference papers have not been reviewed. Besides, the local articles have not been considered.Practical implicationsOne of the most critical cloud computing topics is finding appropriate services depending on consumer demand in real-world scenarios. Effective discovery, finding and selection of relevant services are necessary to gain the best efficiency. Practitioners can thus readily understand various perspectives relevant to cloud service discovery mechanisms. This paper's findings will also benefit academicians and provide insights into future study areas in this field. Besides, the drawbacks and benefits of the analyzed mechanisms have been analyzed, which causes the development of more efficient and practical mechanisms for service discovery in cloud environments in the future.Originality/valueThis survey will assist academics and practical professionals directly in their understanding of developments in service discovery mechanisms. It is a unique paper investigating the current and important cloud discovery methods based on a logical categorization to the best of the authors’ knowledge.

A new SLA-aware method for discovering the cloud services using an improved nature-inspired optimization algorithm

Cloud computing is one of the most important computing patterns that use a pay-as-you-go manner to process data and execute applications. Therefore, numerous enterprises are migrating their applications to cloud environments. Not only do intensive applications deal with enormous quantities of data, but they also demonstrate compute-intensive properties very frequently. The dynamicity, coupled with the ambiguity between marketed resources and resource requirement queries from users, remains important issues that hamper efficient discovery in a cloud environment. Cloud service discovery becomes a complex problem because of the increase in network size and complexity. Complexity and network size keep increasing dynamically, making it a complex NP-hard problem that requires effective service discovery approaches. One of the most famous cloud service discovery methods is the Ant Colony Optimization (ACO) algorithm; however, it suffers from a load balancing problem among the discovered nodes. If the workload balance is inefficient, it limits the use of resources. This paper solved this problem by applying an Inverted Ant Colony Optimization (IACO) algorithm for load-aware service discovery in cloud computing. The IACO considers the pheromones’ repulsion instead of attraction. We design a model for service discovery in the cloud environment to overcome the traditional shortcomings. Numerical results demonstrate that the proposed mechanism can obtain an efficient service discovery method. The algorithm is simulated using a CloudSim simulator, and the result shows better performance. Reducing energy consumption, mitigate response time, and better Service Level Agreement (SLA) violation in the cloud environments are the advantages of the proposed method.

Cloud Computing Services: Taxonomy of Discovery Approaches and Extraction Solutions

Cloud computing offers new features of sharing resources and applications to meet users’ computing requirements. It is a model by which the users can access computing resources as services offered on the Internet (cloud services). Cloud service providers offer a highly diverse range of asymmetric cloud services with heterogeneous features, which makes it difficult for the users to find the best service that fits his needs. Many research studies have been done on cloud service discovery, and several models and solutions that applied different techniques have been proposed. This paper aims at presenting the state of the art in the area of cloud services discovery by exploring the current approaches, techniques, and models. Furthermore, it proposes a taxonomy of cloud service discovery approaches. An integrative review approach was used to explore the related literature. Then, by analyzing the existing cloud service discovery solutions, a taxonomy of discovery approaches was suggested based on several perspectives including the discovery environment and the discovery process methods. The proposed taxonomy allows easily classifying and comparing cloud services discovery solutions. Moreover, it may reveal issues and gaps for further research and expose new insights for more innovative and effective cloud services discovery solutions.

A Reliable Agent System for Cloud Service Discovery using MFA Technique

Reliable Agent system for discovering suitable cloud services are very interesting and challenging process. In this research work, the proposed reliable agents’ authentication is enforced to provide security for cloud data’s. In this proposed method the agent client requires service with specific functional and technical requirements. For privacy concern, we provide authentication for each agent in cloud by using Multifactor authentication process. By using authentication id agents discover the cloud service based on service engine. In our system, we enhance the privacy of the cloud search engine by introducing new authentication technique. The authentication process is done by the use of multi factors such the secret key which is generated based on AES algorithm, biometric of user and the user’s password credentials. Focused Selection Contract Net Protocol is performed to find the relevant cloud service and it plays multiple roles in cloud service discovery. The similarity between the agent’s request and cloud service is computed based on service specifications and consumer service using Cloud ontology. Each legitimate agent provides reliable cloud services for agent clients (users).Service capability Table records the list of cloud service and their corresponding ID and services for reliable cloud discovery