CloudOntoViz

Cloud computing has emerged as one of the hottest area in the field of computer technology, which is used to deliver resources on demand. Users of the cloud can access the services published by the cloud providers. The availability of many cloud providers throughout the world has introduced the need for research in cloud service repository model and discovery. Searching a suitable cloud provider and a cloud service has become a tedious and time consuming task for the service user. There is a need to publish, store and discover suitable cloud providers and services for the users. The cloud service discovery mechanisms support the discovery of services based on functional and non-functional properties. This paper reviews various cloud service description architectures based on the location of availability of cloud service descriptions. It also classifies and gives taxonomy of cloud service discovery methods defined in the literature. The paper also classifies the discovery methods based on the cloud deployment models and describes the various service discovery algorithms used in the literature for cloud service discovery.

Agent-based cloud computing is concerned with the design and development of software agents for bolstering cloud service discovery, service negotiation, and service composition. The significance of this work is introducing an agent-based paradigm for constructing software tools and testbeds for cloud resource management. The novel contributions of this work include: 1) developing Cloudle: an agent-based search engine for cloud service discovery, 2) showing that agent-based negotiation mechanisms can be effectively adopted for bolstering cloud service negotiation and cloud commerce, and 3) showing that agent-based cooperative problem-solving techniques can be effectively adopted for automating cloud service composition. Cloudle consists of 1) a service discovery agent that consults a cloud ontology for determining the similarities between providers' service specifications and consumers' service requirements, and 2) multiple cloud crawlers for building its database of services. Cloudle supports three types of reasoning: similarity reasoning, compatibility reasoning, and numerical reasoning. To support cloud commerce, this work devised a complex cloud negotiation mechanism that supports parallel negotiation activities in interrelated markets: a cloud service market between consumer agents and broker agents, and multiple cloud resource markets between broker agents and provider agents. Empirical results show that using the complex cloud negotiation mechanism, agents achieved high utilities and high success rates in negotiating for cloud resources. To automate cloud service composition, agents in this work adopt a focused selection contract net protocol (FSCNP) for dynamically selecting cloud services and use service capability tables (SCTs) to record the list of cloud agents and their services. Empirical results show that using FSCNP and SCTs, agents can successfully compose cloud services by autonomously selecting services.

In recent years, due to global economic downfall, many of the organisations have resorted to downsizing their Information Technology (IT) expenses by adopting innovative computing models like cloud computing, which allows business houses to reduce their fixed IT costs by promising a greener, scalable, cost-effective alternative to utilise the IT resources. A growing number of pay-per-use cloud services are now available on the web in the form of Software as a Service (SaaS), Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). With the increase in the number of services, there has also been an increase in demand and adoption of cloud services making cloud service identification and discovery a challenging task. This is due to varied service, descriptions, non-standardised naming conventions, heterogeneity in type and features of cloud services. Thus, selecting an appropriate cloud service according to consumer requirements is a daunting task, especially for applications that use a composition of different cloud services. In this paper, we have designed an ontology-based cloud infrastructure service discovery and selection system that defines functional and non-functional concepts, attributes and relations of infrastructure services. We have shown how the system enables one to discover appropriate services optimally as requested by consumers.

The composition of cloud services to satisfy customer requirements is still a complex and tricky task, requiring care and skill owing to the huge number of Cloud services which are currently available on the market. Recently the concept of Cloud Pattern emerged as a way to describe the composition and orchestration of Cloud Services in order to satisfy particular application requirements. Cloud Patterns can be considered as a particular Pattern category, focusing on the description of problems and solutions related to Cloud Computing. In this paper a methodology for the discovery and composition of Cloud services, guided by Cloud Patterns, is presented.

With the spread of services related to cloud environment, it is tiresome and time consuming for users to look for the appropriate service that meet with their needs. Therefore, finding a valid and reliable service is essential. However, in case a single cloud service cannot fulfil every user requirements, a composition of cloud services is needed. In addition, the need to treat uncertainty in cloud service discovery and composition induces a lot of concerns in order to minimize the risk. Risk includes some sort of either loss or damage which is possible to be received by a target (i.e., the environment, cloud providers or customers). In this paper, we will focus on the uncertainty application for cloud service discovery and composition. A set of existing approaches in literature are reviewed and categorized according to the risk modeling.

In the context of the efforts to organize the knowledge in the new and emerging area of Cloud Computing we performed an analysis of relevant existing developments and built on this basis a framework for a semantic registry of cloud services. The framework contains core ontological definitions and extension mechanisms used to define ontologies for cloud services, related to the aspects of semantic discovery and composition of cloud services. The relevance of the proposed registry can be assessed in relation with cloud interoperability, cloud service composition, as well as software services that offer support for finding and selecting cloud services and for marketing advantages of different cloud providers.

Despite the research and standardization efforts carried out both by academia and commercial enterprises, the composition of existing cloud services which fully satisfy customers’ requirements is still a complex and tricky task. This situation is due to the high number of cloud services currently available on the market, which either expose non-standard interfaces or implement different standards, according to their specific objective. As in the past design patterns have been applied to software design to bring order and help developers in better building, composing and reusing their application, nowadays cloud patterns offer the opportunity to leverage best practices in services composition to ease the design and deployment of cloud-oriented applications. However, due to differences in semantics which affect services’, operations’ and parameters’ descriptions, cloud patterns alone cannot solve the cloud service composition problem. In this paper a methodology for the discovery and composition of cloud services, guided by cloud patterns, is presented. Such a methodology is supported by semantic Web technologies, such as OWL, OWL-S and SPARQL, to solve incongruence between interfaces’ and parameters’ descriptions, and to automatize the whole composition process.

Cloud computing has experienced rapid growth in recent years and has become a critical computing paradigm. Combining multiple cloud services to satisfy complex user requirements has become a research hotspot in cloud computing. Service composition in multi-cloud environments is characterized by high energy consumption, which brings attention to the importance of energy consumption in cross-cloud service composition. Nonetheless, prior research has mainly focused on finding a service composition that maximizes the quality of service (QoS) and overlooks the energy consumption generated during service invocation. Additionally, the dynamic nature of multi-cloud environments challenges the adaptability and scalability of cloud service composition methods. Therefore, we propose the skyline-enhanced deep reinforcement learning approach (SkyDRL) to address these challenges. Our approach defines an energy consumption model for cloud service composition in multi-cloud environments. The branch and bound skyline algorithm is leveraged to reduce the search space and training time. Additionally, we enhance the basic deep Q-network (DQN) algorithm by incorporating double DQN to address the overestimation problem, incorporating Dueling Network and Prioritized Experience Replay to speed up training and improve stability. We evaluate our proposed method using comparative experiments with existing methods. Our results demonstrate that our approach effectively reduces energy consumption in cloud service composition while maintaining good adaptability and scalability in service composition problems. According to the experimental results, our approach outperforms the existing approaches by demonstrating energy savings ranging from 8% to 35%.

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.

The crucial role of networking in Cloud computing calls for federated management of both computing and networking resources for end-to-end service provisioning. Application of the Service-Oriented Architecture (SOA) in both Cloud computing and networking enables a convergence of network and Cloud service provisioning. One of the key challenges to network -- Cloud convergence lies in QoS-aware composition of network and Cloud services. In this paper, we propose a QoS-aware service composition method to tackle this challenging issue. We first present a system model for network -- Cloud service composition and formulate the service composition problem as a variant of Multi-Constrained Optimal Path (MCOP) problem. We then develop an algorithm to solve the problem and give theoretical analysis on properties of the algorithm to show its effectiveness and efficiency for QoS-aware network-Cloud service composition. Performance of the proposed algorithm is evaluated through extensive simulation experiments and the obtained results indicate that the proposed method achieves better performance in service composition than the best currently available MCOP approach.

SummaryCloud computing is considered the latest emerging computing paradigm and has brought revolutionary changes in computing technology. With the advancement in this field, the number of cloud users and service providers is increasing continuously with more diversified services. Consequently, the selection of appropriate cloud service has become a difficult task for a new cloud customer. In case of inappropriate selection of a cloud services, a cloud customer may face the vendor locked‐in issue and data portability and interoperability problems. These are the major obstacles in the adoption of cloud services. To avoid these complexities, a cloud customer needs to select an appropriate cloud service at the initial stage of the migration to the cloud. Many researches have been proposed to overcome the issues, but problems still exist in intercommunication standards among clouds and vendor locked‐in issues. This research proposed an IEEE multiagent Foundation for Intelligent Physical Agent (FIPA) compliance multiagent reference architecture for cloud discovery and selection using cloud ontology. The proposed approach will mitigate the prevailing vendor locked‐in issue and also alleviate the portability and interoperability problems in cloud computing. To evaluate the proposed reference architecture and compare it with the state‐of‐the‐art approaches, several experiments have been performed by utilizing the commonly used performance measures. Analysis indicates that the proposed approach enables significant improvements in cloud service discovery and selection in terms of search efficiency, execution, and response time.

Over the past few years, cloud computing has been more and more attractive as a new computing paradigm due to high flexibility for provisioning on-demand computing resources that are used as services through the Internet. In cloud computing, the unique characteristics in cloud services such as dynamic and diverse services offered at different levels, together with the lack of standardized description languages pose significant challenges for effective cloud service discovery. In this paper, we propose a cloud service search engine that exploits a novel ontology-based technique for identifying cloud service categories to improve the accuracy of cloud services searching in real environments. Our approach has the capability to automatically identify and categorize cloud services by detecting cloud service concepts from cloud service sources. Initially, we focus on building the cloud service ontology by using NIST (US National Institute of Standards and Technology). Then, we utilize our cloud service categorization method to investigate cloud service ontology's concepts in a real-world cloud services dataset which contains the metadata of 5,883 real cloud services. After that, we generate cloud service clusters by using cosine similarity to build the cloud service categorization. Our cloud service categorization is helpful in determining whether a given web source is a cloud service. Furthermore, the new web resource which has been categorized as a cloud service can be used to boost knowledge of cloud service categorization cumulatively. The proposed approach is validated by using real-world cloud services available on the World Wide Web and the experimental results show the effectiveness of the approach in cloud service discovery.

The service composition problem in Cloud computing is formulated as a multiple criteria decision making problem. Due to the extensive search space, Cloud service composition is addressed as an NP-hard problem. Using a proper dataset is considered one of the main challenges to evaluate the efficiency of the developed service composition algorithms. According to the work in this paper, a new dataset has been introduced, called Integrated Cloud Services Dataset (ICSD). This dataset is constructed by amalgamating the Google cluster-usage traces, and a real QoS dataset. To evaluate the efficiency of the ICSD dataset, a proof of concept has been done by implementing and evaluating an existing Cloud service compositing approach; PSO algorithm with skyline operator using ICSD dataset. According to the implementation results, it is found that the ICSD dataset achieved a high degree of optimality with low time complexity, which significantly increases the ICSD dataset accuracy in Cloud services composition environment.

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.

With the spread of services related to cloud environment, it is tiresome and time consuming for users to look for the appropriate service that meet with their needs. Therefore, finding a valid and reliable service is essential. However, in case a single cloud service cannot fulfil every user requirements, a composition of cloud services is needed. In addition, the need to treat uncertainty in cloud service discovery and composition induces a lot of concerns in order to minimize the risk. Risk includes some sort of either loss or damage which is possible to be received by a target (i.e., the environment, cloud providers or customers). In this paper, we will focus on the uncertainty application for cloud service discovery and composition. A set of existing approaches in literature are reviewed and categorized according to the risk modeling.