Selecting Cloud Deployment Model Using a Delphi Analytic Hierarchy Process (DAHP)

Cloud computing is a significant paradigm shift in information technology (IT) service offerings that has been receiving enormous attention in academic and IT industry. Recent years has seen exponential growth in cloud use adoption, where many organizations are moving their IT resources into cloud due to flexibility and low-cost. However, on account of rapid innovation and growth in cloud technologies and service providers, selecting the right cloud services, provider and strategy is becoming increasing a common challenge to organization during cloud adoption. In an attempt to address this challenge, we propose application of Delphi Analytic Hierarchy Process (DAHP) method in selecting cloud deployment model. There are several cloud deployment models and organizations must identify the right model that best suits their business needs. The proposed approach facilitates a collaborative decision making process, consisting a number of decision makers whom, with consensus facilitated by the DAHP process, identifies feasible approaches, decision making factors and ultimate selection of a cloud deployment model alternative that is based on organizational business needs and capabilities. The DAHP process is illustrated by a means of a case study. The DAHP result analysis, as was illustrated in the case study, helps in explaining and justifying the choice selected as the best cloud deployment model

USER-ORIENTED CLOUD SERVICE DESIGN BASED ON MARKET RESEARCH TECHNIQUES

For the IT industry, cloud computing has a disruptive effect, since it fundamentally changes how IT resources are produced, distributed, consumed, and priced. Designing cloud services remains a challenge, as the markets are very dynamic and cloud users are heterogeneous, locally distributed and not within the reach of the organization. This research-in-progress paper suggests the use of market research techniques, namely conjoint analysis, in the requirements elicitation process for cloud services. The contribution is a method component that extends existing requirements engineering methods. It supports cloud service providers in addressing specific questions of cloud service design: to analyse user preferences and the many trade-offs between different functional, non-functional and economic properties, to identify customer segments and develop tailored offerings, to analyse willingness-to-pay for specific features and to simulate market reactions of new designs

Selecting cloud computing service provider with fuzzy ahp

With the growing demand for outsourcing the ICT section of enterprises, Cloud Computing service providers increased their popularity. Selecting the most appropriate provider for a demanding enterprise depends on many criteria that are based on the strategies, requirements, and resources of the enterprise. Since this problem is a kind of decision problem and depends on criteria of decision-maker, it can be modeled as Multi-criteria Decision Making (MCDM) problem. In this research, a pilot case study is conducted in which the Cloud Computing service provider selection problem is modeled as a MCDM problem. For selecting the most appropriate provider, Fuzzy Extend Analysis is implemented in the case study

Location-aware deep learning-based framework for optimizing cloud consumer quality of service-based service composition

The expanding propensity of organization users to utilize cloud services urges to deliver services in a service pool with a variety of functional and non-functional attributes from online service providers. brokers of cloud services must intense rivalry competing with one another to provide quality of service (QoS) enhancements. Such rivalry prompts a troublesome and muddled providing composite services on the cloud using a simple service selection and composition approach. Therefore, cloud composition is considered a non-deterministic polynomial (NP-hard) and economically motivated problem. Hence, developing a reliable economic model for composition is of tremendous interest and to have importance for the cloud consumer. This paper provides “A location-aware deep learning framework for improving the QoS-based service composition for cloud consumers”. The proposed framework is firstly reducing the dimensions of data. Secondly, it applies a combination of the deep learning long short-term memory network and particle swarm optimization algorithm additionally to considering the location parameter to correctly forecast the QoS provisioned values. Finally, it composes the ideal services need to reduce the customer cost function. The suggested framework's performance has been demonstrated using a real dataset, proving that it superior the current models in terms of prediction and composition accuracy

Analysing Trust Issues in Cloud Identity Environments

Trust acts as a facilitator for decision making in environments, where decisions are subject to risk and uncertainty. Security is one of the factors contributing to the trust model that is a requirement for service users. In this paper we ask, What can be done to improve end user trust in choosing a cloud identity provider? Security and privacy are central issues in a cloud identity environment and it is the end user who determines the amount of trust they have in any identity system. This paper is an in-depth literature survey that evaluates identity service delivery in a cloud environment from the perspective of the service user

Rapid health data repository allocation using predictive machine learning

Health-related data is stored in a number of repositories that are managed and controlled by different entities. For instance, Electronic Health Records are usually administered by governments. Electronic Medical Records are typically controlled by health care providers, whereas Personal Health Records are managed directly by patients. Recently, Blockchain-based health record systems largely regulated by technology have emerged as another type of repository. Repositories for storing health data differ from one another based on cost, level of security and quality of performance. Not only has the type of repositories increased in recent years, but the quantum of health data to be stored has increased. For instance, the advent of wearable sensors that capture physiological signs has resulted in an exponential growth in digital health data. The increase in the types of repository and amount of data has driven a need for intelligent processes to select appropriate repositories as data is collected. However, the storage allocation decision is complex and nuanced. The challenges are exacerbated when health data are continuously streamed, as is the case with wearable sensors. Although patients are not always solely responsible for determining which repository should be used, they typically have some input into this decision. Patients can be expected to have idiosyncratic preferences regarding storage decisions depending on their unique contexts. In this paper, we propose a predictive model for the storage of health data that can meet patient needs and make storage decisions rapidly, in real-time, even with data streaming from wearable sensors. The model is built with a machine learning classifier that learns the mapping between characteristics of health data and features of storage repositories from a training set generated synthetically from correlations evident from small samples of experts. Results from the evaluation demonstrate the viability of the machine learning technique used. © The Author(s) 2020

Supporting User Requirements and Preferences in Cloud Plan Selection

With the cloud emerging as a successful paradigm for conveniently storing, accessing, processing, and sharing information, the cloud market has seen an incredible growth. An ever-increasing number of providers offer today several cloud plans, with different guarantees in terms of service properties such as performance, cost, or security. While such a variety naturally corresponds to a diversified user demand, it is far from trivial for users to identify the cloud providers and plans that better suit their specific needs. In this paper, we address the problem of supporting users in cloud plan selection. We characterize different kinds of requirements that may need to be supported in cloud plan selection and introduce a very simple and intuitive, yet expressive, language that captures different requirements as well as preferences users may wish to express. The corresponding formal modeling permits to reason on requirements satisfaction to identify plans that meet the constraints imposed by requirements, and to produce a preference-based ranking among such plans