Cloud Computing Adoption: A Mapping Of Service Delivery And Deployment Models

The recent upward trend in adopting cloud computing by businesses worldwide reflects the captivating opportunity of cost effective computing brought by cloud computing to replace the traditional IT computing services model. However, the decision to adopt cloud computing is somewhat complex. This paper will review the literature of cloud computing service and deployment models with the aim to determine the relevant characteristics of both service delivery and deployment models. Then, the authors will develop a mapping between the two sets of characteristics of cloud computing models. The mapping will lead to the development of a decision-making framework for managing cloud-computing adoption

A Framework for Strategic Cloud Migration

This paper presents a novel framework for organisations to carry out a structured feasibility study on Cloud migration and to decide Cloud Migration Strategy. Following the framework helps an organisation to decide whether Cloud migration is a feasible option for them, and if so, the best strategic approach towards Cloud migration. It is a crucial and sensitive part for any organisation to decide whether they should move to Cloud Computing platform. The decision requires strategic approach with proper feasibility study. Several technological, human, security and financial factors are involved in decision making process to move to the Cloud. The proposed framework helps an organisation to carry out a feasibility study to decide whether to move to the Cloud, and if so, what would be the best approach towards Cloud migration. The proposed framework addresses the factors that an organisation must explore to decide on Cloud migration. Cloud Computing has its own pros and cons. A whimsical decision to move to the Cloud may be disastrous for an organisation. Following the proposed framework will help organisations to carry out a structured and integrated feasibility study deal with the decision on Cloud migration

Cloud Computing based Learning Management System (CC-LMS) implementation model in Malaysia Higher Education Institutions

In the education arena, cloud computing is more apparent in technology to ensure the availability and sharing of resources through virtualization. Despite its attractiveness and benefits, the Higher Education Institution (HEIs) are still hesitant to implement cloud computing services due to insufficient details on issues and priorities in implementing cloud computing services. Therefore, this study aims to explore the potential benefits and obstacles of cloud computing in Learning Management System (LMS) and determine the key success factors of cloud computing implementation for LMS (CC-LMS) operations in HEIs. By synthesizing literature from various industries, this study proposes a conceptual model of Critical Success Factors (CSFs) based cloud computing implementation for HEIs. This framework was extracted from the various aspects of the industry and integrated into the Technological, Organizational, and Environmental (TOE) framework. The research methodology consists of rigorous data collection and quantitative and qualitative data analysis that allows for more substantive conclusions to enable viable CC-LMS operation. The Delphi technique was adapted to assist in the data collection and judgment process. The two-round Delphi survey has been conducted with 18 (1st round) and 13 (2nd round) cloud computing technology and LMS experts from local HEIs and service vendors to assist in the judgment process. This analysis resulted in a consensus after the second round of Delphi survey with the suggestions on the high importance of several factors in implementing a cloud computing system for LMS in HEIs. As a result, on the benefit of this technology, most participants agreed that this technology enhances the technology infrastructure and maintenance, which is synchronized with the main obstacle of CC-LMS implementation, poor infrastructure performance. Finally, the study is expected to provide HEIs decision-makers with a better understanding and guidelines of cloud computing implementation characteristics with the relevant perception of current services

A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning

Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloud computing system. However, a complete cloud resource allocation framework exhibits high dimensions in state and action spaces, which prohibit the usefulness of traditional RL techniques. In addition, high power consumption has become one of the critical concerns in design and control of cloud computing systems, which degrades system reliability and increases cooling cost. An effective dynamic power management (DPM) policy should minimize power consumption while maintaining performance degradation within an acceptable level. Thus, a joint virtual machine (VM) resource allocation and power management framework is critical to the overall cloud computing system. Moreover, novel solution framework is necessary to address the even higher dimensions in state and action spaces. In this paper, we propose a novel hierarchical framework for solving the overall resource allocation and power management problem in cloud computing systems. The proposed hierarchical framework comprises a global tier for VM resource allocation to the servers and a local tier for distributed power management of local servers. The emerging deep reinforcement learning (DRL) technique, which can deal with complicated control problems with large state space, is adopted to solve the global tier problem. Furthermore, an autoencoder and a novel weight sharing structure are adopted to handle the high-dimensional state space and accelerate the convergence speed. On the other hand, the local tier of distributed server power managements comprises an LSTM based workload predictor and a model-free RL based power manager, operating in a distributed manner.Comment: accepted by 37th IEEE International Conference on Distributed Computing (ICDCS 2017

A Framework for Uncertain Cloud Data Security and Recovery Based on Hybrid Multi-User Medical Decision Learning Patterns

Machine learning has been supporting real-time cloud based medical computing systems. However, most of the computing servers are independent of data security and recovery scheme in multiple virtual machines due to high computing cost and time. Also, this cloud based medical applications require static security parameters for cloud data security. Cloud based medical applications require multiple servers to store medical records or machine learning patterns for decision making. Due to high Uncertain computational memory and time, these cloud systems require an efficient data security framework to provide strong data access control among the multiple users. In this work, a hybrid cloud data security framework is developed to improve the data security on the large machine learning patterns in real-time cloud computing environment. This work is implemented in two phases’ i.e. data replication phase and multi-user data access security phase. Initially, machine decision patterns are replicated among the multiple servers for Uncertain data recovering phase. In the multi-access cloud data security framework, a hybrid multi-access key based data encryption and decryption model is implemented on the large machine learning medical patterns for data recovery and security process. Experimental results proved that the present two-phase data recovering, and security framework has better computational efficiency than the conventional approaches on large medical decision patterns

A framework for cloud computing adoption by Saudi Government overseas agencies

This study aims to identify key factors that organizations should consider when deciding whether to adopt cloud computing, and sets out a framework for how these factors can be weighed in order to make a decision. The study uses the Saudi Government agencies as a case study and makes several specific recommendations that pertain to the Saudi authorities' implementation of cloud computing. Although there are many benefits associated with deployment of cloud computing applications, there are also several challenges, such as compliance, legal issues, hosting issues, security, trust and privacy. There are also inadequate resources and guidelines for the policy makers and managers to inform their decision of whether or not to adopt cloud computing. This study identifies a number of factors; technological, environmental, organizational and societal, which need to be considered when an organization decides whether or not to adopt cloud computing. After identifying these factors, the study develops a comprehensive framework for organizations to assess their readiness for cloud computing. In addition, the feasibility of cloud computing applications is assessed so that different delivery and deployment models can be taken into account, and cloud computing evaluated from both business and customer perspectives

Holistic Approach Framework for Cloud Computing Strategic Decision-Making in the Healthcare Sector (HAF-CCS)

Cloud Computing is an evolving information technology paradigm that impacts many sectors in many countries. Cloud Computing offers IT services anytime, anywhere via any device and is applicable to healthcare organisations, offering a potential cost saving of 15% to 37%. This research investigates Cloud Computing as a facilitating technology to solve some of the challenges experienced by healthcare organisations such as the high cost of implementing IT solutions. The purpose of this research is to develop and apply an Holistic Approach Framework for Cloud Computing Strategic Decision-Making in the Healthcare Sector (HAF-CCS) to provide a systematic approach to the adoption of Cloud Computing that considers different perspectives. Although, Cloud Computing is becoming widely used, there is limited evidence in the literature concerning its application in the Saudi healthcare sector. In the thesis, current cloud adoption decision-making frameworks are analysed and the need to develop a strategic framework for Cloud Computing decision-making processes which emphasises a multidisciplinary holistic approach is identified. Understanding the different strategic aspects of Cloud Computing is important and could encourage organisations to adopt this model of computing since the decision regarding whether to adopt Cloud Computing is potentially a complex process; there are many perspectives to be considered, and studying this process requires a multiple perspective framework. The framework developed in this thesis aims to support decision-makers in healthcare organisations by covering five perspectives of Cloud Computing adoption: Organisation, Technology, Environment, Human and Business. The framework integrates the TOE (Technology-Organisation-Environment) framework with the Information Systems Strategy Triangle (IS Triangle) and the HOT-fit (Human- Organisation-Technology) model to support an holistic evaluation of the determinants of Cloud Computing adoption in healthcare organisations. The factors that will affect Cloud Computing adoption in healthcare organisations in Saudi Arabia have been identified using quantitative and qualitative methods, and a case study approach was implemented to validate the framework. The results of the validation showed that the framework can support decision-makers in understanding an organisation’s position regarding Cloud Computing and identifying any gaps that may hinder Cloud Computing adoption. The framework can also provide healthcare organisations with a strategic assessment tool to help in gaining the advantages of Cloud Computing

Investigating Cloud Access Security Broker In A Healthcare Service : Creating A Cloud Access Security Broker (CASB) Discussion Frame-work For Evaluating Security in Cloud Healthcare Services

Master's thesis in Cyber security (IKT523)Covid-19 accentuated the importance of accessible services, causing a major increase in the adoption of cloud services for enterprises. Cloud computing is a new paradigm that promises significant benefits for organizations in healthcare services. However, cloud computing also transforms enterprise architectures and introduces new problems of information security. Decision-makers in a large healthcare service provider need to justify decisions on cloud adoption, but such a task is convoluted given the different views on cloud computing and the potential impact of cyberthreats on critical infrastructures. As a consequence, cloud security controls need to be selected and implemented to complement cloud services. Our research focuses on the decision-making process for selecting a Cloud Access Security Broker (CASB) in a large public healthcare ICT provider in Norway. This thesis applies Action Design Research (ADR) to design a decision support tool for cloud security control selection in healthcare organizations. The result is a framework for evaluating cloud security controls that facilitates the decision-making process by considering multiple aspects of enterprise security architectures. Participants in the decision-making process can achieve a common understanding of cloud security control and a tailored assessment of how the cloud will impact information security in the organization. We present the design process and apply the framework to the CASB selection problem. As a practical implication, our findings suggest that selecting a cloud security control in a healthcare service provider is an ill-structured or “wicked” problem that requires a unique problem-solving approac

Towards a cloud migration decision support system for Small and Medium enterprises in Tamil Nadu

Cloud computing is a promising computing paradigm which has the potential to speed up Information Technology adoption among SMEs in developing economies like India. The user friendly, pay per use cloud computing model offers SMEs access to highly scalable and reliable cloud infrastructure without having to invest on buying and maintaining expensive Information Technology resources. However, moving data and application to a cloud infrastructure is not straightforward and can be very challenging as decision makers need to consider numerous aspects before deciding to adopt cloud infrastructure. A review of the literature reveals that there are frameworks available to support cloud migration. However, there are no frameworks, models or tools available to support the whole cloud migration process. This research aims to fill that gap by proposing a conceptual framework for cloud migration decision support system targeted for SMEs in Tamil Nadu

The Cloud Adoption Toolkit: Addressing the Challenges of Cloud Adoption in Enterprise

Cloud computing promises a radical shift in the provisioning of computing resource within the enterprise. This paper: i) describes the challenges that decision makers face when attempting to determine the feasibility of the adoption of cloud computing in their organisations; ii) illustrates a lack of existing work to address the feasibility challenges of cloud adoption in the enterprise; iii) introduces the Cloud Adoption Toolkit that provides a framework to support decision makers in identifying their concerns, and matching these concerns to appropriate tools/techniques that can be used to address them. The paper adopts a position paper methodology such that case study evidence is provided, where available, to support claims. We conclude that the Cloud Adoption Toolkit, whilst still under development, shows signs that it is a useful tool for decision makers as it helps address the feasibility challenges of cloud adoption in the enterprise