Cloud Computing Service Model Research Articles

Compute functional analysis leveraging the IAAS private cloud computing service model in packstack development

Analyzing compute functions by utilizing the IAAS model for private cloud computing services in packstack development is one of the large-scale data storage solutions. Problems that often occur when implementing various applications are the increased need for server resources, the monitoring process, performance efficiency, time constraints in building servers and upgrading hardware. These problems have an impact on long server downtime. The development of private cloud computing technology could become a solution to the problem. This research employed Openstack and Packstack by applying one server controller node and two servers compute nodes. Server administration with IAAS and self-service approaches made scalability testing simpler and time-efficient. The resizing of the virtual server (instance) that has been carried out in a running condition shows that the measurement of the overhead value in private cloud computing is more optimal with a downtime of 16 seconds.

A Comprehensive Survey on Security Threats and Countermeasures of Cloud Computing Environment

Cloud computing technology is one of the biggest breakthroughs in technology and business boom. This technology is service oriented that focuses on reducing costs, minimizing devices and paying for used service only. This paper surveys recent research on security threats and countermeasures of cloud computing environment. It begins by explaining cloud computing definition with its layers. In addition, it provides a brief explanation of the common characteristics of cloud computing service models and deployment models. Authentication and access control in cloud computing will be given at end of this work.

An energy-efficient big data workflow scheduling algorithm under budget constraints for heterogeneous cloud environment

Infrastructure as a service model of cloud computing provides a tremendous amount of high-performance computing systems for the execution of scientific workflow applications. However, due to explosive growth in energy consumption and high charge cost of using these cloud systems, energy-efficient workflow scheduling under budget constraints becomes the most challenging issue. Very few research works have been done that consider the stated issue. Most of them mainly focus on the minimization of schedule length under user-specified budget constraints or energy consumption constraints. In this article, we propose an energy-efficient workflow scheduling algorithm named reducing energy consumption using fair pre-assignment of available budget (RECFPAB) that reduces energy consumption under client-specified budget constraints. The RECFPAB introduces a flexible mechanism to save energy consumption with the inclusion of energy and cost coefficient factor that enables fair distribution of available budget for unscheduled tasks of the workflow application. In order to compare the performance of the proposed algorithm, an energy-efficient version of the popular existing algorithms such as heterogeneous budget constrained scheduling and minimizing schedule length using budget level are introduced. The experimental evaluation based on Genome, LIGO, and Montage applications shows that RECFPAB gives significant results in comparison with considered algorithms.

Decision-Making Model to Evaluate Cloud Computing Service Model Using Analytic Hierarchy Process (AHP) and Benefits, Costs, Opportunities, and Risks (BCOR) Analysis

Background & Objective: Organizations have started evaluating business applications which are on hosted model. Organizations need not to focus on choosing the deployment model or buying the hardware or setup infrastructure instead they are choosing business application which are easily available on SaaS model or platform model. Organizations are analyzing the adoption of Cloud Computing service models due to reduced the load of IT overheads and to bring agility in deploying applications. An organization should have the scientific way to choose and identify the right service model to fulfill its IT and business needs. Methods: This research paper focuses on proposing a decision model with the help of Analytic Hierarchy Process (AHP) and benefit-cost- opportunity-risk (BCOR) analysis to select the appropriate service models i.e. SaaS or PaaS or IaaS to fulfill the business needs. It suggests a range of key attributes which act as building blocks of the model based on Analytic Hierarchy Process (AHP). The model will provide the benefits, cost, opportunity and risks of cloud computing services for the three service models of cloud computing. Conclusion: This study results will be useful for organizations to adopt cloud computing service model which suites the requirements as well as rapid deployment of the applications.

Identification of Various Security Issues, Threats and A Novel Service Model in Cloud Computing

Cloud Computing is a robust, less cost, and an effective platform for providing services. Nowadays, it is applied in various services such as consumer business or Information Technology (IT) carried over the Internet. This cloud computing has some risks of security because, the services which are required for its effective compilation is outsources often by the third party providers. This makes the cloud computing more hard to maintain and monitor the security and privacy of data and also its support. This sudden change in the process of storing data towards the cloud computing technology improved the concerns about different issues in security and also the various threats present in this cloud storage. In the concept of security in cloud storage, various threats and challenges are noted by recent researchers. Hence, an effective framework of providing security is required. The main aim of this paper is to analyze various issues in securing the cloud data threats present in the cloud storage and to propose a novel methodology to secure it. This paper also identifies the most crucial components that can be incorporated in the already existing security measures while designing the storage systems based on cloud. This study also provides us to identify all the available solutions for the challenges of security and privacy in cloud storage.

Evaluation of University Libraries in Turkey within the Context of Cloud Computing

Cloud computing is a service model that allows access to information resources such as computer networks, servers, databases and computer applications at any time and from anywhere. University libraries, like other libraries, already started to benefit from the cloud computing service model that provides benefits such as diversity, flexibility, continuity, scalability, and cost effectiveness in their information services. In recent years, the increase in the number of universities has also increased the number of university libraries in Turkey. Cloud computing will enable university libraries, especially those with limited budgets, to provide information services more cost effectively. In this study it is aimed to evaluate the position of university libraries in Turkey within the context of cloud computing. The structured interview technique was used in the research. SPSS program was used to evaluate the data. Percentage values, frequency and Chi square statistic calculations were used to analyze the data. As a result of the research, the existing conditions and problems in the usage cloud service model in university libraries have been tried to be identified, a road map for solving these problems has been put into in this context. Keywords: Cloud computing; Library services; Information services; University libraries DOI: 10.7176/JSTR/6-11-05

Efficient and Enhanced Data Encryption in Cloud Computing

Cloud computing transforms the way information technology (IT) is consumed and managed, promisingimproved cost efficiencies, accelerated innovation, faster time-to-market, and the ability to scaleapplications on demand. However, as the shape of the cloud computing is emerging and developingrapidly both conceptually and in reality, the legal/contractual, economic, service quality,interoperability, security and privacy issues still pose significant challenges. In this project, we describevarious service and deployment models of cloud computing and identify major challenges. In particular,we discuss three critical challenges: security and privacy issues in cloud computing. Some solutionsto mitigate these challenges are also proposed along with a brief presentation on the future trendsin cloud computing deployment. The use of cloud computing has increased rapidly in many organizations.Cloud computing provides many benefits in terms of low cost and accessibility of data. Ensuring thesecurity of cloud computing is a major factor in the cloud computing environment, as users often storesensitive information with cloud storage providers but these providers may not be trusted.

Developing a Conceptual Model for the Intention to Adopt CC Saas

Cloud Computing (CC) is an evolving information technology paradigm providing on-demand IT resources on a pay-as-you-go basis. A cloud computing service model called Software as a Service (SaaS) emerged to offers IT services, effectively utilise computing and power consumption while achieving performance, decreasing cost, and increasing revenue. However, the adoption rates of CC-SaaS among the organisations are inadequate, and its diffusion is not fully explored due to issues related to data security, privacy concerns and trust which affects the adoption of CC-SaaS. There is a lack of empirical studies investigating factors affecting the adoption of CC-SaaS from the organisational perspectives specifically in Iraq. Therefore, the primary aim of this study is to identify the most influencing factors for the intention to adopt CC-SaaS in Iraqi organisations. In order to meet the primary aim, such factors are reviewed and selected from the literature on a high frequency of occurrence and developed the proposed model. The result of the factors is validated by expert and the values of assessment are statistically analysed using SPSS for t-test analysis. The proposed model can give a guide for the decision makers to plan the successful adoption of CC-SaaS in the organisation.

Cloud Computing: Difficulties in Applying Current and Proposed Nexus and Profit Allocation Rules in a Cross-Border Scenario

This article discusses the international tax challenges with respect to business profits arising from the digital economy using the cloud computing service model. In particular, it examines issues that arise from the OECD Model Convention (2017) and other OECD documents, the EU Proposal for a Directive on a Significant Digital Presence and the OECD Secretariat Proposal for a “Unified Approach” under Pillar One.

Network and Information Security of Cloud Computing Services

The security of network and information systems has become a critical issue for both the public and private sectors in the last decade. This article reviews the current EU directive in this area, looking for safety and reliability requirements. Next, we get an overview of the different models of cloud computing services. Finally, the relationship between the identified security and reliability requirements and the cloud computing service models will be described.

Review on Security Challenges of Cloud Computing

Cloud computing is the quickest developing innovation these days it has wonderful situation in business. Cloud Computing has changed the parts of conventional computing technologies, Cloud computing is a big shift from the traditional way business to IT resources. it is the least complex administration accessible through web to begin and look after business. Services of this technology are provided through internet by giving amount based upon prerequisite of resources in an easier and effective mode with the facility of increase or decrease the requirements of resources. This technology has several advantages, even though it has not satisfied its maturity level due to the lack of protection and other issues. This paper deals with the various security challenges of cloud computing. It also points out the types of cloud and several service models of cloud computing.

Cloud Federations: Economics, Games and Benefits

Sharing economy is a game-changing business paradigm that is currently permeating several industrial sectors. This paper aims to build a fundamental theory of the sharing economy of the computational capacity resource of Cloud Service Providers (CSPs). CSPs aim to cost-efficient serve geographically dispersed customers that often request computational resource-demanding services. The formation of CSP federations arises as an effective means to manage these diverse and time-varying service requests. In this paper, we introduce innovative federation models and policies for profitable federations that also achieve adequate QoS for their customers. Taking in account the flexible cloud computing service model, we abstract the virtualized infrastructure of each CSP to an M/M/1 queueing system, we formulate the CSP revenue and cost functions, and we study the task forwarding-based (TF) and the capacity sharing-based (CS) federation approaches. Under TF, each CSP may forward part of its workload to other federated CSPs, while under CS each CSP may share parts of its computational infrastructure with others. For both approaches, we propose two operation modes with different degree of CSPs’ cooperation: $(i)$ the joint business mode, where the CSPs fully cooperate: they jointly decide on the federation policies that maximize the total federation profit which is shared fairly among them; $(ii)$ the reward-driven mode, where self-interested CSPs participate in a game: they adjust their responses to federation policies aiming to maximize their individual profits. The results reveal that our policies lead to effective federations, which are beneficial both for CSPs and for customers.

Optimization of lightweight task offloading strategy for mobile edge computing based on deep reinforcement learning

With the maturity of 5G technology and the popularity of intelligent terminal devices, the traditional cloud computing service model cannot deal with the explosive growth of business data quickly. Therefore, the purpose of mobile edge computing (MEC) is to effectively solve problems such as latency and network load. In this paper, deep reinforcement learning (DRL) is first proposed to solve the offloading problem of multiple service nodes for the cluster and multiple dependencies for mobile tasks in large-scale heterogeneous MEC. Then the paper uses the LSTM network layer and the candidate network set to improve the DQN algorithm in combination with the actual environment of the MEC. Finally, the task offloading problem is simulated by using iFogSim and Google Cluster Trace. The simulation results show that the offloading strategy based on the improved IDRQN algorithm has better performance in energy consumption, load balancing, latency and average execution time than other algorithms.

Current Development on Cloud Computing Service Models for End Users Data Analysis

The emerging cloud computing has lead society to accept this technology advancement in their daily life. Referring to the past studies, it is found that there are few factors that effecting the usage satisfaction in using cloud computing services. Many cloud computing providers are always competing to give users the best feature out of their offered services. However, it should consider knowing the factors that contribute to the usage satisfaction. This paper reviewed the previous study on cloud computing services. We compare previous models on cloud computing to analyse current gaps on critical factors toward future framework development. The reviews contain relevant factors such as security, efficiency and performance, cost, organisation and technology developed based from the literature.

Cloud computing model selection for e-commerce enterprises using a new 2-tuple fuzzy linguistic decision-making method

Cloud computing is truly transforming the way e-commerce firms do business. While there has been a sharp increase in the use of cloud computing in e-commerce, the benefits of cloud service models have yet to be explored, particularly for small-to-medium-sized businesses. A strong e-commerce offering depends on a reliable and secure online store, therefore it is important for decision makers to adopt the optimal cloud computing service model such as software-as-a-service (SaaS), platform-as-a-service (PaaS), or infrastructure-as-a-service (IaaS), which is a multi-criteria decision-making problem (MCDM). To address this MCDM problem, we propose a novel 2-tuple fuzzy linguistic multi-criteria group decision-making method based on the technique for order preference by similarity to ideal solution (TOPSIS) and rely upon a technology-organization-environment (TOE) framework to determine a set of appropriate criteria. The proposed methodology is applied to a small-to-medium-sized company to facilitate assessing the factors associated with cloud-based e-commerce and making the decision. The result analysis indicates that SaaS is the best choice for small and medium-sized e-commerce businesses considering criteria such as complexity, reliability, security and privacy, organization readiness and firm size, while the selection of PaaS or IaaS can be reinforced considering their compatibility and scalability.

Study on Various Approaches used by Security as a Service Model in Cloud Computing Study on Various Approaches used by Security as a Service Model in Cloud Computing

Study on Various Approaches used by Security as a Service Model in Cloud Computing Study on Various Approaches used by Security as a Service Model in Cloud Computing

Study on Various Approaches used by Security as a Service Model in Cloud Computing

Study on Various Approaches used by Security as a Service Model in Cloud Computing

An efficient job management of computing service using integrated idle VM resources for high-performance computing based on OpenStack

In recent years, various studies on OpenStack-based high-performance computing have been conducted. OpenStack combines off-the-shelf physical computing devices and creates a resource pool of logical computing. The configuration of the logical computing resource pool provides computing infrastructure according to the user’s request and can be applied to the infrastructure as a service (laaS), which is a cloud computing service model. The OpenStack-based cloud computing can provide various computing services for users using a virtual machine (VM). However, intensive computing service requests from a large number of users during large-scale computing jobs may delay the job execution. Moreover, idle VM resources may occur and computing resources are wasted if users do not employ the cloud computing resources. To resolve the computing job delay and waste of computing resources, a variety of studies are required including computing task allocation, job scheduling, utilization of idle VM resource, and improvements in overall job’s execution speed according to the increase in computing service requests. Thus, this paper proposes an efficient job management of computing service (EJM-CS) by which idle VM resources are utilized in OpenStack and user’s computing services are processed in a distributed manner. EJM-CS logically integrates idle VM resources, which have different performances, for computing services. EJM-CS improves resource wastes by utilizing idle VM resources. EJM-CS takes multiple computing services rather than single computing service into consideration. EJM-CS determines the job execution order considering workloads and waiting time according to job priority of computing service requester and computing service type, thereby providing improved performance of overall job execution when computing service requests increase.

Tweet Sentiment Analysis (TSA) for Cloud Providers Using Classification Algorithms and Latent Semantic Analysis

The availability and advancements of cloud computing service models such as IaaS, SaaS, and PaaS; introducing on-demand self-service, auto scaling, easy maintenance, and pay as you go, has dramatically transformed the way organizations design and operate their datacenters. However, some organizations still have many concerns like: security, governance, lack of expertise, and migration. The purpose of this paper is to discuss the cloud computing customers’ opinions, feedbacks, attitudes, and emotions towards cloud computing services using sentiment analysis. The associated aim, is to help people and organizations to understand the benefits and challenges of cloud services from the general public’s perspective view as well as opinions about existing cloud providers, focusing on three main cloud providers: Azure, Amazon Web Services (AWS) and Google Cloud. The methodology used in this paper is based on sentiment analysis applied to the tweets that were extracted from social media platform (Twitter) via its search API. We have extracted a sample of 11,000 tweets and each cloud provider has almost similar proportion of the tweets based on relevant hashtags and keywords. Analysis starts by combining the tweets in order to find the overall polarity about cloud computing, then breaking the tweets to find the specific polarity for each cloud provider. Bing and NRC Lexicons are employed to measure the polarity and emotion of the terms in the tweets. The overall polarity classification of the tweets across all cloud providers shows 68.5% positive and 31.5% negative percentages. More specifically, Azure shows 63.8% positive and 36.2% negative tweets, Google Cloud shows 72.6% positive and 27.4% negative tweets and AWS shows 69.1% positive and 30.9% negative tweets.

Cloud Computing Research in Nigeria: A Bibliometric and Content Analysis

In recent times, there is a growing global interest in cloud computing which has necessitated a huge financial and intellectual investment in cloud research and development and developing countries are not left out, especially Nigeria. The aim of this paper is to measure Nigeria’s contribution to the global cloud computing research endeavours in terms of quantity and impact. Bibliometric and content analysis was performed on research documents that were retrieved from the Scopus database based on defined search terms relating to cloud computing. The year of publication and the author’s country was limited to “2016” and “Nigeria” respectively. A total of 51 documents met the search criteria and the oldest article on cloud computing was published in 2011, while the publication output has increased consistently with the highest number of the document published in 2016. Covenant University emerged as the most productive institution. Malaysia emerged as the country with the highest number of collaborations with Nigeria. The trend of cloud computing research in Nigeria revealed a majority of original researches and research interests are focused on Education and the Software-as-a- Service model of cloud computing. Although, cloud computing research in Nigeria is still very nascent, the publication trends portend more growth in the research area in the nearest future.