Current Issue : January - March Volume : 2017 Issue Number : 1 Articles : 5 Articles
The Cloud Adoption Risk Assessment Model is designed to help cloud customers in assessing the risks that they face\nby selecting a specific cloud service provider. It evaluates background information obtained from cloud customers\nand cloud service providers to analyze various risk scenarios. This facilitates decision making an selecting the cloud\nservice provider with the most preferable risk profile based on aggregated risks to security, privacy, and service\ndelivery. Based on this model we developed a prototype using machine learning to automatically analyze the risks of\nrepresentative cloud service providers from the Cloud Security Alliance Security, Trust & Assurance Registry....
Despite the wide utilization of cloud computing (e.g., services, applications, and resources), some of the services, applications, and\nsmart devices are not able to fully benefit from this attractive cloud computing paradigm due to the following issues: (1) smart\ndevices might be lacking in their capacity (e.g., processing, memory, storage, battery, and resource allocation), (2) they might\nbe lacking in their network resources, and (3) the high network latency to centralized server in cloud might not be efficient for\ndelay-sensitive application, services, and resource allocations requests. Fog computing is promising paradigm that can extend\ncloud resources to edge of network, solving the abovementioned issue. As a result, in this work, we propose an architecture of IoT\nservice delegation and resource allocation based on collaboration between fog and cloud computing. We provide new algorithm\nthat is decision rules of linearized decision tree based on three conditions (services size, completion time, and VMs capacity) for\nmanaging and delegating user request in order to balance workload.Moreover, we propose algorithm to allocate resources to meet\nservice level agreement (SLA) and quality of services (QoS) as well as optimizing big data distribution in fog and cloud computing.\nOur simulation result shows that our proposed approach can efficiently balance workload, improve resource allocation efficiently,\noptimize big data distribution, and show better performance than other existing methods....
Although several cloud storage systems have been proposed, most of them can provide highly efficient point\nqueries only because of the key-value pairs storing mechanism. For these systems, satisfying complex\nmulti-dimensional queries means scanning the whole dataset, which is inefficient. In this paper, we propose a\nmultidimensional index framework, based on the Skip-list and Octree, which we refer to as Skip-Octree. Using a\nrandomized skip list makes the hierarchical Octree structure easier to implement in a cloud storage system. To\nsupport the Skip-Octree, we also propose a series of index operation algorithms including range query algorithm,\nindex maintenance algorithms, and dynamic index scaling algorithms. Through experimental evaluation, we show\nthat the Skip-Octree index is feasible and efficient....
Cloud computing is high technology that extends existing IT capabilities and requirements. Recently, the cloud computing\nparadigm is towards mobile with advances of mobile network and personal devices. As concept of mobile cloud, the number of\nproviders rapidly increases for various mobile cloud services. Despite development of cloud computing, most service providers\nused their own policies to deliver their services to user. In other words, quality criteria for mobile cloud service assessment are\nnot clearly established yet. To solve the problem, there were some researches that proposed models for service quality assessment.\nHowever, they did not consider various metrics to assess service quality. Although existing research considers various metrics,\nthey did not consider newly generated Service Level Agreement. In this paper, to solve the problem, we proposed a mobile\ncloud service assessment model called mCSQAM and verify our model through few case researches. To apply the mobile cloud,\nproposed assessment model is transformed from ISO/IEC 9126 which is an international standard for software quality assessment.\nmCSQAM can provide service quality assessment and determine raking of the service. Furthermore, if Cloud Service Broker\nincludes mCSQAM, appropriate services can be recommended for service users using user and service conditions....
Cloud computing system is a huge cluster of interconnected servers residing in a datacenter\nand dynamically provisioned to clients on-demand via a front-end interface. Scientific applications\nscheduling in the cloud computing environment is identified as NP-hard problem\ndue to the dynamic nature of heterogeneous resources. Recently, a number of metaheuristics\noptimization schemes have been applied to address the challenges of applications\nscheduling in the cloud system, without much emphasis on the issue of secure global\nscheduling. In this paper, scientific applications scheduling techniques using the Global\nLeague Championship Algorithm (GBLCA) optimization technique is first presented for\nglobal task scheduling in the cloud environment. The experiment is carried out using Cloud-\nSim simulator. The experimental results show that, the proposed GBLCA technique produced\nremarkable performance improvement rate on the makespan that ranges between\n14.44% to 46.41%. It also shows significant reduction in the time taken to securely schedule\napplications as parametrically measured in terms of the response time. In view of the experimental\nresults, the proposed technique provides better-quality scheduling solution that is\nsuitable for scientific applications task execution in the Cloud Computing environment than\nthe MinMin, MaxMin, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) scheduling\ntechniques....
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