Current Issue : January - March Volume : 2015 Issue Number : 1 Articles : 4 Articles
Significant innovations in mobile technologies are enabling mobile users to make real-time actionable decisions based\non balancing opportunities and risks to take coordinated actions with other users in their workplace. This requires a\nnew distributed analytic framework that collects relevant information from internal and external sources, performs\nreal-time distributed analytics, and delivers a critical analysis to any user at any place in a given time frame through\nthe use of mobile devices such as smartphones and tablets. This paper discusses the advantages and challenges of\nutilizing mobile devices for distributed analytics by showing its feasibility with Hadoop analytic framework....
Many efforts have been made in optimizing cloud service resource management for efficient service provision and\ndelivery, yet little research addresses how to consume the provisioned service resources efficiently. Meanwhile,\ntypical existing resource scaling management approaches often rest on single monitor category statistics and are\ndriven by certain threshold algorithms, they usually fail to function effectively in case of dealing with complicated\nand unpredictable workload patterns. Fundamentally, this is due to the inflexibility of using static monitor, threshold\nand scaling parameters. This paper presents Off-the-Cloud Service Optimization (OCSO), a novel user-side optimization\nsolution which specifically deals with service resource consumption efficiency from the service consumer perspective.\nOCSO rests on an intelligent resource scaling algorithm which relies on multiple service monitor metrics plus dynamic\nthreshold and scaling parameters. It can achieve proactive and continuous service optimizations for both real-world\nIaaS and PaaS services, through OCSO cloud service API. From the two series of experiments conducted over Amazon\nEC2 and Elastic Beanstalk using OCSO prototype, it is demonstrated that the proposed approach can make significant\nimprovement over Amazon native automated service provision and scaling options, regardless of scaling up/down or\nin/out....
Cloud based scientific data management - storage, transfer, analysis, and inference extraction - is attracting interest. In\nthis paper, we propose a next generation cloud deployment model suitable for data intensive applications. Our model\nis a flexible and self-service container-based infrastructure that delivers - network, computing, and storage resources\ntogether with the logic to dynamically manage the components in a holistic manner. We demonstrate the strength of\nour model with a bioinformatics application. Dynamic algorithms for resource provisioning and job allocation suitable\nfor the chosen data set are packaged and delivered in a privileged virtual machine as part of the container. We tested\nthe model on our private internal experimental cloud that is built on low-cost commodity hardware. We demonstrate\nthe capability of our model to create the required network and computing resources and allocate submitted jobs. The\nresults obtained shows the benefits of increased automation in terms of both a significant improvement in the time\nto complete a data analysis and a reduction in the cost of analysis. The algorithms proposed reduced the cost of\nperforming analysis by 50% at 15 GB of data analysis. The total time between submitting a job and writing the results\nafter analysis also reduced by more than 1 hr at 15 GB of data analysis....
Virtualization and broadband Internet connections enabled what is known today under the term cloud. Benefits like\nscalability and cost reduction by pay per use agreements are accompanied by potential harm to the users data. Since\nexisting cloud solutions can be considered synonymous with unknown locations and potentially hostile environments\nsecurity protection objectives can not be guaranteed. Motivated by cloud�s potential we propose a way to get rid of\nthis drawback. We present ?-Cloud, a personal secure cloud, that enables users to benefit from cloud computing and\nretain data sovereignty by federating the users own resources. More precisely we present a cloud resource manager\nthat is able to keep control on the devices forming the user�s ?-Cloud as well as the services running on them...
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