Current Issue : April - June Volume : 2016 Issue Number : 2 Articles : 4 Articles
Online Education (OE) system is an effective and efficient way to perform the education in all sectors\nof government and non-government educational organization. Low performance and minimum\nspeed are major overhead in the current ongoing OE system due to the increase of users and\nsome system issues. Base on the previous study and recent practical issues, a model is proposed to\nEnhancing the Performance of Online Education System (EPOES) to examine the bare metal virtualization,\nisolation and virtual machine templates. Bare metal virtualization has led the native execution,\nisolation isolated the running application and Virtual Machine Template has help to increase\nefficiency, avoiding the repetitive installation and operate the server in less time. The proposed\nmodel boosts the performance of the current OE system, and examines the benefits of the\nadaptation of cloud computing and virtualization which can be used to overcome the existing\nchallenges and barriers of the current OE System....
Despite the rapid expansion and uptake of cloud based services, lack of trust in the provenance of such services\nrepresents a significant inhibiting factor in the further expansion of such service. This paper explores an approach\nto assure trust and provenance in cloud based services via the generation of digital signatures using properties or\nfeatures derived from their own construction and software behaviour. The resulting system removes the need for a\nserver to store a private key in a typical Public/Private-Key Infrastructure for data sources. Rather, keys are generated\nat run-time by features obtained as service execution proceeds. In this paper we investigate several potential software\nfeatures for suitability during the employment of a cloud service identification system. The generation of stable and\nunique digital identity from features in Cloud computing is challenging because of the unstable operation environments\nthat implies the features employed are likely to vary under normal operating conditions. To address this, we introduce a\nmulti-dimensional key generation technology which maps from multi-dimensional feature space directly to a key space.\nSubsequently, a smooth entropy algorithm is developed to evaluate the entropy of key space....
Cloud service providers negotiate SLAs for customer services they offer based on the reliability of performance and\navailability of their lower-level platform infrastructure. While availability management is more mature, performance\nmanagement is less reliable. In order to support a continuous approach that supports the initial static infrastructure\nconfiguration as well as dynamic reconfiguration and auto-scaling, an accurate and efficient solution is required. We\npropose a prediction technique that combines a workload pattern mining approach with a traditional collaborative\nfiltering solution to meet the accuracy and efficiency requirements. Service workload patterns abstract common\ninfrastructure workloads from monitoring logs and act as a part of a first-stage high-performant configuration\nmechanism before more complex traditional methods are considered. This enhances current reactive rule-based\nscalability approaches and basic prediction techniques by a hybrid prediction solution. Uncertainty and noise are\nadditional challenges that emerge in multi-layered, often federated cloud architectures. We specifically add log\nsmoothing combined with a fuzzy logic approach to make the prediction solution more robust in the context of these\nchallenges....
The current cloud computing market lacks of clear comparison between the Cloud service providers (CSPs) offerings.\nThis is due to the heterogeneity in the virtual machines (VMs) configurations and their prices which differ among the\nCSPs. Big players in the market offer different configurations of fixed size VMs. Cloud customers have to choose the CSP\nthat best fits their requirements. In the actual market, and with the limited performance information provided by the\nCSPs to the cloud users, the choice of the CSP can be a problem for the customers. In our paper, and in the context of\nthe Easi-Clouds (project I, Easi Clouds. http://www.easi-clouds.eu/) a European ITEA 2 research project, we propose a\nset of performance tests based on real measurements to classify the CSPs based on their performance score as well as\ntheir proposed price. We used a set of benchmarks to test the performances of four VMs� sizes (Small (S), Medium (M),\nLarge (L), and Xlarge (XL)) from each one of the biggest eight CSPs (Amazon, Softlayer, Rackspace, Google, Microsoft\nAzure, Aruba, Digital Ocean, Joyent). We try to compare the performance based on seven different metrics (CPU\nperformance, Memory performance, Disk I/O performance, Mean Response time (MRT), Provisioning time, Availability,\nand Variability). In a second step, we include the price to have a performance vs. price value figure. In a final step, we\npropose a new method that let the user specify the importance of each performance�s metric as well as the importance\nof the price to classify the CSPs based on the criterions of the customers. We come up with a unified customer aware\nfigure of merit helping the cloud customers to select the most suitable CSP based on their own requirements....
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