Resource Optimization and Security for Cloud Services by Kaiqi Xiong

By Kaiqi Xiong

This e-book contains a research of trustworthiness, percentile reaction time, carrier availability, and authentication within the networks among clients and cloud carrier companies, and at provider stations or websites which may be owned by way of diverse provider prone. the 1st a part of the publication comprises an research of percentile reaction time, that is essentially the most vital SLA (service point agreements) metrics. potent and actual numerical strategies for the calculation of the percentile reaction time in single-class and multi-class queueing networks are got. Then, the numerical resolution is included in a source allocation challenge. particularly, the authors current an process for the source optimization that minimizes the complete rate of machine assets required whereas holding a given percentile of the reaction time. within the moment half, the procedure is prolonged to contemplate trustworthiness, carrier availability, and the percentile of reaction time in internet prone. those QoS metrics are basically outlined and their quantitative research supplied. The authors then bear in mind those QoS metrics in a trust-based source allocation challenge within which a suite of cloud computing assets is utilized by a merchant to host a regular net prone software for single-class buyer companies and multipleclass consumer providers respectively.

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The service load in cloud computing is dynamically changed upon end users’ service requests. info 52 Resource Optimization and Security for Cloud Services users and generates service requests at a given rate to be processed at the service center hosted by the service provider through the cloud, according to QoS requirements and for a given fee. Clearly, a customer is, in general, concerned about response time rather than throughput in QoS requirements. So, we do not include throughput as a metric in this study.

15]. info Single Class Customers 47 We are interested in investigating the difference between the solutions of problems I and II. Let us first study the property of these solutions. Assume that μ ˆ1 = μ ˆ∗1 and μ ˆ2 = μ ˆ∗2 be the solution of problem I, denoted by (ˆ μ∗1 , μ ˆ∗2 ) ← (Problem I) Clearly, the constraint function G(ˆ μ1 , μ ˆ2 ) − (1 − γ%) is a symmetric function with respect to the line μ ˆ1 = μ ˆ2 . Furthermore, if cˆ1 = cˆ2 , then the objective function cˆ1 ∗ μ ˆ1 + cˆ2 ∗ μ ˆ2 is a symmetric function with respect to the line μ ˆ1 = μ ˆ2 as well, which implies that μ ˆ1 = μ ˆ∗2 and μ ˆ2 = μ ˆ∗1 is the solution of problem I as well.

Otherwise, print “the problem cannot be solved”. 3. e. among stations 1 to m. In this figure, a customer upon completion of its service at the mth station exits the system with probability α, or returns to the beginning of the system with probability 1 − α. 3 can be easily extended to a network of queues arbitrarily connected. 2: Λ as the external arrival rate, λd , λ and λj as the effective arrival rates to the second infinite server and station j, and μj as the service rate at station j, where j = 1, 2, · · · , m.

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