Resource Optimization and Security for Cloud Services by Kaiqi Xiong

By Kaiqi Xiong

This publication encompasses a learn of trustworthiness, percentile reaction time, carrier availability, and authentication within the networks among clients and cloud carrier services, and at provider stations or websites that could be owned by means of varied carrier companies. the 1st a part of the publication comprises an research of percentile reaction time, that is the most vital SLA (service point agreements) metrics. powerful and actual numerical recommendations for the calculation of the percentile reaction time in single-class and multi-class queueing networks are received. Then, the numerical answer is integrated in a source allocation challenge. particularly, the authors current an method for the source optimization that minimizes the whole expense of desktop assets required whereas retaining a given percentile of the reaction time. within the moment half, the strategy is prolonged to contemplate trustworthiness, provider availability, and the percentile of reaction time in net providers. those QoS metrics are sincerely outlined and their quantitative research supplied. The authors then take note of those QoS metrics in a trust-based source allocation challenge during which a collection of cloud computing assets is utilized by a merchant to host a standard internet companies software for single-class consumer prone and multipleclass client companies respectively.

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Info Exit 32 Resource Optimization and Security for Cloud Services The infinite server represents the total propagation delay from the user to the service provider and back, and also from station 1 to m. Each station carries out a particular function. For instance, it could be a database server, a file server, a Web server, a group of CPUs and local disks, etc. In this chapter, we consider only a single-class customer. In the following discussion, each station is modeled as a single M /M /1 queue with arrival rate λj and service rate ψ(nj )μj , where ψ(nj ) is a known function of nj and depends on the configuration of servers at each station.

G. see [NAB 04]). In Chapter 5, we will consider a resource allocation problem in Web services subject to all three QoS metrics, expressed by trustworthiness, percentile response time and service availability. When we consider all QoSs consisting of trustworthiness, percentile response time and service availability, it becomes extremely difficult to solve a trust-based resource allocation problem whereby a set of computer resources is used by a service provider to host a typical distributed computing application for single-class and multiple-class customer services, respectively.

The remain of the chapter is organized as follows. 1, we define the service-level agreement (SLA) performance metric considered in this chapter. 2 formulates the resource optimization problem. 3, we present two typical real-life models and propose an approach for solving the optimization problem. 4, numerical simulations demonstrate the applicability and validity of the proposed approach. 7. 1. The percentile of response time An SLA is a contract between a customer and a service provider that defines all aspects of the service that is to be provided.

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