AbstractWith the rapid development of Cloud computing technologies and wideadopt of Cloud services and applications, QoS provisioning in Clouds be-comes an important research topic. In this paper, we propose an admis-sion control mechanism for Cloud computing. In particular we considerthe high volume of simultaneous requests for Cloud services and developadmission control for aggregated trac ows to address this challenge. Byemploy network calculus, we determine e ective bandwidth for aggregateow, which is used for making admission control decision. In order toimprove network resource allocation while achieving Cloud service QoS,we investigate the relationship between e ective bandwidth and equiva-lent capacity. We have also conducted extensive experiments to evaluateperformance of the proposed admission control mechanism. 1 Introduction Recently the emerging Cloud computing has been developing very quickly[1, 2, 3]. With the rapid development of Cloud computing technologies and wideadoption of Cloud-based applications, the huge amount of trac generated bya large number of users for accessing Cloud services bring in a series challengeto the Internet. The best-e ort service model in the current Internet cannotmeet users’ requirements for Quality of Service (QoS). Call Admission Control(CAC) o ers an e ective approach to controlling network trac and avoiding1
Network calculus was initially invented by Chang @cite_8 and Cruz @cite_5 @cite_13 and then further developed by other researchers (e.g., @cite_15 @cite_0 @cite_16 @cite_3 ) into an effective quantitative tool for analyzing network performance. Network calculus uses arrival curve and service curve to determine some key QoS factors of networking systems such as delay and backlog @cite_14 @cite_11 . Compared to traditional queuing analysis methods, network calculus can provide performance bounds for networking systems to obtain work-case performance, which allows strict QoS guarantee @cite_9 @cite_17 . Network calculus has been widely applied in network performance evaluation, through which tight performance bounds can be obtained for making admission decisions @cite_7 @cite_4 .
@cite_14 @cite_4 @cite_7 @cite_8 @cite_9 @cite_17 @cite_3 @cite_0 @cite_5 @cite_15 @cite_16 @cite_13 @cite_11
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Network calculus is a min-plus system theory for performance evaluation of queuing networks. Its elegance steins from intuitive convolution formulas for concatenation of deterministic servers. Recent research dispenses with the worst-case assumptions of network calculus to develop a probabilistic equivalent that benefits from statistical multiplexing. Significant achievements have been made, owing for example to the theory of effective bandwidths; however, the outstanding scalability set up by concatenation of deterministic servers has not been shown. This paper establishes a concise, probabilistic network calculus with moment generating functions. The presented work features closed-form, end-to-end, probabilistic performance bounds that achieve the objective of scaling linearly in the number of servers in series. The consistent application of moment generating functions put forth in this paper utilizes independence beyond the scope of current statistical multiplexing of flows. A relevant additional gain is demonstrated for tandem servers with independent cross-traffic The stochastic network calculus is an evolving new methodology for backlog and delay analysis of networks that can account for statistical multiplexing gain. This paper advances the stochastic network calculus by deriving a network service curve, which expresses the service given to a flow by the network as a whole in terms of a probabilistic bound. The presented network service curve permits the calculation of statistical end-to-end delay and backlog bounds for broad classes of arrival and service distributions. The benefits of the derived service curve are illustrated for the exponentially bounded burstiness (EBB) traffic model. It is shown that end-to-end performance measures computed with a network service curve are bounded by O(Hlog H), where H is the number of nodes traversed by a flow. Using currently available techniques that compute end-to-end bounds by adding single node results, the corresponding performance measures are bounded by O(H3). Network calculus is an established theory for deterministic quality of service analysis of fixed networks. Due to the failures inherent in fading channels it is, however, not applicable to radio systems. Emerging probabilistic equivalents allow closing this gap. Based on the recent network calculus with moment generating functions we present a methodology for performance analysis of fading channels. We use a service curve representation of radio links which facilitates an efficient analysis of radio networks. We investigate fading channels with memory and our results show that the fading speed impacts service guarantees significantly. Numerical performance bounds are provided for an example taken from cellular radio communications for which the effects of opportunistic scheduling are quantified. Simulation results are shown which confirm the efficiency of the approach. The stochastic network calculus is an evolving new methodology for backlog and delay analysis of networks that can account for statistical multiplexing gain. This paper advances the stochastic network calculus by deriving a network service curve, which expresses the service given to a flow by the network as a whole in terms of a probabilistic bound. The presented network service curve permits the calculation of statistical end-to-end delay and backlog bounds for broad classes of arrival and service distributions. The benefits of the derived service curve are illustrated for the exponentially bounded burstiness (EBB) traffic model. It is shown that end-to-end performance measures computed with a network service curve are bounded by spl Oscr (H log H), where H is the number of nodes traversed by a flow. Using currently available techniques, which compute end-to-end bounds by adding single node results, the corresponding performance measures are bounded by spl Oscr (H sup 3 ).