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
Cloud admission control has started attracting more attention of the research community @cite_6 @cite_10 . @cite_6 and his colleagues proposed a single flow-based admission control method for Cloud services. However, wide the rapid development of Cloud computing a large number of users may send service requests in parallel. Therefore, single flow-based admission control limits the scalability of Cloud service provisioning. Le @cite_2 proposed the concepts of delay-based Effective Bandwidth (EB) and backlog-based Equivalent Capacity (EC), which can be used for network call admission control. However, application of EB and EC in Cloud admission control is still an open issue.
@cite_10 @cite_6 @cite_2
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There is a growing adoption of cloud computing services, attracting users with different requirements and budgets to run their applications in cloud infrastructures. In order to match users' needs, cloud providers can offer multiple service classes with different pricing and Service Level Objective (SLO) guarantees. Admission control mechanisms can help providers to meet target SLOs by limiting the demand at peak periods. This paper proposes a prediction-based admission control model for IaaS clouds with multiple service classes, aiming to maximize request admission rates while fulfilling availability SLOs defined for each class. We evaluate our approach with trace-driven simulations fed with data from production systems. Our results show that admission control can reduce SLO violations significantly, specially in underprovisioned scenarios. Moreover, our predictive heuristics are less sensitive to different capacity planning and SLO decisions, as they fulfill availability SLOs for more than 91 of requests even in the worst case scenario, for which only 56 of SLOs are fulfilled by a simpler greedy heuristic and as little as 0.2 when admission control is not used. An admission control algorithm must coordinate between flows to provide guarantees about how the medium is shared. In wired networks, nodes can monitor the medium to see how much bandwidth is being used. However, in ad hoc networks, communication from one node may consume the bandwidth of neighboring nodes. Therefore, the bandwidth consumption of flows and the available resources to a node are not local concepts, but related to the neighboring nodes in carrier-sensing range. Current solutions do not address how to perform admission control in such an environment so that the admitted flows in the network do not exceed network capacity. In this paper, we present a scalable and efficient admission control framework - contention-aware admission control protocol (CACP) - to support QoS in ad hoc networks. We present several options for the design of CACP and compare the performance of these options using both mathematical analysis and simulation results. We also demonstrate the effectiveness of CACP compared to existing approaches through extensive simulations. Abstract Infrastructure as a Service (IaaS) cloud providers typically offer multiple service classes to satisfy users with different requirements and budgets. Cloud providers are faced with the challenge of estimating the minimum resource capacity required to meet Service Level Objectives (SLOs) defined for all service classes. This paper proposes a capacity planning method that is combined with an admission control mechanism to address this challenge. The capacity planning method uses analytical models to estimate the output of a quota-based admission control mechanism and find the minimum capacity required to meet availability SLOs and admission rate targets for all classes. An evaluation using trace-driven simulations shows that our method estimates the best cloud capacity with a mean relative error of 2.5 with respect to the simulation, compared to a 36 relative error achieved by a single-class baseline method that does not consider admission control mechanisms. Moreover, our method exhibited a high SLO fulfillment for both availability and admission rates, and obtained mean CPU utilization over 91 , while the single-class baseline method had values not greater than 78 . This paper presents a novel approach for stream-based admission control and job scheduling for video transcoding called SBACS (Stream-Based Admission Control and Scheduling). SBACS uses queue waiting time of transcoding servers to make admission control decisions for incoming video streams. It implements stream-based admission control with per stream admission. To ensure efficient utilization of the transcoding servers, video streams are segmented at the Group of Pictures level. In addition to the traditional rejection policy, SBACS also provides a stream deferment policy, which exploits cloud elasticity to allow temporary deferment of the incoming video streams. In other words, the admission controller can decide to admit, defer, or reject an incoming stream and hence reduce rejection rate. In order to prevent transcoding jitters in the admitted streams, we introduce a job scheduling mechanism, which drops a small proportion of video frames from a video segment to ensure continued delivery of video contents to the user. The approach is demonstrated in a discrete-event simulation with a series of experiments involving different load patterns and stream arrival rates.