Proactive planning of bandwidth resource using simulation-based what-if predictions for Web services in the cloud
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Proactive planning of bandwidth resource using simulation-based what-if predictions for Web services in the cloud Jianpeng HU1,2 , Linpeng HUANG
1
, Tianqi SUN2 , Ying FAN2 , Wenqiang HU2, Hao ZHONG1
1 Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China 2 School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China c Higher Education Press 2020
Abstract Resource planning is becoming an increasingly important and timely problem for cloud users. As more Web services are moved to the cloud, minimizing network usage is often a key driver of cost control. Most existing approaches focus on resources such as CPU, memory, and disk I/O. In particular, CPU receives the most attention from researchers, but the bandwidth is somehow neglected. It is challenging to predict the network throughput of modern Web services, due to the factors of diverse and complex response, evolving Web services, and complex network transportation. In this paper, we propose a methodology of what-if analysis, named Log2Sim, to plan the bandwidth resource of Web services. Log2Sim uses a lightweight workload model to describe user behavior, an automated mining approach to obtain characteristics of workloads and responses from massive Web logs, and traffic-aware simulations to predict the impact on the bandwidth consumption and the response time in changing contexts. We use a real-life Web system and a classic benchmark to evaluate Log2Sim in multiple scenarios. The evaluation result shows that Log2Sim has good performance in the prediction of bandwidth consumption. The average relative error is 2% for the benchmark and 8% for the real-life system. As for the response time, Log2Sim cannot produce accurate predictions for every single service request, but the simulation results always show similar trends on average response time with the increase of workloads in different changing contexts. It can provide sufficient information for the system administrator in proactive bandwidth planning.
faced by users of Infrastructures-as-a-Service (IaaS) clouds is to plan the capacity of the cloud resources adequately [1]. A central managerial goal of system operations in the cloud is to minimize the consumption of cloud resources while maintaining service quality, in particular, Response Times (RT) [2]. Under-provisioning of resources causes service level agreements (SLA) violation and quality of service (QoS) dropping. On the other hand, over-provisioning of resources increases costs and energy-wasting [3]. For example, Fig. 1 gives an illustration of different QoS levels in different situations for a network-intensive service of picture downloading [4]. In the normal situation with sufficient provisioning of bandwidth, the overwhelming majority of service requests have RT no more than 2 seconds; when the network throughput is approaching or slightly exceeding the threshold of bandwidth limitation, about 10% of service requests have RT more than 2 seconds; however
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