Abstract-Cloud computing is being widely accepted and utilized in the business world. From the perspective of businesses utilizing the cloud, it is critical to meet their customers' requirements by achieving service-level-objectives. Hence, the ability to accurately characterize and optimize cloud-service performance is of great importance. In this paper a stochastic multi-tenant framework is proposed to model the service of customer requests in a cloud infrastructure composed of heterogeneous virtual machines. Two cloudservice performance metrics are mathematically characterized, namely the percentile and the mean of the stochastic response time of a customer request, in closed form. Based upon the proposed multi-tenant framework, a workload allocation algorithm, termed maxmin-cloud algorithm, is then devised to optimize the performance of the cloud service. A rigorous optimality proof of the max-min-cloud algorithm is also given. Furthermore, the resource-provisioning problem in the cloud is also studied in light of the max-min-cloud algorithm. In particular, an efficient resource-provisioning strategy is proposed for serving dynamically arriving customer requests. These findings can be used by businesses to build a better understanding of how much virtual resource in the cloud they may need to meet customers' expectations subject to cost constraints. Index Terms-Cloud computing, heterogeneous computing, multi-tenant model, performance analysis, workload allocation, resource provisioning LOUD computing is having a profound effect in today's business world. Many services, including email service, application hosting, data storage, e-commerce and so on, have been implemented on cloud infrastructures. When a cloud consumer (refers to a business in this paper) is deploying a cloud-based service, it is essential for the business to deliver services that satisfy customers' requirements by having adequate computing resources, namely virtual machines (VMs). Meanwhile, it is also important for the business to avoid the cost of having unnecessary VMs (i.e., excessive computing power beyond its need). Therefore, an analytical model that can accurately predict and optimize cloud-service performance is vital as it would be of great benefit to enhancing the quality of service while keeping the cost of the business within a budget. In recent years, numerous works have looked at modeling cloud services and analyzing the performance. [5] . In most of these existing works, the analysis of the cloud-service performance is based upon queuing theory (a detailed review of the related work will be given in Section 2). Queuing models have proven their value in studying the response time, throughput and stability for cloud services. However, a necessary assumption adopted by the queuing models is that the service rate for all the tasks are the same. Due to the business (namely cost) concern, a modern cloudservice virtual infrastructure typically contains a cluster of heterogeneous VMs. Although the virtual infrastructure can start with near-homogeneous VMs, the facility will likely grow more heterogeneous over time due to upgrades and replacement [5] . Furthermore, it is of great benefit to have an analytical model that could simultaneously address and investigate the essential characteristics and concerns that are critical in operating a cloud service including (i) stochastic response time of a customer request with a general probability distribution, (ii) service-performance optimization by means of workload allocation, and (iii) efficient resource provisioning for serving dynamically arriving customer requests. Therefore and to the best of our knowledge, no existing analytical work is suitable to solve the problem that we considered in this paper. To fill this gap, we develop a general analytical framework to complement existing models for analyzing and optimizing the computational performance of cloud services. In the proposed framework, we consider a cluster of heterogeneous VMs that are utilized to construct a cloud-
A mechanism has been designed REF , to handle the service of requests of customers.
Optimizing Cloud-Service Performance: Efficient Resource Provisioning via Optimal Workload Allocation
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
Computer Science