Minimizing Deployment Cost of Cloud-Based Web Application with Guaranteed QoS

Abstract

Cloud computing provides a reliable and costeffective setting for deploying large-scale web applications. However, choosing and configuring an appropriate cloud Infrastructure-as-a-Service (IaaS), e.g., the appropriate database and computing instances and acceptable service rates, is a daunting task. The task is also challenging when trying to optimize the IaaS for conflicting objectives such as performance and cost. Furthermore, due to lack of understanding of the pricing model and the cloud IaaS, a cloud consumer may pay more than necessary or may not fully utilize the purchased resources. For this reason, we propose an algorithm that suggests the most costeffective configuration meeting the QoS requirements and budget constraints. In contrast to existing cost optimization proposals, our proposed algorithm maps the minimum requirements of the to-be-deployed web application to deployment costs according to the price model set by cloud providers. The algorithm also considers QoS requirements for different resource types in the cloud, namely, database servers, computing servers, storage, and service rate. The proposed algorithm is evaluated by a series of experiments on a web application with seven different workload scenarios. The experimental results show the effectiveness of the proposed algorithm in achieving a solution with the minimum deployment cost for each scenario while satisfying QoS requirements.

Optimizing the Procurement of IaaS Reservation Contracts via Workload Predicting and Integer Programming

“…For web applications providers, it has become very necessary to optimize the provisioning of IaaS resources for saving cost. However, most of the existing approaches have employed the deterministic resources provisioning schemes [6][7][8][9][10]. In these studies, the uncertain nature of the user's demands is neglected by assuming the demand as a deterministic value.…”

“…Besides, planning resource for the future business cycle of a web application requires an estimation/prediction of the future workloads. Some studies have employed the simulated workloads [6,9,11]; meanwhile, some studies directly take the workloads in the historical cycle as an estimation of the workloads in the future cycle [14,15,17]. But, the two approaches generally could not obtain a good accuracy.…”

Cost Minimization of Virtual Machine Allocation in Public Clouds Considering Multiple Applications

“…In the literature there are only two similar works ( [9] and [15]) that consider cost optimization of several applications on a cloud computing environment. However, they are limited by the type of resources they support and they only perform an static (off-line) analysis.…”

“…However, they are limited by the type of resources they support and they only perform an static (off-line) analysis. Thus, in [9] the authors propose an algorithm that finds the most cost-effective allocation which meets the QoS requirements with the lowest cost. Its main drawbacks are that it is limited to on-demand VMs and it does not take advantage of reserved VMs.…”

“…Hiring too much means a waste of money; on the other hand, hiring too little may reduce profits or, even worse, incur economic penalties if SLAs are not met. Therefore, there is a broad research in order to answer this question and to find the most cost-effective allocation, such as [1], [4,5,6], [9,10], [14,15] or [18].…”