Toward Designing Cost-Optimal Policies to Utilize IaaS Clouds with Online Learning

Abstract

Many businesses possess a small infrastructure that they can use for their computing tasks, but also often buy extra computing resources from clouds. Cloud vendors such as Amazon EC2 offer two types of purchase options: on-demand and spot instances. As tenants have limited budgets to satisfy their computing needs, it is crucial for them to determine how to purchase different options and utilize them (in addition to possible self-owned instances) in a cost-effective manner while respecting their response-time targets. In this paper, we propose a framework to design policies to allocate self-owned, on-demand and spot instances to arriving jobs. In particular, we propose a near-optimal policy to determine the number of self-owned instances and an optimal policy to determine the number of on-demand instances to buy and the number of spot instances to bid for at each time unit. Our policies rely on a small number of parameters and we use an online learning technique to infer their optimal values. Through numerical simulations, we show the effectiveness of our proposed policies, in particular that they achieve a cost reduction of up to 64.51% when spot and on-demand instances are considered and of up to 43.74% when self-owned instances are considered, compared to previously proposed or intuitive policies.

Market-Oriented Online Bi-Objective Service Scheduling for Pleasingly Parallel Jobs with Variable Resources in Cloud Environments

“…Rare studies design algorithms from the perspective of SaaS providers and take both the scheduling and resource acquiring into consideration at the same time, while simultaneous consideration increases the difficulty significantly. Wu et al [18] design a policy to decide how to purchase on-demand and spot instances for the jobs. The aim is to minimize the cost of purchasing instances.…”

“…The two algorithms mentioned above are online algorithms which do not consider variable resources. In [18], Wu et al design a policy to allocate self-owned, on-demand, and spot instances to the arriving pleasingly parallel jobs, using CAP3 in bioinformatics as an example. Their goal is cost optimization.…”

“…Thus we can get equation (19) from equation (18). Since the cost of rejected users must no larger than the total cost of serving all users, we can get equation (20) from equation (19).…”

“…(5) Dynalloc: This algorithm considers to use self-owned, on-demand and spot instances at the same time to execute jobs, with the aim of cost optimization [18]. The main idea of this algorithm is distributing the workloads equally among the entire deadline and minimizing the integer instance hours of on-demand instances.…”