Towards Cost-Optimal Policies for DAGs to Utilize IaaS Clouds with Online Learning

TL;DR

This paper introduces an online learning framework for cost-optimal resource allocation in DAG-based cloud computing, significantly reducing costs by optimizing instance types and deadlines.

Contribution

It proposes a novel framework with policies for deadline and resource allocation that adapt online to cloud market dynamics, improving cost efficiency over existing methods.

Findings

Cost reduction of up to 24.87% with spot and on-demand instances.

Cost reduction of up to 59.05% when including self-owned instances.

Effective use of online learning for dynamic cloud resource management.

Abstract

Premier cloud service providers (CSPs) offer two types of purchase options, namely on-demand and spot instances, with time-varying features in availability and price. Users like startups have to operate on a limited budget and similarly others hope to reduce their costs. While interacting with a CSP, central to their concerns is the process of cost-effectively utilizing different purchase options possibly in addition to self-owned instances. A job in data-intensive applications is typically represented by a directed acyclic graph which can further be transformed into a chain of tasks. The key to achieving cost efficiency is determining the allocation of a specific deadline to each task, as well as the allocation of different types of instances to the task. In this paper, we propose a framework that determines the optimal allocation of deadlines to tasks. The framework also features an optimal policy to determine the allocation of spot and on-demand instances in a predefined time window, and a near-optimal policy for allocating self-owned instances. The policies are designed to be parametric to support the usage of online learning to infer the optimal values against the dynamics of cloud markets. Finally, several intuitive heuristics are used as baselines to validate the cost improvement brought by the proposed solutions. We show that the cost improvement over the state-of-the-art is up to 24.87% when spot and on-demand instances are considered and up to 59.05% when self-owned instances are considered.