Reliable Provisioning of Spot Instances for Compute-intensive Applications

TL;DR

This paper presents a fault-aware resource provisioning strategy for running compute-intensive applications on spot instances, combining price estimation, runtime prediction, and fault tolerance techniques to ensure reliable and economical execution.

Contribution

It introduces a novel resource allocation policy that integrates fault tolerance methods with price and runtime estimation for spot instances.

Findings

Effective execution of applications on spot instances despite failures

Improved cost efficiency with fault-aware provisioning

Successful maintenance of QoS constraints during execution

Abstract

Cloud computing providers are now offering their unused resources for leasing in the spot market, which has been considered the first step towards a full-fledged market economy for computational resources. Spot instances are virtual machines (VMs) available at lower prices than their standard on-demand counterparts. These VMs will run for as long as the current price is lower than the maximum bid price users are willing to pay per hour. Spot instances have been increasingly used for executing compute-intensive applications. In spite of an apparent economical advantage, due to an intermittent nature of biddable resources, application execution times may be prolonged or they may not finish at all. This paper proposes a resource allocation strategy that addresses the problem of running compute-intensive jobs on a pool of intermittent virtual machines, while also aiming to run applications in a fast and economical way. To mitigate potential unavailability periods, a multifaceted fault-aware resource provisioning policy is proposed. Our solution employs price and runtime estimation mechanisms, as well as three fault tolerance techniques, namely checkpointing, task duplication and migration. We evaluate our strategies using trace-driven simulations, which take as input real price variation traces, as well as an application trace from the Parallel Workload Archive. Our results demonstrate the effectiveness of executing applications on spot instances, respecting QoS constraints, despite occasional failures.