RRFT: A Rank-Based Resource Aware Fault Tolerant Strategy for Cloud Platforms

TL;DR

This paper introduces RRFT, a resource-aware fault tolerance strategy for cloud platforms that ranks components based on significance and uses a Markov Decision Process to optimize replica allocation, reducing resource overhead.

Contribution

It presents a novel ranking-based fault tolerance approach combined with MDP to dynamically determine replicas, improving resource efficiency in cloud applications.

Findings

Reduces virtual machine usage by ~10%

Decreases physical machine requirements by ~4.2%

Maintains fault tolerance with fewer resources

Abstract

The applications that are deployed in the cloud to provide services to the users encompass a large number of interconnected dependent cloud components. Multiple identical components are scheduled to run concurrently in order to handle unexpected failures and provide uninterrupted service to the end user, which introduces resource overhead problem for the cloud service provider. Furthermore such resource-intensive fault tolerant strategies bring extra monetary overhead to the cloud service provider and eventually to the cloud users. In order to address these issues, a novel fault tolerant strategy based on the significance level of each component is developed. The communication topology among the application components, their historical performance, failure rate, failure impact on other components, dependencies among them, etc., are used to rank those application components to further decide on the importance of one component over others. Based on the rank, a Markov Decision Process (MDP) model is presented to determine the number of replicas that varies from one component to another. A rigorous performance evaluation is carried out using some of the most common practically useful metrics such as, recovery time upon a fault, average number of components needed, number of parallel components successfully executed, etc., to quote a few, with similar component ranking and fault tolerant strategies. Simulation results demonstrate that the proposed algorithm reduces the required number of virtual and physical machines by approximately 10% and 4.2%, respectively, compared to other similar algorithms.