A Holistic Approach for Collaborative Workload Execution in Volunteer Clouds

The demand for provisioning, using, and maintaining distributed computational resources is growing hand in hand with the quest for ubiquitous services. Centralized infrastructures such as cloud computing systems provide suitable solutions for many applications, but their scalability could be limited in some scenarios, such as in the case of latency-dependent applications. The volunteer cloud paradigm aims at overcoming this limitation by encouraging clients to offer their own spare, perhaps unused, computational resources. Volunteer clouds are thus complex, large-scale, dynamic systems that demand for self-adaptive capabilities to offer effective services, as well as modeling and analysis techniques to predict their behavior. In this article, we propose a novel holistic approach for volunteer clouds supporting collaborative task execution services able to improve the quality of service of compute-intensive workloads. We instantiate our approach by extending a recently proposed ant colony optimization algorithm for distributed task execution with a workload-based partitioning of the overlay network of the volunteer cloud. Finally, we evaluate our approach using simulation-based statistical analysis techniques on a workload benchmark provided by Google. Our results show that the proposed approach outperforms some traditional distributed task scheduling algorithms in the presence of compute-intensive workloads

Replicated Computations Results (RCR) report for “A holistic approach for collaborative workload execution in volunteer clouds”

“A Holistic Approach for Collaborative Workload Execution in Volunteer Clouds” [3] proposes a novel approach to task scheduling in volunteer clouds. Volunteer clouds are decentralized cloud systems based on collaborative task execution, where clients voluntarily share their own unused computational resources. By using simulation-based statistical analysis techniques—in particular, statistical model checking—the authors show that their approach can outperform existing distributed task scheduling algorithms in the case of computation-intensive workloads. The analysis considered a realistic workload benchmark provided by Google. This replicated computations results report focuses on the prototypical tool implementation used in the article to perform such analysis. The software was straightforward to install and use, and a representative part of the experimental results from the article could be reproduced in reasonable time using a standard laptop.</jats:p