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
