PECS : a pareto-efficient and envy-free cloud resource scheduler

In recent years, cloud computing platforms have attracted more and more attention. A key challenge is that existing schedulers provide limited support for heterogeneous types of resources. This makes it difficult to allocate resources in a timely manner adaptively and efficiently to perform different kinds of tasks. In this paper, we develop and evaluate a scheduling system with QoS guarantees when multiple tasks are involved, in which we show a possible design driven by the use of utilities for task-based resource allocation. We call the resulting method PECS, which meets the following unique properties compared to existing systems: Pareto efficiency, i.e. no other assignment can increase the sum of utility of all tasks without harming at least one specific task; freedom of envyness, i.e. no task will find the allocation of resource for another task better for its own utility; finally, improvements based on equal distribution are guaranteed, that is, the utility of all tasks is at least as high as the equal distribution of all resources.