A Carbon-aware Workload Dispatcher in Cloud Computing Systems

The amount of carbon emission associated with the computational energy consumption in data centers depends, in a significant way, on the schedule of the workloads. Due to the inconsistent availability of renewable energy over time, in addition to the existence of various sources of power in grid regions, the carbon intensity of data centers changes over time and location. Thus, the placement and scheduling of flexible workloads, based on the carbon intensity of power sources in data centers, can remarkably decrease the carbon emission. In this paper, we address the problem of placement and scheduling of workloads over geographically distributed data centers. We propose two algorithms that take the variability of carbon intensity of the power sources of the data centers, as well as their computational resource availability, into account when deciding about the placement and scheduling of the workloads. The first is a randomized rounding approximation algorithm that provides solutions that are guaranteed to be within a given distance from the optimal solution. The second is a sample-based algorithm that improves the solutions obtained by the randomized rounding approximation algorithm. The experimental results show that the proposed algorithms can solve the problem efficiently.