A Carbon-aware Workload Dispatcher in Cloud Computing Systems

Abstract

The amount of carbon emission associated with the computational energy consumption in data centers significantly depends 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 massively decrease the carbon emission of computational energy consumption of data centers. In this paper, we address the problem of placement and scheduling of workloads over geographically distributed data centers. We analyze the complexity of the problem and prove that it is NP-hard. We propose two carbon-aware workload scheduling and placement 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 solves the problem in polynomial time and 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. We conduct an extensive experimental analysis to evaluate the performance of the algorithms using real-world data.