FollowMe@LS: Electricity price and source aware resource management in geographically distributed heterogeneous datacenters

With rapid availability of renewable energy sources and growing interest in their use in the datacenter industry presents opportunities for service providers to reduce their energy related costs, as well as, minimize the ecological impact of their infrastructure. However, renewables are largely intermittent and can, negatively affect users' applications and their performance, therefore, the profit of the service providers. Furthermore, services could be offered from those geographical locations where electricity is relatively cheaper than other locations; which may degrade the applications' performance and potentially increase users' costs. To ensure larger providers' profits and lower users' costs, certain non-interactive workloads could be either: moved and executed in geographical locations offering the lowest energy prices; or could be queued and delayed to execute later (in day or night time) when renewables, such as solar and wind energies, are at peak. However, these may have negative impacts on the energy consumption, workloads performance, and users' costs. Therefore, to ensure energy, performance and cost efficiencies, appropriate workload scheduling, placement, migration, and resource management techniques are required to mange the infrastructure resources, workloads, and energy sources. In this paper, we propose a workload placement and three different migration policies that maximize the providers' revenues, ensure the workload performance, reduce energy consumption, along with reducing ecological impacts and users' costs. Using real workload traces and electricity prices for several geographical locations and distributed, heterogeneous, datacenters, our experimental evaluation suggest that the proposed approaches could save significant amount of energy (similar to 15.26%), reduces service monetary costs (similar to 0.53%- similar to 19.66%),improves (similar to 1.58%) or, at least, maintains the expected level of applications' performance, and increases providers' revenue along with environmental sustainability, against the well-known first fit (FF), best fit (BF) heuristic algorithms, and other closest rivals. (c) 2021 Elsevier Inc. All rights reserved.