New Heuristics for Scheduling and Distributing Jobs under Hybrid Dew Computing Environments

Abstract

Mobile grid computing has been a popular topic for researchers due to mobile and IoT devices’ ubiquity and their evergrowing processing potential. While many scheduling algorithms for harnessing these resources exist in the literature for standard grid computing scenarios, surprisingly, there is little insight into this matter in the context of hybrid-powered computing resources, typically found in Dew and Edge computing environments. This paper proposes new algorithms aware of devices’ power source for scheduling tasks in hybrid environments, i.e., where the battery- and non-battery-powered devices cooperate. We simulated hybrid Dew/Edge environments by extending DewSim, a simulator that models battery-driven devices’ battery behavior using battery traces profiled from real mobile devices. We compared the throughput and job completion achieved by algorithms proposed in this paper using as a baseline a previously developed algorithm that considers computing resources but only from battery-dependent devices called Enhanced Simple Energy-Aware Schedule (E-SEAS). The obtained results in the simulation reveal that our proposed algorithms can obtain up to a 90% increment in overall throughput and around 95% of completed jobs in hybrid environments compared to E-SEAS. Finally, we show that incorporating these characteristics gives more awareness of the type of resources present and can enable the algorithms to manage resources more efficiently in more hybrid environments than other algorithms found in the literature.

Solving Task Scheduling Problems in Dew Computing via Deep Reinforcement Learning

“…The main advantage of this approach is adaptability. As discussed by Sanabria et al [7], no current heuristic seems to dominate across all Dew environments. This makes sense, since each Dew environment might have considerably different characteristics.…”

“…These methods outperformed traditional scheduling algorithms (such as Round Robin) but had one limitation: they only consider battery-dependent devices. This problem was recently addressed by Sanabria et al [7]. Sanabria et al [7] studied hybrid-mobile topologies, which combine both battery-dependent and non-battery-dependent devices and proposed heuristic methods that consider the device's battery level, computing score, and current work load in order to distribute jobs in Dew environments.…”

“…To define a Dew environment in terms of S, A, r, and p, our starting point was a state-of-the-art simulator called EdgeDewSim [7,21]. EdgeDewSim simulates real Dew environments with high accuracy.…”

“…For the Dew environment simulator, we used the modified version of the DewSim simulator [21] proposed by Sanabria et al [7]. This version can handle both batterydependant and non-battery-dependant devices.…”

“…These policies try to balance the workload among the devices by following a set of predefined rules. Some examples include the Simple Energy-Aware Scheduler (SEAS) [6], the Batch Processing Algorithm (BPA) [7], and Round Robin (RR) [8]. Unfortunately, these methods are unable to adapt to the particular features of a given Dew environment and, as a result, they make suboptimal decisions and waste valuable resources.…”