A Greedy Scheduling Approach for Peripheral Mobile Intelligent Systems

Abstract

Smart, pervasive devices have recently experienced accelerated technological development in the fields of hardware, software, and wireless connections. The promotion of various kinds of collaborative mobile computing requires an upgrade in network connectivity with wireless technologies, as well as enhanced peer-to-peer communication. Mobile computing also requires appropriate scheduling methods to speed up the implementation and processing of various computing applications by better managing network resources. Scheduling techniques are relevant to the modern architectural models that support the IoT paradigm, particularly smart collaborative mobile computing architectures at the network periphery. In this regard, load-balancing techniques have also become necessary to exploit all the available capabilities and thus the speed of implementation. However, since the problem of scheduling and load-balancing, which we addressed in this study, is known to be NP-hard, the heuristic approach is well justified. We thus designed and validated a greedy scheduling and load-balancing algorithm to improve the utilization of resources. We conducted a comparison study with the longest cloudlet fact processing (LCFP), shortest cloudlet fact processing (SCFP), and Min-Min heuristic algorithms. The choice of those three algorithms is based on the efficiency and simplicity of their mechanisms, as reported in the literature, for allocating tasks to devices. The simulation we conducted showed the superiority of our approach over those algorithms with respect to the overall completion time criterion.

Big data and machine learning: A roadmap towards smart plants

“…Although many techniques can be used to schedule such operations (Lohmer and Lasch, 2021), the ones often used in the industry are based on "greedy approaches", which have extremely short-term economic horizons (Khalaf et al, 2010;Hosseini et al, 2020;Baykasoğlu and Madenoglu, 2021;Fadlallah et al, 2021;Zhou et al, 2021;Hong et al, 2022). Maintenance is mostly invariably disruptive to the production process, ranging from reduced capacity operation during maintenance to complete shutdown.…”