The problem that we worked with was a dynamic scheduling problem. For this problem, we are given a set of tasks to be scheduled in an allotted time slot, so that the total value of the tasks done is maximized. Each task has a duration, value. Each task also has one or more periods...
Combining asynchronous training and the actor–critic method reduces calculation time and adapts to dynamic network changes. Yuan et al. [139] proposes a DRL-based Scheduler at the edge server to minimize the task response time. The scheduling using DRL employs the deep Q-network (DQN) for ...
Naouri Abdenacer has introduced the greedy task graph partitioning offloading algorithm. To help with job scheduling based on device processing power and reduce task communication costs, he employed greedy optimization techniques [4]. Mahenge Michael Pendo John proposed a task uninstallation scheme that...
Greedy Iterative Particle Swarm Optimization is used to disrupt the particle locations. This disruption helps avoid getting stuck in local optima, enabling improved search space exploration and hypothetically generating better solutions. The first step in the method is to create a preliminary TS for cha...
GreedyFilling is proposed, a novel heuristic designed for our speedup model, and we demonstrate that PropScheduling and GreedyFilling are 2-approximation algorithms. In the evaluation, employing synthetic data sets and task graphs arising from multifrontal factorization, the proposed optimized variants ...
This paper proposes an improved multiobjective multi-verse optimizer (IMOMVO) as a novel population optimization technique to solve task scheduling problems. The IMOMVO is introduced to overcome the drawbacks risen in the original MVO and its latest enhanced version mMVO. The proposed method ...
problem, they jointly optimized the number of deployed UAVs and the offloading decision for each IoT device. Then, they determined the optimization objective of minimizing system energy consumption while ensuring that all offloading tasks were completed. And an efficient collaborative greedy task ...
Our paper focuses on task scheduling problem and mobility is not our concern at this step. Nevertheless, our optimization method may be used in conjunction with some prediction method to address mobility issues. Table1compares the different works in terms of optimization method and optimization criter...
Minimizing energy consumption for MDs [51] A greedy Select Maximum Saved Energy First (SMSEF) algorithm. Minimize the energy consumption at the mobile devices [49] Improved branch and bound algorithm SDN based Reduce latency [46] Dynamic Task Offloading and Scheduling problem (DTOS) as a Mixed ...
Particle swarm optimization with cocktail decoding method for hybrid flow shop scheduling problems with multiprocessor tasks This paper addresses the problem of multiprocessor task-scheduling in a hybrid flow shop (HFS) problem to minimize the makespan. Due to the complex nature ... Chou,Fuh-Der -...