Neural network‐based multi‐agent approach for scheduling in distributed systems

Abstract

A distributed system consists of a collection of autonomous heterogeneous resources that provide resource sharing and a common platform for running parallel compute-intensive applications. The different application characteristics combined with the heterogeneity and performance variations of the distributed system make it difficult to find the optimal set of needed resources. When deployed, user applications are usually handled by application domain experts or system administrators who depending on the infrastructure provide a scheduling strategy for selecting the best candidate resource over a set of available resources. However, the provided strategy is usually generic, aimed at handling a wide array of applications and does not take into consideration specific application resource requirements. As such, an intelligent method for selecting the best resources based on expert knowledge is needed. In this paper, we propose a neural network-based multi-agent resource selection technique capable of mimicking the services of an expert user. In addition, to cope with the geographical distribution of the underlying system, we employ a multi-agent coordination mechanism. The proposed neural network-based scheduling framework combined with the multi-agent intelligence is a unique approach to efficiently deal with the resource selection problem. Results run on a simulated environment show the efficiency of our proposed method. Several scheduling simulations were conducted to compare the performance of some conventional resource selection methods against the proposed agent-based neural network technique. The results obtained indicate that the agent-based approach outperformed the classical algorithms by reducing the amount of time required to search for suitable resources irrespective of the resource size.Our system relies on both ANN and MPGP to train and coordinate team of agents on how to discover and select best candidate resource that matches users' application resource needs.

Energy saving scheduling in a fog-based IoT application by Bayesian taskclassification approach

“…Practical programs can be modeled as the workflows in a directed acyclic graph (DAG). CC also represents infrastructure, platform, and software as a service [16]. When the techniques and the devices of IoT entered the life of ordinary people more and more, the current CC pattern could hardly support the need for mobility, location awareness, and low latency.…”

“…The convergence of the Q-learning algorithm has also been speeded up and at the end of each section, a semioptimized policy has been reached. As in [16], in matters of resource scheduling, due to the concentrating on a special purpose like minimizing the executing time or workload and the lack of using the CC features, classification and regression tree and modified bacterial foraging optimization algorithms have been proposed. In [22], the authors recommended a multiagent resource selection technique based on a neural network which would be able to imitate the services of an expert user in the distributed systems like grids and clouds.…”

Heterogeneous multi-agent task allocation based on graph neural network ant colony optimization algorithms

“…While traditional ACO algorithms have been successfully applied to solve combinatorial optimization problems, they may face challenges in complex multi-agent environments [28] . To address these issues, we propose a novel paradigm that combines GHNNs with ACO algorithms to enhance the task allocation process in heterogeneous multi-agent systems [29,30] . The GHNN-ACO algorithms present an innovative approach to solving the assignment problem in heterogeneous multi-agent systems [31] .…”