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A BDI Agent-Based Task Scheduling Framework for Cloud Computing

Yikun Yang, Fenghui Ren, Minjie Zhang

TL;DR

The paper tackles the problem of scalable, robust task scheduling in large-scale cloud environments where central schedulers struggle with distribution and uncertainty. It introduces a decentralized framework based on Belief-Desire-Intention (BDI) agents with asynchronous communication, featuring an Asynchronous Recommendation Algorithm (ARA) for initial scheduling and a separate agent-based rescheduling mechanism to handle uncertain events. Implemented in JADEX and evaluated on CloudSim, the framework demonstrates reductions in makespan, improved load balancing, and higher task success rates under disruption, compared to several baselines. The work suggests that distributing scheduling decisions across autonomous agents with local coordination yields scalable, robust cloud scheduling suitable for dynamic, open environments.

Abstract

Cloud computing is an attractive technology for providing computing resources over the Internet. Task scheduling is a critical issue in cloud computing, where an efficient task scheduling method can improve overall cloud performance. Since cloud computing is a large-scale and geographically distributed environment, traditional scheduling methods that allocate resources in a centralized manner are ineffective. Besides, traditional methods are difficult to make rational decisions timely when the external environment changes. This paper proposes a decentralized BDI (belief-desire-intention) agent-based scheduling framework for cloud computing. BDI agents have advantages in modelling dynamic environments because BDI agents can update their beliefs, change desires, and trigger behaviours based on environmental changes. Besides, to avoid communication stuck caused by environmental uncertainties, the asynchronous communication mode with a notify listener is employed. The proposed framework covers both the task scheduling and rescheduling stages with the consideration of uncertain events that can interrupt task executions. Two agent-based algorithms are proposed to implement the task scheduling and rescheduling processes, and a novel recommendation mechanism is presented in the scheduling stage to reduce the impact of information synchronization delays. The proposed framework is implemented by JADEX and tested on CloudSim. The experimental results show that our framework can minimize the task makespan, balance the resource utilization in a large-scale environment, and maximize the task success rate when uncertain events occur.

A BDI Agent-Based Task Scheduling Framework for Cloud Computing

TL;DR

The paper tackles the problem of scalable, robust task scheduling in large-scale cloud environments where central schedulers struggle with distribution and uncertainty. It introduces a decentralized framework based on Belief-Desire-Intention (BDI) agents with asynchronous communication, featuring an Asynchronous Recommendation Algorithm (ARA) for initial scheduling and a separate agent-based rescheduling mechanism to handle uncertain events. Implemented in JADEX and evaluated on CloudSim, the framework demonstrates reductions in makespan, improved load balancing, and higher task success rates under disruption, compared to several baselines. The work suggests that distributing scheduling decisions across autonomous agents with local coordination yields scalable, robust cloud scheduling suitable for dynamic, open environments.

Abstract

Cloud computing is an attractive technology for providing computing resources over the Internet. Task scheduling is a critical issue in cloud computing, where an efficient task scheduling method can improve overall cloud performance. Since cloud computing is a large-scale and geographically distributed environment, traditional scheduling methods that allocate resources in a centralized manner are ineffective. Besides, traditional methods are difficult to make rational decisions timely when the external environment changes. This paper proposes a decentralized BDI (belief-desire-intention) agent-based scheduling framework for cloud computing. BDI agents have advantages in modelling dynamic environments because BDI agents can update their beliefs, change desires, and trigger behaviours based on environmental changes. Besides, to avoid communication stuck caused by environmental uncertainties, the asynchronous communication mode with a notify listener is employed. The proposed framework covers both the task scheduling and rescheduling stages with the consideration of uncertain events that can interrupt task executions. Two agent-based algorithms are proposed to implement the task scheduling and rescheduling processes, and a novel recommendation mechanism is presented in the scheduling stage to reduce the impact of information synchronization delays. The proposed framework is implemented by JADEX and tested on CloudSim. The experimental results show that our framework can minimize the task makespan, balance the resource utilization in a large-scale environment, and maximize the task success rate when uncertain events occur.
Paper Structure (21 sections, 7 figures, 5 tables, 2 algorithms)

This paper contains 21 sections, 7 figures, 5 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Task Scheduling in Cloud Computing
  4. Multiagent-based Task Scheduling
  5. Problem Description
  6. Scheduling Environments in Cloud Computing
  7. Scheduling Objectives
  8. Agent-Based Framework Design
  9. Framework Architecture
  10. User Agent Definition
  11. Host Agent Definition
  12. Supervise Agent Definition
  13. Initial Scheduling
  14. Asynchronous Recommendation Algorithm
  15. Rescheduling
  16. ...and 6 more sections

Figures (7)

  • Figure 1: Framework of the considered cloud computing environment and proposed MAS.
  • Figure 2: Belief-Desire-Intention Model.
  • Figure 3: Synchronous and Asynchronous Agent Communication Modes.
  • Figure 4: Initial Scheduling Stage Sequence Diagram
  • Figure 5: Makespan of our framework
  • ...and 2 more figures