A TAXONOMY OF TASK SCHEDULING ALGORITHMS IN THE GRID

2007 ◽  
Vol 17 (04) ◽  
pp. 439-454 ◽  
Author(s):  
FANGPENG DONG
Keyword(s):  
Grid Computing ◽  
Task Scheduling ◽  
Scheduling Algorithms ◽  
Scheduling Problem ◽  
Distributed Resources ◽  
Research Problems ◽  
Key Features ◽  
New Research

One motivation of Grid computing is to aggregate the power of widely distributed resources, and provide non-trivial services to users. To achieve this goal, efficient task scheduling algorithms are essential. However, scheduling algorithms in the Grid present high diversities that need to be classified. In this paper, with the help of an abstract scheduling architecture, some key features of the task scheduling problem in the Grid are discussed, followed by a taxonomy of the scheduling algorithms. Some typical examples are given in each category to present a picture of the current research and help to find new research problems.

scholarly journals Static vs. Dynamic List-Scheduling Performance Comparison

10.14311/490 ◽  
2003 ◽  
Vol 43 (6) ◽  
Author(s):  
T. Hagras ◽  
J. Janeček
Keyword(s):  
Task Scheduling ◽  
Optimal Solution ◽  
Scheduling Algorithms ◽  
Low Complexity ◽  
Performance Comparison ◽  
List Scheduling ◽  
Scheduling Problem ◽  
Computing Environments ◽  
Np Complete ◽  
List Scheduling Algorithms

The problem of efficient task scheduling is one of the most important and most difficult issues in homogeneous computing environments. Finding an optimal solution for a scheduling problem is NP-complete. Therefore, it is necessary to have heuristics to find a reasonably good schedule rather than evaluate all possible schedules. List-scheduling is generally accepted as an attractive approach, since it pairs low complexity with good results. List-scheduling algorithms schedule tasks in order of priority. This priority can be computed either statically (before scheduling) or dynamically (during scheduling). This paper presents the characteristics of the two main static and the two main dynamic list-scheduling algorithms. It also compares their performance in dealing with random generated graphs with various characteristics.

Task Scheduling in Cloud Computing Using Spotted Hyena Optimizer

2021 ◽  
pp. 136-149
Author(s):  
Amandeep Kaur ◽  
Gaurav Dhiman ◽  
Meenakshi Garg
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithms ◽  
Search Space ◽  
Population Based ◽  
Optimization Techniques ◽  
Spotted Hyena ◽  
Internet Users ◽  
Effective Performance ◽  
Research Problems

Cloud computing provides internet users with quick and efficient tools to access and share the data. One of the most important research problems that need to be addressed is the effective performance of cloud-based task scheduling. Different cloud-based task scheduling algorithms based on metaheuristic optimization techniques like genetic algorithm (GA) and particle swarm optimization (PSO) scheduling algorithms are demonstrated and analyzed. In this chapter, cloud computing based on the spotted hyena optimizer (SHO) is proposed with a novel task scheduling technique. SHO algorithm is population-based and inspired by nature's spotted hyenas to achieve global optimization over a given search space. The findings show that the suggested solution performs better than other competitor algorithms.

scholarly journals Project Scheduling Heuristics-Based Standard PSO for Task-Resource Assignment in Heterogeneous Grid

2011 ◽  
Vol 2011 ◽  
pp. 1-20 ◽  
Author(s):  
Ruey-Maw Chen ◽  
Chuin-Mu Wang
Keyword(s):  
Grid Computing ◽  
Task Scheduling ◽  
Project Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Resource Assignment ◽  
Grid Environment ◽  
Standard Particle Swarm Optimization ◽  
Communication Topology ◽  
Np Complete

The task scheduling problem has been widely studied for assigning resources to tasks in heterogeneous grid environment. Effective task scheduling is an important issue for the performance of grid computing. Meanwhile, the task scheduling problem is an NP-complete problem. Hence, this investigation introduces a named “standard“ particle swarm optimization (PSO) metaheuristic approach to efficiently solve the task scheduling problems in grid. Meanwhile, two promising heuristics based on multimode project scheduling are proposed to help in solving interesting scheduling problems. They are the best performance resource heuristic and the latest finish time heuristic. These two heuristics applied to the PSO scheme are for speeding up the search of the particle and improving the capability of finding a sound schedule. Moreover, both global communication topology and local ring communication topology are also investigated for efficient study of proposed scheme. Simulation results demonstrate that the proposed approach in this investigation can successfully solve the task-resource assignment problems in grid computing and similar scheduling problems.

scholarly journals An efficient population-based multi-objective task scheduling approach in fog computing systems

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Zahra Movahedi ◽  
Bruno Defude ◽  
Amir mohammad Hosseininia
Keyword(s):  
Energy Consumption ◽  
Task Scheduling ◽  
Heuristic Algorithms ◽  
Rapid Development ◽  
Fog Computing ◽  
Scheduling Problem ◽  
Distributed Resources ◽  
Iot Applications ◽  
Whale Optimization ◽  
Iot Devices

AbstractWith the rapid development of Internet of Things (IoT) technologies, fog computing has emerged as an extension to the cloud computing that relies on fog nodes with distributed resources at the edge of network. Fog nodes offer computing and storage resources opportunities to resource-less IoT devices which are not capable to support IoT applications with computation-intensive requirements. Furthermore, the closeness of fog nodes to IoT devices satisfies the low-latency requirements of IoT applications. However, due to the high IoT task offloading requests and fog resource limitations, providing an optimal task scheduling solution that considers a number of quality metrics is essential. In this paper, we address the task scheduling problem with the aim of optimizing the time and energy consumption as two QoS parameters in the fog context. First, we present a fog-based architecture for handling the task scheduling requests to provide the optimal solutions. Second, we formulate the task scheduling problem as an Integer Linear Programming (ILP) optimization model considering both time and fog energy consumption. Finally, we propose an advanced approach called Opposition-based Chaotic Whale Optimization Algorithm (OppoCWOA) to enhance the performance of the original WOA for solving the modelled task scheduling problem in a timely manner. The efficiency of the proposed OppoCWOA is shown by providing extensive simulations and comparisons with the original WOA and some existing meta-heuristic algorithms such as Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).

scholarly journals An Improved Min-Min Task Scheduling Algorithm with Grid Utilization and Minimized Makespan

2015 ◽  
Vol 14 (8) ◽  
pp. 5960-5966 ◽  
Author(s):  
Lalla Singh ◽  
Neha Agarwal
Keyword(s):  
Distributed Systems ◽  
Load Balancing ◽  
Grid Computing ◽  
Task Scheduling ◽  
Completion Time ◽  
Scheduling Algorithm ◽  
Scheduling Algorithms ◽  
Basic Algorithm ◽  
Software Infrastructure ◽  
Task Scheduling Algorithm

Grid computing is hardware and software infrastructure which offers a economical, distributable, coordinated and credible access to strong computational abilities [1]. For optimal use of the abilities of large distributed systems, necessitate for successful and proficient scheduling algorithms is enforced. For diminution of total completion time and improvement of load balancing, many algorithms have been executed. In this paper, our goal is to propose new scheduling algorithm based on well known task scheduling algorithm i.e. Min-Min[1]. The proposed algorithm tries to use the advantages of this basic algorithm and excludes its drawbacks with better grid utilization and minimized makespan. In comparison to existing algorithms like Min-Min and improved Min-Min algorithm[1], our proposed algorithm is achieving better results for considered parameters.

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