Solving Technician and Task Scheduling Problems with an Intelligent Decision Heuristic

2016 ◽  
pp. 63-75 ◽  
Author(s):  
Amy Khalfay ◽  
Alan Crispin ◽  
Keeley Crockett
Keyword(s):  
Task Scheduling ◽  
Scheduling Problems ◽  
Intelligent Decision ◽  
And Task

An effective multi-objective task scheduling and resource optimization in cloud environment using hybridized metaheuristic algorithm

2021 ◽  
pp. 1-13
Author(s):  
Raj Kumar Kalimuthu ◽  
Brindha Thomas
Keyword(s):  
Resource Allocation ◽  
Task Scheduling ◽  
State Of The Art ◽  
Particle Swarm ◽  
Ant Colony ◽  
Scheduling Problems ◽  
Average Response Time ◽  
Multiple Sources ◽  
Computing Paradigm ◽  
And Task

In today’s world, cloud computing plays a significant role in the development of an effective computing paradigm that adds more benefits to the modern Internet of Things (IoT) frameworks. However, cloud resources are considered to be dynamic and the demands necessitated for resource allocation for a certain task are different. These diverse factors may cause load and power imbalance which also affect the resource utilization and task scheduling in the cloud-based IoT environment. Recently, a bio-inspired algorithm can work effectually to solve task scheduling problems in the cloud-based IoT system. Therefore, this work focuses on efficient task scheduling and resource allocation through a novel Hybrid Bio-Inspired algorithm with the hybridized of Improvised Particle Swarm Optimization and Ant Colony Optimization. The vital objective of hybridizing these two approaches is to determine the nearest multiple sources to attain discrete and continuous solutions. Here, the task has been allocated to the virtual machine through a particle swarm and continuous resource management can be carried out by an ant colony. The performance of the proposed approach has been evaluated using the CloudSim simulator. The simulation results manifest that the proposed Hybridized algorithm efficiently scheduling the task in the cloud-based IoT environment with a lesser average response time of 2.18 sec and average waiting time of 3.6 sec as compared with existing state-of-the-art algorithms.

Fast Pruning Algorithm and Task Scheduling under Map/Reduce

2020 ◽  
Vol 16 (10) ◽  
pp. 1627
Author(s):  
Pei Shujun ◽  
Zhang Yu ◽  
Liang Chao
Keyword(s):  
Task Scheduling ◽  
Map Reduce ◽  
Pruning Algorithm ◽  
And Task

Task Scheduling in cloud using ACO

2020 ◽  
Vol 13 ◽  
Author(s):  
Yuvaraj Natarajan ◽  
Srihari Kannan ◽  
Gaurav Dhiman
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Limited Resource ◽  
Cloud Environment ◽  
Clear Cut ◽  
Strategic Approach ◽  
User Demand ◽  
Scheduling Strategies ◽  
Cloud Users ◽  
And Task

Background: Cloud computing is a multi-tenant model for computation that offers various features for computing and storage based on user demand. With increasing cloud users, the usage increases that highlights the problem of load balancing with limited resource availability based on dynamic cloud environment. In such cases, task scheduling creates fundamental issue in cloud environment. Introduction: Certain problems such as, inefficiencies in load balancing latency, throughput ratio, proper utilization of the cloud resources, better energy consumption and response time have been observed. These drawbacks can be efficiently resolved through the incorporation of efficient load balancing and task scheduling strategies. Method: In this paper, we develop an efficient co-operative method to solve the most recent approaches against load balancing and task scheduling have been proposed using Ant Colony Optimization (ACO). These approaches enables in the clear cut identification of the problems associated with the load balancing and task scheduling strategies in the cloud environment. Results: The simulation is conducted to find the efficacy of the improved ACO system for load balancing in cloud than the other methods. The result shows that the proposed method obtains reduced execution time, reduced cost and delay. Conclusion: A unique strategic approach is developed in this paper, Load Balancing, which works with the ACO in relation to the cloud workload balancing task through the incorporation of the ACO technique. The strategy for determining the applicant nodes is based on which the load balancing approach would essentially depend. By incorporating two different approaches: the maximum minute rules and the forward-backward ant, this reliability task can be established. This method is intended to articulate the initialization of the pheromone and thus upgrade the relevant cloud-based physical properties.

Cost-efficient mobility offloading and task scheduling for microservices IoVT applications in container-based fog cloud network

2021 ◽  
Author(s):  
Abdullah Lakhan ◽  
Muhammad Suleman Memon ◽  
Qurat-ul-ain Mastoi ◽  
Mohamed Elhoseny ◽  
Mazin Abed Mohammed ◽  
...  
Keyword(s):  
Task Scheduling ◽  
Cost Efficient ◽  
Cloud Network ◽  
And Task
Sign in / Sign up

Export Citation Format

Share Document