scholarly journals Analysis of Cloud Network Management Using Resource Allocation and Task Scheduling Services

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
K.C. Okafor ◽  
F.N.Ugwoke ◽  
Obayi, Adaora ◽  
V.C Chijindu ◽  
O.U Oparaku
Keyword(s):  
Resource Allocation ◽  
Network Management ◽  
Task Scheduling ◽  
Cloud Network ◽  
And Task

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

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.

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