Big Data Analytics for Improved Risk Management and Customer Segregation in Banking Applications
Volume-3 | Issue-3
Design of Deep Learning Algorithm for IoT Application by Image based Recognition
Volume-3 | Issue-3
Analysis of Serverless Computing Techniques in Cloud Software Framework
Volume-3 | Issue-3
Health Record Management System – A Web-based Application
Volume-3 | Issue-4
A Novel Signal Processing Based Driver Drowsiness Detection System
Volume-3 | Issue-3
IoT Based Monitoring and Control System using Sensors
Volume-3 | Issue-2
Secure Data Sharing Platform for Portable Social Networks with Power Saving Operation
Volume-3 | Issue-3
Review of Internet of Wearable Things and Healthcare based Computational Devices
Volume-3 | Issue-3
Stock Index Prediction with Financial News Sentiments and Technical Indicators
Volume-4 | Issue-3
Hybrid Framework on Automatic Detection and Recognition of Traffic Display board Signs
Volume-3 | Issue-3
Suspicious Human Activity Detection System
Volume-2 | Issue-4
ROBOT ASSISTED SENSING, CONTROL AND MANUFACTURE IN AUTOMOBILE INDUSTRY
Volume-1 | Issue-3
EFFICIENT RESOURCE ALLOCATION AND QOS ENHANCEMENTS OF IOT WITH FOG NETWORK
Volume-1 | Issue-2
Live Streaming Architectures for Video Data - A Review
Volume-2 | Issue-4
IoT Based Monitoring and Control System using Sensors
Volume-3 | Issue-2
Big Data Analytics for Improved Risk Management and Customer Segregation in Banking Applications
Volume-3 | Issue-3
A Novel Signal Processing Based Driver Drowsiness Detection System
Volume-3 | Issue-3
IoT BASED AIR AND SOUND POLLUTION MONITIORING SYSTEM USING MACHINE LEARNING ALGORITHMS
Volume-2 | Issue-1
Analysis of Serverless Computing Techniques in Cloud Software Framework
Volume-3 | Issue-3
Hybrid Intrusion Detection System for Internet of Things (IoT)
Volume-2 | Issue-4
Volume - 5 | Issue - 4 | december 2023
Published
21 November, 2023
Cloud computing technologies have quickly changed how companies and organizations manage their IT resources. The core of this transformation has evolved as cloud datacenters, which offer scalable and affordable options for hosting and administering a variety of applications and services. One information technology typology that has been widely employed to deliver a range of services via the Internet is cloud computing. It guarantees simpler access to premium services and resources. Cloud systems' operation needs to be planned in order to effectively deliver services to individuals. Task scheduling seeks to maximize system throughput and distribute diverse computational resources to software programs. The unpredictability of the scenario grows as the task and has a strong potential for successful resolution. The study begins with an experimental setup to analyse the various performance metrics of task scheduling algorithms. Every experiment has several important stages. To replicate scenarios found in the real world where jobs are divided across many computing resources, the tasks are assigned to available data centers. A number of experiments were carried out to analyse the performance of First Come First Service (FCFS), Shortest Job First (SJF), Round Robin (RR) and Particle Swarm Optimization (PSO) scheduling algorithms using the parameters: makespan, average completion time, average waiting time, and average cost consumption. Thus, this study provides a description of task scheduling and the performance analysis of algorithms to task scheduling that is employed in cloud computing environments.
KeywordsCloud Data Centers FCFS SJF RR PSO CloudSim
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