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 - 2 | Issue - 1 | march 2020
Published
20 March, 2020
The edge paradigm that is intended as prominent computing due to its low computation latencies faces multiple issues and challenges due to the restrictions in the computing capabilities and its resource availability especially in the huge populace scenarios. To examine the problems faced during the task scheduling when the edge computing is called up by multiple users at time, the paper puts forward the game theory approach. Utilizing the game theory strategy the paper puts forth the a novel multitasking scheduling in the edge computing from the user perception developing an algorithm taking into consideration the consistency of the stable tasks. The analysis of the proposed algorithm used in the allocation of the tasks is done on terms of average time consumed for the execution of the task and the waiting time. The results acquired showed that the proposed method provides a maximized throughput, minimizing the waiting time compared to the conventional methods used in optimizing the parameters of scheduling.
KeywordsEdge Computing Cloud Computing Game Theory Approach Task Scheduling Co-Operative and Non-Co-Operative
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