Top

Wireless Personal Communications

Published in:

10-04-2019

Multi-Objective Task Scheduling Using Hybrid Genetic-Ant Colony Optimization Algorithm in Cloud Environment

Authors: A. M. Senthil Kumar, M. Venkatesan

Published in: Wireless Personal Communications | Issue 4/2019

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Task allocation within the cloud computing environment is a nondeterministic polynomial time class problem that is laborious to get the best solution. It is an important issue in the cloud computing setting. The usage of cloud based applications and cloud users are increasing tremendously. In order to handle the massive cloud user’s requests, effective multi-objective Hybrid Genetic Algorithm–Ant Colony Optimization (HGA–ACO) based task allocation technique is proposed in this paper. Utility based scheduler identifies the task order and suitable resources to be scheduled. The proposed HGA–ACO considers the utility based scheduler output and finds the best task allocation method based on response time, completion time and throughput. The HGA–ACO algorithm combines Genetic and Ant Colony Optimization algorithms together. Genetic algorithm (GA) initializes the effective pheromone for ant colony optimization (ACO). ACO is used to enhance the GA solutions for crossover operation of GA. The experimental results show that the proposed framework has better performance in task allocation and ensuring quality of service parameters.

previous article Serial Decoding Algorithm with Continuous Backtracking for LDPC Convolutional Codes

next article Impact of Variable Packet Length on the Performance of Heterogeneous Multimedia Wireless Sensor Networks

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems, 25(6), 599–616.CrossRef

Qiyi, H., & Tinglei, H. (2010). An optimistic job scheduling strategy based on QoS for cloud computing. In IEEE international conference on intelligent computing and integrated systems (ICISS) (pp. 673–675).

Pan, B. L., Wang, Y. P., Li, H. X., & Qian, J. (2014). Task scheduling and resource allocation of cloud computing based on QoS. Advanced Materials Research, 915, 1382–1385.CrossRef

MadniI, S. H. H., LatiffI, M. S. A., CoulibalyI, Y., & AbdulhamidI, S. M. (2016). Resource scheduling for infrastructure as a service (IaaS) in cloud computing: Challenges and opportunities. Journal of Network and Computer Applications, 68, 173–200.CrossRef

Pacini, E., Mateos, C., & Garino, C. G. (2015). Balancing throughput and response time in online scientific clouds via Ant colony optimization. Advances in Engineering, 84(1), 31–47.

Panda, S. K., & Jana, P. K. (2015). A multi-objective task scheduling algorithm for heterogeneous multi-cloud environment. In International conference on electronic design, computer networks and automated verification (EDCAV) (pp. 82–87).

Arianyan, E., Maleki, D., Yari, A., & Ariayan, I. (2012). Efficient resource allocation in cloud data centers through genetic algorithm. In 6th International symposium on telecommunications (pp. 566–570).

Ramezani, F., Lu, J., & Hussain, F. (2013). Task scheduling optimization in cloud computing applying multi-objective particle swarm optimization. In Service-oriented computing. ICSOC 2013 (pp. 237–251).

Wang, T., Liu, Z., Chen, Y., Xu, Y., & Dai, X. (2014). Load balancing task scheduling based on genetic algorithm in cloud computing. In Dependable, autonomic and secure computing (DASC) (pp. 146–152).

10.

Xue, S., Li, M., Xu, X., Chen, J., & Xue, S. (2014). An ACO-LB algorithm for task scheduling in the cloud environment. Journal of Software, 9, 466–473.

11.

Fan, Z., Shen, H., Wu, Y., & Li, Y. (2013) Simulated-annealing load balancing for resource allocation in cloud environments. In International conference on parallel and distributed computing applications and technologies (PDCAT) (pp. 1–6).

12.

Wang, T., Liu, Z., Chen, Y., Xu, Y., & Dai, X. (2014). Load balancing task scheduling based on genetic algorithm in cloud computing. In International conference on dependable, autonomic and secure computing (DASC) (pp. 146–152).

13.

Kaur, Shaminder. (2012). An efficient approach to genetic algorithm for task scheduling in cloud computing environment. International Journal of Information Technology and Computer Science, 10, 74–79.CrossRef

14.

Wang, L., & Ai, L. (2012). Task scheduling policy based on ant colony optimization in cloud computing environment. In International conference on logistics, informatics and service science (LISS2012) (pp. 953–957).

15.

Ping, G., Chunbo, X., Yi, C., Jing, L., & Yanqing, L. (2014). Adaptive ant colony optimization algorithm. In International conference on mechatronics and control (ICMC) (pp. 95–98).

16.

Dai, Y., Lou, Y., & Lu, X. (2015) A task scheduling algorithm based on genetic algorithm and ant colony optimization algorithm with multi-QoS constraints in cloud computing. In 7th international conference on intelligent human-machine systems and cybernetics (IHMSC) (pp. 428–431).

17.

Liu, C. Y., Zou, C.-M., Wu, P. (2014). A task scheduling algorithm based on genetic algorithm and ant colony optimization in cloud computing. In 13th international symposium on distributed computing and applications to business, engineering and science (pp. 68–72).

Title: Multi-Objective Task Scheduling Using Hybrid Genetic-Ant Colony Optimization Algorithm in Cloud Environment
Authors: A. M. Senthil Kumar
M. Venkatesan
Publication date: 10-04-2019
Publisher: Springer US
Published in: Wireless Personal Communications / Issue 4/2019
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI: https://doi.org/10.1007/s11277-019-06360-8

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 4/2019

A Biometric-Based Authenticated Key Agreement Protocol for User-to-User Communications in Mobile Satellite Networks

A Low Complexity PAPR Reduction Architecture for OFDM-DSCK Communication System

Cognitive Wireless Sensor Network for Elderly Home Healthcare

Energy Efficiency Trade-Off Between Duty-Cycling and Wake-Up Radio Techniques in IoT Networks

A Novel Multi-Objective Efficient Offloading Decision Framework in Cloud Computing for Mobile Computing Applications

Novel Approach for Mobile Based App Development Incorporating MAAF