Efficient and Scalable ACO-Based Task Scheduling for Green Cloud Computing Environment

Cloud Computing has emerged as a popular technology that support computing on demand services by allowing users to follow the pay-per-use-on-demand model. Minimizing energy consumption in cloud systems has many benefits that enable green computing. Energy aware task scheduling in cloud to the users by service cloud providers has non negligible influences on optimal resources utilization and thereby on the cost benefit. The traditional algorithms for task scheduling are not well enough for cloud computing. In such environment, tasks should be efficiently scheduled such a way that the makespan is reduced. In this paper, we proposed a biologically inspired scheduling scheme, which is a based on a modified version of the ant colony optimization that aims at reducing the makespan time while ensuring load balancing among resources in order to enable green computing. Experiments of the proposed scheme in various scenario have been conducted in order to elaborate the impact of proposed models in the reduction of makespan. The obtained results demonstrate the effectiveness of the proposal in regards to the compared algorithms.

[1]  Qingshui Li,et al.  Study on Cloud Computing Resource Scheduling Strategy Based on the Ant Colony Optimization Algorithm , 2012 .

[2]  Thomas L. Casavant,et al.  A Taxonomy of Scheduling in General-Purpose Distributed Computing Systems , 1988, IEEE Trans. Software Eng..

[3]  Guiyi Wei,et al.  GA-Based Task Scheduler for the Cloud Computing Systems , 2010, 2010 International Conference on Web Information Systems and Mining.

[4]  Ke Ding,et al.  Application Scheduling in Mobile Cloud Computing with Load Balancing , 2013, J. Appl. Math..

[5]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[6]  Jing Liu,et al.  Job Scheduling Model for Cloud Computing Based on Multi- Objective Genetic Algorithm , 2013 .

[7]  Keke Gai,et al.  Phase-Change Memory Optimization for Green Cloud with Genetic Algorithm , 2015, IEEE Transactions on Computers.

[8]  Thomas Stützle,et al.  Ant colony optimization: artificial ants as a computational intelligence technique , 2006 .

[9]  Meikang Qiu,et al.  An efficient cloud storage system for tele-health services , 2017, The Journal of Supercomputing.

[10]  Jianhua Gu,et al.  A New Resource Scheduling Strategy Based on Genetic Algorithm in Cloud Computing Environment , 2012, J. Comput..

[11]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[12]  Meikang Qiu,et al.  Cloud Infrastructure Resource Allocation for Big Data Applications , 2018, IEEE Transactions on Big Data.

[13]  R. K. Jena,et al.  Task scheduling in cloud environment: A multi-objective ABC framework , 2017 .

[14]  Thomas Stützle,et al.  MAX-MIN Ant System , 2000, Future Gener. Comput. Syst..

[15]  Wenyun Dai,et al.  RaHeC: A Mechanism of Resource Management for Heterogeneous Clouds , 2015, 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems.

[16]  Abdelhak Mourad Guéroui,et al.  Adaptive scheme for collaborative mobile sensing in wireless sensor networks: Bacterial foraging optimization approach , 2016, 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[17]  Yacine Challal,et al.  Data Aggregation Scheduling Algorithms in Wireless Sensor Networks: Solutions and Challenges , 2014, IEEE Communications Surveys & Tutorials.

[18]  Parmeet Kaur,et al.  Resource provisioning and work flow scheduling in clouds using augmented Shuffled Frog Leaping Algorithm , 2017, J. Parallel Distributed Comput..

[19]  Mingfei Chen,et al.  Optimisation of partial collaborative transportation scheduling in supply chain management with 3PL using ACO , 2017, Expert Syst. Appl..

[20]  Ado Adamou Abba Ari,et al.  A power efficient cluster-based routing algorithm for wireless sensor networks: Honeybees swarm intelligence based approach , 2016, J. Netw. Comput. Appl..

[21]  Zenggang Xiong,et al.  E2FS: an elastic storage system for cloud computing , 2016, The Journal of Supercomputing.

[22]  Abdelhak Mourad Guéroui,et al.  Bacterial Foraging Optimization Scheme for Mobile Sensing in Wireless Sensor Networks , 2017, International Journal of Wireless Information Networks.

[23]  Keke Gai,et al.  Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing , 2016, J. Netw. Comput. Appl..