An artificial neural network based approach for energy efficient task scheduling in cloud data centers

Abstract Energy efficiency is considered as a crucial objective in cloud data centers as it reduces cost and meets the standard set in green computing. Task scheduling an important problem becomes more complex and critical under energy efficiency consideration. Key issues in recent research on energy efficient task scheduling are execution overhead and scalability. Machine learning has been widely employed for energy efficient task scheduling problem but mostly used to predict resource consumption only instead of deciding the schedule itself. However, we used the neural network to decide which resource should be assigned to given task independently. In this paper, we proposed an energy efficient independent task scheduler using supervised neural networks with the aim to reduce makespan, energy consumption, execution overhead and number of active racks. Proposed artificial neural network-based scheduler takes incoming task and current cloud environment state as input and predict the best computing resource for given task as output which compiles our aim. We used genetic algorithm to generate a huge dataset (∼18 million training instances) and trained our neural network on this dataset using back propagation algorithm with 99.9% accuracy. We simulated experiments on heavily loaded and lightly loaded cloud environment and compared with well-known approaches: Genetic algorithm, MinMIN-MINMin heuristic and Linear regression based energy efficient task schedulers. Results clearly indicate that proposed work outperforms considered algorithms. In heavily (lightly) loaded environment, it improves makespan by 59% (64%), energy consumption by 45% (71%), execution overhead by 88% (43%) respectively and number of active racks by 70%.

[1]  Ishfaq Ahmad,et al.  A Cooperative Game Theoretical Technique for Joint Optimization of Energy Consumption and Response Time in Computational Grids , 2009, IEEE Transactions on Parallel and Distributed Systems.

[2]  Sasmita Kumari Padhy,et al.  Dynamic task scheduling using a directed neural network , 2015, J. Parallel Distributed Comput..

[3]  S. D. Madhu Kumar,et al.  Power Efficient Resource Allocation for Clouds Using Ant Colony Framework , 2011, ArXiv.

[4]  Chun-xiang Xu,et al.  Energy Efficient Multiresource Allocation of Virtual Machine Based on PSO in Cloud Data Center , 2014 .

[5]  S. Agatonovic-Kustrin,et al.  Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. , 2000, Journal of pharmaceutical and biomedical analysis.

[6]  BuyyaRajkumar,et al.  Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers , 2012 .

[7]  Dongrui Fan,et al.  An Evolutionary Technique for Performance-Energy-Temperature Optimized Scheduling of Parallel Tasks on Multi-Core Processors , 2016, IEEE Transactions on Parallel and Distributed Systems.

[8]  Rajkumar Buyya,et al.  Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers , 2012, Concurr. Comput. Pract. Exp..

[9]  Mitsuo Gen,et al.  Genetic algorithms and engineering optimization , 1999 .

[10]  Bertrand Granado,et al.  Multi-Objective Approach for Energy-Aware Workflow Scheduling in Cloud Computing Environments , 2013, TheScientificWorldJournal.

[11]  Yu Jiong,et al.  Energy-Aware Genetic Algorithms for Task Scheduling in Cloud Computing , 2012, 2012 Seventh ChinaGrid Annual Conference.

[12]  Pascal Bouvry,et al.  Energy-Aware Scheduling on Multicore Heterogeneous Grid Computing Systems , 2013, Journal of Grid Computing.

[13]  Yanqing Zhang,et al.  A Shadow Price Guided Genetic Algorithm for Energy Aware Task Scheduling on Cloud Computers , 2011, ICSI.

[14]  Cheng-Zhong Xu,et al.  URL: A unified reinforcement learning approach for autonomic cloud management , 2012, J. Parallel Distributed Comput..

[15]  Zibin Zheng,et al.  Particle Swarm Optimization for Energy-Aware Virtual Machine Placement Optimization in Virtualized Data Centers , 2013, ICPADS 2013.

[16]  Ying Feng,et al.  CLPS-GA: A case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling , 2014, Appl. Soft Comput..

[17]  Yuping Wang,et al.  Energy-Efficient Multi-Job Scheduling Model for Cloud Computing and Its Genetic Algorithm , 2012 .

[18]  Rajkumar Buyya,et al.  Virtual Machine Consolidation in Cloud Data Centers Using ACO Metaheuristic , 2014, Euro-Par.

[19]  Yi Zhong,et al.  State-of-the-art research study for green cloud computing , 2011, The Journal of Supercomputing.

[20]  Rajkumar Buyya,et al.  Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing , 2012, Future Gener. Comput. Syst..

[21]  Keqiu Li,et al.  Energy Consumption in Cloud Computing Data Centers , 2014, CloudCom 2014.

[22]  Varghese S. Jacob,et al.  Augmented neural networks for task scheduling , 2003, Eur. J. Oper. Res..

[23]  Jordi Torres,et al.  Towards energy-aware scheduling in data centers using machine learning , 2010, e-Energy.

[24]  Enda Barrett,et al.  Applying reinforcement learning towards automating resource allocation and application scalability in the cloud , 2013, Concurr. Comput. Pract. Exp..

[25]  Albert G. Greenberg,et al.  The cost of a cloud: research problems in data center networks , 2008, CCRV.

[26]  Fatos Xhafa,et al.  Genetic Algorithms for Energy-Aware Scheduling in Computational Grids , 2011, 2011 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing.

[27]  Mehmet Demirci,et al.  A Survey of Machine Learning Applications for Energy-Efficient Resource Management in Cloud Computing Environments , 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).

[28]  P. Ganeshkumar,et al.  Multi-objective Task Scheduling to Minimize Energy Consumption and Makespan of Cloud Computing Using NSGA-II , 2018, Journal of Network and Systems Management.

[29]  Amip J. Shah,et al.  Assessing the environmental impact of data centres part 1: Background, energy use and metrics , 2014 .

[30]  Kevin Lee,et al.  Empirical prediction models for adaptive resource provisioning in the cloud , 2012, Future Gener. Comput. Syst..

[31]  Fangchun Yang,et al.  Energy-aware and revenue-enhancing Combinatorial Scheduling in Virtualized of Cloud Datacenter , 2012 .

[32]  Alejandro Chinea,et al.  Understanding the Principles of Recursive Neural networks: A Generative Approach to Tackle Model Complexity , 2009, ICANN 2009.

[33]  Vipin Chaudhary,et al.  Rack Aware Scheduling in HPC Data Centers: An Energy Conservation Strategy , 2011, IPDPS Workshops.

[34]  Isis Truck,et al.  Using Reinforcement Learning for Autonomic Resource Allocation in Clouds: towards a fully automated workflow , 2011 .

[35]  Jordi Torres,et al.  Adaptive Scheduling on Power-Aware Managed Data-Centers Using Machine Learning , 2011, 2011 IEEE/ACM 12th International Conference on Grid Computing.

[36]  Wei Li,et al.  Energy-Efficient Virtual Machine Placement in Data Centers by Genetic Algorithm , 2012, ICONIP.

[37]  Zhi-hui Zhan,et al.  Energy aware virtual machine placement scheduling in cloud computing based on ant colony optimization approach , 2014, GECCO.

[38]  Pericles A. Mitkas,et al.  Reinforcement Learning based scheduling in a workflow management system , 2019, Eng. Appl. Artif. Intell..

[39]  Sarbjeet Singh,et al.  A review of metaheuristic scheduling techniques in cloud computing , 2015 .