Multi-objective Task Scheduling to Minimize Energy Consumption and Makespan of Cloud Computing Using NSGA-II

The utilization of cloud services has significantly increased due to the easiness in accessibility, better performance, and decrease in the high initial cost. In general, cloud users anticipate completing their tasks without any delay, whereas cloud providers yearn for reducing the energy cost, which is one of the major costs in the cloud service environment. However, reducing energy consumption increases the makespan and leads to customer dissatisfaction. So, it is essential to obtain a set of non-domination solutions for these multiple and conflicting objectives (makespan and energy consumption). In order to control the energy consumption efficaciously, the Dynamic Voltage Frequency Scaling system is incorporated in the optimization procedure and a set of non-domination solutions are obtained using Non-dominated Sorting Genetic Algorithm (NSGA-II). Further, the Artificial Neural Network (ANN), which is one of the most successful machine learning algorithms, is used to predict the virtual machines based on the characteristics of tasks and features of the resources. The optimum solutions obtained using the optimization process with the support of ANN and without the support of ANN are presented and discussed.

[1]  Lotfi Mhamdi,et al.  A survey on architectures and energy efficiency in Data Center Networks , 2014, Comput. Commun..

[2]  Dzmitry Kliazovich,et al.  GreenCloud: A Packet-Level Simulator of Energy-Aware Cloud Computing Data Centers , 2010, GLOBECOM.

[3]  K. P. Padmanaban,et al.  Multi-Objective Optimization of Laminated Composite Plate with Diffused Layer Angles using Non-Dominated Sorting Genetic Algorithm (NSGA-II) , 2014 .

[4]  Jesús Carretero,et al.  E-mc2: A formal framework for energy modelling in cloud computing , 2013, Simul. Model. Pract. Theory.

[5]  B. Kermanshahi,et al.  Up to year 2020 load forecasting using neural nets , 2002 .

[6]  R. Bolaños,et al.  A multiobjective non-dominated sorting genetic algorithm (NSGA-II) for the Multiple Traveling Salesman Problem , 2015 .

[7]  Tomislav Bolanča,et al.  Artificial neural network models for advanced oxidation of organics in water matrix - Comparison of applied methodologies , 2014 .

[8]  Mark Beale,et al.  Neural Network Toolbox™ User's Guide , 2015 .

[9]  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..

[10]  Ulrich Kremer,et al.  The design, implementation, and evaluation of a compiler algorithm for CPU energy reduction , 2003, PLDI '03.

[11]  Ka-Veng Yuen,et al.  On the complexity of artificial neural networks for smart structures monitoring , 2006 .

[12]  Christine Morin,et al.  Energy-Aware Ant Colony Based Workload Placement in Clouds , 2011, 2011 IEEE/ACM 12th International Conference on Grid Computing.

[13]  Athanasios V. Vasilakos,et al.  GreenDCN: A General Framework for Achieving Energy Efficiency in Data Center Networks , 2013, IEEE Journal on Selected Areas in Communications.

[14]  Zhiyong Liu,et al.  Scheduling for energy minimization on restricted parallel processors , 2015, J. Parallel Distributed Comput..

[15]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[16]  Dang Minh Quan,et al.  T-Alloc: A practical energy efficient resource allocation algorithm for traditional data centers , 2012, Future Gener. Comput. Syst..

[17]  Adiwijaya,et al.  IMPLEMENTATION OF ANT COLONY OPTIMIZATION ALGORITHM ON THE PROJECT RESOURCE SCHEDULING PROBLEM , 2008 .

[18]  Xinying Zheng,et al.  Energy-aware load dispatching in geographically located Internet data centers , 2011, Sustain. Comput. Informatics Syst..

[19]  Amir Hayat,et al.  Resource management in cloud computing: Taxonomy, prospects, and challenges , 2015, Comput. Electr. Eng..

[20]  Samuel A. Ajila,et al.  Using Machine Learning Algorithms for Cloud Client Prediction Models in a Web VM Resource Provisioning Environment , 2016 .

[21]  YONGHONG LUO,et al.  Power Consumption Optimization Strategy of Cloud Workflow Scheduling Based on SLA , 2014 .

[22]  Manish Parashar,et al.  Energy-efficient application-aware online provisioning for virtualized clouds and data centers , 2010, International Conference on Green Computing.

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

[24]  Narander Kumar,et al.  Resource Management using Feed Forward ANN-PSO in Cloud Computing Environment , 2016, ICTCS.

[25]  Frank Z. Wang,et al.  Balanced Job scheduling Based on ant algorithm for Grid Network , 2010 .

[26]  P. B. Sujit,et al.  Particle swarm optimization approach for multi-objective composite box-beam design , 2007 .

[27]  Tao Zhang,et al.  SGEESS: Smart green energy-efficient scheduling strategy with dynamic electricity price for data center , 2015, J. Syst. Softw..

[28]  Chia-Ming Wu,et al.  A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters , 2014, Future Gener. Comput. Syst..

[29]  El-Ghazali Talbi,et al.  A multi-start local search heuristic for an energy efficient VMs assignment on top of the OpenNebula cloud manager , 2014, Future Gener. Comput. Syst..

[30]  Indrajit Mukherjee,et al.  Cloud Computing Initiative using Modified Ant Colony Framework , 2009 .

[31]  Shuai Gao,et al.  Genetic simulated annealing algorithm for task scheduling based on cloud computing environment , 2010, 2010 International Conference on Intelligent Computing and Integrated Systems.

[32]  Rajkumar Buyya,et al.  A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments , 2010, 2010 24th IEEE International Conference on Advanced Information Networking and Applications.

[33]  Fei Zhang,et al.  Simulation of power consumption of cloud data centers , 2013, Simul. Model. Pract. Theory.

[34]  S. N. Omkar,et al.  Quantum behaved Particle Swarm Optimization (QPSO) for multi-objective design optimization of composite structures , 2009, Expert Syst. Appl..

[35]  Fei Tao,et al.  FC-PACO-RM: A Parallel Method for Service Composition Optimal-Selection in Cloud Manufacturing System , 2013, IEEE Transactions on Industrial Informatics.

[36]  Massoud Pedram,et al.  Energy-Efficient Datacenters , 2012, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[37]  Ariel Oleksiak,et al.  Energy and thermal models for simulation of workload and resource management in computing systems , 2015, Simul. Model. Pract. Theory.

[38]  Inderveer Chana,et al.  Prediction-based proactive load balancing approach through VM migration , 2016, Engineering with Computers.

[39]  Euiseong Seo,et al.  Energy-credit scheduler: An energy-aware virtual machine scheduler for cloud systems , 2014, Future Gener. Comput. Syst..

[40]  S. N. Omkar,et al.  Vector evaluated particle swarm optimization (VEPSO) for multi-objective design optimization of composite structures , 2008 .

[41]  Albert Y. Zomaya,et al.  A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems , 2010, Adv. Comput..

[42]  Liang Liu,et al.  Energy efficient scheduling of virtual machines in cloud with deadline constraint , 2015, Future Gener. Comput. Syst..

[43]  Debabrata Chakraborty,et al.  Artificial neural network based delamination prediction in laminated composites , 2005 .