Energy-aware scheduling algorithm for time-constrained workflow tasks in DVFS-enabled cloud environment

Abstract Energy consumption in cloud data centers is increasing as the use of such services increases. It is necessary to propose new methods of decreasing energy consumption. Green cloud computing helps to reduce energy consumption and significantly decreases both operating costs and greenhouse gas emissions. Scheduling the enormous number of user-submitted workflow tasks is an important aspect of cloud computing. Resources in cloud data centers should compute these tasks using energy efficient techniques. This paper proposed a new energy-aware scheduling algorithm for time-constrained workflow tasks using the DVFS method in which the host reduces the operating frequency using different voltage levels. The goal of this research is to reduce energy consumption and SLA violations and improve resource utilization. The simulation results show that the proposed method performs more efficiently when evaluating metrics such as energy utilization, average execution time, average resource utilization and average SLA violation.

[1]  Edmundo Roberto Mauro Madeira,et al.  A performance-oriented adaptive scheduler for dependent tasks on grids , 2008 .

[2]  Weng-Long Chang,et al.  Quantum Algorithms and Mathematical Formulations of Biomolecular Solutions of the Vertex Cover Problem in the Finite-Dimensional Hilbert Space , 2015, IEEE Transactions on NanoBioscience.

[3]  Lucio Grandinetti,et al.  An approximate ϵϵ-constraint method for a multi-objective job scheduling in the cloud , 2013, Future Gener. Comput. Syst..

[4]  Keqin Li,et al.  Minimizing Energy Consumption of Real-Time Parallel Applications Using Downward and Upward Approaches on Heterogeneous Systems , 2017, IEEE Transactions on Industrial Informatics.

[5]  Sateesh K. Peddoju,et al.  Energy efficient task scheduling for parallel workflows in cloud environment , 2014, 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT).

[6]  Michael Franz,et al.  Power reduction techniques for microprocessor systems , 2005, CSUR.

[7]  T. Jenifer Nirubah,et al.  ENERGY-EFFICIENT TASK SCHEDULING ALGORITHMS FOR CLOUD DATA CENTERS , 2014 .

[8]  Samee Ullah Khan,et al.  An Energy-Efficient Task Scheduling Algorithm in DVFS-enabled Cloud Environment , 2015, Journal of Grid Computing.

[9]  Keqin Li,et al.  Fast Functional Safety Verification for Distributed Automotive Applications During Early Design Phase , 2018, IEEE Transactions on Industrial Electronics.

[10]  Ming Fan,et al.  Energy minimization for on-line real-time scheduling with reliability awareness , 2017, J. Syst. Softw..

[11]  Keqin Li,et al.  Energy-Aware Processor Merging Algorithms for Deadline Constrained Parallel Applications in Heterogeneous Cloud Computing , 2017, IEEE Transactions on Sustainable Computing.

[12]  Salim Hariri,et al.  Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing , 2002, IEEE Trans. Parallel Distributed Syst..

[13]  Keqin Li,et al.  Resource Consumption Cost Minimization of Reliable Parallel Applications on Heterogeneous Embedded Systems , 2017, IEEE Transactions on Industrial Informatics.

[14]  Jian Li,et al.  Enhanced Energy-Efficient Scheduling for Parallel Applications in Cloud , 2012, 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012).

[15]  Keqin Li,et al.  Energy-Efficient Fault-Tolerant Scheduling of Reliable Parallel Applications on Heterogeneous Distributed Embedded Systems , 2018, IEEE Transactions on Sustainable Computing.

[16]  Rajkumar Buyya,et al.  Power Aware Scheduling of Bag-of-Tasks Applications with Deadline Constraints on DVS-enabled Clusters , 2007, Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid '07).

[17]  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..

[18]  Keqin Li,et al.  Energy-Efficient Scheduling Algorithms for Real-Time Parallel Applications on Heterogeneous Distributed Embedded Systems , 2017, IEEE Transactions on Parallel and Distributed Systems.

[19]  Jeffrey D. Ullman,et al.  NP-Complete Scheduling Problems , 1975, J. Comput. Syst. Sci..

[20]  Keqin Li,et al.  Energy management for multiple real-time workflows on cyber-physical cloud systems , 2017, Future Gener. Comput. Syst..

[21]  Gregor von Laszewski,et al.  Towards Energy Aware Scheduling for Precedence Constrained Parallel Tasks in a Cluster with DVFS , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[22]  Leila Ismail,et al.  EATS: Energy-Aware Tasks Scheduling in Cloud Computing Systems , 2016, ANT/SEIT.

[23]  Ritu Garg,et al.  Energy-Aware Workflow Scheduling in Grid Under QoS Constraints , 2016 .

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

[25]  Mehran Mohsenzadeh,et al.  ATSDS: adaptive two-stage deadline-constrained workflow scheduling considering run-time circumstances in cloud computing environments , 2017, The Journal of Supercomputing.

[26]  Yan Zhang,et al.  On Architecture Design, Congestion Notification, TCP Incast and Power Consumption in Data Centers , 2013, IEEE Communications Surveys & Tutorials.

[27]  Edward A. Lee,et al.  A Compile-Time Scheduling Heuristic for Interconnection-Constrained Heterogeneous Processor Architectures , 1993, IEEE Trans. Parallel Distributed Syst..

[28]  Inderveer Chana,et al.  Energy Efficiency Techniques in Cloud Computing , 2015, ACM Comput. Surv..

[29]  Amin Vahdat,et al.  Managing energy and server resources in hosting centers , 2001, SOSP.

[30]  Rajkumar Buyya,et al.  Environment-conscious scheduling of HPC applications on distributed Cloud-oriented data centers , 2011, J. Parallel Distributed Comput..

[31]  Yongsheng Ding,et al.  Endocrine-based coevolutionary multi-swarm for multi-objective workflow scheduling in a cloud system , 2017, Soft Comput..

[32]  Marios D. Dikaiakos,et al.  Scheduling Workflows with Budget Constraints , 2007, Grid 2007.

[33]  Ishfaq Ahmad,et al.  Dynamic Critical-Path Scheduling: An Effective Technique for Allocating Task Graphs to Multiprocessors , 1996, IEEE Trans. Parallel Distributed Syst..

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

[35]  Albert Y. Zomaya,et al.  Energy-aware parallel task scheduling in a cluster , 2013, Future Gener. Comput. Syst..

[36]  Mehran Mohsenzadeh,et al.  Taxonomy of workflow partitioning problems and methods in distributed environments , 2017, J. Syst. Softw..

[37]  Michael Wallace,et al.  Advanced Configuration and Power Interface , 2009 .

[38]  Luca Benini,et al.  A survey of design techniques for system-level dynamic power management , 2000, IEEE Trans. Very Large Scale Integr. Syst..

[39]  Rajkumar Buyya,et al.  Power-aware provisioning of Cloud resources for real-time services , 2009, MGC '09.

[40]  Sam Jabbehdari,et al.  An autonomic approach for resource provisioning of cloud services , 2016, Cluster Computing.