EATSDCD: A green energy-aware scheduling algorithm for parallel task-based application using clustering, duplication and DVFS technique in cloud datacenters

Energy consumption and performance metrics have become critical issues for scheduling parallel task-based applications in high-performance computing systems such as cloud datacenters. The duplication and clustering strategy, as well as Dynamic Voltage Frequency Scaling (DVFS) technique, have separately been concentrated on reducing energy consumption and optimizing performance parameters such as throughput and makespan. In this paper, a dual-phase algorithm called EATSDCD which is an energy efficient time aware has been proposed. The algorithm uses the combination of duplication and clustering strategies to schedule the precedence-constrained task graph on datacenter processors through DVFS. The first phase focuses on a smart combination of duplication and clustering strategy to reduce makespan and energy consumed by processors in an effort to execute Directed Acyclic Graph (DAG) while satisfying the throughput constraint. The main idea behind EATSDCD intended to minimize energy consumption in the second phase. After determining the critical path and specifying a set of dependent tasks in non-critical paths, the slack time for each task in non-critical paths was distributed among all dependent tasks in that path. Then, the frequency of DVFS-enabled processors is scaled down to execute non-critical tasks as well as idle and communication phases, without extending the execution time of tasks. Finally, a testbed is developed and different parameters are tested on the randomly generated DAG to evaluate and illustrate the effectiveness of EATSDCD. It was also compared against duplication and clustering-based algorithms and DVFS-based algorithms. In terms of energy consumption and makespan, the results show that our proposed algorithm can save up to 8.3% and 20% energy compared against Power Aware List-based Scheduling (PALS) and Power Aware Task Clustering (PATC) algorithms, respectively. Furthermore, there is 16% improvement over Parallel Pipeline Latency Optimization (PaPilo) algorithm with Encur = 1.2Enmin(G). In comparison with Reliability Aware Scheduling with Duplication (RASD) algorithm, the execution time has been reduced in heterogeneous environments.

[1]  S. Ranka,et al.  Applications and performance analysis of a compile-time optimization approach for list scheduling algorithms on distributed memory multiprocessors , 1992, Proceedings Supercomputing '92.

[2]  R. Shriram,et al.  Power management in virtualized datacenter - A survey , 2016, J. Netw. Comput. Appl..

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

[4]  Homayun Motameni,et al.  A trust model between cloud entities using fuzzy mathematics , 2015, J. Intell. Fuzzy Syst..

[5]  Ching-Hsien Hsu,et al.  An optimal control policy to realize green cloud systems with SLA-awareness , 2014, The Journal of Supercomputing.

[6]  Sanjeev Baskiyar,et al.  Energy aware DAG scheduling on heterogeneous systems , 2010, Cluster Computing.

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

[8]  Sang Cheol Kim,et al.  Push-Pull: Deterministic Search-Based DAG Scheduling for Heterogeneous Cluster Systems , 2007, IEEE Transactions on Parallel and Distributed Systems.

[9]  Ranvijay,et al.  Improved real-time energy aware parallel task scheduling in a cluster , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).

[10]  Aihua Liang,et al.  A Novel, Energy-Aware Task Duplication-Based Scheduling Algorithm of Parallel Tasks on Clusters , 2016 .

[11]  Albert Y. Zomaya,et al.  Energy-efficient data replication in cloud computing datacenters , 2013, GLOBECOM Workshops.

[12]  Tao Yang,et al.  A Comparison of Clustering Heuristics for Scheduling Directed Acycle Graphs on Multiprocessors , 1992, J. Parallel Distributed Comput..

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

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

[15]  Albert Y. Zomaya,et al.  Some observations on optimal frequency selection in DVFS-based energy consumption minimization , 2011, J. Parallel Distributed Comput..

[16]  Yang Wang,et al.  Adaptive Scheduling of Task Graphs with Dynamic Resilience , 2017, IEEE Transactions on Computers.

[17]  Chuen-Horng Lin,et al.  Multi-server system with single working vacation , 2009 .

[18]  Ali Movaghar-Rahimabadi,et al.  Performance and power modeling and evaluation of virtualized servers in IaaS clouds , 2017, Inf. Sci..

[19]  Tao Zhang,et al.  A multi-objective co-evolutionary algorithm for energy-efficient scheduling on a green data center , 2016, Comput. Oper. Res..

[20]  Fatma A. Omara,et al.  Genetic algorithms for task scheduling problem , 2010, J. Parallel Distributed Comput..

[21]  Rajkumar Buyya,et al.  Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit: Challenges and opportunities , 2009, 2009 International Conference on High Performance Computing & Simulation.

[22]  Keqin Li,et al.  Future Generation Computer Systems ( ) – Future Generation Computer Systems Multi-objective Scheduling of Many Tasks in Cloud Platforms , 2022 .

[23]  Xiao Qin,et al.  Energy-Aware Duplication Strategies for Scheduling Precedence-Constrained Parallel Tasks on Clusters , 2006, 2006 IEEE International Conference on Cluster Computing.

[24]  Monica Nicoli,et al.  Wireless home automation networks for indoor surveillance: technologies and experiments , 2014, EURASIP J. Wirel. Commun. Netw..

[25]  Kenli Li,et al.  Slack allocation algorithm for energy minimization in cluster systems , 2017, Future Gener. Comput. Syst..

[26]  Rizos Sakellariou,et al.  Using imbalance metrics to optimize task clustering in scientific workflow executions , 2015, Future Gener. Comput. Syst..

[27]  Albert Y. Zomaya,et al.  Author manuscript, published in "Journal of Parallel and Distributed Computing (2011)" A Parallel Bi-objective Hybrid Metaheuristic for Energy-aware Scheduling for Cloud Computing Systems , 2011 .

[28]  Albert Y. Zomaya,et al.  Energy Conscious Scheduling for Distributed Computing Systems under Different Operating Conditions , 2011, IEEE Transactions on Parallel and Distributed Systems.

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

[30]  Kenli Li,et al.  A resource-aware scheduling algorithm with reduced task duplication on heterogeneous computing systems , 2014, The Journal of Supercomputing.

[31]  Mihalis Yannakakis,et al.  Towards an Architecture-Independent Analysis of Parallel Algorithms , 1990, SIAM J. Comput..

[32]  Philippe Chrétienne,et al.  C.P.M. Scheduling with Small Communication Delays and Task Duplication , 1991, Oper. Res..

[33]  Mostafa Langarizadeh,et al.  A novel method for fuzzy diagnostic system design , 2018, Medical journal of the Islamic Republic of Iran.

[34]  Pao-Ann Hsiung,et al.  Multi-objective exploitation of pipeline parallelism using clustering, replication and duplication in embedded multi-core systems , 2013, J. Syst. Archit..

[35]  Mitsuhisa Sato,et al.  Emprical study on Reducing Energy of Parallel Programs using Slack Reclamation by DVFS in a Power-scalable High Performance Cluster , 2006, 2006 IEEE International Conference on Cluster Computing.

[36]  Ishfaq Ahmad,et al.  On Exploiting Task Duplication in Parallel Program Scheduling , 1998, IEEE Trans. Parallel Distributed Syst..

[37]  Sucha Smanchat,et al.  Taxonomies of workflow scheduling problem and techniques in the cloud , 2015, Future Gener. Comput. Syst..

[38]  Anamika Jain,et al.  Working vacations queueing model with multiple types of server breakdowns , 2010 .

[39]  Kwangsik Shin,et al.  Task scheduling algorithm using minimized duplications in homogeneous systems , 2008, J. Parallel Distributed Comput..

[40]  Feng Xia,et al.  A survey on virtual machine migration and server consolidation frameworks for cloud data centers , 2015, J. Netw. Comput. Appl..

[41]  Daniel Sun,et al.  Reliability and energy efficiency in cloud computing systems: Survey and taxonomy , 2016, J. Netw. Comput. Appl..

[42]  Kenli Li,et al.  Reliability-aware scheduling strategy for heterogeneous distributed computing systems , 2010, J. Parallel Distributed Comput..

[43]  Asadullah Shah,et al.  Evaluating power efficient algorithms for efficiency and carbon emissions in cloud data centers: A review , 2015 .

[44]  Wei Wang,et al.  A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing , 2014, EURASIP Journal on Wireless Communications and Networking.

[45]  Ümit V. Çatalyürek,et al.  Compaction of Schedules and a Two-Stage Approach for Duplication-Based DAG Scheduling , 2009, IEEE Transactions on Parallel and Distributed Systems.

[46]  Kenli Li,et al.  Energy-aware task scheduling in heterogeneous computing environments , 2014, Cluster Computing.

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

[48]  Joel H. Saltz,et al.  Optimizing latency and throughput of application workflows on clusters , 2011, Parallel Comput..

[49]  Boontee Kruatrachue,et al.  Grain size determination for parallel processing , 1988, IEEE Software.

[50]  Manpreet Kaur,et al.  Contention-Aware Scheduling with Task Duplication , 2009, JSSPP.

[51]  Albert Y. Zomaya,et al.  Multiple Frequency Selection in DVFS-Enabled Processors to Minimize Energy Consumption , 2012, ArXiv.

[52]  Jing Chen,et al.  Adaptive energy-efficient scheduling algorithm for parallel tasks on homogeneous clusters , 2014, J. Netw. Comput. Appl..

[53]  Manuel Mucientes,et al.  Enhancing discovered processes with duplicate tasks , 2016, Inf. Sci..

[54]  Jorge Ejarque,et al.  Dynamic energy-aware scheduling for parallel task-based application in cloud computing , 2018, Future Gener. Comput. Syst..

[55]  Hassan Taheri,et al.  Novel fuzzy multi objective DVFS-aware consolidation heuristics for energy and SLA efficient resource management in cloud data centers , 2017, J. Netw. Comput. Appl..

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