Scheduling Algorithms for High-Performance Computing: An Application Perspective of Fog Computing

High-performance computing (HPC) demands many computers to perform multiple tasks concurrently and efficiently. For efficient resource utilization and for better response time, different scheduling algorithms have been proposed which aim to increase throughput, scalability, and performance of HPC applications. In this paper, our contribution is twofold. Firstly, the classification of scheduling algorithms on the basis of multiple factors like throughput, waiting time, fairness, overhead, etc. is presented. This paper investigates the recent research that has been carried out from 2009–2017. With this categorization, we aim to provide an easy and concise view of the HPC algorithms. Secondly, the forecasting has been done on HPC applications to predict the growth rate for 2020 and beyond.

[1]  Salabat Khan,et al.  Ant Colony Optimization based scheduling algorithm , 2013, 2013 International Conference on Open Source Systems and Technologies.

[2]  Dejan S. Milojicic,et al.  The Who, What, Why, and How of High Performance Computing in the Cloud , 2013, 2013 IEEE 5th International Conference on Cloud Computing Technology and Science.

[3]  Arif Ali Khan,et al.  A formal framework for web service broker to compose QoS measures , 2015, 2015 SAI Intelligent Systems Conference (IntelliSys).

[4]  Alexandru Iosup,et al.  Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing , 2011, IEEE Transactions on Parallel and Distributed Systems.

[5]  Peter Luksch,et al.  Improving HPC Application Performance in Public Cloud , 2014 .

[6]  Mervat Mosa,et al.  Optimization procedure for algorithms of task scheduling in high performance heterogeneous distributed computing systems , 2011 .

[7]  Philippe Olivier Alexandre Navaux,et al.  High Performance Computing in the cloud: Deployment, performance and cost efficiency , 2012, 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings.

[8]  Tae Young Kim,et al.  The Study of Genetic Algorithm-based Task Scheduling for Cloud Computing , 2012 .

[9]  Josef Weidendorfer,et al.  Case Study on Co-scheduling for HPC Applications , 2015, 2015 44th International Conference on Parallel Processing Workshops.

[10]  Avita Katal,et al.  An Optimized Task Scheduling Algorithm in Cloud Computing , 2016, 2016 IEEE 6th International Conference on Advanced Computing (IACC).

[11]  Fatma A. Omara,et al.  An Enhanced Task Scheduling Algorithm on Cloud Computing Environment , 2016 .

[12]  Albert Y. Zomaya,et al.  CA-DAG: Modeling Communication-Aware Applications for Scheduling in Cloud Computing , 2015, Journal of Grid Computing.

[13]  Zhenlong Li,et al.  Big Data and cloud computing: innovation opportunities and challenges , 2017, Int. J. Digit. Earth.

[14]  Tharam S. Dillon,et al.  Cloud Computing: Issues and Challenges , 2010, 2010 24th IEEE International Conference on Advanced Information Networking and Applications.

[15]  Bartosz Balis,et al.  Porting HPC applications to the cloud: A multi-frontal solver case study , 2017, J. Comput. Sci..

[16]  Jun Zhang,et al.  Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches , 2015, ACM Comput. Surv..

[17]  Abhishek Gupta,et al.  Evaluation of HPC Applications on Cloud , 2011, 2011 Sixth Open Cirrus Summit.

[18]  Kim-Kwang Raymond Choo,et al.  Distributed controller clustering in software defined networks , 2017, PloS one.

[19]  Emmanuel Jeannot,et al.  Topology-aware resource management for HPC applications , 2017, ICDCN.

[20]  Sudhir Shenai,et al.  Survey on Scheduling Issues in Cloud Computing , 2012 .

[21]  Narayan Desai,et al.  Job scheduling strategies for parallel processing : 18th international workshop, JSSPP 2014, Phoenix, AZ, USA, May 23, 2014 : revised selected papers , 2015 .

[22]  Jyh-Horng Chou,et al.  Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm , 2013, Comput. Oper. Res..

[23]  De-fu Zhang,et al.  A Hybrid Genetic Scheduling Algorithm to Heterogeneous Distributed System , 2012 .

[24]  Tao Yu,et al.  Cloud Service Scheduling Algorithm Research and Optimization , 2017, Secur. Commun. Networks.

[25]  Rajkumar Buyya,et al.  A taxonomy and survey on scheduling algorithms for scientific workflows in IaaS cloud computing environments , 2017, Concurr. Comput. Pract. Exp..

[26]  Dejan S. Milojicic,et al.  Evaluating and Improving the Performance and Scheduling of HPC Applications in Cloud , 2016, IEEE Transactions on Cloud Computing.

[27]  Rajkumar Buyya,et al.  Energy-Efficient Scheduling of HPC Applications in Cloud Computing Environments , 2009, ArXiv.