Multi-Objective Task Scheduling Using Smart MPI-Based Cloud Resources

Task Scheduling and Resource Allocation (TSRA) is the key focus of cloud computing. This paper utilizes Smart Message Passing Interface based Approach (SMPIA) and the Roulette Wheel selection method in order to determine the best Alternative Virtual Machine (AVM). To do so, the Virtual MPI Bus (VMPIB) is employed for efficient communication among Virtual Machines (VMs) using SMPIA. In this matter, SMPIA is applied on different resource allocation and task scheduling strategies. MakeSpan (MS) was chosen as an optimization factor and solutions with minimum MS value as the best task mapping performance and reduced cloud consumption. The simulation is conducted using MATLAB. The analysis proves that applying SMPIA reduced the Total Execution Time (TET) of resource allocation, maximum MS time, and increase the Resource Utilization (RU), as compared to non-SMPIA for Greedy, Max-Min, Min-Min algorithms. It is observed that SMPIA can outperform non-SMPIA. The effect of SMPIA is more ∗ Corresponding author Multi-Objective Task Scheduling Using Smart MPI-Based Cloud Resources 105 obvious as change in the MS and the number of cloud workloads increase. Furthermore, regarding the TET and MS of the tasks, the SMPIA can significantly reduce the starvation problem as well as the lack of sufficient resources. In addition, this approach improves the system’s performance more than the previous methods, what reflects effectiveness of the proposed approach concerning the Message Passing Interface (MPI) communication time in the network virtualization. The mentioned text mining work was prepared concurrently after practical evaluation.

[1]  Sanjaya Kumar Panda,et al.  A Customer-Oriented Task Scheduling for Heterogeneous Multi-Cloud Environment , 2016, Int. J. Cloud Appl. Comput..

[2]  Prasanta K. Jana,et al.  Efficient task scheduling algorithms for heterogeneous multi-cloud environment , 2015, 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[3]  Yu Xin,et al.  A Scheduling Algorithm for Cloud Computing System Based on the Driver of Dynamic Essential Path , 2016, PloS one.

[4]  Lee Chao,et al.  Cloud computing for teaching and learning , 2012 .

[5]  Claude Tadonki,et al.  E-HEFT: Enhancement Heterogeneous Earliest Finish Time algorithm for Task Scheduling based on Load Balancing in Cloud Computing , 2018, 2018 International Conference on High Performance Computing & Simulation (HPCS).

[6]  Shujia Zhou,et al.  Case study for running HPC applications in public clouds , 2010, HPDC '10.

[7]  Cloud Computing for Teaching and Learning MPI with Improved Network Communications , 2012, WCLOUD.

[8]  R. Smeliansky,et al.  IMPROVING RESOURCE USAGE IN HPC CLOUDS , 2019 .

[9]  Xingjun Zhang,et al.  A low-power task scheduling algorithm for heterogeneous cloud computing , 2020, The Journal of Supercomputing.

[10]  L. D. Dhinesh Babu,et al.  Honey bee behavior inspired load balancing of tasks in cloud computing environments , 2013, Appl. Soft Comput..

[11]  Fernando Gomez-Folgar,et al.  MPI-Performance-Aware-Reallocation: method to optimize the mapping of processes applied to a cloud infrastructure , 2018, Computing.

[12]  Tian Fu,et al.  A Novel Dynamic Task Scheduling Algorithm Based on Improved Genetic Algorithm in Cloud Computing , 2016 .

[13]  Babur Hayat Malik,et al.  Comparison of Task Scheduling Algorithms in Cloud Environment , 2018 .

[14]  Philippe Olivier Alexandre Navaux,et al.  EagerMap: A Task Mapping Algorithm to Improve Communication and Load Balancing in Clusters of Multicore Systems , 2019, ACM Trans. Parallel Comput..

[15]  Rajkumar Buyya,et al.  Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach , 2018, 2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC).

[16]  Paul Rad,et al.  Low-latency software defined network for high performance clouds , 2015, 2015 10th System of Systems Engineering Conference (SoSE).

[17]  Deep Medhi,et al.  Energy-Efficient dynamic virtual network traffic engineering for north-south traffic in multi-location data center networks , 2017, Comput. Networks.

[18]  Nora Almezeini,et al.  An Enhanced Workflow Scheduling Algorithm in Cloud Computing , 2016, CLOSER.

[20]  K. D. Kumar,et al.  Resource Provisioning in Cloud Computing Using Prediction Models : A Survey , 2018 .

[21]  Emilio Luque,et al.  MCM: A new MPI Communication Management for Cloud Environments , 2017, ICCS.

[22]  Maryam Amiri,et al.  Survey on prediction models of applications for resources provisioning in cloud , 2017, J. Netw. Comput. Appl..

[23]  Tinghuai Ma,et al.  Resource Allocation and Scheduling in Cloud Computing: Policy and Algorithm , 2014 .

[24]  Longda Huang,et al.  Overview of Cloud Computing Resource Allocation and Management Technology , 2019, 2019 6th International Conference on Systems and Informatics (ICSAI).

[25]  Kuo-Chan Huang,et al.  Online scheduling of workflow applications in grid environments , 2011, Future Gener. Comput. Syst..

[26]  Rizos Sakellariou,et al.  A hybrid heuristic for DAG scheduling on heterogeneous systems , 2004, 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings..

[27]  Huankai Chen,et al.  User-priority guided Min-Min scheduling algorithm for load balancing in cloud computing , 2013, 2013 National Conference on Parallel Computing Technologies (PARCOMPTECH).

[28]  Subhash K. Shinde,et al.  Task scheduling and resource allocation in cloud computing using a heuristic approach , 2018, Journal of Cloud Computing.

[29]  Tran Cong Hung,et al.  MMSIA: Improved Max-Min Scheduling Algorithm for Load Balancing on Cloud Computing , 2019, ICMLSC.

[30]  Hu Zhigang,et al.  Task Scheduling Algorithm based on Greedy Strategy in Cloud Computing , 2015 .

[31]  Hong Zhang,et al.  Segmented min-min: a static mapping algorithm for meta-tasks on heterogeneous computing systems , 2000, Proceedings 9th Heterogeneous Computing Workshop (HCW 2000) (Cat. No.PR00556).

[32]  Berkant Barla Cambazoglu,et al.  Improving the Performance of IndependentTask Assignment Heuristics MinMin,MaxMin and Sufferage , 2014, IEEE Transactions on Parallel and Distributed Systems.

[33]  Jin Zhang,et al.  Process Mapping for MPI Collective Communications , 2009, Euro-Par.

[34]  J. R. Mohanty,et al.  GA-Based Customer-Conscious Resource Allocation and Task Scheduling in Multi-cloud Computing , 2018 .

[35]  Mohammed Joda Usman,et al.  Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment , 2017, PloS one.

[36]  Jianlong Zhong,et al.  Network Performance Aware MPI Collective Communication Operations in the Cloud , 2015, IEEE Transactions on Parallel and Distributed Systems.

[37]  Sunita Singhal,et al.  Load Balancing Scheduling Algorithm for Concurrent Workflow , 2018, Comput. Informatics.

[38]  Hamid Arabnejad,et al.  Fairness Resource Sharing for Dynamic Workflow Scheduling on Heterogeneous Systems , 2012, 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications.