Cost Optimised Heuristic Algorithm (COHA) for Scientific Workflow Scheduling in IaaS Cloud Environment

Cloud computing, a multipurpose and high-performance internet-based computing, can model and transform a large range of application requirements into a set of workflow tasks. It allows users to represent their computational needs conveniently for data retrieval, reformatting, and analysis. However, workflow applications are big data applications and often take long hours to finish executing due to their nature and data size. In this paper, we study the cost optimised scheduling algorithms in cloud and proposed a novel task splitting algorithm named Cost optimised Heuristic Algorithm (COHA) for the cloud scheduler to optimise the execution cost. In this algorithm, the large tasks are split into sub-tasks to reduce their execution time. The design purpose is to enable all tasks to adequately meet their deadlines. We have carefully tested the performance of the COHA with a list of workflow inputs. The simulation results have convincingly demonstrated that COHA can effectively perform VM allocation and deployment, and well handle randomly arrived tasks. It can efficiently reduce execution costs while also allowing all tasks to properly finish before their deadlines. Overall, the improvements in our algorithm have remarkably reduced the execution cost by 32.5% for Sipht, 3.9% for Montage, and 1.2% for CyberShake workflows when compared to the state of art work.

[1]  Yuxuan Jiang,et al.  Towards Max-Min Fair Resource Allocation for Stream Big Data Analytics in Shared Clouds , 2018, IEEE Transactions on Big Data.

[2]  Rajkumar Buyya,et al.  Budget-Driven Scheduling of Scientific Workflows in IaaS Clouds with Fine-Grained Billing Periods , 2017, ACM Trans. Auton. Adapt. Syst..

[3]  Jin Sun,et al.  Minimizing cost and makespan for workflow scheduling in cloud using fuzzy dominance sort based HEFT , 2019, Future Gener. Comput. Syst..

[4]  Maria Alejandra Rodriguez Sossa.  Resource provisioning and scheduling algorithms for scientific workflows in cloud computing environments , 2016 .

[5]  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..

[6]  Randy H. Katz,et al.  Above the Clouds: A Berkeley View of Cloud Computing , 2009 .

[7]  Miron Livny,et al.  Pegasus, a workflow management system for science automation , 2015, Future Gener. Comput. Syst..

[8]  Mohammad Masdari,et al.  Towards workflow scheduling in cloud computing: A comprehensive analysis , 2016, J. Netw. Comput. Appl..

[9]  A. I. Awad,et al.  Enhanced Particle Swarm Optimization for Task Scheduling in Cloud Computing Environments , 2015 .

[10]  Sarbjeet Singh,et al.  A review of metaheuristic scheduling techniques in cloud computing , 2015 .

[11]  Laura Savu,et al.  Cloud Computing: Deployment Models, Delivery Models, Risks and Research Challenges , 2011, 2011 International Conference on Computer and Management (CAMAN).

[12]  Bin Luo,et al.  Cost and Energy Aware Scheduling Algorithm for Scientific Workflows with Deadline Constraint in Clouds , 2018, IEEE Transactions on Services Computing.

[13]  Prashant Pandey,et al.  Cloud computing , 2010, ICWET.

[14]  Deo Prakash Vidyarthi,et al.  A Cost-Effective Deadline-Constrained Dynamic Scheduling Algorithm for Scientific Workflows in a Cloud Environment , 2018, IEEE Transactions on Cloud Computing.

[15]  Mei-Hui Su,et al.  Characterization of scientific workflows , 2008, 2008 Third Workshop on Workflows in Support of Large-Scale Science.

[16]  C. P. Katti,et al.  Cost effective deadline aware scheduling strategy for workflow applications on virtual machines in cloud computing , 2017, J. King Saud Univ. Comput. Inf. Sci..

[17]  Jinjun Chen,et al.  Cost optimization for deadline-aware scheduling of big-data processing jobs on clouds , 2017, Future Gener. Comput. Syst..

[18]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[19]  Radu Prodan,et al.  A Multi-objective Approach for Workflow Scheduling in Heterogeneous Environments , 2012, 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012).

[20]  Aida A. Nasr,et al.  Cost-Effective Algorithm for Workflow Scheduling in Cloud Computing Under Deadline Constraint , 2019 .

[21]  Mohammed F. AlRahmawy,et al.  An extended Intelligent Water Drops algorithm for workflow scheduling in cloud computing environment , 2017 .

[22]  Ahmad M. Manasrah,et al.  Workflow Scheduling Using Hybrid GA-PSO Algorithm in Cloud Computing , 2018, Wirel. Commun. Mob. Comput..

[23]  Huifang Deng,et al.  Elastic Scheduling of Scientific Workflows under Deadline Constraints in Cloud Computing Environments , 2018, Future Internet.

[24]  Mansi Bhonsle,et al.  A Study on Scheduling Methods in Cloud Computing , 2012 .

[25]  Salim Bitam,et al.  Bees Life Algorithm for Job Scheduling in Cloud Computing , 2012 .

[26]  Martin Maier,et al.  Workflow Scheduling in Multi-Tenant Cloud Computing Environments , 2017, IEEE Transactions on Parallel and Distributed Systems.

[27]  Aida A. Nasr,et al.  An Enhanced Task Scheduling in Cloud Computing Based on Hybrid Approach , 2019 .

[28]  Marta Mattoso,et al.  Parallelization of Scientific Workflows in the Cloud , 2014 .

[29]  Rajkumar Buyya,et al.  Scheduling dynamic workloads in multi-tenant scientific workflow as a service platforms , 2018, Future Gener. Comput. Syst..

[30]  Ann L. Chervenak,et al.  Characterizing and profiling scientific workflows , 2013, Future Gener. Comput. Syst..

[31]  Ekaba Bisong.  What Is Cloud Computing , 2019 .

[32]  Ewa Deelman,et al.  WorkflowSim: A toolkit for simulating scientific workflows in distributed environments , 2012, 2012 IEEE 8th International Conference on E-Science.

[33]  Upendra Bhoi,et al.  Enhanced Load Balanced Min-min Algorithm for Static Meta Task Scheduling in Cloud Computing , 2015 .