Process mining‐constrained scheduling in the hybrid cloud

Hybrid cloud, typically a combination of public and private cloud deployment models, is a rising paradigm due to the benefits it offers: full control of data and applications in the private cloud and elastic computing resource availability in the public cloud. This combination however brings an extra layer of complexity that can potentially erode the benefits and present serious challenges if not managed well. Among the challenges, ensuring business constraint compliance across the combination of cloud deployment models is a growing concern. Our article brings a sensitive, data‐ and process‐aware framework to bear on task scheduling in hybrid clouds with compliance to business constraints. Our proposed approach utilizes data from a real hybrid cloud‐based hospital billing system that is governed by complex and dynamic data processing rules. Our system successfully employs a process mining controlled algorithm to schedule tasks in the hybrid cloud to comply with the given set of business constraints.

[1]  Rajkumar Buyya,et al.  SLA-Aware Provisioning and Scheduling of Cloud Resources for Big Data Analytics , 2014, 2014 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM).

[2]  Hwangnam Kim,et al.  MR-CloudSim: Designing and implementing MapReduce computing model on CloudSim , 2012, 2012 International Conference on ICT Convergence (ICTC).

[3]  Wil M. P. van der Aalst,et al.  Business Process Management, Models, Techniques, and Empirical Studies , 2000 .

[4]  R. Rajeswara Rao,et al.  A cost-effective SLA-aware scheduling for hybrid cloud environment , 2016, 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC).

[5]  XiaoFeng Wang,et al.  Sedic: privacy-aware data intensive computing on hybrid clouds , 2011, CCS '11.

[6]  Sam Jabbehdari,et al.  An autonomic resource provisioning approach for service-based cloud applications: A hybrid approach , 2018, Future Gener. Comput. Syst..

[7]  Kenneth Kwame Azumah,et al.  Scheduling in the Hybrid Cloud Constrained by Process Mining , 2018, 2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom).

[8]  Paola Mello,et al.  Monitoring business constraints with the event calculus , 2013, ACM Trans. Intell. Syst. Technol..

[9]  Paola Mello,et al.  Map Reduce Autoscaling over the Cloud with Process Mining Monitoring , 2016, CLOSER.

[10]  Sarbjeet Singh,et al.  Deadline and cost based workflow scheduling in hybrid cloud , 2013, 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[11]  Rajkumar Buyya,et al.  CloudSim: A Novel Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services , 2009, ArXiv.

[12]  Mauricio A. Saca.  Refactoring improving the design of existing code , 2017, 2017 IEEE 37th Central America and Panama Convention (CONCAPAN XXXVII).

[13]  Gregory Levitin,et al.  Optimal data partitioning in cloud computing system with random server assignment , 2017, Future Gener. Comput. Syst..

[14]  Sanjay Ghemawat,et al.  MapReduce: simplified data processing on large clusters , 2008, CACM.

[15]  Mário M. Freire,et al.  CloudSim Plus: A cloud computing simulation framework pursuing software engineering principles for improved modularity, extensibility and correctness , 2017, 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM).

[16]  Eric Wohlstadter,et al.  Partitioning of web applications for hybrid cloud deployment , 2014, Journal of Internet Services and Applications.

[17]  Marin Litoiu,et al.  Partitioning applications for hybrid and federated clouds , 2012, CASCON.

[18]  Wei Yan,et al.  Optimal Scheduling Algorithm of MapReduce Tasks Based on QoS in the Hybrid Cloud , 2016, 2016 17th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT).

[19]  Salman Yussof,et al.  A Comparative Analysis of Task Scheduling Algorithms of Virtual Machines in Cloud Environment , 2015, J. Comput. Sci..

[20]  Bogdan Nicolae,et al.  On Exploiting Data Locality for Iterative MapReduce Applications in Hybrid Clouds , 2016, 2016 IEEE/ACM 3rd International Conference on Big Data Computing Applications and Technologies (BDCAT).

[21]  Wil M. P. van der Aalst,et al.  DECLARE: Full Support for Loosely-Structured Processes , 2007, 11th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2007).

[22]  Marek J. Sergot,et al.  A logic-based calculus of events , 1989, New Generation Computing.

[23]  Christian Baun,et al.  A Taxonomy Study on Cloud Computing Systems and Technologies , 2011 .

[24]  Murat Kantarcioglu,et al.  SEMROD: Secure and Efficient MapReduce Over HybriD Clouds , 2015, SIGMOD Conference.

[25]  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..

[26]  Rajkumar Buyya,et al.  Scaling MapReduce Applications Across Hybrid Clouds to Meet Soft Deadlines , 2013, 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA).

[27]  Wil M. P. van der Aalst,et al.  A Declarative Approach for Flexible Business Processes Management , 2006, Business Process Management Workshops.

[28]  Victor I. Chang,et al.  A load-aware resource allocation and task scheduling for the emerging cloudlet system , 2018, Future Gener. Comput. Syst..

[29]  S. Swamynathan,et al.  Structure aware resource estimation for effective scheduling and execution of data intensive workflows in cloud , 2018, Future Gener. Comput. Syst..

[30]  Jie Cao,et al.  Improving task scheduling with parallelism awareness in heterogeneous computational environments , 2019, Future Gener. Comput. Syst..

[31]  Félix García Carballeira,et al.  A heterogeneous mobile cloud computing model for hybrid clouds , 2018, Future Gener. Comput. Syst..