Elsevier

Future Generation Computer Systems

Volume 112, November 2020, Pages 751-766
Future Generation Computer Systems

Toward a correct and optimal time-aware cloud resource allocation to business processes

https://doi.org/10.1016/j.future.2020.06.018Get rights and content

Highlights

  • Formal verification of time-constrained cloud resource allocation to business process.

  • Cost optimization of business process deployment in cloud resources using binary linear programming.

  • Experimental results show the technical doability of our approach.

Abstract

Cloud is an increasingly popular computing paradigm that provides on-demand services to organizations for deploying their business processes over the Internet as it reduces their needs to plan ahead for provisioning resources. Cloud providers offer competitive pricing strategies (e.g., on-demand, reserved, and spot) specified based on temporal constraints to accommodate organizations’ changing and last-minute demands. Despite their varieties and benefits to optimize business process deployment cost, using those pricing strategies can lead to violating time constraints and exceeding budget constraints due to inappropriate decisions when allocating cloud resources to business processes. In this paper, we present an approach to guarantee a correct and optimal time-aware allocation of cloud resources to business processes. Correct because time constraints on these processes are not violated. And, optimal because the deployment cost of these processes is minimized. For this purpose, our approach uses timed automata to formally verify the matching between business processes’ temporal constraints and cloud resources’ time availabilities and linear programming to optimize deployment costs. Experiments demonstrate the technical doability of our proposed approach.

Introduction

Cloud computing is an attractive operational model for organizations that wish, among other reasons, to reduce upfront investment on Information and Communication Technologies (ICT) and to tap into hardware and software resources of cloud providers in return of a fee. Cloud is known for resource elasticity and pay-per-use model making it perfect for organizations that witness a surge of activities during particular periods of the year. For instance, during 2017 Christmas Amazon.com had to temporarily cope with 280 millions online retail transactions calling for immediate provisioning of resources that luckily were released once the load went back to normal.1

In today’s economic world, attracting and retaining customers constitutes a challenge. Indeed, many cloud providers offer competitive pricing strategies (e.g., on-demand, reserved, and spot) to accommodate users’ changing and last-minute demands. However this price variation puts more pressure on cloud providers who need to ensure resource availability on a short-notice, for example. Organizations that “wrestle” with time like shipping need to respond quickly to any unforeseen event and hence, could call upon cloud providers anytime. Indeed, cloud computing allows organizations to optimize their Business Processes (BPs, aka know-how) thanks to different techniques associated with cloud computing such as virtualization and load balancing. But, this optimization should not happen on the expense of increasing operation costs [1] and/or violating time constraints, for example. “Striking” the right balance between cloud resources’ pricing strategies and BPs’ time constraints is one of our objectives in this paper. We achieve this objective by ensuring the temporal correctness of cloud-aware BPs, finding an optimal-deployment cost for these BPs, and validating the deployment of these BPs as well.

Some research works on temporal verification of BPs [2], [3] and allocation of cloud resources to BPs [1], [4] are reported in the literature. Nevertheless, formal satisfaction of BPs’ temporal constraints with respect to cloud resources’ availabilities and identification of optimal deployment cost of BPs over these resources, is either barely touched upon or handled on a case-by-case basis. Our previous work reported in [5] and [6] is one step toward a formal specification and verification of allocating cloud resources to BPs. To this end we used timed-automata networks to check BPs’ time-constraint behaviors like reachability and deadlock-free [7]. We also used linear programming to optimize the deployment cost of these BPs [8]. Although, due to the complexity of linear programming, handling BPs with large number of activities (200 as per our work in [8]) turned out cumbersome and inefficient. In this paper, first, we extend our verification approach presented in [5], [6] to check more advanced properties such as liveness to guarantee better correctness of time-constrained, cloud-aware BPs. Second, we improve our optimization approach presented in [8] to reduce the deployment cost of cloud resources when running more complex and “large” BPs.

Our contributions are, but not limited to, (i) developing a set of rules to transform BP into timed-automata as a step toward a correct time-aware cloud resource allocation in BP, (ii) formalizing the optimization problem as a mathematical model to minimize the deployment cost of time-constrained BPs, and (iii) evaluating the technical doability of our approach for ensuring the correctness and optimization of time-aware cloud resource allocation to BPs.

The remainder of this paper is organized as follows: Section 2 presents the necessary concepts related to our work. Section 3 presents a real use case from France Telecom Orange labs. Sections 4 Verification of cloud resources allocation correctness, 5 Cost optimization of BP deployment cost discuss the verification and optimization steps, respectively. Section 6 details the evaluation. Section 7 presents some related works. Finally, Section 8 presents our conclusions and future works.

Section snippets

Preliminaries

In this section, we present the foundations upon which our approach for the correctness and optimization of BPs over cloud resources, is built. First, we define cloud resources along with their pricing strategies and then, present types of temporal constraints that BPs’ activities could be subject to. Finally, we present timed automata elements.

Case study

For illustration purposes, we consider “supervision service” BP from France Telecom/Orange labs [20], Fig. 1. First, we model this BP’s process model in Business Process Model Notation(BPMN) [21], the standard for BP modeling, and then specify cloud resources and their pricing strategies, and activities’ temporal constraints.

Table 2 lists “supervision service” BP’s temporal constraints along with the capacities expressed in terms of RAM and vCPU that each activity in this process requires. For

Verification of cloud resources allocation correctness

We present, in this section, the necessary steps that we took to ensure the correctness of our time-constrained cloud resources allocation to BPs. First, we developed a set of rules to automatically transform time-constrained BPMN models into a network of timed automata. Then, we formally verified this network against advanced properties known in the community as liveness, deadlock free, and deadline.

Cost optimization of BP deployment cost

We discuss how to optimize the cost of BP deployment in cloud resources. To this end, we use Binary Linear Program (BLP) to define both an objective function and constraints that would guide the optimization. BLP is known for its simplicity, flexibility, and extensive modeling capability [25].

Evaluation

We present the technical doability of our approach for ensuring the correctness and optimization of time-aware cloud resources allocation to BPs. This doability started with model checking to verify some Computation Tree Logic (CTL) properties that support matching temporal constraints of both activities and cloud resources. Then, we analyzed the impact of verification on the optimization approach in terms of objective function and response time. Finally, comparing our results to those of

Related work

In this section, we discuss some works related to formal verification (Section 7.1) and optimization (Section 7.2) of BPM in the context of cloud.

Conclusion

In this paper, we presented an approach for allocating cloud resources to activities of BPs that are subject to time constraints. This allocation needs to be, at run-time, both correct to avoid blockage and optimized to avoid excessive deployment costs. To achieve verification, we developed rules that transform BPMN-based BPs’ process models into a network of timed-automata so that proper matching of activities’ needs of resources to cloud resources is ensured despite the time constraints. And,

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Rania Ben Halima received the graduation degree in computer science engineer from the National Engineering School of Sfax (ENIS), Tunisia, in 2014. She is currently working toward the PhD degree under the joint supervision of Prof. Walid Gaaloul, a professor in the Telecom SudParis School, France, and Prof. Mohamed Jmaiel, a professor in the National Engineering School of Sfax (ENIS), Tunisia. Her research interests include in business process management, temporal constraints, cloud computing

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  • Cited by (0)

    Rania Ben Halima received the graduation degree in computer science engineer from the National Engineering School of Sfax (ENIS), Tunisia, in 2014. She is currently working toward the PhD degree under the joint supervision of Prof. Walid Gaaloul, a professor in the Telecom SudParis School, France, and Prof. Mohamed Jmaiel, a professor in the National Engineering School of Sfax (ENIS), Tunisia. Her research interests include in business process management, temporal constraints, cloud computing and cost optimization.

    Slim Kallel is an associate professor at the University of Sfax in Tunisia. He got his Ph.D. from the Darmstadt University of Technology (Germany) and he obtained an engineering degree and a master’s degree in computer science from the National Engineering School of Sfax (Tunisia). He serves as a teacher in different institutions at the University of Sfax. He is also the coordinator of the Master on Data Science and Engineering at the Faculty of Economics and Management of Sfax. Slim has also participated in various national, Mediterranean, and European projects. Her research interest includes Cloud computing, business process management, and software architectures. Her current research topics include Internet-of Things and blockchain. More details are available on his home page: http://www.redcad.tn/members/kallel.

    Walid Gaaloul is a professor at Telecom SudParis an engineering school in the field of Information and Communication Technology. He is member of the SIMBAD group part of the Computer Science Department of Telecom SudParis and the CNRS research laboratory SAMOVAR. Before joining Telecom SudParis, Walid was a researcher at the Digital Enterprise Research Institute (DERI) and an adjunct lecturer in the National University of Ireland, Galway (NUIG). He holds an M.S. (2002) and a Ph.D. (2006) in computer science from the University of Lorraine, France, and a habilitation (2014) from Pierre et Marie Curie University, Paris, France. He was a junior researcher in the Lorraine Laboratory of IT Research and its Applications (LORIA-INRIA) and a teaching assistant in the University of Lorraine, France. His research interests are on Business Process Management, Process Mining, Cloud Computing, Service Oriented Computing. Walid Gaaloul has published over 100 research papers in these domains. He serves as program committee member and reviewer at many international journals and conferences and has been participating in several national and European research projects.

    Zakaria Maamar is Professor in the College of Technological Innovation at Zayed University, Dubai, United Arab Emirates. His research interests include Internet-of-Things, social computing, and business process management. Zakaria has extensively published in different peer reviewed journals and conferences, regularly serves on the program and organizing committees of several international conferences and workshops, and serves on the editorial boards of many international journals. Zakaria has a PhD in computer science from Laval University, Quebec City, Canada.

    Mohamed Jmaiel obtained his diploma of engineer in Computer Science from Kiel (Germany) University in 1992 and his Ph.D. from the Technical University of Berlin in 1996. He joined the National School of Engineers of Sfax (Tunisia) as Assistant Professor of Computer Science in 1995. He became an Associate Professor in 1997 and full Professor in January 2009. He participated to the initiation of many graduate courses at the University of Sfax. His current research areas include software engineering of distributed systems, formal methods in model driven architecture, self-adaptive and pervasive systems, autonomic middleware. He conducted many research projects and published more than 220 regular and invited papers in international conferences and journals. He was director of the National Engineering School of Sfax (ENIS), from 2011 to 2014. Currently, he is director of the digital research center at the Technopark of Sfax.

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