Elsevier

Computers in Industry

Volume 68, April 2015, Pages 65-77
Computers in Industry

Behavioral modeling and automated verification of a Cloud-based framework to share the knowledge and skills of human resources

https://doi.org/10.1016/j.compind.2014.12.007Get rights and content

Highlights

  • •

    Introducing the Expert Cloud to find, share, exploit, manage and employ the HR.

  • •

    Describing the state diagram of the Expert Cloud.

  • •

    Defining a Kripke structure of the Expert Cloud to provide the relationship between the expanded model and the original state diagram structure.

  • •

    Defining the expected properties of the structure and composition of the Expert Cloud by means of temporal logic languages.

  • •

    Implementing the structure and composition of the Expert Cloud by NuSMV model checker.

Abstract

Expert Cloud as a new class of Cloud computing systems by employing the Internet infrastructures and Cloud computing concepts enables its users to request the skill, knowledge and expertise of human resources without any information about their location. It makes the communication between the HRs more efficient, reduces the cost of service, increases the variety of knowledge and information, facilitates employment of the HR in organizations, decreases customer response time and improves the service delivery methods. However, one facet that is still being less cared and that may introduce potential errors and faults regards the architectural problems and components analysis of Expert Cloud. Therefore, in this paper, we verify and check the specification, composition and architecture of the Expert Cloud via NuSMV model checker, Argo UML and Rebeca Verifier tools. The approach extracts the checking properties in the form of LTL and CTL formulas of control behaviors and automatically verifies the properties in operational behaviors. Also, experimental results indicate that the system is reachable, fair and deadlock-free.

Introduction

Cloud computing provides significant financial advantages (pay only for what you use) for organizations and enterprises while offering high-level collaboration possibilities [1]. It offers numerous advantages for data and software sharing and thus making the management of complex IT systems much simpler [2]. One of the important features of Cloud computing that makes it very popular is its ability for connecting the human society, information space and the physical world [3]. With Cloud computing, new IT services emerge from the convergence of business and technology perspectives which provide users access to IT resources anytime and anywhere, including other devices like mobile phones and tablet computers [4]. Cloud resources include a variety of resources such as storage, processing, memory, network bandwidth and virtual machines. Three forms of such resources are usually well-known: Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS) [5]. But Human Resources (HRs) are not considered in details as Cloud resources in the literatures; however, these resources are very important in many fields of science and technology [6]. Furthermore, they do many activities by means of the Internet and enjoy the convenience of it [7].

Expert Cloud as a new class of Cloud systems enables all human societies such as universities, organizations, businesses, industries, colleges and institutes to share and pool the knowledge, skills and experiences of their HR to meet the demands of today's competitive era. Also, any person who is not a member of any institutes can join the Expert Cloud to share and receive knowledge, skills and experiences with and from other HRs. Therefore, in Expert Cloud there are many HRs with different profession and expertise, including engineers, accountants, police officers, teachers, doctors, painters, musicians, mathematicians, chemists, professors, students and so on. These members (or resources) are from industries, universities, organizations, business, colleges and institutes in different countries and regions.

Model checking and automated verification of the Expert Cloud are very important issues. In the past few years, the research community has been trying to tackle this issue by proposing model-driven approaches based on formal methods (such as [8], [9], [10], [11], [12], [13], [14], [15]). Formal methods describe the verification and specification of the systems using logical and mathematical techniques. Especially, software development communities are increasingly adopting formal techniques to perform different development activities such as requirement definition and analysis, modeling and model transformation, testing and property verification. For many distributed applications the need for model checking becomes clear and outweighs the doubts cast over its use [16]. In general, what we care the most about the verification and specification of Expert Cloud models is determining whether some reachability, fairness, deadlock properties, usually described in temporal logics such as Linear Temporal Logic (LTL) and Computation Tree Logic (CTL), are satisfied. Reachability analysis is at the heart of any verification process. The standard form of reachability analysis asks to compute/approximate the probability of all system paths that start from a given initial state and visit a target state set [17]. Fairness prevents infinite behaviors that are considered unrealistic and are often necessary to establish liveness properties [18]. A deadlock occurs if at least one component in a system is in a nonterminal state. Thus, the entire system has come to a halt, whereas at least one component has the possibility to continue to operate [18]. In the case of Expert Cloud which is defined by a set of layers and related components, the reasoning about the use of verification, temporal logics and model checking techniques is similar. Therefore, in this paper, to verify the structure and composition of the Expert Cloud and analyze its properties, the first step is describing the state diagram of the Management and Application layers of the Expert Cloud; the second step is defining a Kripke structure with marked states which provides the relationship between the expanded model and the original state diagram structure; the third step is defining the expected properties of the structure and composition of the Expert Cloud by means of temporal logic languages; and at the end, the considered model is implemented by NuSMV model checker.

The rest of this paper is organized as follows: the related works are reviewed in Section 2, Section 3 presents the Expert Cloud and its architecture to verify whether a model satisfies a given specification. In Section 4, model checking of Expert Cloud is provided. Section 5 represents system model by Kripke structure to facilitate the verification of a rich set of properties and to check the properties of Expert Cloud such as reachability, fairness, and deadlock the logical properties by using temporal logics are provided in Section 6. Translation of the reduced model into SMV code is presented in Section 7 and in the last section we will conclude the paper.

Section snippets

Related works

There have been many formal methods and verification techniques developed and applied to ensure the correctness of the systems. In this section some popular and new researches that investigate the application of verification methods and model checking techniques for various system are reviewed.

Development and verification of liveness properties of reactive systems using the Event-B method is investigated by Mosbahi and Jemni Ben Ayed [19]. They extended the expressivity and the semantics of a B

Expert Cloud

Expert Cloud enables all human societies to share and pool the knowledge, skills and experiences of their HR. Furthermore, any person who is not a member of any institutes can join the Expert Cloud to share and receive knowledge, skills and experiences with other HRs. The members of Expert Cloud are spread over geographic, governmental and organizational boundaries and collaborate together. Unlike common online networks, in the Expert Cloud many important issues such as trust, reputation,

Model checking of Expert Cloud

From this section to evaluate the architecture of the Expert Cloud, such as many papers that introduce the new systems, especially in Web applications, we provide some models and concepts to verify and check the component of the proposed architecture by formal and automated methods.

While there is a range of different techniques for automated verification, model checking is particularly well-suited for the automated verification of finite-state systems, both for software and hardware [26]. Model

Kripke structure of the model

A Kripke structure is a nondeterministic finite state machine that is used in model checking to represent system model [30]. It is a type of the state transition graph to capture the intuition about the behavior of systems which is used to validate the systems [31].

Definition 2

A Kripke structure is a 5-tuple KSEC=(S,s0,Tk,R,AP), where:

  • •

    S is a finite set of states,

  • •

    s0∈S is an initial state,

  • •

    AP is a set of atomic proposition,

  • •

    Tk⊆S×S is a transition relation, for which it holds that ∀s∈S:∃s′∈S:(s,s′)∈Tk,

  • •

    R:S→2AP

Checking the properties

In this section to facilitate the verification of a rich set of properties and to check the properties of the Expert Cloud such as reachability, fairness, and deadlock the logical properties by using temporal logics such as LTL [26], [33], [34] and CTL [35], [36] are presented. LTL is a formal specification language for specifying and verifying properties of the systems which is widely used in formal verification tools such as the model checkers SPIN [37] and NuSMV4 [32],

Implementation

In the previous sections, we clarified and explained the formal modeling and verification of Expert Cloud by checking-based approaches through the following steps:

  • 1.

    Presenting the state diagram of the Expert Cloud.

  • 2.

    Converting state diagram to Kripke structure.

  • 3.

    Reducing the obtained Kripke model.

Now, we must translate the reduced model into SMV code. Rebeca (reactive object language) [41], [42], [43], [44] as an actor-based language is used to translate the reduced model into SMV code. It is

Conclusions and future works

The Expert Cloud not only finds, exploits, manages and employs the HRs, but also shares their knowledge, skills and experiences which leads to HR virtualization. The Expert Cloud makes the communication between the HR more efficient, reduces the expense and cost of service, increases the variety of knowledge and information, facilitates employment of the HR in organizations, decreases customer response time and improves the service delivery methods. The architecture of Expert Cloud lies on four

Nima Jafari Navimipour received his B.S. in computer engineering, software engineering, from Tabriz Branch, Islamic Azad University, Tabriz, Iran, in 2007; the M.S. in computer engineering, computer architecture, from Tabriz Branch, Islamic Azad University, Tabriz, Iran, in 2009; the Ph.D.in computer engineering, computer architecture, from Science and Research Branch, Islamic Azad University, Tehran, Iran in 2014. He is assistance professor in the Department of Computer Engineering at Tabriz

References (44)

  • A. Zengin et al.

    Formal verification and validation with DEVS-Suite: OSPF Case study

    Simulation Modelling Practice and Theory

    (2012)
  • M. Haydar

    A formal approach for run-time verification of web applications using scope-extended LTL

    Information and Software Technology

    (2013)
  • M.L. Bujorianu et al.

    State constrained reachability for stochastic hybrid systems

    Nonlinear Analysis: Hybrid Systems

    (2011)
  • O. Mosbahi et al.

    A formal approach for the development of reactive systems

    Information and Software Technology

    (2011)
  • J. Bentahar

    Symbolic model checking composite Web services using operational and control behaviors

    Expert Systems with Applications

    (2013)
  • M. El-Menshawy

    Verifying conformance of multi-agent commitment-based protocols

    Expert Systems with Applications

    (2013)
  • A. Souri et al.

    Behavioral modeling and formal verification of a resource discovery approach in Grid computing

    Expert Systems with Applications

    (2014)
  • K.Y. Rozier

    Linear temporal logic symbolic model checking

    Computer Science Review

    (2011)
  • P. Zhang et al.

    A classification and comparison of model checking software architecture techniques

    Journal of Systems and Software

    (2010)
  • N. Bolloju et al.

    A knowledge-based object modeling advisor for developing quality object models

    Expert Systems with Applications

    (2012)
  • S. Salamah et al.

    Validated templates for specification of complex LTL formulas

    Journal of Systems and Software

    (2012)
  • W. Li

    Resource virtualization and service selection in cloud logistics

    Journal of Network and Computer Applications

    (2013)

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    Nima Jafari Navimipour received his B.S. in computer engineering, software engineering, from Tabriz Branch, Islamic Azad University, Tabriz, Iran, in 2007; the M.S. in computer engineering, computer architecture, from Tabriz Branch, Islamic Azad University, Tabriz, Iran, in 2009; the Ph.D.in computer engineering, computer architecture, from Science and Research Branch, Islamic Azad University, Tehran, Iran in 2014. He is assistance professor in the Department of Computer Engineering at Tabriz Branch, Islamic Azad University, Tabriz, Iran. He has published more than 30 papers in various journals and conference proceedings. His research interests include cloud computing, grid systems, traffic control, computational intelligence, evolutionary computing, and wireless networks.

    Ahmad Habibizad Navin was born in 1971. He received his H.N.D. in electronic in1997 and B.Sc. degree in applied mathematics from Tabriz University, Tabriz, Iran, in 1999. He received his M.Sc. degree in computer architecture from Science and Research Branch of Islamic Azad University, Tehran, Iran, in 2003 and his Ph.D. in computer engineering from Science and Research Branch, Islamic Azad University, Tehran, Iran, in 2007. His research interest includes computer architecture, data-oriented approach, robotic, soft computing and probability and statistic.

    Amir Masoud Rahmani received his B.S. in computer engineering from Amir Kabir University, Tehran, in 1996, the M.S. in computer engineering from Sharif University of Technology, Tehran, in 1998 and the Ph.D. degree in computer engineering from IAU University, Tehran, in 2005. He is the postdoctoral researcher at the University of Algarve. He also is associate professor in the Department of Computer and Mechatronics Engineering at the IAU University. He is the author/co-author of more than 80 publications in technical journals and conferences. He served on the program committees of several national and international conferences. His research interests are in the areas of distributed systems, ad hoc and sensor wireless networks, scheduling algorithms and evolutionary computing.

    Mehdi Hosseinzadeh was born in Dezfulin 1981. Received his B.Sc. in Computer Hardware Engineering from Islamic Azad University, Dezful Branch, in 2003. He also received the M.Sc. and Ph.D. degrees in Computer Systems Architecture from Science and Research Branch, Islamic Azad University, Tehran, Iran in 2005 and 2008, respectively. He is currently Assistant Professor in Department of Computer Engineering of Science and Research Branch of Islamic Azad University, Tehran, Iran. His research interests are computer arithmetic with emphasis on residue number system, cryptography, network security and e-commerce.

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