An Approach to Modelling Information System Availability by Using Bayesian Belief Network

In today’s era of the ubiquitous use of information technology (IT), it is expected that the information systems provide services to end-users on continuous basis, regardless of time and location. This is especially true in organizations where information systems (IS) support real-time critical operations, particularly, in the industries in which these systems must continuously operate 24x7x365. This paper presents a modified Bayesian Belief Network model for predicting IS availability. Based on a thorough review of all IS availability dimensions, we proposed a modified set of determinants. The model is parametrized using probability elicitation process with the participation of experts from the BiH financial sector. The results showed that most influential determinants of the IS availability are a timely and precise definition of the availability requirements, quality of IT operations, management and network. This work is licensed under a&nbsp;Creative Commons Attribution-NonCommercial 4.0 International License.</p

A SURVEY ON AVAILABILITY CALCULATION AND DEFINITION FOR INFORMATION TECHNOLOGY SERVICES

Nowadays companies outsource a lot of their IT resources and capabilities by contracting them with external IT providers. The agreements between providers and customers concerning different quality aspects of the contracted services such as availability, maintainability, security and continuity are formalized through Server Level Agreements (SLAs). One of the most important quality aspects and, at the same time, most difficult to agree is the availability level to be reached. Indeed, the process, methods and types of inputs used by providers and customers to calculate this level are still very informal and in many cases the resulting availability target is not suited to the customer requirements and the provider capabilities. In this boarder, this work presents a survey aimed at identifying and analysing the research literature to analyse what are the most used inputs and methods for availability calculation and prediction as wells as to analyse their applicability in the industry

Modeling Information System Availability by using Bayesian Belief Network Approach

Modern information systems are expected to be always-on by providing services to end-users, regardless of time and location. This is particularly important for organizations and industries where information systems support real-time operations and mission-critical applications that need to be available on 24 x 7 x 365 basis. Examples of such entities include process industries, telecommunications, healthcare, energy, banking, electronic commerce and a variety of cloud services. This article presents a modified Bayesian Belief Network model for predicting information system availability, introduced initially by Franke, U. and Johnson, P. (in article “Availability of enterprise IT systems – an expert based Bayesian model”. Software Quality Journal 20(2), 369-394, 2012) based on a thorough review of several dimensions of the information system availability, we proposed a modified set of determinants. The model is parameterized by using probability elicitation process with the participation of experts from the financial sector of Bosnia and Herzegovina. The model validation was performed using Monte Carlo simulation

PREDICTING AVAILABILITY AND RESPONSE TIMES OF IT SERVICES

When IT service providers adapt their IT system landscapes because of new technologies or changing business requirements, the effects of changes to the quality of service must be considered to fulfill service level agreements. Analytical prediction models can support this process in the service design stages, but dependencies between quality aspects are not taken into account. In this paper, a novel approach for predicting availability and response time of an IT service is developed, which is simulation-based to support dynamic analysis of service quality. The correctness of the model as well as its applicability in a real case can be evaluated. Therefore, this work presents a step towards an analytical framework for predicting IT service quality aspects

Reliability Analysis of Component-Based Systems with Multiple Failure Modes

This paper presents a novel approach to the reliability modeling and analysis of a component-based system that allows dealing with multiple failure modes and studying the error propagation among components. The proposed model permits to specify the components attitude to produce, propagate, transform or mask different failure modes. These component-level reliability specifications together with information about systems global structure allow precise estimation of reliability properties by means of analytical closed formulas, probabilistic modelchecking or simulation methods. To support the rapid identification of components that could heavily affect systems reliability, we also show how our modeling approach easily support the automated estimation of the system sensitivity to variations in the reliability properties of its components. The results of this analysis allow system designers and developers to identify critical components where it is worth spending additional improvement efforts

Run-time efficient probabilistic model checking

Since the inception of discontinuous Galerkin (DG) methods for elliptic problems, there has existed a question of whether DG methods can be made more computationally efficient than continuous Galerkin (CG) methods. Fewer degrees of freedom, approximation properties for elliptic problems together with the number of optimization techniques, such as static condensation, available within CG framework made it challenging for DG methods to be competitive until recently. However, with the introduction of a static-condensation-amenable DG method—the hybridizable discontinuous Galerkin (HDG) method—it has become possible to perform a realistic comparison of CG and HDG methods when applied to elliptic problems. In this work, we extend upon an earlier 2D comparative study, providing numerical results and discussion of the CG and HDG method performance in three dimensions. The comparison categories covered include steady-state elliptic and time-dependent parabolic problems, various element types and serial and parallel performance. The postprocessing technique, which allows for superconvergence in the HDG case, is also discussed. Depending on the direct linear system solver used and the type of the problem (steady-state vs. time-dependent) in question the HDG method either outperforms or demonstrates a comparable performance when compared with the CG method. The HDG method however falls behind performance-wise when the iterative solver is used, which indicates the need for an effective preconditioning strategy for the method

A Syntactic-Semantic Approach to Incremental Verification

Software verification of evolving systems is challenging mainstream methodologies and tools. Formal verification techniques often conflict with the time constraints imposed by change management practices for evolving systems. Since changes in these systems are often local to restricted parts, an incremental verification approach could be beneficial. This paper introduces SiDECAR, a general framework for the definition of verification procedures, which are made incremental by the framework itself. Verification procedures are driven by the syntactic structure (defined by a grammar) of the system and encoded as semantic attributes associated with the grammar. Incrementality is achieved by coupling the evaluation of semantic attributes with an incremental parsing technique. We show the application of SiDECAR to the definition of two verification procedures: probabilistic verification of reliability requirements and verification of safety properties.Comment: 22 pages, 8 figures. Corrected typo

Strategy for scalable scenarios modeling and calculation in early software reliability engineering

System scenarios derived from requirements specification play an important role in the early software reliability engineering. A great deal of research effort has been devoted to predict reliability of a system at early design stages. The existing approaches are unable to handle scalability and calculation of scenarios reliability for large systems. This paper proposes modeling of scenarios in a scalable way by using a scenario language that describes system scenarios in a compact and concise manner which can results in a reduced number of scenarios. Furthermore, it proposes a calculation strategy to achieve better traceability of scenarios, and avoid computational complexity. The scenarios are pragmatically modeled and translated to finite state machines, where each state machine represents the behaviour of component instance within the scenario. The probability of failure of each component exhibited in the scenario is calculated separately based on the finite state machines. Finally, the reliability of the whole scenario is calculated based on the components’ behaviour models and their failure information using modified mathematical formula. In this paper, an example related to a case study of an automated railcar system is used to verify and validate the proposed strategy for scalability of system modeling