Strategic Engineering for Cloud Computing and Big Data Analytics

Sustaining and optimising complex systems are often challenging problems as such systems contain numerous variables that are interacting with each other in a nonlinear manner. Application of integrated sustainability principles in a complex system (e.g., the Earth’s global climate, social organisations, Boeing’s supply chain, automotive products and plants’ operations, etc.) is also a challenging process. This is due to the interactions between numerous parameters such as economic, ecological, technological, environmental and social factors being required for the life assessment of such a system. Functionality and flexibility assessment of a complex system is a major factor for anticipating the systems’ responses to changes and interruptions. This study outlines generic mathematical and computational approaches to solving the nonlinear dynamical behaviour of complex systems. The goal is to explain the modelling and simulation of system’s responses experiencing interaction change or interruption (i.e., interactive disruption). Having this knowledge will allow the optimisation of systems’ efficiency and would ultimately reduce the system’s total costs. Although, many research works have studied integrated sustainability behaviour of complex systems, this study presents a generic mathematical and computational framework to explain the behaviour of the system following interactive changes and interruptions. Moreover, a dynamic adaptive response of the global system over time should be taken into account. This dynamic behaviour can capture the interactive behaviour of components and sub-systems within a complex global system. Such assessment would benefit many systems including information systems. Due to emergence and expansion of big data analytics and cloud computing systems, such life-cycle assessments can be considered as a strategic planning framework before implementation of such information systems.

In the life-cycle management of systems under continuous deterioration, studying the sensitivity analysis of the optimised preventive maintenance decisions with respect to the changes in the model parameters is of a great importance. Since the calculations of the mean cost rates considered in the preventive maintenance policies are not sufficiently robust, the corresponding maintenance model can generate outcomes that are not robust and this would subsequently require interventions that are costly. This chapter presents a computationally efficient decision-theoretic sensitivity analysis for a maintenance optimisation problem for systems/structures/assets subject to measurable deterioration using the Partial Expected Value of Perfect Information (PEVPI) concept. Furthermore, this sensitivity analysis approach provides a framework to quantify the benefits of the proposed maintenance/replacement strategies or inspection schedules in terms of their expected costs and in light of accumulated information about the model parameters and aspects of the system, such as the ageing process. In this paper, we consider random variable model and stochastic Gamma process model as two well-known probabilistic models to present the uncertainty associated with the asset deterioration. We illustrate the use of PEVPI to perform sensitivity analysis on a maintenance optimisation problem by using two standard preventive maintenance policies, namely age-based and condition-based maintenance policies. The optimal strategy of the former policy is the time of replacement or repair and the optimal strategies of the later policy are the inspection time and the preventive maintenance ratio. These optimal strategies are determined by minimising the corresponding expected cost rates for the given deterioration models’ parameters, total cost and replacement or repair cost. The robust optimised strategies to the changes of the models’ parameters can be determined by evaluating PEVPI’s which involves the computation of multi-dimensional integrals and is often computationally demanding, and conventional numerical integration or Monte Carlo simulation techniques would not be helpful. To overcome this computational difficulty, we approximate the PEVPI using Gaussian process emulators.

Focusing on safety attributes becomes an essential practice towards the safety critical software system (SCSS) development. The system should be error free for a perfect decision-making and subsequent operations. This paper presents an analysis on error propagation in the modules through a novel safety metric known as SM_EP, which can be characterized depending on the performance rate of the working module. We propose a framework for the analysis of occurrence of error in various modules and the intensity of it is quantified through probabilistic model and universal generating function technique.

System reliability assessment is a critical task for design engineers. Identifying the least reliable components within a to-be system would immensely assist the engineers to improve designs. This represents a pertinent example of data-informed decision-making (DIDM). In this chapter, we have looked into the theoretical frameworks and the underlying structure of system reliability assessment using prior elicitation and analysis of imprecise judgements. We consider the issue of imprecision in the expert’s probability assessments. We particularly examine how imprecise assessments would lead to uncertainty. It is crucial to investigate and assess this uncertainty. Such an assessment would lead to a more realistic representation of the expert’s beliefs, and would avoid artificially precise inferences. In other words, in many of the existing elicitation methods, it cannot be claimed that the resulting distribution perfectly describes the expert’s beliefs. In this paper, we examine suitable ways of modelling the imprecision in the expert’s probability assessments. We would also discuss the level of uncertainty that we might have about an expert’s density function following an elicitation. Our method to elicit an expert’s density function is nonparametric (using Gaussian Process emulators), as introduced by Oakley and O’Hagan [1]. We will modify this method by including the imprecision in any elicited probability judgement. It should be noticed that modelling imprecision does not have any impact on the expert’s true density function, and it only affects the analyst’s uncertainty about the unknown quantity of interest. We will compare our method with the method proposed in [2] using the ‘roulette method’. We quantify the uncertainty of their density function, given the fact that the expert has only specified a limited number of probability judgements, and that these judgements are forced to be rounded. We will investigate the advantages of these methods against each other. Finally, we employ the proposed methods in this paper to examine the uncertainty about the prior density functions of the power law model’s parameters elicited based on the imprecise judgements and how this uncertainty might affect our final inference.

Reliability of software is the capability of itself to maintain its level of stability under specified conditions for a specified period of time. The reliability of software is influenced by process and product factors. Among them, the design mechanism has a considerable impact on overall quality of the software. A well-designed internal structure of software is a required for ensuring better reliable. Based on this, we propose a framework for modeling the influence of design metrics on one of the external quality factors, reliability of the software. Here, multivariate regression analysis is applied to arrive a formal model, which is the linear combination of weighted polynomial equations. These estimation equations are formed based on the intricate relationship between the design properties of software system as represented by CK metric suite and the reliability.

Cloud Systems Simulation Modelling (CSSM) combines three different topic areas in software engineering, apparent in its constituting keywords: cloud system, simulation and modelling. Literally, it involves the simulation of various units of a cloud system—functioning as a holistic body. CSSM addresses various drawbacks of physical modelling of cloud systems, such as time of setup, cost of setup and expertise required. Simulation of cloud systems to explore potential cloud system options for ‘smarter’ managerial and technical decision-making help to significantly eradicate waste of resources that would otherwise be required for physically exploring cloud system behaviours. This chapter provides an in-depth overview of System Dynamics, the most widely adopted implementation of CSSM. This chapter provides an in-depth background to CSSM and its applicability in cloud software engineering—providing a case for the apt suitability of System Dynamics in investigating cloud software projects. It discusses the components of System Dynamic models in CSSM, data sources for effectively calibrating System Dynamic models, role of empirical studies in System Dynamics for CSSM, and the various methods of assessing the credibility of System Dynamic models in CSSM.

Software Process Simulation Modelling has gained recognition in the recent years in addressing a variety of cloud software project development, software risk management and cloud software project management issues. Using Software Process Simulation Modelling, the investigator draws up real-world problems to address in the software domain, and then a simulation approach is used to develop as-is/to-be models—where the models are calibrated using credible empirical data. The simulation outcome of such cloud system project models provide an economic way of predicting implications of various decisions, helping to make with effective and prudent decision-making through the process. This chapter provides an overview of Software Process Simulation Modelling and the present issues it addresses as well as the motivation for its being—particularly related to agile cloud software projects. This chapter also discusses its techniques of implementation, as well as its applications in solving real-world problems.

Virtualisation is one of the key concepts that allows for abstraction of hardware and software resources on the cloud. Virtualisation has been employed for all aspects of cloud computing including big data processing. There have been arguments based on recent research that indicate that computational efficiency could be more efficient via virtualisation compared to their physical counterparts. A data centre not only represents physical resources but also the collection of virtualised entities that in essence, form virtual networks. Methods to monitor these virtual entities for attributes such as network traffic, performance, sustainability, etc., usually tend to be deployed on ad hoc basis. Understanding the network related attributes of virtualised entities on the cloud will help take critical decisions on management activities such as timing, selection and migration of virtual machines (VMs). In corporate physical data networks, it could have been achieved using four of the network management functional areas, i.e., performance management, configuration management, accounting management and fault management. This chapter discusses, with examples, at how network management principles could be contextualised with virtualisation on the cloud. In particular, the discussion will be centred on the application of Simple Network Management Protocol (SNMP) for gathering behavioural statistics from each virtualised entity.

Cloud-based services provide a number of benefits in areas such as scalability, flexibility, availability and productivity for any organisation. These benefits can be further enriched by considering opportunities, which will allow for organisational enrichment at a strategic level. It is important to align businesses, ICT and security strategies to enable more beneficial outputs overall. Moving to cloud computing needs to consider strategic objectives of businesses essentially how IT cost impacts on the needs of future developments for businesses and IT departments within large organisations. Strategically implementing a cloud strategy can also be considered as a disruptive technology. Disruptive technology can be considered favourable in terms of offering organisational benefits. The most immediate benefits are consistent with reducing cost technology ownership, communication time therefore increasing the time benefits thus allowing organisations to become more productive.

This chapter discusses, on strategic level, security considerations related to moving to the cloud. It starts by discussing common cloud threats with brief explanation of each of them. The next section discusses the detailed differences between the cloud and classical data-center in terms of security. Presented in this chapter, the process of prioritizing assets and threats when moving to the cloud. The chapter explains how to evaluate the risks on each asset and how to prioritize the required spending on the information security budget. The chapter concludes with a discussion of general security consideration for the cloud.

Cloud computing has become one of the most game changing technologies in the recent history of computing. It is gaining acceptance and growing in popularity. However, due to its infancy, it encounters challenges in strategy, capabilities, as well as technical, organizational, and legal dimensions. Cloud service providers and customers do not yet have any proper strategy or process that paves the way for a set procedure on how to investigate or go about the issues within the cloud. Due to this gap, they are not able to ensure the robustness and suitability of cloud services in relation to supporting investigations of criminal activity. Moreover, both cloud service providers and customers have not yet established adequate forensic capabilities that could assist investigations of criminal activities in the cloud. The aim of this chapter is to provide an overview of the emerging field of cloud forensics and highlight its capabilities, strategy, investigation, challenges, and opportunities. This paper also provides a detailed discussion in relation to strategic planning for cloud forensics.

Numerous organisations are facing challenges of manipulating large volumes of data generated as a result of their internal business processes. Manual inference from such data often results in poor outcome. Decision makers within such firms are now reliant on the processed data created by the use of business intelligence tools and dashboards. In this chapter, the business intelligence and analytics concepts are explained as a means to manage vast amounts of data. Number of business intelligence tools and relevant strategies are discussed. Case studies and applications, e.g., banking sector are provided.