A Strategic Roadmap for the Manufacturing Industry to Implement Industry 4.0

Industry 4.0 (also referred to as digitization of manufacturing) is characterized by cyber physical systems, automation, and data exchange. It is no longer a future trend and is being employed worldwide by manufacturing organizations, to gain benefits of improved performance, reduced inefficiencies, and lower costs, while improving flexibility. However, the implementation of Industry 4.0 enabling technologies is a difficult task and becomes even more challenging without any standardized approach. The barriers include, but are not limited to, lack of knowledge, inability to realistically quantify the return on investment, and lack of a skilled workforce. This study presents a systematic and content-centric literature review of Industry 4.0 enabling technologies, to highlight their impact on the manufacturing industry. It also provides a strategic roadmap for the implementation of Industry 4.0, based on lean six sigma approaches. The basis of the roadmap is the design for six sigma approach for the development of a new process chain, followed by a continuous improvement plan. The reason for choosing lean six sigma is to provide manufacturers with a sense of familiarity, as they have been employing these principles for removing waste and reducing variability. Major reasons for the rejection of Industry 4.0 implementation methodologies by manufactures are fear of the unknown and resistance to change, whereas the use of lean six sigma can mitigate them. The strategic roadmap presented in this paper can offer a holistic view of phases that manufacturers should undertake and the challenges they might face in their journey toward Industry 4.0 transition

BALANCING NON-FUNCTIONAL REQUIREMENTS IN CLOUD-BASED SOFTWARE: AN APPROACH BASED ON SECURITY-AWARE DESIGN AND MULTI-OBJECTIVE SOFTWARE DYNAMIC MANAGEMENT

Beyond its functional requirements, architectural design, the quality of a software system is also defined by the degree to which it meets its non-functional requirements. The complexity of managing these non-functional requirements is exacerbated by the fact that they are potentially conflicting with one another. For cloud-based software, i.e., software whose service is delivered through a cloud infrastructure, other constraints related to the features of the hosting data center, such as cost, security and performance, have to be considered by system and software designers. For instance, the evaluation of requests to access sensitive resources results in performance overhead introduced by policy rules evaluation and message exchange between the different geographically distributed components of the authorization system. Duplicating policy rule evaluation engines traditionally solves such performance issues, however such a decision has an impact on security since it introduces additional potential private data leakage points. Taking into account all the aforementioned features is a key factor to enhance the perceived quality of service (QoS) of the cloud as a whole. Maximizing users and software developers satisfaction with cloud-based software is a challenging task since trade-off decisions have to be dynamically taken between these conflicting quality attributes to adapt to system requirements evolution. In this thesis, we tackle the challenges of building a decision support method to optimize software deployment in a cloud environment. Our proposed holistic method operates both at the level of 1) Platform as a service (PaaS) by handling software components deployment to achieve an efficient runtime optimization to satisfy cloud providers and customers objectives 2) Guest applications by making inroads into the design of applications to enable the design of secure systems that also meet flexibility, performance and cost requirements. To thoroughly investigate these challenges, we identify three main objectives that we address as follows: The first objective is to achieve a runtime optimization of cloud-based software deployment at the Platform as a service (PaaS) layer, by considering both cloud customers and providers constraints. To fulfill this objective, we leverage the [email protected] paradigm to build an abstraction layer to model a cloud infrastructure. In a second step, we model the software placement problem as a multi-objective optimization problem and we use multi-objective evolutionary algorithms (MOEAs) to identify a set of possible cloud optimal configurations that exhibit best trade-offs between conflicting objectives. The approach is validated through a case study that we defined with EBRC1, a cloud provider in Luxembourg, as a representative of a software component placement problem in heterogeneous distributed cloud nodes. The second objective is to ameliorate the convergence speed of MOEAs that we have used to achieve a run-time optimization of cloud-based software. To cope with elasticity requirements of cloud-based applications, we improve the way the search strategy operates by proposing a hyper-heuristic that operates on top of MOEAs. Our hyper-heuristic uses the history of mutation effect on fitness functions to select the most relevant mutation operators. Our evaluation shows that MOEAs in conjunction with our hyper-heuristic has a significant performance improvement in terms of resolution time over the original MOEAs. The third objective aims at optimizing cloud-based software trade-offs by exploring applications design as a complementary step to the optimization at the level of the cloud infrastructure, tackled in the first and second objectives. We aimed at achieving security trade-offs at the level of guest applications by revisiting current practices in software methods. We focus on access control as a main security concern and we opt for guest applications that manage resources regulated by access control policies specified in XACML2. This focus is mainly motivated by two key factors: 1) Access control is the pillar of computer security as it allows to protect sensitive resources in a given system from unauthorized accesses 2) XACML is the de facto standard language to specify access control policies and proposes an access control architectural model that supports several advanced access requirements such as interoperability and portability. To attain this objective, we advocate the design of applications based on XACML architectural model to achieve a trade-off between security and flexibility and we adopt a three-step approach: First, we identify a lack in the literature in XACML with obligation handling support. Obligations enable to specify user actions that have to be performed before/during/after the access to resources. We propose an extension of the XACML reference model and language to use the history of obligations states at the decision making time. In this step, we extend XACML access control architecture to support a wider range of usage control scenarios. Second, in order to avoid degrading performance while using a secure architecture based on XACML, we propose a refactoring technique applied on access control policies to enhance request evaluation time. Our approach, evaluated on three Java policy-based systems, enables to substantially reduce request evaluation time. Finally, to achieve a trade-off between a safe security policy evolution and regression testing costs, we develop a regression-test-selection approach for selecting test cases that reveal faults caused by policy changes. To sum up, in all aforementioned objectives, we pursue the goal of analysing and improving the current landscape in the development of cloud-based software. Our focus on security quality attributes is driven by its crucial role in widening the adoption of cloud computing. Our approach brings to light a security-aware design of guest applications that is based on XACML architecture. We provide useful guidelines, methods with underlying algorithms and tools for developers and cloud solution designers to enhance tomorrow’s cloud-based software design. Keywords: XACML-policy based systems, Cloud Computing, Trade-offs, Multi-Objective Optimizatio

Advances in Grid Computing

This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems

Automatic Building of a Powerful IDS for The Cloud Based on Deep Neural Network by Using a Novel Combination of Simulated Annealing Algorithm and Improved Self- Adaptive Genetic Algorithm

Cloud computing (CC) is the fastest-growing data hosting and computational technology that stands today as a satisfactory answer to the problem of data storage and computing. Thereby, most organizations are now migratingtheir services into the cloud due to its appealing features and its tangible advantages. Nevertheless, providing privacy and security to protect cloud assets and resources still a very challenging issue. To address the aboveissues, we propose a smart approach to construct automatically an efficient and effective anomaly network IDS based on Deep Neural Network, by using a novel hybrid optimization framework “ISAGASAA”. ISAGASAA framework combines our new self-adaptive heuristic search algorithm called “Improved Self-Adaptive Genetic Algorithm” (ISAGA) and Simulated Annealing Algorithm (SAA). Our approach consists of using ISAGASAA with the aim of seeking the optimal or near optimal combination of most pertinent values of the parametersincluded in building of DNN based IDS or impacting its performance, which guarantee high detection rate, high accuracy and low false alarm rate. The experimental results turn out the capability of our IDS to uncover intrusionswith high detection accuracy and low false alarm rate, and demonstrate its superiority in comparison with stateof-the-art methods