In cloud computing, load balancing is required to distribute the dynamic local workload evenly across all the nodes. It helps to achieve a high user satisfaction and resource utilization by ensuring an efficient and fair allocation of every computing resource. Load balancing aids in minimizing resource consumption and avoids bottlenecks. Although many load balancing schemes have been presented, there is no scheme providing the elasticity in cloud computing. In this paper, a Max–Min task scheduling algorithm for load balance in the elastic cloud is proposed. To realize the load balancing, the proposed algorithm maintains a task status table to estimate the real-time load of virtual machines and the expected completion time of tasks, which can allocate the workload among nodes and realize the load balance. The extensive experiments demonstrate that the proposed Max–Min task scheduling algorithm can improve the resource utilization as well as reduce the respond time of tasks.

Max–Min Task Scheduling Algorithm for Load Balance in Cloud Computing

The 21st century has witnessed precipitous changes spanning from the way of life to the technologies that emerged. We have entered a nascent paradigm shift (industry 4.0) where science fictions have become science facts, and technology fusion is the main driver. Thus, ensuring that any advancement in technology reach and benefit all is the ideal opportunity for everyone. In this study, disruptive technologies of industry 4.0 were explored and quantified in terms of the number of their appearances in published literature. The study aimed at identifying industry 4.0 key technologies which have been ill-defined by previous researchers and to enumerate the required skills of industry 4.0. Comprehensive literature survey covering the field of engineering, production, and management was done in multidisciplinary databases: Google Scholar, Science Direct, Scopus, Sage, Taylor & Francis, and Emerald Insight. From the electronic survey, 35 disruptive technologies were quantified and 13 key technologies: Internet of Things, Big Data, 3D printing, Cloud computing, Autonomous robots, Virtual and Augmented reality, Cyber-physical system, Artificial intelligence, Smart sensors, Simulation, Nanotechnology, Drones, and Biotechnology were identified. Both technical and personal skills to be imparted into the human workforce for industry 4.0 were reported. The review identified the need to investigate the capability and the readiness of developing countries in adapting industry 4.0 in terms of the changes in the education systems and industrial manufacturing settings. This study proposes the need to address the integration of industry 4.0 concepts into the current education system.

https://downloads.hindawi.com/journals/je/2020/4280156.pdf

Exponential Disruptive Technologies and the Required Skills of Industry 4.0

The emergence of Industry 4.0, also referred to as the fourth industrial revolution, has entirely transformed how the industry or business functions and evolves. It can be attributed to its broadening focus on automation, decentralization, system integration, cyber-physical systems, etc. Its implementation promises numerous benefits in terms of higher productivity, greater volatility, better control and streamlining of processes, accelerated enterprise growth, sustainable development, etc. Despite the worldwide recognition and realization of Industry 4.0, its holistic adoption is constrained by the requirements of specific skills among the workforce. The personnel are expected to acquire adaptive thinking, cognitive and computational skills, predominantly in the area of information technology, data analytics, etc. Thus, the universities that laid the foundation for future talents or trends in society have to adapt and modernize the existing programs, facilities, and infrastructure. This reshaping of higher education in consonance with the vision of Industry 4.0 possesses its opportunities and challenges. There are, of course, a multitude of factors involved and they need a reasonable assessment to strategically plan this metamorphosis. Therefore, this work aims to explore and analyze the different factors that influence the progression and enactment of Industry 4.0 in universities for sustainable education. For this purpose, a systematic approach based on a questionnaire as well as a SWOT (strengths (S), weaknesses (W), opportunities (O), and threats (T)) integrated with the analytic hierarchy process (AHP) is adopted. The questionnaires are administered to university employees and students (or stakeholders) to assess their viewpoint, as well as to estimate the priority values for individual factors to be included in SWOT. The AHP is implemented to quantify the different factors in terms of weights using a pairwise comparison matrix. Finally, the SWOT matrix is established depending on the questionnaire assessment and the AHP weights to figure out stakeholders’ perspectives, in addition to the needed strategic scheme. The SWOT implementation of this research proposes an aggressive approach for universities, where they must make full use of their strengths to take advantage of the emerging opportunities in Industry 4.0. The results also indicate that there are fundamental requirements for universities in Industry 4.0, including effective financial planning, skilled staff, increased industrial partnerships, advanced infrastructure, revised curricula, and insightful workshops. This investigation undoubtedly underlines the importance of practical expertise and the implementation of digital technologies at the university level to empower novices with the requisite skills and a competitive advantage for Industry 4.0.

Adapting Universities for Sustainability Education in Industry 4.0: Channel of Challenges and Opportunities

Industry 4.0 is a term that represents the radical transformation of Industry that has resulted from the integration of emerging technologies. It implies that we are witnessing the proximity of the Fourth Industrial Revolution. Industry 4.0 aims to enable intelligent factories to produce personalized output utilizing greener and more efficient processes. However, to accomplish this, manufacturers must overcome several barriers, such as the lack of qualified talent to develop and manage various high-tech systems. This deficiency means that Industry 4.0 demands a change in the labor market, explicitly requiring trained professionals who have the competencies and skills to thrive in this new environment. What should a set of competencies be in the modern professional profiles? In this work, we review the required competencies, the technologies that aid in developing them, and the methods of assessing them.

Competencies for Industry 4.0

Industry 4.0 (I4), defined as the integration of information and communication technology with industrial advances to develop digital factories, will transform production processes by making them more efficient, green, and flexible. Manufacturers must overcome barriers to implement this approach; one of them is the lack of qualified talent to manage I4 systems. This paper is a review of the state-of-art of I4. The goal of this research is to describe the technologies that are enablers of I4, analyze the human talent needed and the qualifications (competencies/skills) required to manage I4 systems, describe the courses and practices that leading universities in engineering and technology are teaching for developing the I4 workforce, and provide a look to the future by presenting trends related to education in I4. Autonomous robots, simulation, horizontal and vertical integration, the Industrial Internet of Things, additive manufacturing, augmented reality, cybersecurity, the cloud, and Big Data and analytics are technologies considered pillars of the I4 approach. Nowadays, there are universities already offering programs related to I4. These range from those aimed at presenting the concepts of I4 to those in which students learn how to handle related technologies in real work in global enterprises. I4 is still evolving, and we present only a glimpse of what is required based on its current state. Continuous review of the I4 environment is necessary to determine opportunely the path that it will follow and, in consequence, the needs that will emerge in areas such as curricula content, competency requirements, and technologies.

Engineering education for smart 4.0 technology: a review

Industry 4.0 is originally a future vision described in the high-tech strategy of the German government that is conceived upon information and communication technologies like Cyber-Physical Systems, Internet of Things, Physical Internet, and Internet of Services to achieve a high degree of flexibility in production (individualized mass production), higher productivity rates through real-time monitoring and diagnosis, and a lower wastage rate of material in production. An important part of the tasks in the preparation for Industry 4.0 is the adaption of the higher education to the requirements of this vision, in particular the engineering education. In this work, we introduce a road map consisting of three pillars describing the changes/enhancements to be conducted in the areas of curriculum development, lab concept, and student club activities. We also report our current application of this road map at the Turkish German University, Istanbul.

https://www.mdpi.com/2227-7080/7/1/10/pdf

Adapting Engineering Education to Industry 4.0 Vision

Storage area networks (SANs) connect groups of storage devices to servers over fast interconnects using protocols like Fibre Channel or iSCSI, so that storage resources can be assigned to servers in a flexible and scalable way. An important challenge is controlling the complexity of the SAN configuration resulting from the high scalability of the network and the diversity and the interconnectivity of the devices. Policy- based validation has been proposed earlier as a solution to this configuration problem. We propose a light-weight, SQL-based solution that uses existing well-known technologies to implement such a validation system. Our approach is based on a relational database which stores configuration data extracted from the system via a WBEM standard interface. In contrast to other approaches, we use SQL to define our policy rules as executable checks on these configuration data. Each rule is embedded in a test case, defined by an XML schema, which combines each check with an explanation component and an action component. These components retrieve information about the SAN components that cause the check to fail and provide methods that will be invoked on a success or a failure of the test. Although the approach is more general, we focus on the application for Storage Area Networks. We also give some example policies for SAN configuration and their implementations and show how in addition all policy types presented by Agrawal et al. (2004) can be formulated as SQL queries by our method.

Sanchk: an sql-based validation system for san configuration

Storage area networks (SANs) connect storage devices to servers over fast network interconnects. We consider the problem of optimal SAN configuration with the goal of meeting service level agreements (SLAs) for server processes while retaining flexibility for future changes. Our approach proceeds in two stages, by setting up pseudo-Boolean constraint problems and solving them with an off-the-shelf solver. First, we give an algorithm for assigning storage devices to applications running on the SANpsilas hosts. This algorithm tries to balance the workload as evenly as possible over all storage devices. Our second algorithm takes these assignments and computes the interconnections (data paths) that are necessary to achieve the desired configuration while respecting redundancy (safety)requirements in the SLAs. Again, this algorithm tries to balance the workload of all connections and devices in proportion to their capacity. Thus, our network configurations respect all SLAs and provide flexibility for future changes by avoiding bottlenecks on storage devices or switches.

/pdf/towards-sla-based-optimal-workload-distribution-in-sans-1nk0adg1p0.pdf

Towards SLA-based optimal workload distribution in SANs

Storage Area Networks (SANs) connect groups of storage devices to servers over fast interconnects. An important challenge lies in managing the complexity of the resulting massive SAN configurations. Policy-based validation using new logical frameworks has been proposed earlier as a solution to this configuration problem. SANchk offers a new solution that uses standard technologies such as SQL, XML, and Java, to implement a rule-based configuration checker. SANchk works as a light-weight extension to the relational databases of storage management systems; current support includes IBM's TPC and the open source Aperi storage manager. Some five dozen best practices rules for SAN configuration are implemented in SANchk, many of them with configurable parameters. Empirical results with several commercial SANs show that the approach is viable in practice.

/pdf/sanchk-sql-based-san-configuration-checking-3cbh9upje3.pdf

SANchk: SQL-based SAN configuration checking

In automotive industry, validation and maintenance of product configuration data is a complex task Both orders from the customers and new product line designs from the R&D department are subject to a set of configuration rules to be satisfied In this work, non-monotonic computational logic, answer set programming in particular, is applied to industrial-scale automotive product configuration problems This methodology provides basic validation of the product configuration documentation and validation of single product orders, where Reiter style diagnosis provides minimal changes needed to correct an invalid order or a product configuration rule set In addition, a method for discovering groups of product configuration variables that are strongly related can be obtained by small modification of the basic logic program, and by the usage of cautious and brave reasoning methods As a result, options that are used in every, or respectively in no configuration, can easily be identified, as well as groups of options that are always used together or not at all Finally it is possible to single out mandatory and obsolete options, relative to a preselected set of included or excluded options Experimental results on an industrial dataset show applicability, example results, and computational feasibility with computation times on the order of seconds using a state-of-the-art answer set solver on standard PC hardware

Eray Gençay

Papers

Adapting Engineering Education to Industry 4.0 Vision

Sanchk: an sql-based validation system for san configuration

Towards SLA-based optimal workload distribution in SANs

SANchk: SQL-based SAN configuration checking

Applications of non-monotonic reasoning to automotive product configuration using answer set programming