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Reports on a new methodology for formation of virtual (“extended”) machining cells using generic capability patterns termed “resource elements”. Resource elements are used to uniquely describe the processing requirements of the component mix and dynamically match them to the processing capabilities of the machining shop. The virtual cell formation methodology is based on four steps: component requirement analysis and generation of processing alternatives; definition of virtual cell capability boundaries; machine tool selection; and system evaluation. The proposed methodology facilitates the dynamic formation of virtual manufacturing structures by providing accurate assessment of the component processing requirements and their matching with the available capabilities of the existing manufacturing facilities.

This paper aims to develop the Dragonfly-based exponential gravitational search algorithm to VMM strategy for effective load balancing in cloud computing. Due to widespread growth of cloud users, load balancing is the essential criterion to deal with the overload and underload problems of the physical servers. DEGSA-VMM is introduced, which calculates the optimized position to perform the virtual machine migration (VMM).

This paper presents an algorithm Dragonfly-based exponential gravitational search algorithm (DEGSA) that is based on the VMM strategy to migrate the virtual machines of the overloaded physical machine to the other physical machine keeping in mind the energy, migration cost, load and quality of service (QoS) constraints. For effective migration, a fitness function is provided, which selects the best fit that possess minimum energy, cost, load and maximum QoS contributing toward the maximum energy utilization.

For the performance analysis, the experimentation is performed with three setups, with Setup 1 composed of three physical machines with 12 virtual machines, Setup 2 composed of five physical machines and 19 virtual machines and Setup 3 composed of ten physical machines and 28 virtual machines. The performance parameters, namely, QoS, migration cost, load and energy, of the proposed work are compared over the other existing works. The proposed algorithm obtained maximum resource utilization with a good QoS at a rate of 0.19, and minimal migration cost at a rate of 0.015, and minimal energy at a rate of 0.26 with a minimal load at a rate of 0.1551, whereas with the existing methods like ant colony optimization (ACO), gravitational search algorithm (GSA) and exponential gravitational search algorithm, the values of QoS, load, migration cost and energy are 0.16, 0.1863, 0.023 and 0.29; 0.16, 0.1863, 0.023 and 0.28 and 0.18, 0.1657, 0.016 and 0.27, respectively.

This paper presents an algorithm named DEGSA based on VMM strategy to determine the optimum position to perform the VMM to achieve a better load balancing.

Current industrial scenario is largely dependent on cloud computing paradigms. On-demand services provided by cloud data centre are paid as per use. Hence, it is very important to make use of the allocated resources to the maximum. The resource utilization is highly dependent on the allocation of resources to the incoming request. The allocation of requests is done with respect to the physical machines present in the datacenter. While allocating the tasks to these physical machines, it needs to be allocated in such a way that no physical machine is underutilized or over loaded. To make sure of this, optimal load balancing is very important.

The paper proposes an algorithm which makes use of the fitness functions and duopoly game theory to allocate the tasks to the physical machines which can handle the resource requirement of the incoming tasks. The major focus of the proposed work is to optimize the load balancing in a datacenter. When optimization happens, none of the physical machine is neither overloaded nor under-utilized, hence resulting in efficient utilization of the resources.

The performance of the proposed algorithm is compared with different existing load balancing algorithms such as round-robin load (RR) ant colony optimization (ACO), artificial bee colony (ABC) with respect to the selected parameters response time, virtual machine migrations, host shut down and energy consumption. All the four parameters gave a positive result when the algorithm is simulated.

The contribution of this paper is towards the domain of cloud load balancing. The paper is proposing a novel approach to optimize the cloud load balancing process. The results obtained show that response time, virtual machine migrations, host shut down and energy consumption are reduced in comparison to few of the existing algorithms selected for the study. The proposed algorithm based on the duopoly function and fitness function brings in an optimized performance compared to the four algorithms analysed.

The aim of this study is to investigate the effects of different virtual product experiences (VPEs, interpersonal and machine) on sense of virtual community (SOVC) for consumers. Furthermore, parasocial interaction (PSI) and the sequence of VPEs likely moderate the relationship between different combinations of VPEs and SOVC.

The study employed a factorial online experimental design: 8 (combinations of VPEs, between subjects) x 2 (sequences of VPEs, between subjects). Participants were randomly assigned to the 16 groups.

The results show that with a high degree of PSI, the “escapism + social” VPEs group has the greatest beneficial effects on SOVC, whereas with a low degree of PSI, the “aesthetics + information” VPEs group has the greatest beneficial effects on SOVC. Besides, in VPES sequence 1 (interpersonal → machine), the “escapism + social” VPEs group has a greater effect on SOVC, whereas in sequence 2 (machine → interpersonal), there was no statistically significant difference for VPEs combinations relative to SOVC.

This study determines that consumers with different degrees of PSI differ from one another regarding their interactions with web sites and communities. Therefore, the characteristics of PSI provide manufacturers with various marketing strategies for interaction. The combinations and sequences of interpersonal and machine VPEs in this study can provide manufacturers with suggestions for appropriate online shopping web site design.

Although the importance of these virtual experiences is continuously increasing, there is still a lack of studies that empirically analyze from the combinations of machine interaction and interpersonal interaction of VPEs of the effects on consumer behavior.

The purpose of this paper is to develop an efficient and effective preventive maintenance (PM) plan that considers machines’ maintenance needs in addition to their reliability factor.

Similarity coefficient method in group technology (GT) philosophy is used. Machines’ reliability factor is considered to develop virtual machine cells based on their need for maintenance according to the type of failures they encounter.

Using similarity coefficient method in GT philosophy for PM planning results in grouping machines based on their common failures and maintenance needs. Using machines' reliability factor makes the plan more efficient since machines will be maintained at the same time intervals and when their maintenance is due. This helps to schedule a standard and efficient maintenance process where maintenance material, tools and labor are scheduled accordingly.

The proposed procedure will assist maintenance managers in developing an efficient and effective PM plans. These maintenance plans provide better inventory management for the maintenance materials and tools needed using the developed virtual machine cells.

This paper presents a new procedure to implement PM using the similarity coefficient method in GT. A new similarity coefficient equation that considers machines reliability is developed. Also a clustering algorithm that calculates the similarity between machine groups and form virtual machine cells is developed. A numerical example adopted from the literature is solved to demonstrate the proposed heuristic method.

The lack of geometric and dimensional accuracy of parts produced by additive manufacturing (AM) is directly related to the machine, material and process used. This paper aims to propose a method for the analysis and compensation of machine-related geometric errors applicable to any AM machine, regardless of the manufacturing process and technology used.

For this purpose, an error calculation model inspired by those used in computerized numerical control machines and coordinate measuring machines was developed. The error functions of the model were determined from the position deviations of a set of virtual points that are not sensitive to material and process errors. These points were obtained from the measurement of an ad hoc designed and manufactured master artefact. To validate the model, off-line compensation was applied to both the original designed artefact and an example part.

The geometric deviations in both cases were significantly smaller than those found before applying the geometric compensation. Dimensional enhancements were also achieved on the example part by using a correction parameter available in the three-dimensional printing software, whose value was adjusted from the measurement of the geometrically compensated master artefact.

The errors that persist in the part derive from both material and process. Compensation for these type of errors requires a detailed analysis of the influencing parameters, which will be the subject of future research.

The use of the virtual-point-based error model increases the quality of additively manufactured parts and can be used in any AM system.

Virtual machines (VMs) are suggested by the providers of cloud services as the services for the users over the internet. The consolidation of VM is the tactic of the competent and smart utilization of resources from cloud data centers. Placement of a VM is one of the significant issues in cloud computing (CC). Physical machines in a cloud environment are aware of the way of the VM placement (VMP) as the mapping VMs. The basic target of placement of VM issue is to reduce the physical machines' items that are running or the hosts in cloud data centers. The VMP methods have an important role in the CC. However, there is no systematic and complete way to discuss and analyze the algorithms. The purpose of this paper is to present a systematic survey of VMP techniques. Also, the benefits and weaknesses connected with selected VMP techniques have been debated, and the significant issues of these techniques are addressed to develop the more efficient VMP technique for the future.

Because of the importance of VMP in the cloud environments, in this paper, the articles and important mechanisms in this domain have been investigated systematically. The VMP mechanisms have been categorized into two major groups, including static and dynamic mechanisms.

The results have indicated that an appropriate VMP has the capacity to decrease the resource consumption rate, energy consumption and carbon emission rate. VMP approaches in computing environment still need improvements in terms of reducing related overhead, consolidation of the cloud environment to become an extremely on-demand mechanism, balancing the load between physical machines, power consumption and refining performance.

This study aimed to be comprehensive, but there were some limitations. Some perfect work may be eliminated because of applying some filters to choose the original articles. Surveying all the papers on the topic of VMP is impossible, too. Nevertheless, the authors are trying to present a complete survey over the VMP.

The consequences of this research will be valuable for academicians, and it can provide good ideas for future research in this domain. By providing comparative information and analyzing the contemporary developments in this area, this research will directly support academics and working professionals for better knowing the growth in the VMP area.

The gathered information in this paper helps to inform the researchers with the state of the art in the VMP area. Totally, the VMP's principal intention, current challenges, open issues, strategies and mechanisms in cloud systems are summarized by explaining the answers.

An object‐oriented event‐driven immersive virtual environment is described for the creation of virtual labs (VLs) for simulating physical experiments. Discussion focuses on a number of aspects of the VLs, including interface devices, software objects, and various applications. The VLs interface with output devices, including immersive stereoscopic screen(s) and stereo speakers; and a variety of input devices, including body tracking (head and hands), haptic gloves, wand, joystick, mouse, microphone, and keyboard. The VL incorporates the following types of primitive software objects: interface objects, support objects, geometric entities, and finite elements. Each object encapsulates a set of properties, methods, and events that define its behavior, appearance, and functions. A “container” object allows grouping of several objects. Applications of the VLs include viewing the results of the physical experiment, viewing a computer simulation of the physical experiment, simulation of the experiment’s procedure, computational steering, and remote control of the physical experiment. In addition, the VL can be used as a risk‐free (safe) environment for training. The implementation of virtual structures testing machines, virtual wind tunnels, and a virtual acoustic testing facility is described.

This paper aims to describe the problems faced by MyAIS, an open access repository of scholarly content, the reasons for moving to a cloud infrastructure and the design of the system.

MyAIS is supported by an IBM HS22V multiple blade server using virtualization technology. A single blade server runs both MyAIS's back‐end that handles the processing of digital repository and front‐end that handles the web server. The whole system runs on a Linux platform. The virtualized servers deliver an infrastructure as a service (IaaS) to the Faculty of Computer Science, University of Malaya researchers as well as students.

Sharing resources in the cloud cuts maintenance cost and leaves a smaller carbon footprint due to its modular design. Virtualization makes it easy to monitor access traffic and usage of hardware resources on‐the‐fly. The scalability and expansion of MyAIS is improved. It allows for faster system recovery in case of hard disk failure and speedy file move during migration. Larger bandwidth improves reliability and management of bottlenecks.

There are still bottlenecks at the wide area network level resulting in sluggishness during peak hours.

There is significant improvement in storage expansion through shared hardware resources.

Cloud as an IaaS helps reduce the total cost of ownership and maintenance, increase system scalability to handle increased traffic, meet increasing demand for storage space for full text resources, and reduce downtime. It makes backup and disaster recovery easier. It is possible to accommodate other university repositories in future.

The software virtual machine (SVM) concept is described as a methodology to reduce the manpower required to implement and maintain finite element software. A SVM provides the engineering programmer with high‐level languages to facilitate the structuring and management of data, to define and interface process modules, and to manage computer resources. A prototype finite element system has been successfully implemented using the SVM approach. Development effort is significantly reduced compared to a conventional all‐FORTRAN approach. The impact on execution efficiency of the SVM is described along with special procedures developed to minimize overhead in compute‐bound modules. Planned extensions of capabilities in the SVM used by the authors are outlined.