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The purpose of this paper is to consider how and why virtual machines (VMs) and cloud computing and related development environments built on cloud-based resources may be used to support and enhance the technological elements of library and information science (LIS) education.

It is based on analysis of available technologies and relevant applications.

Cloud computing and virtualization offer a basis for creating a robust computing infrastructure for LIS education.

In the context of LIS education, cloud computing is relevant in two respects. First, many important library and archival services already rely heavily on cloud-based infrastructures, and in the near future, cloud computing is likely to define a much larger part of the computing environment on which libraries and archives rely. Second, cloud computing affords a highly flexible and efficient environment that is ideal for learning about VMs, operating systems and a wide variety of applications. What is more important, it constitutes an environment for teaching and learning that is vastly superior to the ones that currently support most LIS degree programs. From a pedagogical perspective, the key aspect of teaching and learning in the cloud environment is the VM. So, the article focuses a significant portion of its attentions on questions related to the deployment and use of VMs and Linux Containers, within and without cloud-based infrastructures, as means of learning about computer systems, applications and networking and achieving an understanding of essential aspects of both cloud computing and VM environments.

Based on a search of available literature in computer science and library and information science, the paper has no counterparts.

By introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.

This paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.

(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.

The originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.

Founded on the concept of organizational ambidexterity and the competing value model, the purpose of this paper is to develop an information technology (IT) ambidexterity framework to underscore the importance of a balanced and harmonious IT environment in enterprise cloud adoption.

With survey responses from 165 IT executives in a managerial position who are in charge of cloud computing implementation, partial least square method is used to test the research model.

Cloud absorptive capacity plays an important role for firms to secure a competitive advantage. The synergy of the two capabilities (flexibility and control), which have conflicting characteristics, contributes to the enhancement of cloud absorptive capacity and leads to a firm’s knowledge accumulation and performance.

This study is the very first attempt that empirically establishes the relationship between a firm’s competitiveness and cloud computing absorptive capacity. This study provides a comprehensive framework that integrates ambidexterity theory with the competing value framework (CVF) with extending the concept of absorptive capacity that is bounded within an organizational perspective into a cloud computing context.

Firms should treat cloud computing as a strategic consideration to secure a competitive advantage in the contemporary business environment. For a firm’s performance, a dual governance structure, that encompasses flexibility and control, is required to achieve competitive advantage from cloud computing adoption.

To facilitate organizational effort in achieving a harmonious cloud environment, the authors propose a comprehensive ambidexterity framework integrating the CVF approach. This framework maps IT ambidexterity onto the CVF. As CVF considers internal and external factors that ambidexterity theory does not cover, integrating two theories can provide more comprehensive implications and discussions regarding cloud computing adoption.

Cloud computing is relatively a new type of technology demanding a new method of management techniques to attain security and privacy leading to customer satisfaction regarding “Business Protection” measure. As cloud computing businesses are usually composed of multiple colocation sites/departments, the purpose of this paper is to propose a benchmark operation to measure and compare the overall integrated people-process-performance (PPP) among different departments within cloud computing organization. The purpose of this paper is to motivate staff/units to improve the process performance and meet the standards in a competitive approach among business units.

The research method was conducted at Cirrus Ltd, which is a cloud computing service provider where a focus group consists of six IT professionals/managers. The objective of the focus group was to investigate the proposed technique by selecting the best practices relevant criteria, with the relevant sub-criteria as a benchmarking performance tool to measure PPP via an analytic hierarchy processing (AHP) approach. The standard pairwise comparative AHP scale was used to measure the performance of three different teams defined as production team, user acceptance testing team and the development team.

Based on best practice performance measurement (reviewed in this paper) of cloud computing, the proposed AHP model was implemented in a local medium-sized cloud service provider named “Cirrus” with their single site data center. The actual criteria relevant to Cirrus was an adaptation of the “Best practice” described in the literature. The main reason for the adaptation of criteria was that the principle of PPP assumes multiple departments/datacenters located in a different geographical area in large service providers. As Cirrus is a type of SMEs, the adaptation of performance measurement was based on teams within the same data center location. Irrelevant of this adaptation, the objective of measuring vendors KPI using the AHP technique as a specific output of PPP is also a valid situation.

This study provides guidance for achieving cloud computing performance measurement using the AHP technique. Hence, the proposed technique is an integrated model to measure the PPP under monitored cloud environment.

The proposed technique measures and manages the performance of cloud service providers that also implicitly act as a catalyst to attain trust in such high information-sensitive environment leading to organizational effectiveness of managing cloud organizations.

Today, with the rapid growth of cloud computing (CC), there exist several users that require to execute their tasks by the available resources to obtain the best performance, reduce response time and use resources. However, despite the significance of the scheduling issue in CC, as far as the authors know, there is not any systematic and inclusive paper about studying and analyzing the recent methods. This paper aims to review the current mechanisms and techniques, which can be addressed in this area.

The central purpose of this paper refers to offering a complete study of the state-of-the-art planning algorithms in the cloud and also instructions for future research. Besides, this paper offers a methodological analysis of the scheduling mechanisms in the cloud environment.

The central role of this paper is to present a summary of the present issues related to scheduling in the cloud environment, providing a structure of some popular techniques in cloud scheduling scope and defining key areas for the development of cloud scheduling techniques in the future research.

In this paper, scheduling mechanisms are classified into two main categories include deterministic and non-deterministic algorithms; however, it can also be classified into different categories. In addition, the selection of all related papers could not be ensured. It is possible that some appropriate and related papers were removed in the search process.

According to the results of this paper, the requirement for more suitable algorithms exists to allocate tasks for resources in cloud environments. In addition, some principal rules in cloud scheduling should be re-evaluated to achieve maximum productivity and minimize wasted expense and effort. In this direction, to stay away from overloading and under loading of components and resources, the proposed method should execute workloads in an adaptable and scalable way. As the number of users increased in cloud environments, the number of tasks in the cloud that needed to be scheduled proportionally increased. Thus, an efficient mechanism is needed for scheduling tasks in these environments.

The general information gathered in this study makes the researchers acquainted with the state-of-the-art scheduling area of the cloud. Entirely, the answers to the research questions summarized the main objective of scheduling, current challenges, mechanisms and methods in the cloud systems. The authors hope that the results of this paper lead researchers to present more efficient scheduling techniques in cloud systems.

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.

Using the example of audiovisual materials, this paper aims to illustrate how records-related and archival legislation lags behind advances in technology. As more audiovisual materials are created on the cloud, questions arise about the applicability of national laws over the control, ownership, and custody of data and records.

This paper analyses court cases relating to audiovisual materials in the cloud and archival legislation from three Commonwealth countries: Canada, Australia, and Singapore – representing North America, the Pacific, and Asia respectively.

Current records-related and archival legislation does not effectively address the creation, processing, and preservation of records and data in a cloud environment. The paper identifies several records-related risks linked to the cloud – risks related to the ownership and custody of data, legal risks due to transborder data flow, and risks due to differing interpretations on the act of copying and ownership of audiovisual materials.

The paper identifies the need for records professionals to pay greater attention to the implications of the emerging cloud environment. There is a need for further research on how the concept of extraterritoriality and transborder laws can be applied to develop model laws for the management and preservation of records in the cloud.

The paper identifies record-related risks linked to the cloud by analyzing court cases and archival legislation. The paper examines maritime law to find useful principles that the archival field could draw on to mitigate some of these risks.

Cloud services have become very popular among researchers and people recently. In such a scenario, identifying reliable cloud services has become very important. The trust value plays a significant role in recognizing reliable providers. The purpose of this paper is to propose a new method to evaluate the trust metric among the cloud providers. The main goal is to increase the precision and accuracy of the trust evaluation method in the cloud environments.

This paper evaluates the trust metric among the cloud providers and entities by grouping the services and using a behavioral graph. Four parameters, availability, reliability, interaction evolution and identity, are used for evaluating the trust value. The performance of the proposed method is assessed using a simulator which is programmed in the cloud Azure 2013 based on C# codes.

The method is evaluated through various experiments in terms of precision, recall, error-hit, reliability and availability. The obtained results show that the proposed method has better reliability and availability than the FIFO and QoS models. Also, the results show that increasing the number of groups leads to increasing values of trust, precision and availability, and decreasing values of error-hit.

This paper proposes a trust evaluation method in the cloud environment by grouping the services and using a behavioral graph for improving the amount of availability, error-hit, precision and reliability values.

Resource scheduling is the study of how to effectively measure, evaluate, analyze, and dispatch resources in order to meet the demands of corresponding tasks. Aiming at the problem of resource scheduling in the private cloud environment, the purpose of this paper is to propose a resource scheduling approach from an efficiency priority point of view.

To measure the computational efficiencies for the resource nodes in a private cloud environment, the data envelopment analysis (DEA) approach is incorporated and a suitable DEA model is proposed. Then, based on the efficiency scores calculated by the proposed DEA model for the resource nodes, the 0-1 programming technique is introduced to build a simple resource scheduling model.

The proposed DEA model not only has the ability of ranking all the decision-making units into different positions but also can handle non-discretionary inputs and undesirable outputs when evaluating the resource nodes. Furthermore, the resource scheduling model can generate for the calculation tasks an optimal resource scheduling scheme that has the highest total computational efficiency.

The proposed method may also be used in studies of resource scheduling studies in the environments of public clouds and hybrid clouds.

The proposed approach can achieve the goal of resource scheduling in private cloud computing platforms by attaining the highest total computational efficiency, which is very significant in practice.

This paper uses an efficiency priority point of view to solve the problem of resource scheduling in private cloud environments.

Cloud computing gives several on-demand infrastructural services by dynamically pooling heterogeneous resources to cater to users’ applications. The task scheduling needs to be done optimally to achieve proficient results in a cloud computing environment. While satisfying the user’s requirements in a cloud environment, scheduling has been proven an NP-hard problem. Therefore, it leaves scope to develop new allocation models for the problem. The aim of the study is to develop load balancing method to maximize the resource utilization in cloud environment.

In this paper, the parallelized task allocation with load balancing (PTAL) hybrid heuristic is proposed for jobs coming from various users. These jobs are allocated on the resources one by one in a parallelized manner as they arrive in the cloud system. The novel algorithm works in three phases: parallelization, task allocation and task reallocation. The proposed model is designed for efficient task allocation, reallocation of resources and adequate load balancing to achieve better quality of service (QoS) results.

The acquired empirical results show that PTAL performs better than other scheduling strategies under various cases for different QoS parameters under study.

The outcome has been examined for the real data set to evaluate it with different state-of-the-art heuristics having comparable objective parameters.