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This paper aims to provide a method for media resource allocation in Cloud systems for supporting green computing policies, as well as attempting to improve the overall performance of system by optimizing the communication latencies.

A common method for resource allocation is using resource agent that takes the budgets/prices of applicants/resources and creates a probability matrix of allocation according to the policies of system. Two general policies for optimization are latency optimization and green computing. Presented heuristic for latencies is so that the average latencies of communication between applicant and resource are measured, and they will affect the next decision. For gaining green computing, it is attempted to consolidate the allocated resources on smaller number of physical machines. So calculation formula of the price of each resource is modified to decrease the probability of allocating the resources on the machine with least allocated resources.

Results of proposed method indicates its success in both green computing and improving the performance. Experiments show decreasing 21.4 per cent of response time simultaneously with increasing tasks in the tested range. The maximum and minimum of saved energy is acceptable and reported as 79.2 and 16.8 per cent.

Like other centralized solutions, the proposed method suffers from the limitations of centralized resource agent, like bottle neck. But the implementation of distributed resource agent is postponed to future work.

Proposed method presents heuristics for improving the performance and gaining green computing. The key feature is formulating all the details and considering pitch variables for controlling the policies of system.

One of the techniques for improving the performance of distributed systems is data replication, wherein new replicas are created to provide more accessibility, fault tolerance and lower access cost of the data. In this paper, the authors propose a community-based solution for the management of data replication, based on the graph model of communication latency between computing and storage nodes. Communities are the clusters of nodes that the communication latency between the nodes are minimum values. The purpose of this study if to, by using this method, minimize the latency and access cost of the data.

This paper used the Louvain algorithm for finding the best communities. In the proposed algorithm, by requesting a file according to the nodes of each community, the cost of accessing the file located out of the applicant’s community was calculated and the results were accumulated. On exceeding the accumulated costs from a specified threshold, a new replica of the file was created in the applicant’s community. Besides, the number of replicas of each file should be limited to prevent the system from creating useless and redundant data.

To evaluate the method, four metrics were introduced and measured, including communication latency, response time, data access cost and data redundancy. The results indicated acceptable improvement in all of them.

So far, this is the first research that aims at managing the replicas via community detection algorithms. It opens many opportunities for further studies in this area.

The purpose of this paper is to explain the effect of the technology acceptance model in the media environment by using the mediating role of artificial intelligence and the cloud computing approach.

After reviewing the theoretical foundations, a conceptual model framework and research hypotheses were formed. The statistical population of the study included managers, deputies and experts from the National Iranian Oil Company, and a statistical sample of 368 people was selected by simple random sampling.

The results of structural equation modelling with PLS 2.0 software show a positive and significant effect on the artificial intelligence variable in the technology acceptance model with the cloud approach. Artificial intelligence has opened a new space in the digital world, especially in the media, so that its profound impact is quite evident and has affected people’s lives.

The acceleration of various technologies has severely challenged the approach of organizations, especially the media. The media environment with word of the technologies of the Industry 4.0, especially cloud computing technology, has changed the ways of accessing and using products and services.

The purpose of this paper is a transcoding system based on a virtual machine in a cloud computing environment. There are many studies about transmitting realistic media through a network. As the size of realistic media data is very large, it is difficult to transmit them using current network bandwidth. Thus, a method of encoding by compressing the data using a new encoding technique is necessary. The next-generation encoding technique high-efficiency video coding (HEVC) can encode video at a high compressibility rate compared to the existing encoding techniques, MPEG-2 and H.264. Yet, encoding the information takes at least ten times longer than existing encoding techniques.

This paper attempts to solve the tome problem using a virtual machine in a cloud computing environment.

In addition, by calculating the transcoding time of the proposed technique, it found that the time was reduced compared to existing techniques.

To this end, this paper proposed transcoding appropriate for the transmission of realistic media by dynamically allocating the resources of the virtual machine.

Internet has changed radically in the way people interact in the virtual world, in their careers or social relationships. IoT technology has added a new vision to this process by enabling connections between smart objects and humans, and also between smart objects themselves, which leads to anything, anytime, anywhere, and any media communications. IoT allows objects to physically see, hear, think, and perform tasks by making them talk to each other, share information and coordinate decisions. To enable the vision of IoT, it utilizes technologies such as ubiquitous computing, context awareness, RFID, WSN, embedded devices, CPS, communication technologies, and internet protocols. IoT is considered to be the future internet, which is significantly different from the Internet we use today. The purpose of this paper is to provide up-to-date literature on trends of IoT research which is driven by the need for convergence of several interdisciplinary technologies and new applications.

A comprehensive IoT literature review has been performed in this paper as a survey. The survey starts by providing an overview of IoT concepts, visions and evolutions. IoT architectures are also explored. Then, the most important components of IoT are discussed including a thorough discussion of IoT operating systems such as Tiny OS, Contiki OS, FreeRTOS, and RIOT. A review of IoT applications is also presented in this paper and finally, IoT challenges that can be recently encountered by researchers are introduced.

Studies of IoT literature and projects show the disproportionate importance of technology in IoT projects, which are often driven by technological interventions rather than innovation in the business model. There are a number of serious concerns about the dangers of IoT growth, particularly in the areas of privacy and security; hence, industry and government began addressing these concerns. At the end, what makes IoT exciting is that we do not yet know the exact use cases which would have the ability to significantly influence our lives.

This survey provides a comprehensive literature review on IoT techniques, operating systems and trends.

Knowledge management (KM) is widely adopted by organisations to improve their performance and make informed decisions. Prior research has confirmed that Information Systems (IS) play a critical role in effective KM. The purpose of this study is to examine the existing literature on the role of cloud-based KM systems (C-KMS) in small- and medium-sized enterprise (SMEs) by understanding its impact on the five KM processes: knowledge acquisition, creation, storage, sharing and usage.

This study conducted a systematic literature review by examining 133 journal articles and 24 conference papers from 2010 to 2021 on the role of cloud computing in KM for SMEs.

This study revealed that there are numerous empirical analyses on KM processes and tools in SMEs; however, only few studies demonstrate how the whole gamut of KM processes can adopt cloud computing in SMEs. Therefore, SMEs are ineffective at KM with limited IS intervention. This paper offers a proposition on how C-KMS can impact all five KM process, thereby increasing its effectiveness of KM in SMEs. This study analysed the benefits of C-KMS that brings to SMEs in terms of availability, scalability, reliability, security and cost.

This systematic review is restricted to certain databases (ScienceDirect, Sage journals, Scopus and Emerald Insight) and specific IS conference proceedings to source articles. The selection of search criteria and time frame is based on this study’s assessment and choice. This study adds value to our understanding of the role of KM in SMEs, and it reinforces the role of cloud computing in effectively managing knowledge in SMEs. The proposal of C-KMS for the enhancement of KM has significant implications for SMEs to effectively use knowledge for their survival and superior performance.

This study suggests three practical implications. First, adopting and using C-KMS provide a strong foundation to manage knowledge for SMEs in a cost-effective way. Second, C-KMS improves the effectiveness of KM by increasing availability of knowledge artifacts, which in turn aids SMEs’ growth. Third, C-KMS is useful to codify SME’s knowledge, and accordingly supports employees to acquire and use knowledge based on their requirements.

This study discussed C-KMS with contemporary social issues, such as the COVID-19 pandemic challenges for SMEs and demonstrated how C-KMS can support SMEs to handle such crises by managing knowledge effectively.

This research highlights the importance of the implementation of a C-KMS for the enhancement of KM in SMEs. The review provides empirical evidence on the challenges faced by SMEs regarding KM, as they often only have enough resources to focus on a single KM process, predominantly knowledge sharing. Consequently, a holistic approach to KM cannot be realised by SMEs. In this context, the findings of this study offer theoretical and practical insights into the role of cloud computing by addressing the challenges of KM in SMEs.

This study aims to investigate the impact of skills and knowledge of employees, economic situations of the company, current IT infrastructure, payment fashion, cloud availability, and cloud privacy and security on the productivity of the human resources in the COVID-19 era.

Over the past few years, the advent of cloud-assisted technologies has dramatically advanced the Information Technology (IT)-based industries by providing everything as a service. Cloud computing is recognized as a growing technology among companies around the world. One of the most critical cloud applications is deploying systems and organizational resources, especially systems whose deployment costs are high. Manpower is one of the basic and vital resources of the organization, and organizations need an efficient workforce to achieve their goals. But, in the COVID-19 era, human resources' productivity can be reduced due to stress, high labor force, reduced organizational performance and profits, unfavorable organizational conditions, inability to manage and lack of training. Therefore, this study tries to investigate the productivity of human resources in the COVID-19 era. Data were collected from the medium-sized companies through a questionnaire. Distributed questionnaires were conducted on the Likert scale. The model is assessed using the structural equation modeling technique to examine its reliability and validity. The study is a library method and literature review. A case study was conducted through a questionnaire and statistical analysis by SPSS 25 and SMART-PLS.

Based on the findings, the skills and knowledge of employees, the economic situations of the company, payment fashion, cloud availability and the current IT infrastructures of the company have a positive impact on human resource efficiency in the COVID-19 era. But cloud privacy and security have a negative effect on the productivity of human resources. The findings can be the basis for companies and organizations in the COVID-19 era.

This study has some restrictions that need to be considered in evaluating the obtained results. First, due to the prevalence of Coronavirus, access to information from the companies under study was limited. Second, this research may have overlooked other variables that affect human resource productivity in the COVID-19 era. Prospective researchers can examine the impact of Customer Relationship Management (CRM) and Supply Chain Management (SCM) on the human resource's productivity in the COVID-19 era.

The results of this research are applicable for all companies, their departments and human resources in the COVID-19 era.

In this paper, human resources' productivity in the COVID-19 era is pointed out. The presented new model provides a complete framework for investigating cloud-based enterprise resource planning systems affect the productivity of human resources in the COVID-19 era.