Currently, researchers are working to contribute to the emerging fields of cloud computing, edge computing, and distributed systems. The major area of interest is to examine and understand their performance. The major globally leading companies, such as Google, Amazon, ONLIVE, Giaki, and eBay, are truly concerned about the impact of energy consumption. These cloud computing companies use huge data centers, consisting of virtual computers that are positioned worldwide and necessitate exceptionally high-power costs to preserve. The increased requirement for energy consumption in IT firms has posed many challenges for cloud computing companies pertinent to power expenses. Energy utilization is reliant upon numerous aspects, for example, the service level agreement, techniques for choosing the virtual machine, the applied optimization strategies and policies, and kinds of workload. The present paper tries to provide an answer to challenges related to energy-saving through the assistance of both dynamic voltage and frequency scaling techniques for gaming data centers. Also, to evaluate both the dynamic voltage and frequency scaling techniques compared to non-power-aware and static threshold detection techniques. The findings will facilitate service suppliers in how to encounter the quality of service and experience limitations by fulfilling the service level agreements. For this purpose, the CloudSim platform is applied for the application of a situation in which game traces are employed as a workload for analyzing the procedure. The findings evidenced that an assortment of good quality techniques can benefit gaming servers to conserve energy expenditures and sustain the best quality of service for consumers located universally. The originality of this research presents a prospect to examine which procedure performs good (for example, dynamic, static, or non-power aware). The findings validate that less energy is utilized by applying a dynamic voltage and frequency method along with fewer service level agreement violations, and better quality of service and experience, in contrast with static threshold consolidation or non-power aware technique.
Currently, cybersecurity plays an essential role in computing and information technology due to its direct effect on organizations’ critical assets and information. Cybersecurity is applied using integrity, availability, and confidentiality to protect organizational assets and information from various malicious attacks and vulnerabilities. The COVID-19 pandemic has generated different cybersecurity issues and challenges for businesses as employees have become accustomed to working from home. Firms are speeding up their digital transformation, making cybersecurity the current main concern. For software and hardware systems protection, organizations tend to spend an excessive amount of money procuring intrusion detection systems, antivirus software, antispyware software, and encryption mechanisms. However, these solutions are not enough, and organizations continue to suffer security risks due to the escalating list of security vulnerabilities during the COVID-19 pandemic. There is a thriving need to provide a cybersecurity awareness and training framework for remote working employees. The main objective of this research is to propose a CAT framework for cybersecurity awareness and training that will help organizations to evaluate and measure their employees’ capability in the cybersecurity domain. The proposed CAT framework will assist different organizations in effectively and efficiently managing security-related issues and challenges to protect their assets and critical information. The developed CAT framework consists of three key levels and twenty-five core practices. Case studies are conducted to evaluate the usefulness of the CAT framework in cybersecurity-based organizational settings in a real-world environment. The case studies’ results showed that the proposed CAT framework can identify employees’ capability levels and help train them to effectively overcome the cybersecurity issues and challenges faced by the organizations.
Security is an essential attribute of high‐quality software. However, effectively incorporating security practices into different phases of the software development life cycle (SDLC) remains challenging. Owing to less mature secure testing processes, organizations are prone to ineffective testing practices for defect detection, including severe security‐related failures. Thus, in this study, we present a maturity model for secure software testing (MMSST) to assist software development organizations in improving the secure testing of software applications. We conducted a multivocal literature review and identified 68 primary studies from the formal and gray literature. Then, based on the available evidence, 27 process areas were identified to develop the proposed MMSST. The MMSST includes five main categories: governance, contrive and design, execution, deployment and configuration, and mature. The MMSST was subsequently evaluated using case studies related to practical environments. Results demonstrate that the proposed MMSST is useful for estimating the maturity level of an organization with respect to the secure testing phase of the SDLC. The participants of the case studies also agreed that the proposed MMSST is useful in terms of structure, user satisfaction, and ease of use. We believe that the proposed MMSST can help organizations evaluate and improve software security testing practices. In addition, the proposed MMSST is expected to provide researchers and industry practitioners with an effective foundation for developing new secure testing approaches and tools.
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