Biblio

Dew Computing (DC) is a comparatively modern field with a wide range of applications. By examining how technological advances such as fog, edge and Dew computing, and distributed intelligence force us to reconsider traditional Cloud Computing (CC) to serve the Internet of Things. A new dew estimation theory is presented in this article. The revised definition is as follows: DC is a software and hardware cloud-based company. On-premises servers provide autonomy and collaborate with cloud networks. Dew Calculation aims to enhance the capabilities of on-premises and cloud-based applications. These categories can result in the development of new applications. In the world, there has been rapid growth in Information and Communication Technology (ICT), starting with Grid Computing (GC), CC, Fog Computing (FC), and the latest Edge Computing (EC) technology. DC technologies, infrastructure, and applications are described. We’ll go through the newest developments in fog networking, QoE, cloud at the edge, platforms, security, and privacy. The dew-cloud architecture is an option concerning the current client-server architecture, where two servers are located at opposite ends. In the absence of an Internet connection, a dew server helps users browse and track their details. Data are primarily stored as a local copy on the dew server that starts the Internet and is synchronized with the cloud master copy. The local dew pages, a local online version of the current website, can be browsed, read, written, or added to the users. Mapping between different Local Dew sites has been made possible using the dew domain name scheme and dew domain redirection.

Nowadays, cybersecurity threat is a big challenge to all organizations that present their services over the Internet. Distributed Denial of Service (DDoS) attack is the most effective and used attack and seriously affects the quality of service of each E-organization. Hence, mitigation this type of attack is considered a persistent need. In this paper, we used Network Load Balancing (NLB) and High Availability Proxy (HAProxy) as mitigation techniques. The NLB is used in the Windows platform and HAProxy in the Linux platform. Moreover, Internet Information Service (IIS) 10.0 is implemented on Windows server 2016 and Apache 2 on Linux Ubuntu 16.04 as web servers. We evaluated each load balancer efficiency in mitigating synchronize (SYN) DDoS attack on each platform separately. The evaluation process is accomplished in a real network and average response time and average CPU are utilized as metrics. The results illustrated that the NLB in the Windows platform achieved better performance in mitigation SYN DDOS compared to HAProxy in the Linux platform. Whereas, the average response time of the Window webservers is reduced with NLB. However, the impact of the SYN DDoS on the average CPU usage of the IIS 10.0 webservers was more than those of the Apache 2 webservers.