The Impact of Terrestrial Radiation on FPGAs in Data Centers

Field programmable gate arrays (FPGAs) are used in large numbers in data centers around the world. They are used for cloud computing and computer networking. The most common type of FPGA used in data centers are re-programmable SRAM-based FPGAs. These devices offer potential performance and power consumption savings. A single device also carries a small susceptibility to radiation-induced soft errors, which can lead to unexpected behavior. This article examines the impact of terrestrial radiation on FPGAs in data centers. Results from artificial fault injection and accelerated radiation testing on several data-center-like FPGA applications are compared. A new fault injection scheme provides results that are more similar to radiation testing. Silent data corruption (SDC) is the most commonly observed failure mode followed by FPGA unavailable and host unresponsive. A hypothetical deployment of 100,000 FPGAs in Denver, Colorado, will experience upsets in configuration memory every half-hour on average and SDC failures every 0.5–11 days on average.

Vigorous IDS on Nefarious Operations and Threat Analysis Using Ensemble Machine Learning

The geometric increase in the usage of computer networking activities poses problems with the management of network normal operations. These issues had drawn the attention of network security researchers to introduce different kinds of intrusion detection systems (IDS) which monitor data flow in a network for unwanted and illicit operations. The violation of security policies with nefarious motive is what is known as intrusion. The IDS therefore examine traffic passing through networked systems checking for nefarious operations and threats, which then sends warnings if any of these malicious activities are detected. There are 2 types of detection of malicious activities, misuse detection, in this case the information about the passing network traffic is gathered, analyzed, which is then compared with the stored predefined signatures. The other type of detection is the Anomaly detection which is detecting all network activities that deviates from regular user operations. Several researchers have done various works on IDS in which they employed different machine learning (ML), evaluating their work on various datasets. In this paper, an efficient IDS is built using Ensemble machine learning algorithms which is evaluated on CIC-IDS2017, an updated dataset that contains most recent attacks. The results obtained show a great increase in the rate of detection, increase in accuracy as well as reduction in the false positive rates (FPR).

An Adaptive Routing Framework for Efficient Power Consumption in Software-Defined Datacenter Networks

Data Center Networks (DCNs) form the backbone of many Internet applications and services that have become necessary in daily life. Energy consumption causes both economic and environmental issues. It is reported that 10% of global energy consumption is due to ICT and network usage. Computer networking equipment is designed to accommodate network traffic; however, the level of use of the equipment is not necessarily proportional to the power consumed by it. For example, DCNs do not always run at full capacity yet the fact that they are supporting a lighter load is not mirrored by a reduction in energy consumption. DCNs have been shown to unnecessarily over-consume energy when they are not fully loaded. In this paper, we propose a new framework that reduces power consumption in software-defined DCNs. The proposed approach is composed of a new Integer Programming model and a heuristic link utility-based algorithm that strikes a balance between energy consumption and performance. We evaluate the proposed framework using an experimental platform, which consists of an optimization tool called LinGo for solving convex and non-convex optimization problems, the POX controller and the Mininet network emulator. Compared with the state-of-the-art approach, the equal cost multi-path algorithm, the results show that the proposed method reduces the power consumption by up to 10% when the network is experiencing a high traffic load and 63.3% when the traffic load is low. Based on these results, we outline how machine learning approaches could be used to further improve our approach in future work.

Metric Dimension on Path-Related Graphs

Graph theory has a large number of applications in the fields of computer networking, robotics, Loran or sonar models, medical networks, electrical networking, facility location problems, navigation problems etc. It also plays an important role in studying the properties of chemical structures. In the field of telecommunication networks such as CCTV cameras, fiber optics, and cable networking, the metric dimension has a vital role. Metric dimension can help us in minimizing cost, labour, and time in the above discussed networks and in making them more efficient. Resolvability also has applications in tricky games, processing of maps or images, pattern recognitions, and robot navigation. We defined some new graphs and named them s − middle graphs, s -total graphs, symmetrical planar pyramid graph, reflection symmetrical planar pyramid graph, middle tower path graph, and reflection middle tower path graph. In the recent study, metric dimension of these path-related graphs is computed.

Efficient incremental data backup of unison synchronize approach

Data communication and computer networks have enormously grown in every aspect of businesses. Computer networks are being used to offer instantaneous access to information in online libraries around the world. The popularity and importance of data communication has produced a strong demand in all sectors job for people with more computer networking expertise. Companies need workers to plan, use and manage the database system aspects of security. The security policy must apply data stored in a computer system as well as information transfer a network. This paper aimed to define computer data backup policies of the Incremental backup by using Unison synchronization as a file-synchronization tool and load balancing file synchronization management (LFSM) for traffic management. The policy is to be able to perform a full backup only at first as a one time from obtaining a copy of the data. The easiest aspect of value to assess is replacement for restoring the data from changes only and processing the correct information. As a result, the new synchronization technique was able to improve the performance of data backup and computer security system.

A Newly Proposed Prospective and Robust Computer Networking Model Architecture Based on the Infrastructure of Cloud Computing Contrivance

Computer Networking Play’s a major role for data communication or data sharing and data transmissions from one location to another, which are geographically differ, but in today’s scenario where the main and primary major concerns are not to data transfer but also utilize all resources with greater efficiency and also preserves the confidentiality and integrity of the messages with respect to speed and time with lower Bandwidth and also consume a very low computational costs with low power supply and redirect to optimality. Cloud Computing also play’s a significant role to access data at geographically different locations. So In this paper we create a fusion of Computer Networking Architecture and Cloud Computing Architecture and released a very much superior fundamentally strong Cloud computing based Computer Networking model, which works on the concepts of ‘Virtualization’. Because when the number of hardware components (Servers) drastically increases all factors which are responsible to make possible networking among nodes are also consume each resources at extreme level, and networking becomes complex and slow, that’s why we used the concept of Virtual Machine. In this paper we proposed a Computer Networking model using the concepts of Cloud Computing. This model also suitable for data transmission but also take concern the most significant feature of Computer Networking, which is Data Security. This model also used some Proxy servers/ firewalls to take concern some security mechanisms. In this paper we also proposed Communication Oriented model among the Intercluster domains that how one node which belongs to another CLOUD cluster make possible communication among other InterCLOUD clusters with respect to data security measures. In this paper we proposed three models related to this networking model, which is CLOUD Networking Infrastructure, Connection Oriented model, Communication Oriented model. The detailed description of all three models are in the upcoming sections of this paper. Keywords: Cloud computing based computer networking model, A virtual model for computer networking, Computer Networking model based on virtualization, Virtualization based computer networking model.

Balancing Collaboration and Cognitive Load to Optimize Individual and Group Desirable Difficulties

We sought to identify factors that optimize individual learning in complex, technology-enhanced learning environments. Undergraduates viewed tutorials and played a simulation-based game either alone or in groups and in either high or low cognitive load sequences and later took tests measuring comprehension of tutorials and transfer of computer networking skills. A cognitive load by collaboration interaction was found for both immediate and delayed transfer measures, but not comprehension measures. Students working in groups performed best under high cognitive load whereas students working individually performed best under low cognitive load. These findings support the notions of optimal individual and group cognitive load and have implications for leveraging technology to design learning environments that allow students to collaborate and maximize individual learning.