Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

Data center resilience assessment : storage, networking and security.

Data centers (DC) are the core of the national cyber infrastructure. With the incredible growth of critical data volumes in financial institutions, government organizations, and global companies, data centers are becoming larger and more distributed posing more challenges for operational continuity in the presence of experienced cyber attackers and occasional natural disasters. The main objective of this research work is to present a new methodology for data center resilience assessment, this methodology consists of: • Define Data center resilience requirements. • Devise a high level metric for data center resilience. • Design and develop a tool to validate and the metric. Since computer networks are an important component in the data center architecture, this research work was extended to investigate computer network resilience enhancement opportunities within the area of routing protocols, redundancy, and server load to minimize the network down time and increase the time period of resisting attacks. Data center resilience assessment is a complex process as it involves several aspects such as: policies for emergencies, recovery plans, variation in data center operational roles, hosted/processed data types and data center architectures. However, in this dissertation, storage, networking and security are emphasized. The need for resilience assessment emerged due to the gap in existing reliability, availability, and serviceability (RAS) measures. Resilience as an evaluation metric leads to better proactive perspective in system design and management. The proposed Data center resilience assessment portal (DC-RAP) is designed to easily integrate various operational scenarios. DC-RAP features a user friendly interface to assess the resilience in terms of performance analysis and speed recovery by collecting the following information: time to detect attacks, time to resist, time to fail and recovery time. Several set of experiments were performed, results obtained from investigating the impact of routing protocols, server load balancing algorithms on network resilience, showed that using particular routing protocol or server load balancing algorithm can enhance network resilience level in terms of minimizing the downtime and ensure speed recovery. Also experimental results for investigating the use social network analysis (SNA) for identifying important router in computer network showed that the SNA was successful in identifying important routers. This important router list can be used to redundant those routers to ensure high level of resilience. Finally, experimental results for testing and validating the data center resilience assessment methodology using the DC-RAP showed the ability of the methodology quantify data center resilience in terms of providing steady performance, minimal recovery time and maximum resistance-attacks time. The main contributions of this work can be summarized as follows: • A methodology for evaluation data center resilience has been developed. • Implemented a Data Center Resilience Assessment Portal (D$-RAP) for resilience evaluations. • Investigated the usage of Social Network Analysis to Improve the computer network resilience

Securing Internet Protocol (IP) Storage: A Case Study

Storage networking technology has enjoyed strong growth in recent years, but security concerns and threats facing networked data have grown equally fast. Today, there are many potential threats that are targeted at storage networks, including data modification, destruction and theft, DoS attacks, malware, hardware theft and unauthorized access, among others. In order for a Storage Area Network (SAN) to be secure, each of these threats must be individually addressed. In this paper, we present a comparative study by implementing different security methods in IP Storage network.Comment: 10 Pages, IJNGN Journa

Near infrared spectroscopy for fibre based gas detection

Gas sensing systems based on fibre optic linked near infra red absorption cells are potentially a flexible and effective tool for monitoring accumulations of hazardous and noxious gases in enclosed areas such as tunnels and mines. Additionally the same baseline technology is readily modified to measure concentrations of hydrocarbon fuels - notably but not exclusively methane, and monitoring emissions of greenhouse gases. Furthermore the system can be readily implemented to provide intrinsically safe monitoring over extensive areas at up to ~250 points from a single interrogation unit. In this paper we review our work on fibre coupled gas sensing systems. We outline the basic principles through which repeatable and accurate self calibrating gas measurements may be realised, including the recover of detailed line shapes for non contact temperature and / or pressure measurements in addition to concentration assessments in harsh environments. We also outline our experience in using these systems in extensive networks operating under inhospitable conditions over extended periods extending to several years

CERN openlab Whitepaper on Future IT Challenges in Scientific Research

This whitepaper describes the major IT challenges in scientific research at CERN and several other European and international research laboratories and projects. Each challenge is exemplified through a set of concrete use cases drawn from the requirements of large-scale scientific programs. The paper is based on contributions from many researchers and IT experts of the participating laboratories and also input from the existing CERN openlab industrial sponsors. The views expressed in this document are those of the individual contributors and do not necessarily reflect the view of their organisations and/or affiliates

Fibre Channel Switch Modeling at Fibre Channel-2 Level for Large Fabric Storage Area Network Simulations using OMNeT++

Abstract—Typically, in the current enterprise data centers dedicated fabrics or networks are implemented to meet their LAN, Inter-Processor communication and storage traffic requirements. The storage traffic requirements of a group of servers are met through multiple storage area networks based on fibre channel, which has become the standard connection type. Typically, this fibre channel storage area networks are small (maximum of 32 switches/directors in a single fabric) and do not experience any scaling, stability and other performance issues.The advent of I/O consolidation in enterprise data centers for multiple traffic types to converge on to a single fabric or network (typically Ethernet platform) to reduce hardware, energy and management costs has also the potential to allow implementation of large storage area networks based on the fibre channel standards. Large storage area networks are being planned with more than two hundred switches/directors in a single fabric or network in addition to servers and storages connected to the fabric on Ethernet platforms. Even though these large storage area networks are envisioned to operate on Ethernet platform, they still have to satisfy the stringent operating and performance requirement set forth by the fibre channel standards. The two important issues of concern with large storage area networks are scaling and stability. The scaling and stability issues are dependent on the interactions and performance capabilities of various fabric servers located on each switch/director in the fabric in order to provide fabric services. In order to determine the extent of scaling and stability issues of a large fabric first the detailed models of the switch/director addressing the operations of the individual fabric servers are required. Next, the interactions of the switches/directors using the detailed models are to be simulated to study the scaling and stability issues.In this paper, the detailed modeling of the fibre channel switch and the fabric servers using the OMNeT++ discrete event simulator is presented first. Detailed models are developed addressing the behavior of the switch at the level-2 of the fibre channel protocol since this layer addresses the requirements and operations of various mandatory fabric services like fabric build, directory, login, nameserver, management, etc. Next, using the OMNET++ discrete event simulator large fabrics are simulated. The results from the simulation are compared against the test bed traffic and the accuracy is demonstrated. Also, results and analysis of multiple simulations with increasing fabric size are presented

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller