Energy efficiency for edge multimedia elastic applications

With the rise of edge computing paradigms, multimedia applications will have to tackle unprecedented management issues, pursuing an optimal balance between performance, Quality of Service (QoS), and power consumption. In this paper, we investigate a novel paradigm to deploy multimedia elastic applications at the edge in a very energy-efficient manner. Our approach is based on pre-provisioning virtual resources that remain \u201cfrozen\u201d until the application scales out. Frozen resources are treated in a special way by the infrastructure, leveraging aggressive power-saving mechanisms that keep negligible their impact on energy consumption and performance. We report extensive measurements on QoS and power consumption that we carried out in a real testbed, which is the first working implementation of the proposed paradigm. Our work shows how resource utilization and performance can be increased by leveraging SDN technologies and conscious setting of cloud parameters. We investigate the trade-off between performance and power consumption (i.e., energy efficiency), in relation to different consolidation strategies. Finally, we measure power consumption and estimate energy saving for an elastic video transcoding application deployed at the network edge

Boosting energy efficiency and quality of service through orchestration tools

In this paper, we describe a novel paradigm to pre-provision virtual machines (VMs) in an energy-efficient manner for cloud elastic applications. Our approach is based on the definition of dynamic context for VMs, which can be easily updated by software orchestration tools. We show the feasibility of our approach and improvement over existing state of the art with an experimental setup

Industrial Control System-Anomaly Detection Dataset (ICS-ADD) for Cyber-Physical Security Monitoring in Smart Industry Environments

The increasing integration of cyber-physical systems in industrial environments has underscored the critical need of robust security mechanisms to counteract evolving cyber threats. To allow a full performance evaluation of these security mechanisms as well as the extension of their detection skills concerning new cyber-physical-attacks, this paper introduces an open-source dataset, called Industrial Control System - Anomaly Detection Dataset (ICS-ADD). ICS-ADD would like to be a valuable resource for researchers and practitioners who aim to develop, test, and benchmark new cyber-physical security monitoring and detection technologies. ICS-ADD comprises raw network traffic captures of an industrial control system (ICS) subjected to a variety of simulated cyber-attacks, including but not limited to denial of service (DoS), man-in-the-middle (MITM), and malware infiltration. In addition to raw network traffic, ICS-ADD includes the output of two widely utilized open-source security monitoring tools, OSSIM (Open Source Security Information Management) and Suricata, which offer insights concerning the detection and analysis capabilities of existing security frameworks against threats. The analysis appearing in this paper highlights the complexity and variety of modern cyber threats in industrial environments and the novelty of ICS-ADD with respect to publicly available datasets. The reported performance analysis of OSSIM and Suricata by using ICS-ADD reveals areas of improvement for the detection of new attacks, which will be object of future research concerning the protection of industrial control systems