ARcode: HPC Application Recognition Through Image-encoded Monitoring Data

Knowing HPC applications of jobs and analyzing their performance behavior play important roles in system management and optimizations. The existing approaches detect and identify HPC applications through machine learning models. However, these approaches rely heavily on the manually extracted features from resource utilization data to achieve high prediction accuracy. In this study, we propose an innovative application recognition method, ARcode, which encodes job monitoring data into images and leverages the automatic feature learning capability of convolutional neural networks to detect and identify applications. Our extensive evaluations based on the dataset collected from a large-scale production HPC system show that ARcode outperforms the state-of-the-art methodology by up to 18.87% in terms of accuracy at high confidence thresholds. For some specific applications (BerkeleyGW and e3sm), ARcode outperforms by over 20% at a confidence threshold of 0.8

Fine-grained characterization of edge workloads

Edge computing is an emerging paradigm within the field of distributed computing, aimed at bringing data processing capabilities closer to the data-generating sources to enable real-time processing and reduce latency. However, the lack of representative data in the literature poses a significant challenge for evaluating the effectiveness of new algorithms and techniques developed for this paradigm. A part of the process towards alleviating this problem includes creating realistic and relevant workloads for the edge computing community. Research has already been conducted towards this goal, but resulting workload characterizations from these studies have been shown to not give an accurate representation of the workloads. This research gap highlights the need for developing new methodologies that can accurately characterize edge computing workloads. In this work we propose a novel methodology to characterize edge computing workloads, which leverages hardware performance counters to capture the behavior and characteristics of edge workloads in high detail. We explore the concept of representing workloads in a high-dimensional space, and develop a "proof-of-concept" classification model, that classifies workloads on a continuous "imprecise" data spectrum, to demonstrate the effectiveness and potential of the proposed characterization methodology. This research contributes to the field of edge computing by identifying and addressing the limitations of existing edge workload characterization techniques, and also opens up further avenues of research with regards to edge computing workload characterization

A Survey of FPGA Optimization Methods for Data Center Energy Efficiency

This article provides a survey of academic literature about field programmable gate array (FPGA) and their utilization for energy efficiency acceleration in data centers. The goal is to critically present the existing FPGA energy optimization techniques and discuss how they can be applied to such systems. To do so, the article explores current energy trends and their projection to the future with particular attention to the requirements set out by the European Code of Conduct for Data Center Energy Efficiency. The article then proposes a complete analysis of over ten years of research in energy optimization techniques, classifying them by purpose, method of application, and impacts on the sources of consumption. Finally, we conclude with the challenges and possible innovations we expect for this sector.Comment: Accepted for publication in IEEE Transactions on Sustainable Computin

A survey of intrusion detection techniques in Cloud

Cloud computing provides scalable, virtualized on-demand services to the end users with greater flexibility and lesser infrastructural investment. These services are provided over the Internet using known networking protocols, standards and formats under the supervision of different managements. Existing bugs and vulnerabilities in underlying technologies and legacy protocols tend to open doors for intrusion. This paper, surveys different intrusions affecting availability, confidentiality and integrity of Cloud resources and services. It examines proposals incorporating Intrusion Detection Systems (IDS) in Cloud and discusses various types and techniques of IDS and Intrusion Prevention Systems (IPS), and recommends IDS/IPS positioning in Cloud architecture to achieve desired security in the next generation networks