Systems performance analysis is an important skill for all computer users, whether you're trying to understand why your laptop is slow, or optimizing the performance of a large-scale production environment. It is the study of both operating system (kernel) and application performance, but can also lead to more specialized performance topics, for specific languages or applications.
This course gives an introduction to modeling, analysis and simulation of computer and networking systems. The focus of the course is on discrete-event simulation. Simulation is widely used to evaluate systems in general, computer and communication networks in particular. In this course we will emphasize the simulation of wired and wireless communication systems. Some topics of the course are:
computer system performance analysis raj jain pdf 13
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My research interests are in the area of computer architecture, with emphasis on the design of server systems. I work on the entire computing stack, from server software and operating systems, to networks and processor microarchitecture. My current research projects include FPGA accelerator integration into server environments (e.g., Intel HARP, Microsoft Catapult, and Amazon F1), FPGA programmability (e.g., virtual memory and high-level synthesis), accelerators for machine learning (e.g., transformers and convolutional neural networks), efficient network processing and software-defined networking, speculative performance and energy-enhancing techniques for high-performance processors, and programming models and mechanisms for emerging memory technologies (e.g., HBM and 3D XPoint).
Furthermore, the previous works did not show detailed performance analyses of data transactions in the system. Very few works in literature considered these issues in a comprehensive manner, especially using queuing network models. Bouloukakis et al. in a recent work, ref. [37] presented several queuing models to represent QoS settings of IoT interactions. Nevertheless, the models are for different purposes of performance analysis without a proper consideration of system/network architecture. While, we propose in our study the adoption of a queuing network based message exchange architecture to represent the data transaction in an edge/fog infrastructure for smart buildings. Volochiy et al. in the most related work, ref. [38] proposed a queuing network for availability and safety assessment of data services in a general IoT infrastructure. We extensively propose a comprehensive queuing network based message exchange architecture to capture the data transaction in a specific IoT sensor network for smart buildings for the sake of performance evaluation. Our study presents a significant progress and contribution compared to the work [38], as well as many other above-mentioned works in the performance assessment of IoT sensor networks in smart buildings using queuing networks.
Among the above-cited papers, none explored layers of edge and fog. Previous work has also not explored the analysis of the impact of resource capacity variation on system performance. Furthermore, this work considered sensors grouped by location, an essential characteristic when monitoring more than one environment. Therefore, this paper proposes a queuing network based message exchange architecture to evaluate IoT systems for smart buildings supported by fog-edge. The contributions of this paper are as follows:
Some previous works adopted Petri nets to represent the data flows in a system for availability evaluation, mainly. Some others used queuing Petri nets for performance evaluation but not at a detailed level. Very few works presented comprehensive performance evaluation with detailed sensitivity analysis using DoE to assimilate the impact of different factors on the system performance, especially using queuing network models. As above mentioned, refs. [37,38] are the most related works that presented the use of queuing models for comprehensive performance assessment of data transactions and services in IoT infrastructures. We propose to use a queuing network-based message exchange architecture to comprehend the exact performance behaviors and evaluation of the data transactions. We employ a common type of queuing model but extensively construct a queuing network to represent the data transactions in an edge/fog based IoT infrastructure for smart buildings.
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