Optimizing simultaneous autoscaling for serverless cloud computing

This paper explores resource allocation in serverless cloud computing platforms and proposes an optimization approach for autoscaling systems. Serverless computing relieves users from resource management tasks, enabling focus on application functions. However, dynamic resource allocation and function replication based on changing loads remain crucial. Typically, autoscalers in these platforms utilize threshold-based mechanisms to adjust function replicas independently. We model applications as interconnected graphs of functions, where requests probabilistically traverse the graph, triggering associated function execution. Our objective is to develop a control policy that optimally allocates resources on servers, minimizing failed requests and response time in reaction to load changes. Using a fluid approximation model and Separated Continuous Linear Programming (SCLP), we derive an optimal control policy that determines the number of resources per replica and the required number of replicas over time. We evaluate our approach using a simulation framework built with Python and simpy. Comparing against threshold-based autoscaling, our approach demonstrates significant improvements in average response times and failed requests, ranging from 15% to over 300% in most cases. We also explore the impact of system and workload parameters on performance, providing insights into the behavior of our optimization approach under different conditions. Overall, our study contributes to advancing resource allocation strategies, enhancing efficiency and reliability in serverless cloud computing platforms

Place Alexis-Nihon, Montréal, 1967

Photographie: Pierre-Richard Bisson, 1972.04.3

Place Alexis-Nihon, Montréal, 1967

Photographie: Pierre-Richard Bisson, 1972.04.30Au centre: Maison mère des Soeurs de la Congrégation de Notre-Dame convertie de 1985 à 1988 pour loger le Collège Dawson, un cégep anglophon

Separable Nonlinear Least-Squares Parameter Estimation for Complex Dynamic Systems

Nonlinear dynamic models are widely used for characterizing processes that govern complex biological pathway systems. Over the past decade, validation and further development of these models became possible due to data collected via high-throughput experiments using methods from molecular biology. While these data are very beneficial, they are typically incomplete and noisy, which renders the inference of parameter values for complex dynamic models challenging. Fortunately, many biological systems have embedded linear mathematical features, which may be exploited, thereby improving fits and leading to better convergence of optimization algorithms. In this paper, we explore options of inference for dynamic models using a novel method of separable nonlinear least-squares optimization and compare its performance to the traditional nonlinear least-squares method. The numerical results from extensive simulations suggest that the proposed approach is at least as accurate as the traditional nonlinear least-squares, but usually superior, while also enjoying a substantial reduction in computational time

Nuclear heat power systems for merchant ships : a paper presented before the Washington Section, American Society of Mechanical Engineers, December 4, 1956 /

"College of Engineering, University of Michigan, Ann Arbor, Michigan and Office of Ship Construction and Repair, Maritime Administration, Washington, D.C."--Cover.Includes bibliographical references (page 12).Mode of access: Internet.This bibliographic record is available under the Creative Commons CC0 "No Rights Reserved" license. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law