the proceedings contain 10 papers. the special focus in this conference is on Analytical and Stochastic Modeling Techniques and Applications. the topics include: Optimal Allocation of Tasks to Networked Comp...
ISBN:
(纸本)9783031707520
the proceedings contain 10 papers. the special focus in this conference is on Analytical and Stochastic Modeling Techniques and Applications. the topics include: Optimal Allocation of Tasks to Networked computing Facilities;revenue Management for Parallel Services with Fully Observable Queues;Deep Reinforcement Learning for Weakly Coupled MDP’s with Continuous Actions;a Lazy Abstraction Algorithm for Markov Decision Processes: theory and Initial Evaluation;queueing Analysis of an Ensemble Machine Learning system;analysis of Load Balancing Prioritization for Heterogeneous M/M/c/K Server Clusters in the Stationary Mean-Field Regime;An Algebraic Proof of the Relation of Markov Fluid Queues and QBD Processes;stability Condition for the Multi-server Job Queuing Model: Sensitivity Analysis.
the proceedings contain 152 papers. the topics discussed include: Solving SLICOT benchmarks for continuous-time algebraic Riccati equations by Hamiltonian solvers;invariant sets for discrete time-delay systems: set fa...
ISBN:
(纸本)9781479984817
the proceedings contain 152 papers. the topics discussed include: Solving SLICOT benchmarks for continuous-time algebraic Riccati equations by Hamiltonian solvers;invariant sets for discrete time-delay systems: set factorization and state representation;interval systems - construction and exploitation of diagonal Lyapunov functions;B-spline based repetitive controller revisited: error shift, higher-order polynomials and smooth pass-to-pass transition;rotating consensus and tracking of second-order multi-agent systems in 3-D under directed interaction topologies;cascade controller design using controller synthesis;modeling and optimization of bioethanol production process;parameter identification of the fermentative production of fructo-oligosaccharides by aureobasidium pullulans;and adaptive optimal control of a continuous stirred tank bioreactor.
Distributed allocation of computing tasks over network resources is meant to decrease the cost of centralized allocation. However, existing analytical models consider practically indistinguishable resources, e.g., loc...
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ISBN:
(纸本)9783031707520;9783031707537
Distributed allocation of computing tasks over network resources is meant to decrease the cost of centralized allocation. However, existing analytical models consider practically indistinguishable resources, e.g., located in the data center. Withthe rise of edge computing, it becomes important to account for the impact of diverse latency values imposed by edge/cloud data center locations. In this paper, we study the optimization of computing task allocation considering boththe delays to reach edge/cloud data centers and the response times of servers. We explicitly evaluate the resulting performance under different scenarios. We show, through numerical analysis and real experiments, that differences in delays to reach data center locations cannot be neglected. We also study the price of anarchy of a distributed implementation of the computing task allocation and unveil important properties such as the price of anarchy being generally small, except when the system is overloaded, and its maximum can be computed with low complexity.
Recent advances in AI/ML technologies have accelerated the development of various ML applications. One of the major trends in AI/ML application development is the increasing use of multiple ML models to support high-a...
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ISBN:
(纸本)9783031707520;9783031707537
Recent advances in AI/ML technologies have accelerated the development of various ML applications. One of the major trends in AI/ML application development is the increasing use of multiple ML models to support high-accuracy inference in a complex end-to-end ML serving. However, testing the right configuration of multiple ML models is expensive, and the application requirements for ML inferences are highly dependent on various factors like the quality of ML models, computing resource performance, and data quality. In this context, techniques and methods that help to emulate and analyze ML inference characteristics using queueing theory can reduce the development effort and cost for ML services encapsulating ML models but also the entire ML system. In this paper, we modeled and analyzed a queueing model for an ML systemthat uses ensemble learning as an inference method with a new rule and clarified the impacts of model design in ensemble learning on the system's performance. As a result, we demonstrate the usefulness of the analysis for understanding possible configurations and their efficiency in the ML systemthrough queueing analysis and simulation.
作者：
Rana, V.M.Civil Engineering Department
Institute of Technology NIRMA University Sarkhej Gandhinagar Highway Near Vaishnodevi Circle Gujarat Ahmedabad382421 India
the aim of this work is to present a graphical response analysis of multiple canal pool system using Matlab/Simulink based on unsteady state simulation results. Firstly, it has started by describing the system which i...
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the two main notions of control in quantum programming languages are often referred to as "quantum" control and "classical" control. Withthe latter, the control flow is based on classical inf...
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this paper establishes an optimization model for carbon reduction operation of power systems with renewable energy under the coordinated configuration of generators with traditional and new energy. Using duality theor...
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Hyperproperties are properties over sets of traces (or runs) of a system, as opposed to properties of just one trace. they were introduced in 2010 and have been much studied since, in particular via an extension of th...
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