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resource allocation optimization for quantitative service differentiation on server clusters

JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2008年第9期68卷 1250-1262页

作者： Zhou, Xiaobo Ippoliti, Dennis Univ Colorado Dept Comp Sci Colorado Springs CO 80933 USA

The goal of service differentiation is to provide different service quality levels to meet changing system configuration and resource availability and to satisfy different requirements and expectations of applications and users. In this paper, we investigate the problem of quantitative service differentiation on cluster-based delay-sensitive servers. The goal is to support a system-wide service quality optimization with respect to resource allocation on a computer system while provisioning proportionality fairness to clients. We first propose and promote a square-root proportional differentiation model. Interestingly, both popular delay factors, queueing delay and slowdown, are reciprocally proportional to the allocated resource usage. We formulate the problem of quantitative service differentiation as a generalized resource allocation optimization towards the minimization of system delay, defined as the sum of weighted delay of client requests. We prove that the optimization-based resource allocation scheme essentially provides square-root proportional service differentiation to clients. We then study the problem of service differentiation provisioning from an important relative performance metric, slowdown. We give a closed-form expression of the expected slowdown of a popular heavy-tailed workload model with respect to resource allocation on a server cluster. We design a two-tier resource management framework, which integrates a dispatcher-based node partitioning scheme and a server-based adaptive process allocation scheme. We evaluate the resource allocation framework with different models via extensive simulations. Results show that the square-root proportional model provides service differentiation at a minimum cost of system delay. The two-tier resource allocation framework can provide fine-grained and predictable service differentiation on cluster-based servers. (C) 2008 Elsevier Inc. All rights reserved.

关键词： quantitative service differentiation slowdown system quality resource allocation optimization square-root proportional model

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resource allocation optimization in Mobile Multiuser Molecular Communication by Deep Neural Network

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IEEE TRANSACTIONS ON MOLECULAR BIOLOGICAL AND MULTI-SCALE COMMUNICATIONS 2024年第3期10卷 409-421页

作者： Cheng, Zhen Yan, Jun Sun, Jie Zhang, Shubin Chi, Kaikai Zhejiang Univ Technol Sch Comp Sci & Technol Hangzhou 310023 Peoples R China

Mobile molecular communication (MMC) is expected to be a promising technology for drug delivery. This paper studies a multiuser MMC system in a three-dimensional diffusive environment, which is composed of multiple transmitter nanomachines and one receiver nanomachine. Considering that all transmitter nanomachines release the same type of molecules for information transmission, the mechanism of time division multiple access (TDMA) is employed in this system. Under the release resource constraint which requires that the total number of released molecules of all transmitter nanomachines is fixed, the resource allocation optimization plays a significant role in the performance of this system. When the environmental variables in this multiuser MMC system change, the traditional optimization algorithms need to reoptimize the resource allocation to minimize the average bit error probability (BEP) of this system, which results in more run time. In order to reduce the run time, we propose an algorithm designed based on deep neural network (DNN) to obtain the optimal resource allocation scheme. For the trained DNN, once the input is given, it does not need to re-execute the optimization process and the output can be instantaneously obtained. The numerical results show that the proposed algorithm has a shorter run time and lower average BEP compared with other existing traditional optimization algorithms used in MMC, including bisection algorithm and genetic algorithm. The optimization results are approximate to the optimal solutions obtained by the exhaustive search. These analysis results can provide help in designing a multiuser MMC with optimal resource allocation.

关键词： Nanobioscience Transmitters optimization resource management Receivers Drugs Artificial neural networks Multiuser mobile molecular communication deep neural network resource allocation optimization

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resource allocation optimization OF DISTRIBUTED RADAR IMAGING SYSTEM BASED ON SPATIAL SPECTRUM ANALYSIS 39

RESOURCE ALLOCATION OPTIMIZATION OF DISTRIBUTED RADAR IMAGIN...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Xu, Fanyun Wang, Rufei Mao, Deqing Zhang, Yongchao Zhang, Yin Huang, Yulin Yang, Jianyu Univ Elect Sci & Technol China Chengdu 611731 Sichuan Peoples R China

ISBN: (纸本)9781538691540

Distributed radar imaging utilizes expanded array elements in space to form a large aperture and obtain high imaging resolution. A great number of array elements are required in traditional distributed radar system which uses multiple platforms. The distribution of spatial spectrum is affected by the number and the signal form of array elements. In this research, to improve the utilization efficiency of platform resources, a resource allocation optimization method based on Unmanned Aerial Vehicle(UAV) is proposed. It chooses the optimized bandwidth and sampling frequency points of array elements by analyzing the relationship between spatial spectrum and imaging performance. This method can use a small number of UAVs to maintain high imaging resolution. Simulation results verified the effectiveness of the resource allocation optimization method for image quality improvement.

关键词： resource allocation optimization distributed radar radar imaging wavenumber domain spatial spectrum

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An Adaptive Continuous-Time Algorithm for Nonsmooth Convex resource allocation optimization

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第11期67卷 6038-6044页

作者： Jia, Wenwen Liu, Na Qin, Sitian Harbin Inst Technol Dept Math Weihai 264209 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

This article develops a novel continuous-time algorithm based on the idea of adaptive strategy for solving a resource allocation optimization with nonsmooth objective functions and constraints over multiagent network. It is proved that the state solution is globally bounded and finally converges to an optimal solution to the nonsmooth convex resource allocation problem. Compared with the existing algorithms, the strong/strict convexity of the objective function is relaxed and only convexity is required. Moreover, by employing an exact penalty approach for the distributed optimization, the primal-dual variables is avoided to introduce. Therefore, the proposed algorithm has a simple structure with low dimensionality of state variables. To show the effectiveness and practicability of the presented algorithm, a numerical example and an application in power system are presented.

关键词： optimization resource management Complexity theory Aerospace electronics Technological innovation Linear programming Laplace equations Continuous-time algorithm multiagent network resource allocation optimization

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Comparison of Search-Based Software Engineering Algorithms for resource allocation optimization

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JOURNAL OF INTELLIGENT SYSTEMS 2016年第4期25卷 629-642页

作者： Bibi, Nazia Anwar, Zeeshan Ahsan, Ali Ctr Adv Studies Engn Dept Comp Engn Islamabad 44000 Pakistan Ctr Adv Studies Engn Dept Engn Management Islamabad 44000 Pakistan

A project manager balances the resource allocation using resource leveling algorithms after assigning resources to project activities. However, resource leveling does not ensure optimized allocation of resources. Furthermore, the duration and cost of a project may increase after leveling resources. The objectives of resource allocation optimization used in our research are to (i) increase resource utilization, (ii) decrease project cost, and (iii) decrease project duration. We implemented three search-based software engineering algorithms, i.e. multiobjective genetic algorithm, multiobjective particle swarm algorithm (MOPSO), and elicit nondominated sorting evolutionary strategy. Twelve experiments to optimize the resource allocation are performed on a published case study. The experimental results are analyzed and compared in the form of Pareto fronts, average Pareto fronts, percent increase in resource utilization, percent decrease in project cost, and percent decrease in project duration. The experimental results show that MOPSO is the best technique for resource optimization because after optimization with MOPSO, resource utilization is increased and the project cost and duration are reduced.

关键词： resource allocation search-based software engineering evolutionary algorithms resource allocation optimization skills management

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Delayed resource allocation optimization with Applications in Population Dynamics

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IFAC-PapersOnLine 2016年第10期49卷 1-6页

作者： Verriest, Erik I. Dirr, Gunther Helmke, Uwe School of ECE Georgia Institute of Technology AtlantaGA United States Institute of Mathematics University of Würzburg Würzburg97074 Germany

The contribution of this paper is twofold. First a simplified derivation of the maximum principle for optimal control problems with delay is presented based on first principles. Then the result is applied to an investigation into the dynamic optimal resource allocation problem in population dynamics involving delayed action. © 2016

关键词： resource allocation Optimal control systems Population dynamics Delay systems First principles Maximum principle for optimal control Optimal controls Optimal resource allocation resource allocation optimization

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resource allocation optimization STRATEGY USING IMPROVED DIFFERENTIAL EVOLUTION ALGORITHM IN MULTI-RADIO MULTI-CHANNEL WSNS

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2020年第3期16卷 923-937页

作者： Qiu, Bin Shantou Polytech Dept Comp Haojiang Dist 515078 Shantou Peoples R China

In order to improve the performance of multi-radio and multi-channel wireless sensor networks (WSNs), a resource allocation optimization strategy using improved differential evolution algorithm in multi-radio and multi-channel WSNs is proposed. Firstly, considering the interaction among WSNs node energy consumption, channel allocation, power control and slot allocation, the channel model of multi-node communication and the interference model of the system are constructed. Then, taking the interference and conflict of links as constraints, and taking the objects that reduce energy consumption, increase network capacity and improve the balance of resource allocation as objective functions, a multi-objective optimization model of resource allocation is constructed. The establishment of multi-objective optimization model highlights the trade-off among different objectives. Two-population differential evolution algorithm is employed to solve the model constructed in this paper, and the results are compared with those of standard differential evolution algorithms. The experimental results show that the performance of the network in all aspects has been greatly improved by the use of the proposed algorithm. The simulation results show that the link collisions can be effectively avoided and the network interference is reduced with the utilization of the proposed algorithm. Compared with the existing algorithms, the capacity of key links can be raised by two to three times, and the network capacity and resource allocation balance are improved as a result of application of the proposed algorithm.

关键词： resource allocation optimization Wireless sensor networks Multi-radio multi-channel Improved differential evolution algorithms Multi-objective optimization model Slot allocation Link capacity

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Improving wastewater treatment plant performance: an ANN-based predictive model for managing average daily overflow and resource allocation optimization using Tabu search

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Asian Journal of Civil Engineering 2024年第2期25卷 1427-1441页

作者： Ismail, Rabah Civil Engineering Department Jadara University Irbid Jordan

The effective management of wastewater treatment facilities is of paramount importance to safeguard the environment and ensure the well-being of the general public. This research paper presents a novel approach utilizing an artificial neural network (ANN) as a predictive model to enhance the precision of estimating the average daily overflow in wastewater treatment plants. In addition, the study aims to optimize the allocation of resources at these plants. The effectiveness of the model was assessed by employing various metrics, including root mean square error (RMSE), mean squared error (MSE), mean absolute error (MAE), and R-squared (R ²). The examination of various neural network topologies demonstrated that higher numbers of layers resulted in improved accuracy and precision. However, additional research is required to reduce prediction errors and improve the model’s ability to explain phenomena, thus making it more suitable for practical application. The validation of these models in operational settings necessitates the undertaking of fieldwork, establishing collaborations with industry stakeholders, and actively engaging with policymakers. The integration of real-time data and advanced neural network methodologies has the potential to significantly improve both the accuracy and flexibility of the system. To enhance the progress of sustainable wastewater management, it is crucial to broaden the scope of performance indicators beyond the conventional measures of average daily overflow and resource allocation. This study establishes a theoretical framework for enhancing the efficacy of wastewater treatment facilities and advocating for environmentally conscious practices. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Artificial neural network (ANN) Average daily overflow Performance evaluation resource allocation optimization Tabu search Wastewater treatment

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Research on Kill Chain resource allocation optimization Based on Reinforcement Learning and Game Theory 2

Research on Kill Chain Resource Allocation Optimization Base...

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2nd International Conference on Computer Vision and Intelligent Technology

作者： Xiao, Hang Sun, Shangjing Li, Dong Nanjing Res Inst Elect Engn Nanjing Peoples R China

ISBN: (纸本)9798331540050;9798331540043

This paper investigates a resource allocation optimization method based on reinforcement learning and game theory for resource management in kill chain tasks. By constructing a game-theoretic model to analyze the interaction between attackers and defenders and integrating deep reinforcement learning (DRL) algorithms for adaptive strategy optimization, this approach aims to enhance the efficiency of resource allocation for the defender. The proposed method leverages a combination of game theory's strategic interaction modeling and reinforcement learning's decision-making capability in uncertain environments. Simulation results demonstrate that the method effectively optimizes resource allocation in dynamic, adversarial scenarios, improving both defense success rate and resource utilization efficiency. Additionally, the performance of the method is compared with traditional heuristic algorithms, showing a clear improvement in terms of adaptability and resource management.

关键词： Kill chain resource allocation optimization reinforcement learning game theory

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Blackboard Meets Dijkstra for resource allocation optimization 18th

Blackboard Meets Dijkstra for Resource Allocation Optimizati...

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18th International Conference on Computational Science (ICCS)

作者： Vorhemus, Christian Schikuta, Erich Univ Vienna Fac Comp Sci Wahringerstr 29 A-1090 Vienna Austria

ISBN: (纸本)9783319937137;9783319937120

This paper presents the integration of Dijkstra's algorithm into a Blackboard framework to optimize the selection of web resources from service providers. The architectural framework of the implementation of the proposed Blackboard approach and its components in a real life scenario is laid out. For justification of approach, and to show practical feasibility, a sample implementation architecture is presented.

关键词： Web-service selection resource allocation optimization Blackboard method Dijkstra algorithm

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