We consider the problem of simultaneous scheduling and resource allocation of an incoming flow of requests to a set of computing units. By representing each computing unit as a node, we model the overall system as a multi-queue scheme. Inspired by congestion control approaches in communication networks, we propose an AIMD-like (additive increase multiplicative decrease) admission control policy that is stable irrespective of the total number of nodes and AIMD parameters. The admission policy allows us to establish an event-driven discrete model, triggered by a locally identifiable enabling condition. Subsequently, we propose a decentralized resource allocation strategy via a simple nonlinear state feedback controller, guaranteeing global convergence to a bounded set in finite time. Last, we reveal the connection of these properties with Quality of Service specifications, by calculating local queuing time via a simple formula consistent with Little's Law.
This paper studies optimal scheduling and resource allocation under allowable over-scheduling. Formulating an optimisation problem where over-scheduling is embedded, we derive an optimal solution that can be implemented by means of a new additive increase multiplicative decrease (AIMD) algorithm. After describing the AIMD-like scheduling mechanism as a switching system, we show convergence of the scheme, based on the joint spectral radius of symmetric matrices, and propose two methods for fitting an optimal AIMD tuning to the optimal solution derived. Finally, we demonstrate the overall optima…
In a federated setting, agents coordinate with a central agent or a server to solve an optimization problem in which agents do not share their information with each other. Wirth and his co-authors, in a recent paper, describe how the basic additive-increase multiplicative-decrease (AIMD) algorithm can be modified in a straightforward manner to solve a class of optimization problems for federated settings for a single shared resource with no inter-agent communication. The AIMD algorithm is one of the most successful distributed resource allocation algorithms currently deployed in practice. It i…
Cellular networks provide communication for different applications. Some applications have strict and very short latency requirements, while others require high bandwidth with varying priorities. The challenge of satisfying the requirements grows in congested traffic where some packets might miss their deadlines. Unfortunately, we prove that the problem is NP-Hard. To overcome this, we propose a new scheduling policy for packets with multiple priorities, latency requirements, and strict deadlines. To alleviate the complexity, our solution incorporates a novel time domain relaxation solved by l…
In wireless caching networks, the design of the content delivery method must consider random user requests, caching states, network topology, and interference management. In this paper, we establish a general framework for content delivery in wireless caching networks without stringent assumptions that restrict the network structure, delivery link, and interference model. Based on the framework, we propose a dynamic and distributed link scheduling and power allocation scheme for content delivery that is assisted by belief-propagation (BP) algorithms. Considering content-requesting users and po…
Max-Min Fair Sensor Scheduling: Game-theoretic Perspective and Algorithmic Solution
Hong Kong University of Science and Technology（香港科技大学）；KTH Royal Institute of Technology（皇家理工学院）；Institute of Systems Science, Chinese Academy of Sciences（中国科学院系统科学研究所）
Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）；Research Center for Information Technology (FZI)（信息技术研究中心）
In order to fully exploit the advantages inherent to cooperating heterogeneous multi-robot teams, sophisticated coordination algorithms are essential. Time-extended multi-robot task allocation approaches assign and schedule a set of tasks to a group of robots such that certain objectives are optimized and operational constraints are met. This is particularly challenging if cooperative tasks, i.e. tasks that require two or more robots to work directly together, are considered. In this paper, we present an easy-to-implement criterion to validate the feasibility, i.e. executability, of solutions …
Goal-oriented Resource Allocation for Collaborative Integrated Sensing and Communication
CentraleSupélec, Université Paris-Saclay（中央苏佩莱克高等电力学院，巴黎萨克雷大学）；University of Engineering and Technology, Vietnam National University, Hanoi（越南国立大学河内工程与技术大学）
Agentic DDQN-Based Scheduling for Licensed and Unlicensed Band Allocation in Sidelink Networks
Department of Electronics and Electrical Engineering（电子与电气工程系）；Institute of Communications Engineering（通讯工程研究所）；National Yang Ming Chiao Tung University（阳明交通大学）；Research Center for Information Technology Innovation（信息技术创新研究中心）；Academia Sinica（台湾“中央研究院”）；Bradley Department of Electrical and Computer Engineering（电气与计算机工程系）
In this paper, we optimize user scheduling, power allocation and beamforming in distributed multiple-input multiple-output (MIMO) networks implementing user-centric clustering. We study both the coherent and non-coherent transmission modes, formulating a weighted sum rate maximization problem for each; finding the optimal solution to these problems is known to be NP-hard. We use tools from fractional programming, block coordinate descent, and compressive sensing to construct an algorithm that optimizes the beamforming weights and user scheduling and converges in a smooth non-decreasing pattern…
