Cluster-Aware LVP: Enhancing Task Allocation with Growth Dynamics

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Cluster-Aware LVP: Enhancing Task Allocation with Growth Dynamics. / Акинфиев, Иван Андреевич ; Tarasova, E.

In: IFAC-PapersOnLine, Vol. 59, No. 14, 2025, p. 155-160.

Research output: Contribution to journal › Conference article › peer-review

BibTeX

@article{c7d8a4d8f2e8447fb88a00990b6c3279,

title = "Cluster-Aware LVP: Enhancing Task Allocation with Growth Dynamics",

abstract = "This paper presents a novel decentralized algorithm for adaptive task allocation and load balancing in multi-agent systems operating under heterogeneous and evolving workloads. The proposed Cluster-Aware Local Voting Protocol (CaLVP) integrates local voting mechanisms with cluster-specific parameter tracking to enable context-sensitive scheduling decisions. Each agent independently adjusts its routing strategy based on performance feedback and statistical characteristics of task clusters, allowing the system to dynamically adapt to real-time load conditions. The algorithm builds on the principles of stochastic approximation and consensus-based coordination, offering theoretical robustness under noise and minimal communication assumptions. Experimental results on a real-world call center dataset demonstrate the effective-ness of the approach in minimizing agent workload imbalance and average task waiting time. The method is scalable, robust to dynamic task streams, and suitable for applications requiring fine-grained, decentralized control. {\textcopyright} {\textcopyright} 2025 The Authors.",

keywords = "adaptive control, cluster control, load-balancing, multi-agent systems, task-redistribution, Adaptive control systems, Approximation algorithms, Approximation theory, Decentralized control, Dynamics, Intelligent agents, Resource allocation, Stochastic control systems, Stochastic systems, Adaptive Control, Adaptive task allocations, Cluster control, Decentralized algorithms, Growth dynamics, Load-Balancing, Multiagent systems (MASs), Task allocation, Task re distributions, Voting protocols, Multi agent systems",

author = "Акинфиев, {Иван Андреевич} and E. Tarasova",

note = "Export Date: 16 February 2026; Cited By: 0; Conference name: 15th IFAC Workshop on Adaptive and Learning Control Systems, ALCOS 2025; Conference location: Mexico City; Conference date: 2025-07-02 through 2025-07-04",

year = "2025",

doi = "10.1016/j.ifacol.2025.12.142",

language = "Английский",

volume = "59",

pages = "155--160",

journal = "IFAC-PapersOnLine",

issn = "2405-8971",

publisher = "Elsevier",

number = "14",

}

RIS

TY - JOUR

T1 - Cluster-Aware LVP: Enhancing Task Allocation with Growth Dynamics

AU - Акинфиев, Иван Андреевич

AU - Tarasova, E.

N1 - Export Date: 16 February 2026; Cited By: 0; Conference name: 15th IFAC Workshop on Adaptive and Learning Control Systems, ALCOS 2025; Conference location: Mexico City; Conference date: 2025-07-02 through 2025-07-04

PY - 2025

Y1 - 2025

N2 - This paper presents a novel decentralized algorithm for adaptive task allocation and load balancing in multi-agent systems operating under heterogeneous and evolving workloads. The proposed Cluster-Aware Local Voting Protocol (CaLVP) integrates local voting mechanisms with cluster-specific parameter tracking to enable context-sensitive scheduling decisions. Each agent independently adjusts its routing strategy based on performance feedback and statistical characteristics of task clusters, allowing the system to dynamically adapt to real-time load conditions. The algorithm builds on the principles of stochastic approximation and consensus-based coordination, offering theoretical robustness under noise and minimal communication assumptions. Experimental results on a real-world call center dataset demonstrate the effective-ness of the approach in minimizing agent workload imbalance and average task waiting time. The method is scalable, robust to dynamic task streams, and suitable for applications requiring fine-grained, decentralized control. © © 2025 The Authors.

AB - This paper presents a novel decentralized algorithm for adaptive task allocation and load balancing in multi-agent systems operating under heterogeneous and evolving workloads. The proposed Cluster-Aware Local Voting Protocol (CaLVP) integrates local voting mechanisms with cluster-specific parameter tracking to enable context-sensitive scheduling decisions. Each agent independently adjusts its routing strategy based on performance feedback and statistical characteristics of task clusters, allowing the system to dynamically adapt to real-time load conditions. The algorithm builds on the principles of stochastic approximation and consensus-based coordination, offering theoretical robustness under noise and minimal communication assumptions. Experimental results on a real-world call center dataset demonstrate the effective-ness of the approach in minimizing agent workload imbalance and average task waiting time. The method is scalable, robust to dynamic task streams, and suitable for applications requiring fine-grained, decentralized control. © © 2025 The Authors.

KW - adaptive control

KW - cluster control

KW - load-balancing

KW - multi-agent systems

KW - task-redistribution

KW - Adaptive control systems

KW - Approximation algorithms

KW - Approximation theory

KW - Decentralized control

KW - Dynamics

KW - Intelligent agents

KW - Resource allocation

KW - Stochastic control systems

KW - Stochastic systems

KW - Adaptive Control

KW - Adaptive task allocations

KW - Cluster control

KW - Decentralized algorithms

KW - Growth dynamics

KW - Load-Balancing

KW - Multiagent systems (MASs)

KW - Task allocation

KW - Task re distributions

KW - Voting protocols

KW - Multi agent systems

U2 - 10.1016/j.ifacol.2025.12.142

DO - 10.1016/j.ifacol.2025.12.142

M3 - статья в журнале по материалам конференции

VL - 59

SP - 155

EP - 160

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

SN - 2405-8971

IS - 14

ER -

ID: 148838065