Accelerated Decentralized Load Balancing in Multi-Agent Networks

Decentralized load balancers are gaining in popularity because they offer scalability, resilience, and the ability to handle high-demand workloads in distributed network systems. In practice, decentralized algorithms face such network issues as connection losses, dropped packets during data transmis...

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Main Authors: Victoria Erofeeva, Oleg Granichin, Elena Volodina
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10738720/
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author Victoria Erofeeva
Oleg Granichin
Elena Volodina
author_facet Victoria Erofeeva
Oleg Granichin
Elena Volodina
author_sort Victoria Erofeeva
collection DOAJ
description Decentralized load balancers are gaining in popularity because they offer scalability, resilience, and the ability to handle high-demand workloads in distributed network systems. In practice, decentralized algorithms face such network issues as connection losses, dropped packets during data transmission, network latency. They should also account for the physical limitations of real systems. Existing works primarily consider different meta-heuristic approaches to carry out load balancing. Nevertheless, theoretically grounded algorithms that work under non-stationary conditions are of interest. In this paper, we improve the convergence rate of an existing decentralized load balancing protocol based on Local Voting Protocol (LVP) to obtain a solution that tends towards the optimal load balancing strategy over time. We propose a new Accelerated-LVP protocol and derive its parameters required to achieve the acceleration. The simulation demonstrates superiority of the proposed solution over the existing approaches in terms of convergence rate. In our experiments, we consider two scenarios: steady and bursty. In the first scenario, we observe that, on average, the proposed algorithm achieves the lowest error rate 15% faster than the nearest competitor. In the second scenario, on average, the proposed algorithm achieves an error rate that is 10% less than the nearest competitor.
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spelling doaj-art-8f1fbb786a7345b3bfcfdac65f0edb7f2025-08-20T02:12:53ZengIEEEIEEE Access2169-35362024-01-011216195416196710.1109/ACCESS.2024.348839910738720Accelerated Decentralized Load Balancing in Multi-Agent NetworksVictoria Erofeeva0https://orcid.org/0000-0001-5107-0293Oleg Granichin1https://orcid.org/0000-0002-3631-7347Elena Volodina2https://orcid.org/0009-0005-6133-9723Centre for Artificial Intelligence and Data Science, St. Petersburg State University, Saint Petersburg, RussiaCentre for Artificial Intelligence and Data Science, St. Petersburg State University, Saint Petersburg, RussiaFaculty of Mathematics and Mechanics, St. Petersburg State University, Saint Petersburg, RussiaDecentralized load balancers are gaining in popularity because they offer scalability, resilience, and the ability to handle high-demand workloads in distributed network systems. In practice, decentralized algorithms face such network issues as connection losses, dropped packets during data transmission, network latency. They should also account for the physical limitations of real systems. Existing works primarily consider different meta-heuristic approaches to carry out load balancing. Nevertheless, theoretically grounded algorithms that work under non-stationary conditions are of interest. In this paper, we improve the convergence rate of an existing decentralized load balancing protocol based on Local Voting Protocol (LVP) to obtain a solution that tends towards the optimal load balancing strategy over time. We propose a new Accelerated-LVP protocol and derive its parameters required to achieve the acceleration. The simulation demonstrates superiority of the proposed solution over the existing approaches in terms of convergence rate. In our experiments, we consider two scenarios: steady and bursty. In the first scenario, we observe that, on average, the proposed algorithm achieves the lowest error rate 15% faster than the nearest competitor. In the second scenario, on average, the proposed algorithm achieves an error rate that is 10% less than the nearest competitor.https://ieeexplore.ieee.org/document/10738720/Load balancingdecentralized networksaccelerated algorithmsnon-stationary optimizationLocal Voting Protocolnetwork disruptions
spellingShingle Victoria Erofeeva
Oleg Granichin
Elena Volodina
Accelerated Decentralized Load Balancing in Multi-Agent Networks
IEEE Access
Load balancing
decentralized networks
accelerated algorithms
non-stationary optimization
Local Voting Protocol
network disruptions
title Accelerated Decentralized Load Balancing in Multi-Agent Networks
title_full Accelerated Decentralized Load Balancing in Multi-Agent Networks
title_fullStr Accelerated Decentralized Load Balancing in Multi-Agent Networks
title_full_unstemmed Accelerated Decentralized Load Balancing in Multi-Agent Networks
title_short Accelerated Decentralized Load Balancing in Multi-Agent Networks
title_sort accelerated decentralized load balancing in multi agent networks
topic Load balancing
decentralized networks
accelerated algorithms
non-stationary optimization
Local Voting Protocol
network disruptions
url https://ieeexplore.ieee.org/document/10738720/
work_keys_str_mv AT victoriaerofeeva accelerateddecentralizedloadbalancinginmultiagentnetworks
AT oleggranichin accelerateddecentralizedloadbalancinginmultiagentnetworks
AT elenavolodina accelerateddecentralizedloadbalancinginmultiagentnetworks