Differentiated Consensuses in Decentralized Load Balancing Problem with Randomized Topology, Noise, and Delays

Natalia Amelina, Oleg Granichin, Olga Granichina, Yuming Jiang

Результат исследований: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаярецензирование

13 Цитирования (Scopus)

Аннотация

In this paper, a new consensus problem, termed differentiated consensuses, is studied. This consensus problem is that, in a system with multiple classes, consensus is targeted for each class, which may be different among classes. Specifically, we investigate differentiated consensuses in a distributed stochastic network system of nodes (or agents), where tasks, classified with different priorities, are serviced. The network system is assumed to have switched topology, noise and delay in measurement, and cost on the topology. The goal is to reach/maintain balanced (equal) load, i.e. consensus, across the network and at the same time to meet the topology cost constraint, both for every priority class. A control protocol is proposed. We prove that the proposed control protocol is able to meet the topology cost constraint and achieve approximate consensus for each of the priority classes in the network.

Язык оригиналаАнглийский
Название основной публикации2014 IEEE 53RD ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC)
ИздательIEEE Canada
Страницы6969-6974
Число страниц6
Том2015-February
ИзданиеFebruary
DOI
СостояниеОпубликовано - 2014
Событие53rd IEEE Annual Conference on Decision and Control (CDC) - Los Angeles, Канада
Продолжительность: 15 дек 201417 дек 2014

Серия публикаций

НазваниеProceedings of the IEEE Conference on Decision and Control
ИздательInstitute of Electrical and Electronics Engineers Inc.
ISSN (печатное издание)0743-1546

конференция

конференция53rd IEEE Annual Conference on Decision and Control (CDC)
Страна/TерриторияКанада
ГородLos Angeles
Период15/12/1417/12/14

Предметные области Scopus

  • Системотехника
  • Моделирование и симуляция
  • Теория оптимизации

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