1 resultado para the lies
em Boston University Digital Common
Filtro por publicador
- JISC Information Environment Repository (1)
- KUPS-Datenbank - Universität zu Köln - Kölner UniversitätsPublikationsServer (1)
- Abertay Research Collections - Abertay University’s repository (3)
- Aberystwyth University Repository - Reino Unido (2)
- Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España (4)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (1)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (3)
- Aquatic Commons (18)
- ArchiMeD - Elektronische Publikationen der Universität Mainz - Alemanha (6)
- Archive of European Integration (17)
- Aston University Research Archive (18)
- Biblioteca Digital da Câmara dos Deputados (2)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (8)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (12)
- Biblioteca Digital de la Universidad Católica Argentina (2)
- Biblioteca Digital de Teses e Dissertações Eletrônicas da UERJ (1)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (30)
- Boston University Digital Common (1)
- Brock University, Canada (6)
- Bucknell University Digital Commons - Pensilvania - USA (1)
- Bulgarian Digital Mathematics Library at IMI-BAS (1)
- CaltechTHESIS (7)
- Cambridge University Engineering Department Publications Database (5)
- CentAUR: Central Archive University of Reading - UK (46)
- Central European University - Research Support Scheme (4)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (12)
- Cochin University of Science & Technology (CUSAT), India (5)
- Comissão Econômica para a América Latina e o Caribe (CEPAL) (11)
- CORA - Cork Open Research Archive - University College Cork - Ireland (2)
- Corvinus Research Archive - The institutional repository for the Corvinus University of Budapest (2)
- Dalarna University College Electronic Archive (3)
- Deakin Research Online - Australia (69)
- Digital Commons - Michigan Tech (1)
- Digital Commons - Montana Tech (2)
- Digital Commons @ DU | University of Denver Research (2)
- Digital Commons @ Winthrop University (2)
- Digital Commons at Florida International University (16)
- Digital Peer Publishing (5)
- DigitalCommons - The University of Maine Research (1)
- DigitalCommons@The Texas Medical Center (7)
- DigitalCommons@University of Nebraska - Lincoln (2)
- Diposit Digital de la UB - Universidade de Barcelona (1)
- Doria (National Library of Finland DSpace Services) - National Library of Finland, Finland (1)
- DRUM (Digital Repository at the University of Maryland) (3)
- Duke University (4)
- Ecology and Society (1)
- eResearch Archive - Queensland Department of Agriculture; Fisheries and Forestry (3)
- Glasgow Theses Service (1)
- Greenwich Academic Literature Archive - UK (5)
- Helda - Digital Repository of University of Helsinki (11)
- Indian Institute of Science - Bangalore - Índia (36)
- Instituto Gulbenkian de Ciência (1)
- Iowa Publications Online (IPO) - State Library, State of Iowa (Iowa), United States (1)
- Lume - Repositório Digital da Universidade Federal do Rio Grande do Sul (1)
- Massachusetts Institute of Technology (2)
- Ministerio de Cultura, Spain (1)
- National Center for Biotechnology Information - NCBI (32)
- Nottingham eTheses (2)
- Portal de Revistas Científicas Complutenses - Espanha (5)
- Publishing Network for Geoscientific & Environmental Data (51)
- QSpace: Queen's University - Canada (1)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (47)
- Queensland University of Technology - ePrints Archive (185)
- RCAAP - Repositório Científico de Acesso Aberto de Portugal (2)
- Repositorio Académico de la Universidad Nacional de Costa Rica (2)
- Repositório Científico do Instituto Politécnico de Lisboa - Portugal (1)
- Repositório digital da Fundação Getúlio Vargas - FGV (12)
- Repositório Digital da UNIVERSIDADE DA MADEIRA - Portugal (1)
- Repositório Institucional da Universidade de Aveiro - Portugal (4)
- Repositorio Institucional de la Universidad de Málaga (1)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (41)
- SAPIENTIA - Universidade do Algarve - Portugal (3)
- Savoirs UdeS : plateforme de diffusion de la production intellectuelle de l’Université de Sherbrooke - Canada (1)
- Universidad de Alicante (7)
- Universidad del Rosario, Colombia (1)
- Universidad Politécnica de Madrid (15)
- Universidade Complutense de Madrid (3)
- Universidade de Lisboa - Repositório Aberto (2)
- Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP) (1)
- Universidade Federal do Pará (1)
- Universitätsbibliothek Kassel, Universität Kassel, Germany (1)
- Université de Lausanne, Switzerland (1)
- Université de Montréal (5)
- Université de Montréal, Canada (41)
- Université Laval Mémoires et thèses électroniques (2)
- University of Connecticut - USA (1)
- University of Michigan (17)
- University of Queensland eSpace - Australia (11)
- University of Washington (3)
- WestminsterResearch - UK (3)
Resumo:
A well-known paradigm for load balancing in distributed systems is the``power of two choices,''whereby an item is stored at the less loaded of two (or more) random alternative servers. We investigate the power of two choices in natural settings for distributed computing where items and servers reside in a geometric space and each item is associated with the server that is its nearest neighbor. This is in fact the backdrop for distributed hash tables such as Chord, where the geometric space is determined by clockwise distance on a one-dimensional ring. Theoretically, we consider the following load balancing problem. Suppose that servers are initially hashed uniformly at random to points in the space. Sequentially, each item then considers d candidate insertion points also chosen uniformly at random from the space,and selects the insertion point whose associated server has the least load. For the one-dimensional ring, and for Euclidean distance on the two-dimensional torus, we demonstrate that when n data items are hashed to n servers,the maximum load at any server is log log n / log d + O(1) with high probability. While our results match the well-known bounds in the standard setting in which each server is selected equiprobably, our applications do not have this feature, since the sizes of the nearest-neighbor regions around servers are non-uniform. Therefore, the novelty in our methods lies in developing appropriate tail bounds on the distribution of nearest-neighbor region sizes and in adapting previous arguments to this more general setting. In addition, we provide simulation results demonstrating the load balance that results as the system size scales into the millions.