A hierarchical cluster algorithm for dynamic, centralized timestamps
Partial-order data structures used in distributed-system observation tools typically use vector timestamps to efficiently determine event precedence. Unfortunately all current dynamic vector-timestamp algorithms either require a vector of size equal to the number of processes in the computation or r...
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| creator | Ward, P.A.S. Taylor, D.J. |
| description | Partial-order data structures used in distributed-system observation tools typically use vector timestamps to efficiently determine event precedence. Unfortunately all current dynamic vector-timestamp algorithms either require a vector of size equal to the number of processes in the computation or require a graph search operation to determine event precedence. This fundamentally limits the scalability of such observation systems. In this paper we present an algorithm for hierarchical, clustered vector time-stamps. We present results for a variety of computation environments that demonstrate such timestamps can reduce space consumption by more than an order-of-magnitude over Fidge/Mattern timestamps while still providing acceptable time bounds for computing timestamps and determining event precedence. |
| doi_str_mv | 10.1109/ICDSC.2001.918989 |
| format | Conference Proceeding |
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Unfortunately all current dynamic vector-timestamp algorithms either require a vector of size equal to the number of processes in the computation or require a graph search operation to determine event precedence. This fundamentally limits the scalability of such observation systems. In this paper we present an algorithm for hierarchical, clustered vector time-stamps. We present results for a variety of computation environments that demonstrate such timestamps can reduce space consumption by more than an order-of-magnitude over Fidge/Mattern timestamps while still providing acceptable time bounds for computing timestamps and determining event precedence.</description><identifier>ISBN: 0769510779</identifier><identifier>ISBN: 9780769510774</identifier><identifier>DOI: 10.1109/ICDSC.2001.918989</identifier><language>eng</language><publisher>IEEE</publisher><subject>Clustering algorithms ; Computer science ; Control systems ; Costs ; Data structures ; Data visualization ; Distributed computing ; Heuristic algorithms ; Monitoring ; Scalability</subject><ispartof>Proceedings 21st International Conference on Distributed Computing Systems, 2001, p.585-593</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/918989$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2051,4035,4036,27904,54898</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/918989$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Ward, P.A.S.</creatorcontrib><creatorcontrib>Taylor, D.J.</creatorcontrib><title>A hierarchical cluster algorithm for dynamic, centralized timestamps</title><title>Proceedings 21st International Conference on Distributed Computing Systems</title><addtitle>ICDCS</addtitle><description>Partial-order data structures used in distributed-system observation tools typically use vector timestamps to efficiently determine event precedence. Unfortunately all current dynamic vector-timestamp algorithms either require a vector of size equal to the number of processes in the computation or require a graph search operation to determine event precedence. This fundamentally limits the scalability of such observation systems. In this paper we present an algorithm for hierarchical, clustered vector time-stamps. We present results for a variety of computation environments that demonstrate such timestamps can reduce space consumption by more than an order-of-magnitude over Fidge/Mattern timestamps while still providing acceptable time bounds for computing timestamps and determining event precedence.</description><subject>Clustering algorithms</subject><subject>Computer science</subject><subject>Control systems</subject><subject>Costs</subject><subject>Data structures</subject><subject>Data visualization</subject><subject>Distributed computing</subject><subject>Heuristic algorithms</subject><subject>Monitoring</subject><subject>Scalability</subject><isbn>0769510779</isbn><isbn>9780769510774</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2001</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj71OwzAURi0hJKD0AWDyA5Dg6zhxPFYpP5UqMdDO1bV9Q4yStrLNUJ6eSuVbznSO9DH2AKIEEOZ51S0_u1IKAaWB1rTmit0J3ZgahNbmhs1T-hbnqRpaBbdsueBDoIjRDcHhyN34kzJFjuPXIYY8TLw_RO5Pe5yCe-KO9jniGH7J8xwmShmnY7pn1z2Oieb_nLHt68umey_WH2-rbrEuAgiVC9C-NcpI76yxwqvGopWirrSrNEknnAKvDFoCUAoaTVpSKyslrTtL2Fcz9njpBiLaHWOYMJ52l5_VHz-vSW4</recordid><startdate>2001</startdate><enddate>2001</enddate><creator>Ward, P.A.S.</creator><creator>Taylor, D.J.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>2001</creationdate><title>A hierarchical cluster algorithm for dynamic, centralized timestamps</title><author>Ward, P.A.S. ; Taylor, D.J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i104t-17d89492dcb9b0d46bab20537c37e2c0c41d49abe1144167e72e82342bc92daf3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2001</creationdate><topic>Clustering algorithms</topic><topic>Computer science</topic><topic>Control systems</topic><topic>Costs</topic><topic>Data structures</topic><topic>Data visualization</topic><topic>Distributed computing</topic><topic>Heuristic algorithms</topic><topic>Monitoring</topic><topic>Scalability</topic><toplevel>online_resources</toplevel><creatorcontrib>Ward, P.A.S.</creatorcontrib><creatorcontrib>Taylor, D.J.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ward, P.A.S.</au><au>Taylor, D.J.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A hierarchical cluster algorithm for dynamic, centralized timestamps</atitle><btitle>Proceedings 21st International Conference on Distributed Computing Systems</btitle><stitle>ICDCS</stitle><date>2001</date><risdate>2001</risdate><spage>585</spage><epage>593</epage><pages>585-593</pages><isbn>0769510779</isbn><isbn>9780769510774</isbn><abstract>Partial-order data structures used in distributed-system observation tools typically use vector timestamps to efficiently determine event precedence. Unfortunately all current dynamic vector-timestamp algorithms either require a vector of size equal to the number of processes in the computation or require a graph search operation to determine event precedence. This fundamentally limits the scalability of such observation systems. In this paper we present an algorithm for hierarchical, clustered vector time-stamps. We present results for a variety of computation environments that demonstrate such timestamps can reduce space consumption by more than an order-of-magnitude over Fidge/Mattern timestamps while still providing acceptable time bounds for computing timestamps and determining event precedence.</abstract><pub>IEEE</pub><doi>10.1109/ICDSC.2001.918989</doi><tpages>9</tpages></addata></record> |
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| ispartof | Proceedings 21st International Conference on Distributed Computing Systems, 2001, p.585-593 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Clustering algorithms Computer science Control systems Costs Data structures Data visualization Distributed computing Heuristic algorithms Monitoring Scalability |
| title | A hierarchical cluster algorithm for dynamic, centralized timestamps |
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