Approximate sojourn time distribution of a discriminatory processor sharing queue with impatient customers View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-06

AUTHORS

Sunggon Kim

ABSTRACT

We consider a two-class processor sharing queueing system scheduled by the discriminatory processor sharing discipline. Poisson arrivals of customers and exponential amounts of service requirements are assumed. At any moment of being served, a customer can leave the system without completion of its service. In the asymptotic regime, where the ratio of the time scales of the two-class customers is infinite, we obtain the conditional sojourn time distribution of each class customers. Numerical experiments show that the time scale decomposition approach developed in this paper gives a good approximation to the conditional sojourn time distribution as well as the expectation of it. More... »

PAGES

411-430

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00186-017-0623-z

DOI

http://dx.doi.org/10.1007/s00186-017-0623-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1093026489


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0806", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information Systems", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Seoul", 
          "id": "https://www.grid.ac/institutes/grid.267134.5", 
          "name": [
            "Department of Statistics, University of Seoul, 02504, Seoul, Republic of Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kim", 
        "givenName": "Sunggon", 
        "id": "sg:person.014540736405.72", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014540736405.72"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1080/00207160008804916", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004881708"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/321386.321388", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009840287"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1389-1286(03)00200-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011920010"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1090/s0002-9947-1957-0091566-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019493158"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0166-5316(94)90053-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033787356"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0166-5316(94)90053-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033787356"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11134-006-7586-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034319109", 
          "https://doi.org/10.1007/s11134-006-7586-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/322203.322212", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037330703"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jkss.2013.04.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038048417"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.aeue.2005.11.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042270234"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/j.1538-7305.1985.tb00440.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044578156"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01158767", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053307428", 
          "https://doi.org/10.1007/bf01158767"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01158767", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053307428", 
          "https://doi.org/10.1007/bf01158767"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/imamat/16.1.57", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059684473"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnet.2002.1012364", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061714296"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1239/aap/1086957584", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064440606"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1287/opre.22.6.1232", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064728557"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/infcom.2005.1498528", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093502873"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/infcom.2004.1356984", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094528056"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1017/cbo9780511759550", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098674744"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-06", 
    "datePublishedReg": "2018-06-01", 
    "description": "We consider a two-class processor sharing queueing system scheduled by the discriminatory processor sharing discipline. Poisson arrivals of customers and exponential amounts of service requirements are assumed. At any moment of being served, a customer can leave the system without completion of its service. In the asymptotic regime, where the ratio of the time scales of the two-class customers is infinite, we obtain the conditional sojourn time distribution of each class customers. Numerical experiments show that the time scale decomposition approach developed in this paper gives a good approximation to the conditional sojourn time distribution as well as the expectation of it.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00186-017-0623-z", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1053187", 
        "issn": [
          "1432-2994", 
          "1432-5217"
        ], 
        "name": "Mathematical Methods of Operations Research", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "87"
      }
    ], 
    "name": "Approximate sojourn time distribution of a discriminatory processor sharing queue with impatient customers", 
    "pagination": "411-430", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "37f57a86ab12fb861ed2e33d436876196d4e9a224f62ac84563521308ec9105e"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00186-017-0623-z"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1093026489"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00186-017-0623-z", 
      "https://app.dimensions.ai/details/publication/pub.1093026489"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T22:25", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8690_00000484.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/s00186-017-0623-z"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00186-017-0623-z'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00186-017-0623-z'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00186-017-0623-z'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00186-017-0623-z'


 

This table displays all metadata directly associated to this object as RDF triples.

117 TRIPLES      21 PREDICATES      45 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00186-017-0623-z schema:about anzsrc-for:08
2 anzsrc-for:0806
3 schema:author N4eeb9ddf75494f28b55d45657eb51185
4 schema:citation sg:pub.10.1007/bf01158767
5 sg:pub.10.1007/s11134-006-7586-8
6 https://doi.org/10.1002/j.1538-7305.1985.tb00440.x
7 https://doi.org/10.1016/0166-5316(94)90053-1
8 https://doi.org/10.1016/j.aeue.2005.11.007
9 https://doi.org/10.1016/j.jkss.2013.04.001
10 https://doi.org/10.1016/s1389-1286(03)00200-7
11 https://doi.org/10.1017/cbo9780511759550
12 https://doi.org/10.1080/00207160008804916
13 https://doi.org/10.1090/s0002-9947-1957-0091566-1
14 https://doi.org/10.1093/imamat/16.1.57
15 https://doi.org/10.1109/infcom.2004.1356984
16 https://doi.org/10.1109/infcom.2005.1498528
17 https://doi.org/10.1109/tnet.2002.1012364
18 https://doi.org/10.1145/321386.321388
19 https://doi.org/10.1145/322203.322212
20 https://doi.org/10.1239/aap/1086957584
21 https://doi.org/10.1287/opre.22.6.1232
22 schema:datePublished 2018-06
23 schema:datePublishedReg 2018-06-01
24 schema:description We consider a two-class processor sharing queueing system scheduled by the discriminatory processor sharing discipline. Poisson arrivals of customers and exponential amounts of service requirements are assumed. At any moment of being served, a customer can leave the system without completion of its service. In the asymptotic regime, where the ratio of the time scales of the two-class customers is infinite, we obtain the conditional sojourn time distribution of each class customers. Numerical experiments show that the time scale decomposition approach developed in this paper gives a good approximation to the conditional sojourn time distribution as well as the expectation of it.
25 schema:genre research_article
26 schema:inLanguage en
27 schema:isAccessibleForFree false
28 schema:isPartOf N33301b9cf4bb483cb2c5e18f2e485ba6
29 N9920cf4e272d49be9f109d7e98f4a3dd
30 sg:journal.1053187
31 schema:name Approximate sojourn time distribution of a discriminatory processor sharing queue with impatient customers
32 schema:pagination 411-430
33 schema:productId N53fa2aa5414f49f5ace3aaf0767ac626
34 Nbdaf14a04c8c44be9fd8189005c5b535
35 Ncfb9f1170db74c89a6946f9f83432f6c
36 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093026489
37 https://doi.org/10.1007/s00186-017-0623-z
38 schema:sdDatePublished 2019-04-10T22:25
39 schema:sdLicense https://scigraph.springernature.com/explorer/license/
40 schema:sdPublisher N704700bcf1c147c297c7f054c72a5d14
41 schema:url http://link.springer.com/10.1007/s00186-017-0623-z
42 sgo:license sg:explorer/license/
43 sgo:sdDataset articles
44 rdf:type schema:ScholarlyArticle
45 N33301b9cf4bb483cb2c5e18f2e485ba6 schema:issueNumber 3
46 rdf:type schema:PublicationIssue
47 N4eeb9ddf75494f28b55d45657eb51185 rdf:first sg:person.014540736405.72
48 rdf:rest rdf:nil
49 N53fa2aa5414f49f5ace3aaf0767ac626 schema:name dimensions_id
50 schema:value pub.1093026489
51 rdf:type schema:PropertyValue
52 N704700bcf1c147c297c7f054c72a5d14 schema:name Springer Nature - SN SciGraph project
53 rdf:type schema:Organization
54 N9920cf4e272d49be9f109d7e98f4a3dd schema:volumeNumber 87
55 rdf:type schema:PublicationVolume
56 Nbdaf14a04c8c44be9fd8189005c5b535 schema:name doi
57 schema:value 10.1007/s00186-017-0623-z
58 rdf:type schema:PropertyValue
59 Ncfb9f1170db74c89a6946f9f83432f6c schema:name readcube_id
60 schema:value 37f57a86ab12fb861ed2e33d436876196d4e9a224f62ac84563521308ec9105e
61 rdf:type schema:PropertyValue
62 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
63 schema:name Information and Computing Sciences
64 rdf:type schema:DefinedTerm
65 anzsrc-for:0806 schema:inDefinedTermSet anzsrc-for:
66 schema:name Information Systems
67 rdf:type schema:DefinedTerm
68 sg:journal.1053187 schema:issn 1432-2994
69 1432-5217
70 schema:name Mathematical Methods of Operations Research
71 rdf:type schema:Periodical
72 sg:person.014540736405.72 schema:affiliation https://www.grid.ac/institutes/grid.267134.5
73 schema:familyName Kim
74 schema:givenName Sunggon
75 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014540736405.72
76 rdf:type schema:Person
77 sg:pub.10.1007/bf01158767 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053307428
78 https://doi.org/10.1007/bf01158767
79 rdf:type schema:CreativeWork
80 sg:pub.10.1007/s11134-006-7586-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034319109
81 https://doi.org/10.1007/s11134-006-7586-8
82 rdf:type schema:CreativeWork
83 https://doi.org/10.1002/j.1538-7305.1985.tb00440.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1044578156
84 rdf:type schema:CreativeWork
85 https://doi.org/10.1016/0166-5316(94)90053-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033787356
86 rdf:type schema:CreativeWork
87 https://doi.org/10.1016/j.aeue.2005.11.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042270234
88 rdf:type schema:CreativeWork
89 https://doi.org/10.1016/j.jkss.2013.04.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038048417
90 rdf:type schema:CreativeWork
91 https://doi.org/10.1016/s1389-1286(03)00200-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011920010
92 rdf:type schema:CreativeWork
93 https://doi.org/10.1017/cbo9780511759550 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098674744
94 rdf:type schema:CreativeWork
95 https://doi.org/10.1080/00207160008804916 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004881708
96 rdf:type schema:CreativeWork
97 https://doi.org/10.1090/s0002-9947-1957-0091566-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019493158
98 rdf:type schema:CreativeWork
99 https://doi.org/10.1093/imamat/16.1.57 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059684473
100 rdf:type schema:CreativeWork
101 https://doi.org/10.1109/infcom.2004.1356984 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094528056
102 rdf:type schema:CreativeWork
103 https://doi.org/10.1109/infcom.2005.1498528 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093502873
104 rdf:type schema:CreativeWork
105 https://doi.org/10.1109/tnet.2002.1012364 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061714296
106 rdf:type schema:CreativeWork
107 https://doi.org/10.1145/321386.321388 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009840287
108 rdf:type schema:CreativeWork
109 https://doi.org/10.1145/322203.322212 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037330703
110 rdf:type schema:CreativeWork
111 https://doi.org/10.1239/aap/1086957584 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064440606
112 rdf:type schema:CreativeWork
113 https://doi.org/10.1287/opre.22.6.1232 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064728557
114 rdf:type schema:CreativeWork
115 https://www.grid.ac/institutes/grid.267134.5 schema:alternateName University of Seoul
116 schema:name Department of Statistics, University of Seoul, 02504, Seoul, Republic of Korea
117 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...