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However, multiprocessor devices encounter inherent challenges when handling on\u2010chip shared resources. The concurrent access to these resources by the tasks requesting more than one shared resource and running on multiple processors may face huge blocking times, which can lead to missed hard real\u2010time deadlines and cause a catastrophic system failure. To reduce the task blocking time, we propose a task allocation algorithm that takes advantage of inter\u2010resource affinity and allocates all the tasks accessing multiple shared resources having inter\u2010resource affinity on the same processor of the multiprocessor. The proposed algorithm reduces global resources and remote blocking, which subsequently increase schedulability of task sets and reduce the processor utilization. In our experiments, we compare the proposed task allocation algorithm SRTA with existing well\u2010known task allocation strategies SPA, ROP, and blocking\u2010agnostic FFD. The experimental results reveal that the SRTA, on the average, can allocate 1.80, 2.34, and 3.14 times more task sets as compared to ROP, SPA, and FFD, respectively and reduces the number of globally shared resources significantly.<\/jats:p>","DOI":"10.1002\/cpe.5177","type":"journal-article","created":{"date-parts":[[2019,4,8]],"date-time":"2019-04-08T15:27:13Z","timestamp":1554737233000},"page":"1-1","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Real\u2010time partitioned scheduling: Exploiting the inter\u2010resource affinity for task allocation on multiprocessors"],"prefix":"10.1002","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7409-9304","authenticated-orcid":false,"given":"Naveed","family":"Akram","sequence":"first","affiliation":[{"name":"Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University  Beijing China"},{"name":"School of Computer Science and Engineering Beihang University  Beijing China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8813-450X","authenticated-orcid":false,"given":"Jianxin","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University  Beijing China"},{"name":"School of Computer Science and Engineering Beihang University  Beijing China"}]},{"given":"Yan","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Engineering and Technology University of Washington Tacoma  WA USA"}]},{"given":"Yangyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University  Beijing China"},{"name":"School of Computer Science and Engineering Beihang University  Beijing China"}]}],"member":"311","published-online":{"date-parts":[[2019,4,8]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-4000-7"},{"key":"e_1_2_8_3_1","unstructured":"GaiP Di NataleM LipariG FerrariA GabelliniC MarcecaP.A comparison of MPCP and MSRP when sharing resources in the Janus multiple\u2010processor on a chip platform. 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