Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/5406
Título: Task Assignment Algorithms for Heterogeneous Multiprocessors
Autor: Raravi, Gurulingesh
Nélis, Vincent
Palavras-chave: Heterogeneous multiprocessors
Real-time scheduling
Data: 2014
Editora: ACM
Relatório da Série N.º: ACM Transactions on Embedded Computing Systems (TECS);Article No. 159
Resumo: Consider the problem of assigning implicit-deadline sporadic tasks on a heterogeneous multiprocessor platform comprising a constant number (denoted by t) of distinct types of processors—such a platform is referred to as a t-type platform. We present two algorithms, LPGIM and LPGNM, each providing the following guarantee. For a given t-type platform and a task set, if there exists a task assignment such that tasks can be scheduled to meet their deadlines by allowing them to migrate only between processors of the same type (intra-migrative), then: (i) LPGIM succeeds in finding such an assignment where the same restriction on task migration applies (intra-migrative) but given a platform in which only one processor of each type is 1 + α × t-1/t times faster and (ii) LPGNM succeeds in finding a task assignment where tasks are not allowed to migrate between processors (non-migrative) but given a platform in which every processor is 1 + α times faster. The parameter α is a property of the task set; it is the maximum of all the task utilizations that are no greater than one. To the best of our knowledge, for t-type heterogeneous multiprocessors: (i) for the problem of intra-migrative task assignment, no previous algorithm exists with a proven bound and hence our algorithm, LPGIM, is the first of its kind and (ii) for the problem of non-migrative task assignment, our algorithm, LPGNM, has superior performance compared to state-of-the-art.
Peer review: yes
URI: http://hdl.handle.net/10400.22/5406
DOI: 10.1145/2660494
Versão do Editor: http://dl.acm.org/citation.cfm?id=2660494
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