2017/04/07 by Sanjiva Prasad
Computer Science · #Computer network #Computer science #Core (optical fiber) #Distributed computing #Efficient energy use #Embedded Systems Design Techniques #Energy consumption #Frame (networking) #Interconnection Networks and Systems #Multi-core processor #Parallel Computing and Optimization Techniques #Parallel computing #Quality of service #Resource (disambiguation) #Resource allocation #Scheme (mathematics) #Sequence (biology) #cs.LO #cs.PF
paper · pdf · doi:10.4204/eptcs.246.10
published as EPTCS 246, 2017, pp. 61-71 · In Proceedings PLACES 2017, arXiv:1704.02418
openalex publication_date 2017/04/07 · arxiv created 2017/04/11 · arxiv updated 2017/04/17 · openalex created_date 2018/06/13 · openalex updated_date 2026/08/06
Energy consumption is a major concern in multicore systems. Perhaps the simplest strategy for reducing energy costs is to use only as many cores as necessary while still being able to deliver a desired quality of service. Motivated by earlier work on a dynamic (heterogeneous) core allocation scheme for H.264 video decoding that reduces energy costs while delivering desired frame rates, we formulate operationally the general problem of executing a sequence of actions on a reconfigurable machine while meeting a corresponding sequence of absolute deadlines, with the objective of reducing cost. Using a transition system framework that associates costs (e.g., time, energy) with executing an action on a particular resource configuration, we use the notion of amortised cost to formulate in terms of simulation relations appropriate notions for comparing deadline-conformant executions. We believe these notions can provide the basis for an operational theory of optimal cost executions and performance guarantees for approximate solutions, in particular relating the notion of simulation from transition systems to that of competitive analysis used for, e.g., online algorithms.