This paper presents a uni-processor real-time scheduling algorithm called the Generic Utility Scheduling algorithm (which we will refer to simply as GUS). GUS solves an open real-time scheduling problem -scheduling application activities that have time constraints specified using arbitrarily shaped time/utility functions, and have mutual exclusion resource constraints. A time/utility function is a time constraint specification that describes an activity's utility to the system as a function of that activity's completion time. Given such time and resource constraints, we consider the scheduling objective of maximizing the total utility that is accrued by the completion of all activities. Since this problem is N P-hard, GUS heuristically computes schedules with a polynomial-time cost of O(n 3 ) at each scheduling event, where n is the number of activities in the ready queue. We evaluate the performance of GUS through simulation and by an actual implementation on a real-time POSIX operating system. Our simulation studies and implementation measurements reveal that GUS performs close to, if not better than, the existing algorithms for the cases that they apply. Furthermore, we analytically establish several timeliness and non-timeliness properties of GUS.Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.

No abstract

No abstract

With the emergence of AI application ecology, an increasing number of AI applications are being developed and deployed on end devices. For some applications, due to various reasons (such as delay, bandwidth and privacy issues), inference must be performed on the edge nodes. To realize the efficient deployment of multiple networks on different chips, this paper uses cloud service technology and container resource management technology to achieve cloud deployment and uses a variety of model optimization technologies, such as model format conversion, graph optimization, chip optimization, model low-precision calculation optimization, model cutting and distillation. To achieve the effect of saving considerable memory and reducing energy consumption on the premise of satisfying the accuracy.