Meng Xu, Linh Thi Xuan Phan, and Hyon-Young Choi (University of Pennsylvania); Yuhan Lin (Northeastern University); Haoran Li and Chenyang Lu (Washington University in St. Louis); and Insup Lee (University of Pennsylvania) are the recipients of the Best Paper Award at the 25th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), part of the Cyber-Physical Systems Week (CPSWeek), that took place in Montreal, Canada on 15-18 April 2019. Their paper titled “Holistic Resource Allocation for Multicore Real-Time Systems” presents a holistic cache and memory bandwidth resource allocation strategy for multicore real-time systems. Their strategy exploits the relationship between the allocation of cache and memory bandwidth resources and a task's WCET to map tasks onto cores and to compute the resource allocation for each core, to fully utilize resources while ensuring timing guarantees. Extensive evaluations using real-world benchmarks show that their strategy offers near optimal schedulability performance while being highly efficient, and that it substantially outperforms state-of-the-art solutions.
Linh, and her former/current doctoral students (Saeed Abedi, Neeraj Gandhi, Henri Maxime Demoulin, Yang Li, and Yang Wu), also won RTAS Best Student Paper Award. Their paper titled “RTNF: Predictable Latency for Network Function Virtualization” presents a scalable framework for the online resource allocation and scheduling of NFV applications that provides predictable end-to-end latency guarantees. RTNF is based on a novel time-aware abstraction algorithm that transforms complex NFV graphs and their performance requirements into sets of scheduling interfaces; these can then be used by the resource manager and the scheduler on each node to efficiently allocate resources and to schedule NFV requests at runtime. Their evaluation, based on simulations and an experimental prototype, shows that RTNF can schedule DAG-based NFV applications with solid timing guarantees while incurring only a small overhead, and that it substantially outperforms state-of-the-art techniques.
Congratulations to all!