In this paper, we propose a Bayesian optimization-driven multi-objective scheduler, PaMO, that can proactively explore the systempricing preference by pairwise comparing outcome vectors of all ob-jectives. Moreover, PaMO designs a heuristic scheduling algorithmwith a zero-delay jitter guarantee to avoid performance degrada-tion caused by resource contention and uses a revised Bayesianoptimization algorithm to make video configuration and scheduling decisions.
In this paper, we propose an online long-range PCR scheme in VANETs, called LoRaPCR, where vehicles achieve long-range registration through multi-hop short-range highly-accurate registrations.
In this paper, we propose a scalableonline video analytics scheme, called Novas, which automaticallymakes precise service configuration adjustments upon constantvideo content changes. Specifically, Novas leverages the filteredconfidence sum and a two-window t-test to online detect accuracyfluctuations without ground truth information. In such cases, Novas efficiently estimates the performance of all potentialservice configurations through a singular value decomposition(SVD)-based collaborative filtering method. Finally, given theNP-hardness of the optimal scheduling problem, a heuristicscheduling strategy that maximizes the minimum remainingresources is devised to schedule the most suitable configurationsto servers for execution.
In this work, we design and develop ENTS, the first edge-native task scheduling system, to manage the distributed edge resources and facilitate efficient task scheduling to optimize the performance of edge-native applications. ENTS extends Kubernetes with the unique ability to collaboratively schedule computation and networking resources by comprehensively considering job profile and resource status.