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Modern developers rely on container-orchestration frameworks like Kubernetes to deploy and manage hybrid workloads that span the edge and cloud. When network conditions between the edge and cloud change unexpectedly, a workload must adapt its internal behavior. Unfortunately, container-orchestration frameworks do not offer an easy way to express, deploy, and manage adaptation strategies. As a result, fine-tuning or modifying a workload's adaptive behavior can require modifying containers built from large, complex codebases that may be maintained by separate development teams. This paper presents BumbleBee, a lightweight extension for container-orchestration frameworks that separates the concerns of application logic and adaptation logic. BumbleBee provides a simple in-network programming abstraction for making decisions about network data using application semantics. Experiments with a BumbleBee prototype show that edge ML-workloads can adapt to network variability and survive disconnections, edge stream-processing workloads can improve benchmark results between 37.8% and 23x , and HLS video-streaming can reduce stalled playback by 77%. 

Increasingly, vehicles sold today are connected cars: they offer vehicle-to-infrastructure connectivity through built-in WiFi and cellular interfaces, and they act as mobile hotspots for devices in the vehicle. We study the connection quality available to connected cars today, focusing on user-facing, latency-sensitive applications. We find that network latency varies significantly and unpredictably at short time scales and that high tail latency substantially degrades user experience. We also find an increase in coverage options available due to commercial WiFi offerings and that variations in latency across network options are not well-correlated. Based on these findings, we develop RAVEN, an in-kernel MPTCP scheduler that mitigates tail latency and network unpredictability by using redundant transmission when confidence about network latency predictions is low. RAVEN has several novel design features. It operates transparently, without application modification or hints, to improve interactive latency. It seamlessly supports three or more wireless networks. Its in-kernel implementation allows proactive cancellation of transmissions made unnecessary through redundancy. Finally, it explicitly considers how the age of measurements affects confidence in predictions, allowing better handling of interactive applications that transmit infrequently and networks that exhibit periods of temporary poor performance. Results from speech, music, and recommender applications in both emulated and live vehicle experiments show substantial improvement in application response time 

Abstract Relaxor ferroelectrics (RFEs) are being actively investigated for energy‐storage applications due to their large electric‐field‐induced polarization with slim hysteresis and fast energy charging–discharging capability. Here, a novel nanograin engineering approach based upon high kinetic energy deposition is reported, for mechanically inducing the RFE behavior in a normal ferroelectric Pb(Zr_0.52Ti_0.48)O₃(PZT), which results in simultaneous enhancement in the dielectric breakdown strength (E_DBS) and polarization. Mechanically transformed relaxor thick films with 4 µm thickness exhibit an exceptionalE_DBSof 540 MV m⁻¹and reduced hysteresis with large unsaturated polarization (103.6 µC cm⁻²), resulting in a record high energy‐storage density of 124.1 J cm⁻³and a power density of 64.5 MW cm⁻³. This fundamental advancement is correlated with the generalized nanostructure design that comprises nanocrystalline phases embedded within the amorphous matrix. Microstructure‐tailored ferroelectric behavior overcomes the limitations imposed by traditional compositional design methods and provides a feasible pathway for realization of high‐performance energy‐storage materials.