Serverless computing promises fine-grained resource elasticity and billing, making it an attractive way to build complex applications as multi-stage workflows. Nonetheless, existing workflow orchestration ignores the heterogeneous demands of the computation and communication parts within a stage, potentially resulting in resource inefficiency on either side. In this paper, we advocate for computation-communication-separated orchestration to unleash hybrid resource (i.e., compute and network) elasticity. We present HyFaaS, a serverless workflow orchestrator that improves performance while ensuring cost efficiency. It seamlessly decouples computation and communication as a series of hybrid stages re-expressed within HyDAG, a novel workflow abstraction. HyFaaS uses a gray-box profiling model to identify their Pareto-optimal saturated configurations, and then deploys the saturated workflow to juggle communication and scaling overheads through two-level HyDAG partitioning. Along with event-driven runtime fine-tuning, HyFaaS further scales down the non-critical stages to reduce cost via branch-aware coordination. Experimental results show that HyFaaS surpasses existing solutions by 32.7%–50.4% on end-to-end latency, while lowering cost by up to 1.37×.

Recent years have witnessed video streaming grad- ually evolve into one of the most popular Internet applications. With the rapidly growing personalized demand for real-time video streaming services, maximizing their Quality of Experience (QoE) is a long-standing challenge. The emergence of the server- less computing paradigm has potential to meet this challenge through its fine-grained management and highly parallel comput- ing structures. However, it is still ambiguous how to implement and configure serverless components to optimize video streaming services. In this paper, we propose EAVS, an Edge-assisted Adaptive Video streaming system with Serverless pipelines, which facilitates fine-grained management for multiple concurrent video transmission pipelines. Then, we design a chunk-level optimiza- tion scheme to address video bitrate adaptation. We propose a Deep Reinforcement Learning (DRL) algorithm based on Proximal Policy Optimization (PPO) with a trinal-clip mechanism to make bitrate decisions efficiently for better QoE. Finally, we implement the serverless video streaming system prototype and evaluate the performance of EAVS on various real-world network traces. Our results show that EAVS significantly improves QoE and reduces the video stall rate, achieving over 9.1% QoE improvement and 60.2% latency reduction compared to state- of-the-art solutions.

Rail corrugation, one of the most deleterious types of track wear, affects the railroad industries in many countries. Rail corrugation leads not only to vehicle and track vibration, lifespan reduction of infrastructure, and safety and passenger comfort issues but it also produces track noise. This paper, based on the track vibration theory, studies the initiation and development of rail corrugation in both the time and frequency domains. First, four track models for the frequency-domain analysis are developed to model the common short sleeper track, vibration absorbing fastener track, elastic short sleeper track and ladder sleeper track. The frequency response analysis is performed to calculate the characteristic frequencies of the track models. In order to analyse the relationship between the natural vibration of track structures and rail corrugation, the characteristic frequencies are compared with the measured passing frequencies of tracks due to rail corrugation. Second, the vehicle–track–tunnel three-dimensional dynamic models are developed for the four track structures to study the initiation of rail corrugation under the influence of track vibration using the time-domain analysis. Finally, the development of rail corrugation for the four track structures is investigated by means of the wear growth rate. Moreover, the influence of other factors, such as the curve radius, the rail–sleeper relative position, the driving velocity and the wheel–rail friction coefficient, is discussed.