The remaining useful life (RUL) degradation under driving conditions is complex. The features from incremental capacity-differential voltage curves and electrochemical impedance spectroscopy (EIS) can be implemented to identify the battery degradation modes and predict RUL. This article proposes a light gradient boosting machine (LightGBM)-based framework with electrochemical theory to achieve RUL prediction under driving conditions. The degradation modes are identified as loss of conductivity, loss of active material, and loss of lithium ion, and the EIS is refined as ohmic resistance, charge transfer resistance, solid electrolyte interphase film resistance, and Warburg resistance. The LightGBM model is improved by the adaptive robust loss function to achieve multiloss functions adaptive adjustment for different cases and limit the effect of measurement noise on gradients. Based on the perspective of multitask learning, the Pearson correlation is analyzed to design the sharing principle, which ensures full use of features and reduce experimental costs. The proposed framework is validated by the database under four vibration cases (static,X-axis,Y-axis, andZ-axis). Experimental results demonstrate that the proposed framework is capable of providing accurate and steady RUL prediction under driving conditions even with measurement noise. The proposed framework could guide periodic maintenance and stable operation to avoid risks.

Real-time video analytics (VA) with requirement of high accuracy necessitates intensive bandwidth resource consumption, calling for an adaptive streaming configuration strategy to strike a balance in VA pipelines. Existing works however suffer from a key limitation: the profiling-based strategy not only wastes unnecessary resources with the golden configuration transmission but also is trapped into only coarse-grained adaptation due the contradiction between profiling granularity and the profiling cost. In this paper, we for the first time reveal the correlation between the video dynamics and motion degree and highlight the limitation of traditional profiling-based strategy. We then propose Maxim, the first learning-based framework that solves the fine-grained VA configuration adaptation problem employing a novel deep reinforcement learning-based methodology without using any golden configuration in inference stage. Maxim could optimize the trade-off between resources cost and inference accuracy. Besides, Maxim also employs an enhanced cross-camera collaboration based on spatial and temporal correlation among cameras, which further improves robustness and performance in a large camera network. Extensive experiments confirm the superiority of our work compared with SOTA works, with a 76.6% improvement in comprehensive.

Modern data-center applications generate a diverse mix of short and long flows with different performance requirements and weaknesses. The short flows are typically delay-sensitive but to suffer the head-of-line blocking and out-of-order problems. Recent solutions prioritize the short flows to meet their latency requirements, while damaging the throughput-sensitive long flows. To solve these problems, we design a Coding-based Adaptive Packet Spraying (CAPS) that effectively mitigates the negative impact of short and long flows on each other. To exploit the availability of multiple paths and avoid the head-of-line blocking, CAPS spreads the packets of short flows to all paths, while the long flows are limited to a few paths with Equal Cost Multi Path (ECMP). Meanwhile, to resolve the out-of-order problem with low overhead, CAPS encodes the short flows using forward error correction (FEC) technology and adjusts the coding redundancy according to the blocking probability. Moreover, since the coding efficiency decreases when the coding unit is too small or large, we demonstrate how to obtain the optimal size of coding unit. The coding layer is deployed between the TCP and IP layers, without any modifications on the existing TCP/IP protocols. The test results of NS2 simulation and small-scale testbed experiments show that CAPS significantly reduces the average flow completion time of short flows by ~30%-70% over the state-of-the-art multipath transmission schemes and achieves the high throughput for long flows with negligible traffic overhead.

In modern data centers, many flow-based and task-based schemes have been proposed to speed up the data transmission in order to provide fast, reliable services for millions of users. However, the existing flow-based schemes treat all flows in isolation, contributing less to or even hurting user experience due to the stalled flows. Other prevalent task-based approaches, such as centralized and decentralized scheduling, are sophisticated or unable to share task information. In this work, we first reveal that the relinquishing bandwidth of leading flows to the stalled ones effectively reduces the task completion time. We further present the design and implementation of a general supporting scheme that shares the flow-tardiness information through a receiver-driven coordination. Our scheme can be flexible and widely integrated with the state-of-the-art TCP protocols designed for data centers in either single stage or multiple stage scenario, while making no modification on switches. Through the testbed experiments and simulations of typical data center applications, we show that in single stage scenario, our scheme reduces the task completion time by 70% and 50% compared with the flow-based protocols (e.g., DCTCP, L2DCT) and task-based scheduling (e.g., Baraat), respectively. Moreover, our scheme also outperforms other approaches by 18%~25% in prevalent topologies of the data center. For multiple stage scenario, our scheme also has up to 50% improvement compared to other schemes.

Modern data-center applications generate a diverse mix of short and long flows with different performance requirements and weaknesses. The short flows are typically delay-sensitive but to suffer the head-of-line blocking and out-of-order problems. Recent solutions prioritize the short flows to meet their latency requirements, while damaging the throughput-sensitive long flows. To solve these problems, we design a Coding-based Adaptive Packet Spraying (CAPS) that effectively mitigates the negative impact of short and long flows on each other. To exploit the availability of multiple paths and avoid the head-of-line blocking, CAPS spreads the packets of short flows to all paths, while the long flows are limited to a few paths with Equal Cost Multi Path (ECMP). Meanwhile, to resolve the out-of-order problem with low overhead, CAPS encodes the short flows using forward error correction (FEC) technology and adjusts the coding redundancy according to the blocking probability. The coding layer is deployed between the TCP and IP layers, without any modifications on the existing TCP/IP protocols. The experimental results of NS2 simulation and Mininet implementation show that CAPS significantly reduces the average flow completion time of short flows by ~30% -70% over the state-of-the-art multipath transmission schemes and achieves the high throughput for long flows with negligible traffic overhead.

In modern data centers, many flow-based and task-based schemes have been proposed to speed up the data transmission in order to provide fast, reliable services for millions of users. However, existing flow-based schemes treat all flows in isolation, contributing less to or even hurting user experience due to the stalled flows. Other prevalent task-based approaches, such as centralized and decentralized scheduling, are sophisticated or unable to share task information. In this work, we first reveal that relinquishing bandwidth of leading flows to the stalled ones effectively reduces the task completion time. We further present the design and implementation of a general supporting scheme that shares the flow-tardiness information through a receiver-driven coordination. Our scheme can be flexibly and widely integrated with the state-of-the-art TCP protocols designed for data centers, while making no modification on switches. Through the testbed experiments and simulations of typical data center applications, we show that our scheme reduces the task completion time by 70% and 50% compared with the flow-based protocols (e.g. DCTCP, L2DCT) and task-based scheduling (e.g. Baraat), respectively. Moreover, our scheme also outperforms other approaches by 18% to 25% in prevalent topologies of data center.

In data center networks, many network-intensive applications leverage large fan-in and many-to-one communication to achieve high performance. However, the special traffic patterns, such as micro-burst and high concurrency, easily cause TCP Incast problem and seriously degrade the application performance. To address the TCP Incast problem, we first reveal theoretically and empirically that alleviating packet burstiness is much more effective in reducing the Incast probability than controlling the congestion window. Inspired by the findings and insights from our experimental observations, we further propose a general supporting scheme Adaptive Pacing (AP), which dynamically adjusts burstiness according to the flow concurrency without any change on switch. Another feature of AP is its broad applicability. We integrate AP transparently into different TCP protocols (i.e., DCTCP, L2DCT and D2TCP). Through a series of large-scale NS2 simulations, we show that AP significantly reduces the Incast probability across different TCP protocols and the network goodput can be increased consistently by on average 7x under severe congestion.