As the demand for streaming services surges, content delivery network (CDN) operators face increasing pressure to scale live video delivery without proportionally increasing infrastructure costs. While best-effort edge resources offer a cost-effective extension to traditional CDN capacity, their limited bandwidth and unstable performance pose significant challenges. Our operational experience shows that naively layering such resources onto existing CDN infrastructure falls short in meeting performance and scalability demands. This paper presents RLive, a robust delivery system that scales CDN capacity by integrating best-effort edge resources. RLive features a redundancy-free multi-source data plane to support reliable and cost-efficient live streaming, along with a multi-layer collaborative control plane that combines the global view with local adaptability for scalable user-to-node mapping. Deployed in ByteDance CDN to support large-scale live streaming services with hundreds of millions of daily viewers, RLive has tripled delivery capacity while reducing rebuffering events by 14.9–20.1%.

The recognition of Activities of Daily Living (ADLs) from event-triggered ambient sensors is an essential task in Ambient Assisted Living, yet existing methods remain constrained by representation-level limitations. Sequence-based approaches preserve temporal order of sensor activations but are sensitive to noise and lack spatial awareness, while image-based approaches capture global patterns and implicit spatial correlations but compress fine-grained temporal dynamics and distort sensor layouts. Naïve fusion (e.g., feature concatenation) fails to enforce alignment between sequence- and image-based representation views, underutilizing their complementary strengths. We propose Contrastive Alignment for ADL Recognition from Event-Triggered Sensor Streams (CARE), an end-to-end framework that jointly optimizes representation learning via Sequence–Image Contrastive Alignment (SICA) and classification via cross-entropy, ensuring both cross-representation alignment and task-specific discriminability. CARE integrates (i) time-aware, noise-resilient sequence encoding with (ii) spatially-informed and frequency-sensitive image representations, and employs (iii) a joint contrastive-classification objective for end-to-end learning of aligned and discriminative embeddings. Evaluated on three CASAS datasets, CARE achieves state-of-the-art performance (89.8% on Milan, 88.9% on Cairo, and 73.3% on Kyoto7) and demonstrates robustness to sensor malfunctions and layout variability, highlighting its potential for reliable ADL recognition in smart homes. We release our code at https://github.com/Jhziiiig/CARE.

Live streaming platforms like Douyin have developed the Live Streaming Hyper-Edge Delivery Network (LSHEDN) to reduce bandwidth cost. In LS-HEDN, the Content Delivery Network (CDN) splits the live streaming into multiple substreams by randomly assigning each frame to them. Hyperedge devices like set-top boxes with cheap and idle bandwidth resources forward a substream from CDN to multiple users. A protocol based on User Datagram Protocol (UDP) is adopted between devices and users, with users detecting packet loss and requesting retransmissions via Negative Acknowledgment (NACK). Given the demand for lower latency and the inherent fluctuations in public network, existing receiver-side packet loss detection and retransmission methods fall short in achieving both timeliness and accuracy simultaneously. This is manifested as frequent rebuffering and excessive redundancy. Notably, when head-of-line blocking(HOL blocking) occurs in the upstream link of the device, these issues become even more pronounced. To address this, we propose Hyper-Edge Loss Detection and Retransmission (HELDR) algorithm. It features a loss detection algorithm tailored to the transmission characteristics in LSHEDN, which improves detection accuracy. Its immediate retransmission mechanism and the backup devices retransmission mechanism enhance timeliness. Large-scale online A/B tests results show that HELDR reduces the average rebuffering rate by 41.2%, reduces the average redundancy rate by 15.7%.

Dynamic binary translation (DBT) is the cornerstone of many important applications. In practice, however, it is quite difficult to maintain the performance efficiency of a DBT system due to its inherent complexity. Although performance regression testing is an effective approach to detect potential performance regression issues, it is not easy to apply performance regression testing to DBT systems, because of the natural differences between DBT systems and common software systems and the limited availability of effective test programs. In this paper, we present FADATest, which devises several novel techniques to address these challenges. Specifically, FADATest automatically generates adaptable test programs from existing real benchmark programs of DBT systems according to the runtime characteristics of the benchmarks. The test programs can then be used to achieve highly efficient and adaptive performance regression testing of DBT systems. We have implemented a prototype of FADATest. Experimental results show that FADATest can successfully uncover the same performance regression issues across the evaluated versions of two popular DBT systems, QEMU and Valgrind, as the original benchmark programs. Moreover, the testing efficiency is improved significantly on two different hardware platforms powered by x86-64 and AArch64, respectively.

System virtualization is a fundamental technology that enables many important applications. However, existing virtualization techniques suffer from a critical limitation due to their limited exploitation of host SIMD hardware resources, especially when a guest application does not have inherently fine-grained data-level parallelism. To bridge this utilization gap and unleash the full potential of host SIMD resources, this paper proposes an effective and unconventional SIMD exploitation technique. The proposed exploitation takes advantage of ample host SIMD registers and powerful host SIMD instructions to generate more efficient host binary code for guest applications even without any fine-grained data-level parallelism. It also mitigates the shortage of general-purpose registers on the host platform, as well as improves the efficiency of accessing guest registers. We have implemented the exploitation in an extensively-used virtualization platform, QEMU. Experimental results on a comprehensive list of benchmarks from PARSEC, SPEC-CPU2017, and Google Octane JavaScript benchmark suite show that an average of 2.2X performance speedup can be achieved for AArch64 binaries on an x86-64 host machine. We believe the proposed technique will provide a new perspective for our community to rethink the exploitation of SIMD hardware resources.

Dynamic binary translation (DBT) has been adopted in many important applications. Due to the large scale and complexity of a DBT system, a minor code change may lead to unexpected impact on the performance. Therefore, it is necessary to conduct performance regression testing for DBT systems. However, existing benchmark suites are not suitable for daily performance testing due to the extremely-long testing time. To address this challenge, we propose PerffiBT, which employs a novel approach to automatically generate test programs from existing long-running benchmarks. The execution times of the generated test programs are much shorter, which allows them to be used for daily performance regression testing of a DBT system. Experimental results demonstrate that the test programs generated by PerfDBT can achieve an average of 71X testing efficiency compared to original benchmarks. Furthermore, it can also deliver similar testing results to original benchmarks.