At internet scale companies like ByteDance, data is generated and consumed at enormously high speed by many different applications. Achieving low latency on such big data jobs is an important problem. However, the naive approach of aggregating all the data required by a job to a single location is not always feasible in a geo-distributed environment. Similarly, existing approaches in geo-distributed job scheduling often try to minimize WAN usage, which may come at the cost of latency. Another crucial element to ensure low latency is resource load balancing among DCs, which enables flexibility in job scheduling and avoids resource bottlenecks. Therefore, to minimize latency, optimizing job completion time (JCT) while maintaining resource utilization balance is important. To this end, we propose ResLake , a global scheduling platform for data-intensive workloads. ResLake aims to reduce JCT of geo-distributed applications while balancing the compute (CPU/Memory) and storage (Disk) usages across DCs and efficiently using WAN interconnections. We have deployed ResLake in ByteDance's production for over 1.5 years. ResLake has scheduled billions of jobs since its deployment. We find that ResLake improves JCT of jobs by at least 20%, and can improve resource utilization balance across DCs by up to 53%.

Deep neural networks (DNNs) have been shown to be vulnerable to adversarial attacks -- subtle, perceptually indistinguishable perturbations of inputs that change the response of the model. In the context of vision, we hypothesize that an important contributor to the robustness of human visual perception is constant exposure to low-fidelity visual stimuli in our peripheral vision. To investigate this hypothesis, we develop RBlur, an image transform that simulates the loss in fidelity of peripheral vision by blurring the image and reducing its color saturation based on the distance from a given fixation point. We show that compared to DNNs trained on the original images, DNNs trained on images transformed by RBlur are substantially more robust to adversarial attacks, as well as other, non-adversarial, corruptions, achieving up to 25% higher accuracy on perturbed data.

Many websites rely on third parties for services (e.g., DNS, CDN, etc.). However, it also exposes them to shared risks from attacks (e.g., Mirai DDoS attack [24]) or cascading failures (e.g., GlobalSign revocation error [21]). Motivated by such incidents, we analyze the prevalence and impact of third-party dependencies, focusing on three critical infrastructure services: DNS, CDN, and certificate revocation checking by CA. We analyze both direct (e.g., Twitter uses Dyn) and indirect (e.g., Netflix uses Symantec as CA which uses Verisign for DNS) dependencies. We also take two snapshots in 2016 and 2020 to understand how the dependencies evolved. Our key findings are: (1) 89% of the Alexa top-100K websites critically depend on third-party DNS, CDN, or CA providers i.e., if these providers go down, these websites could suffer service disruption; (2) the use of third-party services is concentrated, and the top-3 providers of CDN, DNS, or CA services can affect 50%-70% of the top-100K websites; (3) indirect dependencies amplify the impact of popular CDN and DNS providers by up to 25X; and (4) some third-party dependencies and concentration increased marginally between 2016 to 2020. Based on our findings, we derive key implications for different stakeholders in the web ecosystem.

We present C-Saw, a system that measures Internet censorship by offering data-driven censorship circumvention to users. The adaptive circumvention capability of C-Saw incentivizes users to opt-in by offering small page load times (PLTs). As users crowdsource, the measurement data gets richer, offering greater insights into censorship mechanisms over a wider region, and in turn leading to even better circumvention capabilities. C-Saw incorporates user consent in its design by measuring only those URLs that a user actually visits. Using a cross-platform implementation of C-Saw, we show that it is effective at collecting and disseminating censorship measurements, selecting circumvention approaches, and optimizing user experience. C-Saw improves the average PLT by up to 48% and 63% over Lantern and Tor, respectively. We demonstrate the feasibility of a large-scale deployment of C-Saw with a pilot study.

TCP incast congestion happens in many-to-one communication workflow patterns that frequently arise in large-scale datacenter applications such as web search, social networks, and cluster-based storage systems. Incast congestion can severely degrade the performance of applications. This paper studies the effectiveness of randomizing the TCP retransmission timeout (RTO) in mitigating the impact of incast. Our design is based on the observation that under incast, retransmitted packets also get synchronized due to the use of similar RTOs by the senders. Using analysis and experimental evaluation, we show that there exists a tradeoff between the randomization interval (from which the RTO values are picked) and the number of senders involved in incast. Motivated by this insight, we propose three algorithms (TDA, MAA, and FSA) for the dynamic adaptation of the randomization interval that rely on (a) successive timeouts, (b) explicit knowledge of the level of multiplexing, and/or (c) the knowledge of flow sizes (i.e., large interval for long flows and a small interval for short flows), respectively. Our results show that these algorithms improve goodput by 1.5x-11x for up to 64 senders and provide greater improvement for larger number of senders. The proposed algorithms can be readily deployed as they do not require any changes in switches or applications.

Existing research on Internet censorship primarily focuses on either measurements or circumvention. Considering these two in isolation often leads to designs with limited capabilities: Circumvention is not driven by measurement data and end users find little incentive to help gather such data. We present the preliminary design and implementation of C-Saw, a platform that offers both. The circumvention capability of C-Saw incentivizes consumers to opt-in. As more and more consumers crowdsource, the monitoring data gets richer. This, in turn, offers greater insights into the censorship mechanisms over a wider region, offering even better circumvention capabilities. C-Saw is a browser-based platform set up as a lightweight client-side proxy. C-Saw adapts its circumvention approach to the particular censorship mechanism deployed by a user's ISP, achieving a better balance of circumvention effectiveness and performance. In addition, it can also leverage the heterogeneity in filtering mechanisms across ISPs to achieve better circumvention performance. We demonstrate this using page load times across various ISPs and locations in a censored region. Unlike previous measurement approaches, C-Saw does not require the knowledge of a target URL to be tested. In fact, as URLs get blocked, their information can be monitored in real time.