Ion-trap quantum computers offer a large number of possible qubit couplings, each of which requires individual calibration and can be misconfigured. To enhance the duty cycle of an ion trap, we develop a strategy that diagnoses individual miscalibrated couplings using only log-many tests. This strategy is validated on a commercial ion-trap quantum computer, where we illustrate the process of debugging faulty quantum gates. Our methodology provides a scalable pathway towards fault detections on a larger scale ion-trap quantum computers, confirmed by simulations up to 32 qubits.

We have ubiquitous presence of computers today, ranging from simple controllers in modern appliances to smartphones in our pockets that provide a wide range of everyday services, to powerful supercomputers and large data centers that carry out the most computationally intensive tasks. These computational machines have a few things in common: for example, the information they handle is stored in bits (0 or 1), and the procedure for processing the information is specified by a program. A great deal is known about the limits of what such computational machines can and cannot do efficiently. There are many important computational problems that are believed to be very difficult to solve using even the most powerful computers, where the resource requirement-whether it is the size of the machine or the time it takes to finish the task-increases exponentially as a function of the problem size.

Most content providers are interested in providing good video delivery QoE for all users, not just on average. State-of-the-art ABR algorithms like BOLA and MPC rely on parameters that are sensitive to network conditions, so may perform poorly for some users and/or videos. In this paper, we propose a technique called Oboe to auto-tune these parameters to different network conditions. Oboe pre-computes, for a given ABR algorithm, the best possible parameters for different network conditions, then dynamically adapts the parameters at run-time for the current network conditions. Using testbed experiments, we show that Oboe significantly improves BOLA, MPC, and a commercially deployed ABR. Oboe also betters a recently proposed reinforcement learning based ABR, Pensieve, by 24% on average on a composite QoE metric, in part because it is able to better specialize ABR behavior across different network states.

Despite much recent progress, Web page latencies over cellular networks remain much higher than those over wired networks. Proxies that execute Web page JavaScript (JS) and push objects needed by the client can reduce latency. However, a key concern is the scalability of the proxy which must execute JS for many concurrent users. In this paper, we propose to scale the proxies, focusing on a design where the proxy's execution is solely to push the needed objects and the client completely executes the page as normal. Such redundant execution is a simple, yet effective approach to cutting network latencies, which dominate page load delays in cellular settings. We develop whittling, a technique to identify and execute in the proxy only the JS code necessary to identify and push the objects required for the client page load, while skipping other code. Whittling is closely related to program slicing, but with the important distinction that it is acceptable to approximate the program slice in the proxy given the client's complete execution. Experiments with top Alexa Web pages show NutShell can sustain, on average, 27\% more user requests per second than a proxy performing fully redundant execution, while preserving, and sometimes enhancing, the latency benefits.

As popular web sites turn to content delivery networks (CDNs) for full-site delivery, there is an opportunity to improve the end-user experience by optimizing the delivery of entire web pages, rather than just individual objects. In particular, this paper explores page-structure-aware strategies for placing objects in CDN cache hierarchies. The key idea is that the objects in a web page that have the largest impact on page latency should be served out of the closest or fastest caches in the hierarchy. We present schemes for identifying these objects and develop mechanisms to ensure that they are served with higher priority by the CDN, while balancing traditional CDN concerns such as optimizing the delivery of popular objects and minimizing bandwidth costs. To establish a baseline for evaluating improvements in page latencies, we collect and analyze publicly visible HTTP headers that reveal the distribution of objects among the various levels of a major CDN's cache hierarchy. Through extensive experiments on 83 real-world web pages, we show that latency reductions of over 100 ms can be obtained for 30% of the popular pages, with even larger reductions for the less popular pages. Using anonymized server logs provided by the CDN, we show the feasibility of reducing capacity and staleness misses of critical objects by 60% with minimal increase in overall miss rates, and bandwidth overheads of under 0.02%.