Sustainable and cost-effective long-term storage remains an unsolved problem. The most widely used storage technologies today are magnetic (hard disk drives and tape). They use media that degrades over time and has a limited lifetime, which leads to inefficient, wasteful, and costly solutions for long-lived data. This article presents Silica: the first cloud storage system for archival data underpinned by quartz glass, an extremely resilient media that allows data to be left in situ indefinitely. The hardware and software of Silica have been co-designed and co-optimized from the media up to the service level with sustainability as a primary objective. The design follows a cloud-first, data-driven methodology underpinned by principles derived from analyzing the archival workload of a large public cloud service. Silica can support a wide range of archival storage workloads and ushers in a new era of sustainable, cost-effective storage.

The vision of self-maintaining systems is to make cloud hardware automatically servicing and repairing using robotics. We define a self-maintaining system as one where software can control robotics that can automatically perform hardware maintenance tasks and repair operations. This reduces failure service windows and lowers the risk of repairs causing further cascading failures and outages. Self-maintaining systems are not purely reactive to failures, but also do proactive maintenance before failures occur which reduces future hardware failures. Operating an entire datacenter as a self-maintaining system is many years away, and we present four stages of automation, analogous to levels used for autonomous vehicles, required to reach the full vision for datacenters.

Automated storage and retrieval systems (ASRS) are a key component of the modern storage industry, and are used in a wide range of applications, carrying anything from lightweight tape cartridges to entire pallets of goods. Many of these systems are under pressure to maximise the use of space by growing in height and density, but this can create challenges for the the robots that service them. In this context, we present RASCAL, a novel ASRS robot for small payload items in structured environments, with a focus on system-level scalability and redundancy. We describe the design objectives of RASCAL and how they address some of the limitations of existing robotic systems in this area, such as scalability and redundancy. We then demonstrate the viability of our design with a proof-of-concept implementation of a data centre storage media robot, and show through a series of experiments that its design, speed, accuracy, and energy efficiency are appropriate for this application.

Sustainable and cost-effective long-term storage remains an unsolved problem. The most widely used storage technologies today are magnetic (hard disk drives and tape). They use media that degrades over time and has a limited lifetime, which leads to inefficient, wasteful, and costly solutions for long-lived data. This paper presents Silica: the first cloud storage system for archival data underpinned by quartz glass, an extremely resilient media that allows data to be left in situ indefinitely. The hardware and software of Silica have been co-designed and co-optimized from the media up to the service level with sustainability as a primary objective. The design follows a cloud-first, data-driven methodology underpinned by principles derived from analyzing the archival workload of a large public cloud service. Silica can support a wide range of archival storage workloads and ushers in a new era of sustainable, cost-effective storage.

The quality of automatic speech recognition (ASR) is critical to Dialogue Systems as ASR errors propagate to and directly impact downstream tasks such as language understanding (LU). In this paper, we propose multi-task neural approaches to perform contextual language correction on ASR outputs jointly with LU to improve the performance of both tasks simultaneously. To measure the effectiveness of this approach we used a public benchmark, the 2nd Dialogue State Tracking (DSTC2) corpus. As a baseline approach, we trained task specific Statistical Language Models (SLM) and fine-tuned state-of-the-art Generative Pre-training (GPT) Language Model to re-rank the n-best ASR hypotheses, followed by a model to identify the dialog act and slots. i) We further trained ranker models using GPT and Hierarchical CNN-RNN models with discriminatory losses to detect the best output given n-best hypotheses. We extended these ranker models to first select the best ASR output and then identify the dialogue act and slots in an end to end fashion. ii) We also proposed a novel joint ASR error correction and LU model, a word confusion pointer network (WCN-Ptr) with multihead self attention on top, which consumes the word confusions populated from the n-best. We show that the error rates of off the shelf ASR and following LU systems can be reduced significantly by 14% relative with joint models trained using small amounts of in-domain data.

The increasing gap between the growth of datacenter traffic and electrical switch capacity is expected to worsen due to the slowdown of Moore's law, motivating the need for a new switching technology for the post-Moore's law era that can meet the increasingly stringent requirements of hardware-driven cloud workloads. We propose Sirius, an optically-switched network for datacenters providing the abstraction of a single, high-radix switch that can connect thousands of nodes---racks or servers---in a datacenter while achieving nanosecond-granularity reconfiguration. At its core, Sirius uses a combination of tunable lasers and simple, passive gratings that route light based on its wavelength. Sirius' switching technology and topology is tightly codesigned with its routing and scheduling and with novel congestion-control and time-synchronization mechanisms to achieve a scalable yet flat network that can offer high bandwidth and very low end-to-end latency. Through a small-scale prototype using a custom tunable laser chip that can tune in less than 912 ps, we demonstrate 3.84 ns end-to-end reconfiguration atop 50 Gbps channels. Through large-scale simulations, we show that Sirius can approximate the performance of an ideal, electrically-switched non-blocking network with up to 74-77% lower power.

Commerce search engines allow users to discover products, learn about them, and, importantly, make purchases. Commerce search is a challenging problem --- one that is very different to conventional text and web search. In this talk, we discuss what makes commerce search hard, how eBay has solved some of these problems, and what challenges eBay faces in the next generation of its search technologies. We also discuss the recent release of eBay's Cassini engine, share facts and figures about its scale, and outline the progress eBay has made in ranking and relevance for commerce search.