SummaryStore is an approximate time-series store, designed for analytics, capable of storing large volumes of time-series data (~1 petabyte) on a single node; it preserves high degrees of query accuracy and enables near real-time querying at unprecedented cost savings. SummaryStore contributes time-decayed summaries, a novel abstraction for summarizing data streams, along with an ingest algorithm to continually merge the summaries for efficient range queries; in conjunction, it returns reliable error estimates alongside the approximate answers, supporting a range of machine learning and analytical workloads. We successfully evaluated SummaryStore using real-world applications for forecasting, outlier detection, and Internet traffic monitoring; it can summarize aggressively with low median errors, 0.1 to 10%, for different workloads. Under range-query microbenchmarks, it stored 1PB synthetic stream data (10241TB streams), on a single node, using roughly 10 TB (100x compaction) with 95%-ile error below 5% and median cold-cache query latency of 1.3s (worst case latency under 70s).

Many large organizations collect massive volumes of data each day in a geographically distributed fashion, at data centers around the globe. Despite their geographically diverse origin the data must be processed and analyzed as a whole to extract insight. We call the problem of supporting large-scale geo-distributed analytics Wide-Area Big Data (WABD). To the best of our knowledge, WABD is currently addressed by copying all the data to a central data center where the analytics are run. This approach consumes expensive cross-data center bandwidth and is incompatible with data sovereignty restrictions that are starting to take shape. We instead propose WANalytics, a system that solves the WABD problem by orchestrating distributed query execution and adjusting data replication across data centers in order to minimize bandwidth usage, while respecting sovereignty requirements. WANalytics achieves an up to 360x reduction in data transfer cost when compared to the centralized approach on both real Microsoft production workloads and standard synthetic benchmarks, including TPC-CH and Berkeley Big-Data. In this demonstration, attendees will interact with a live geo-scale multi-data center deployment of WANalytics, allowing them to experience the data transfer reduction our system achieves, and to explore how it dynamically adapts execution strategy in response to changes in the workload and environment.

Low latency is critical for interactive networked applications. But while we know how to scale systems to increase capacity, reducing latency --- especially the tail of the latency distribution --- can be much more difficult. In this paper, we argue that the use of redundancy is an effective way to convert extra capacity into reduced latency. By initiating redundant operations across diverse resources and using the first result which completes, redundancy improves a system's latency even under exceptional conditions. We study the tradeoff with added system utilization, characterizing the situations in which replicating all tasks reduces mean latency. We then demonstrate empirically that replicating all operations can result in significant mean and tail latency reduction in real-world systems including DNS queries, database servers, and packet forwarding within networks.

Low latency is critical for interactive networked applications. But while we know how to scale systems to increase capacity, reducing latency --- especially the tail of the latency distribution --- can be much more difficult.

Denial of service protection mechanisms usually require classifying malicious traffic, which can be difficult. Another approach is to price scarce resources. However, while congestion pricing has been suggested as a way to combat DoS attacks, it has not been shown quantitatively how much damage a malicious player could cause to the utility of benign participants. In this paper, we quantify the protection that congestion pricing affords against DoS attacks, even for powerful attackers that can control their packets' routes. Specifically, we model the limits on the resources available to the attackers in three different ways and, in each case, quantify the maximum amount of damage they can cause as a function of their resource bounds. In addition, we show that congestion pricing is provably superior to fair queueing in attack resilience.