Erasure codes are critical tools for building fault-tolerant and resource-efficient storage systems. However developing and maintaining optimized erasure-coding libraries are challenging. We make the case that the growth of fast machine-learning (ML) libraries may serve as a lifeboat for easing the development of current and future optimized erasure-coding libraries: fast erasure-coding libraries for various hardware platforms can be easily implemented by using existing optimized ML libraries. We show that the computation structure of many erasure codes mirrors that common to matrix multiplication, which is heavily optimized in ML libraries. Due to this similarity, one can implement erasure codes using ML libraries in few lines of code and with little knowledge of erasure codes, while immediately adopting the many optimizations within these libraries, without requiring expertise in high-performance programming. We develop prototypes of our proposed approach using an existing ML library. Our prototypes are up to 1.75× faster than state-of-the-art custom erasure-coding libraries.

Shared logs offer linearizable total order across storage shards. However, they enforce this order eagerly upon ingestion, leading to high latencies. We observe that in many modern shared-log applications, while linearizable ordering is necessary, it is not required eagerly when ingesting data but only later when data is consumed. Further, readers are naturally decoupled in time from writers in these applications. Based on this insight, we propose LazyLog, a novel shared log abstraction. LazyLog lazily binds records (across shards) to linearizable global positions and enforces this before a log position can be read. Such lazy ordering enables low ingestion latencies. Given the time decoupling, LazyLog can establish the order well before reads arrive, minimizing overhead upon reads. We build two LazyLog systems that provide linearizable total order across shards. Our experiments show that LazyLog systems deliver significantly lower latencies than conventional, eager-ordering shared logs.

Efficient and correct operation of an IoT network requires the presence of a failure detector and membership protocol amongst the IoT nodes. This paper presents a new failure detector for IoT settings wherein nodes are connected via a wireless ad-hoc network. Our failure detector, named Medley, is fully decentralized, allows IoT nodes to maintain a local membership list of other alive nodes, detects failures quickly (and updates the membership list), and incurs low communication overhead. We adapt a failure detector originally proposed for datacenters (SWIM), for the IoT environment. This adaptation is non-trivial. In Medley each node picks a medley of ping targets in a randomized and skewed manner, preferring nearer nodes. We also provide optimizations to achieve time-bounded detection, as well as to reduce tail detection times. Via analysis, simulation, and Raspberry Pi deployments, we show that Medley can simultaneously optimize detection time and communication traffic.

In this paper, we propose B2-Coupon, an efficient mobile electronic coupon (mobile coupon or coupon in short) distribution system on mobile phones using the Dual Bloom Filter. The Dual Bloom Filter, consisted of the Interest Bloom Filter and the Forwarder Bloom Filter, achieve targeted forwarding and fair reward distributions among coupon forwarders. Particularly, the Interest Bloom Filter narrows down the target receivers (i.e. potential coupon users or forwarders) by user interest matching check, and the Forwarder Bloom Filter records all the coupon forwarder IDs in a space-efficient way. We use Bluetooth Low Energy (BLE) beacons to enable non-intrusive coupon transmission without manual connection setup. We conduct both large-scale simulation-based and implementation-based evaluations on our system. The simulation results show that our system realizes fast coupon propagation and fair reward distributions. The implementation is based on Android phones, such as Google and Samsung phones. The implementation results indicate that our system is capable of real-world deployment. We conclude that B2-Coupon can effectively facilitate efficient and non-intrusive coupon distribution among mobile users.