Datacenter disaggregation provides numerous benefits to both the datacenter operator and the application designer. However switching from the server-centric model to a disaggregated model requires developing new programming abstractions that can achieve high performance while benefiting from the greater elasticity. To explore the limits of datacenter disaggregation, we study an application area that near-maximally benefits from current server-centric datacenters: dense linear algebra. We build NumPyWren, a system for linear algebra built on a disaggregated serverless programming model, and LAmbdaPACK, a companion domain-specific language designed for serverless execution of highly parallel linear algebra algorithms. We show that, for a number of linear algebra algorithms such as matrix multiply, singular value decomposition, Cholesky decomposition, and QR decomposition, NumPyWren's performance (completion time) is within a factor of 2 of optimized server-centric MPI implementations, and has up to 15% greater compute efficiency (total CPU-hours), while providing fault tolerance.

Distributed computing remains inaccessible to a large number of users, in spite of many open source platforms and extensive commercial offerings. While distributed computation frameworks have moved beyond a simple map-reduce model, many users are still left to struggle with complex cluster management and configuration tools, even for running simple embarrassingly parallel jobs. We argue that stateless functions represent a viable platform for these users, eliminating cluster management overhead, fulfilling the promise of elasticity. Furthermore, using our prototype implementation, PyWren, we show that this model is general enough to implement a number of distributed computing models, such as BSP, efficiently. Extrapolating from recent trends in network bandwidth and the advent of disaggregated storage, we suggest that stateless functions are a natural fit for data processing in future computing environments.

Low latency analytics on geographically distributed datasets (across datacenters, edge clusters) is an upcoming and increasingly important challenge. The dominant approach of aggregating all the data to a single datacenter significantly inflates the timeliness of analytics. At the same time, running queries over geo-distributed inputs using the current intra-DC analytics frameworks also leads to high query response times because these frameworks cannot cope with the relatively low and variable capacity of WAN links. We present Iridium, a system for low latency geo-distributed analytics. Iridium achieves low query response times by optimizing placement of both data and tasks of the queries. The joint data and task placement optimization, however, is intractable. Therefore, Iridium uses an online heuristic to redistribute datasets among the sites prior to queries' arrivals, and places the tasks to reduce network bottlenecks during the query's execution. Finally, it also contains a knob to budget WAN usage. Evaluation across eight worldwide EC2 regions using production queries show that Iridium speeds up queries by 3× -- 19× and lowers WAN usage by 15% -- 64% compared to existing baselines.

We have observed two trends, growing wireless capability at the physical layer powered by MIMO-OFDM and growing video traffic as the dominant application traffic. Both the video source and MIMO-OFDM channel components exhibit nonuniform energy distribution. This has motivated us to leverage the source data redundancy at the channel to achieve high video recovery performance. We propose ParCast+ that first separates the source and the channel into independent components, matches the more important source components with higher-gain channel components, allocates power weights with joint consideration to the source and the channel, and uses pseudo-analog modulation for transmission. Such a scheme achieves fine-grained unequal error protection across source components. We implemented ParCast+ in Matlab and on Sora. Extensive evaluation has shown that our scheme outperforms competing schemes by notable margins, sometimes up to 6.4 dB in PSNR for challenging scenarios.

A key challenge for smartphone based visual communication over screen-camera links is imperfect frame synchronization. The difficulty arises from frame rate diversity and variability due to camera capability, lighting conditions, and system factors. On the 4 smartphone cameras we tested, the frame rate varies between 8 and 30 frames per second. If the transmit frame rate is too high, the receiver might lose original frames or capture mixed frames, which are normally not decodable. Previous systems simply reduce the effective screen frame rate to be half the camera frame capture rate, to guarantee receiving a decodable frame every other frame. This under-utilizes the transmitter side capacity and is inefficient.

This paper presents WiSee, a novel gesture recognition system that leverages wireless signals (e.g., Wi-Fi) to enable whole-home sensing and recognition of human gestures. Since wireless signals do not require line-of-sight and can traverse through walls, WiSee can enable whole-home gesture recognition using few wireless sources. Further, it achieves this goal without requiring instrumentation of the human body with sensing devices. We implement a proof-of-concept prototype of WiSee using USRP-N210s and evaluate it in both an office environment and a two- bedroom apartment. Our results show that WiSee can identify and classify a set of nine gestures with an average accuracy of 94%.

As personal information increases in value, the incentives for remote services to collect as much of it as possible increase as well. In the current Internet, the default assumption is that all behavior can be correlated using a variety of identifying information, not the least of which is a user's IP address. Tools like Tor, Privoxy, and even NATs, are located at the opposite end of the spectrum and prevent any behavior from being linked. Instead, our goal is to provide users with more control over linkability---which activites of the user can be correlated at the remote services---not necessarily more anonymity.

This demo presents WiSee, a novel human-computer interaction system that leverages wireless networks (e.g., Wi-Fi), to enable sensing and recognition of human gestures and motion. Since wire- less signals do not require line-of-sight and can traverse through walls, WiSee enables novel human-computer interfaces for remote device control and building automation. Further, it achieves this goal without requiring instrumentation of the human body with sensing devices. We integrate WiSee with applications and demonstrate how WiSee enables users to use gestures and control applications including music players and gaming systems. Specifically, our demo will allow SIGCOMM attendees to control a music player and a lighting control device using gestures.

We observe two trends, growing wireless capability at the physical layer powered by MIMO-OFDM, and growing video traffic as the dominant application traffic. Both the source and MIMO-OFDM channel components exhibit non-uniform energy distribution. This motivates us to leverage the source data redundancy at the channel to achieve high video recovery performance. We propose ParCast that first separates the source and channel into independent components, matches the more important source components with higher-gain channel components, allocates power weights with joint consideration to the source and the channel, and uses analog modulation for transmission. Such a scheme achieves fine-grained unequal error protection across source components. We implemented ParCast in Matlab and on Sora. Extensive evaluation shows that our scheme outperforms competitive schemes by notable margins, sometimes up to 5~dB in PSNR for challenging scenarios.