MobiStreams: A Reliable Distributed Stream Processing System for Mobile Devices

Multi-core phones are now pervasive. Yet, existing applications rely predominantly on a client-server computing paradigm, using phones only as thin clients, sending sensed information via the cellular network to servers for processing. This makes the cellular network the bottleneck, limiting overall application performance. In this paper, we propose Mobi Streams, a Distributed Stream Processing System (DSPS) that runs directly on smartphones. Mobi Streams can offload computing from remote servers to local phones and thus alleviate the pressure on the cellular network. Implementing DSPS on smartphones faces significant challenges: 1) multiple phones can readily fail simultaneously, and 2) the phones' ad-hoc WiFi network has low bandwidth. Mobi Streams tackles these challenges through two new techniques: 1) token-triggered check pointing, and 2) broadcast-based check pointing. Our evaluations driven by two real world applications deployed in the US and Singapore show that migrating from a server platform to a smartphone platform eliminates the cellular network bottleneck, leading to 0.78~42.6X throughput increase and 10%~94.8% latency decrease. Also, Mobi Streams' fault tolerance scheme increases throughput by 230% and reduces latency by 40% vs. prior state-of-the-art fault-tolerant DSPSs

RoadRunner: Infrastructure-less vehicular congestion control

RoadRunner is an in-vehicle app for traffic congestion control without costly roadside infrastructure, instead judiciously harnessing vehicle-to-vehicle communications, cellular connectivity, and onboard computation and sensing to enable large-scale traffic congestion control at higher penetration and finer granularity than previously possible. RoadRunner limits the number of vehicles in a congested region or road by requiring each to possess a token for entry. Tokens can circulate and be reused among multiple vehicles as vehicles move between regions. We built RoadRunner as an Android app utilizing LTE, 802.11p, and 802.11n radios, deployed it on 10 vehicles, and measured cellular access reductions of up to 84% and response time improvements of up to 80%. In a microscopic agent-based traffic simulator, RoadRunner achieved travel speed improvements of up to 7.7% over an industry-strength electronic road pricing system.Singapore-MIT Alliance for Research and TechnologyAmerican Society for Engineering Education. National Defense Science and Engineering Graduate Fellowshi

SignalGuru: Leveraging mobile phones for collaborative traffic signal schedule advisory

While traffic signals are necessary to safely control competing flows of traffic, they inevitably enforce a stop-and-go movement pattern that increases fuel consumption, reduces traffic flow and causes traffic jams. These side effects can be alleviated by providing drivers and their onboard computational devices (e.g., vehicle computer, smartphone) with information about the schedule of the traffic signals ahead. Based on when the signal ahead will turn green, drivers can then adjust speed so as to avoid coming to a complete halt. Such information is called Green Light Optimal Speed Advisory (GLOSA). Alternatively, the onboard computational device may suggest an efficient detour that will save the driver from stops and long waits at red lights ahead. This paper introduces and evaluates SignalGuru, a novel software service that relies solely on a collection of mobile phones to detect and predict the traffic signal schedule, enabling GLOSA and other novel applications. Our SignalGuru leverages windshield-mounted phones to opportunistically detect current traffic signals with their cameras, collaboratively communicate and learn traffic signal schedule patterns, and predict their future schedule. Results from two deployments of SignalGuru, using iPhones in cars in Cambridge (MA, USA) and Singapore, show that traffic signal schedules can be predicted accurately. On average, SignalGuru comes within 0.66s, for pre-timed traffic signals and within 2.45s, for traffic-adaptive traffic signals. Feeding SignalGuru's predicted traffic schedule to our GLOSA application, our vehicle fuel consumption measurements show savings of 20.3%, on average.National Science Foundation (U.S.). (Grant number CSR-EHS-0615175)Singapore-MIT Alliance for Research and Technology Center. Future Urban Mobilit

Enabling System-Level Modeling of Variation-Induced Faults in Networks-on-Chip

Process Variation (PV) is increasingly threatening the reliability of Networks-on-Chips. Thus, various resilient router designs have been recently proposed and evaluated. However, these evaluations assume random fault distributions, which result in 52%--81% inaccuracy. We propose an accurate circuit-level fault-modeling tool, which can be plugged into any system-level NoC simulator, quantify the system-level impact of PV-induced faults at runtime, pinpoint fault-prone router components that should be protected, and accurately evaluate alternative resilient multi-core designs.GigaScale Systems Research CenterFocus Center Research Program. Focus Center for Circuit & System Solutions. Semiconductor Research Corporation. Interconnect Focus Cente

Similitude: Interfacing a Traffic Simulator and Network Simulator with Emulated Android Clients

Mobile phone apps are increasingly part and parcel of today's intelligent transportation systems (ITS). Evaluating these apps at scale requires modeling of phones and networks, along with vehicles, people and roads. In this paper, we present Similitude, a system comprising a traffic simulator, network simulator, and cluster of Android emulators that has applications in mobile app development as well as modern transport simulation. Apps with their wireless network stack are run on an Android emulator, with network packet delivery modeled in detail via a network simulator. Each phone's location and human interaction elements are obtained through interfacing with a microscopic traffic simulator running driver and pedestrian behavioral models. A prototype of the system is shown to scale well up to 300 simultaneous connected Android emulators, with individual system components scaling upwards of thousands of agents. An ITS app that does road space rationing is used as the case study demonstrating a potential use case of Similitude

40.4fJ/bit/mm Low-Swing On-Chip Signaling with Self-Resetting Logic Repeaters Embedded within a Mesh NoC in 45nm SOI CMOS

Mesh NoCs are the most widely-used fabric in high-performance many-core chips today. They are, however, becoming increasingly power-constrained with the higher on-chip bandwidth requirements of high-performance SoCs. In particular, the physical datapath of a mesh NoC consumes significant energy. Low-swing signaling circuit techniques can substantially reduce the NoC datapath energy, but existing low-swing circuits involve huge area footprints, unreliable signaling or considerable system overheads such as an additional supply voltage, so embedding them into a mesh datapath is not attractive. In this paper, we propose a novel low-swing signaling circuit, a self-resetting logic repeater, to meet these design challenges. The SRLR enables single-ended low-swing pulses to be asynchronously repeated, and hence, consumes less energy than differential, clocked low-swing signaling. To mitigate global process variations while delivering high energy efficiency, three circuit techniques are incorporated. Fabricated in 45nm SOI CMOS, our 10mm SRLR-based low-swing datapath achieves 6.83Gb/s/µm bandwidth density with 40.4fJ/bit/mm energy at 4.1Gb/s data rate at 0.8V.United States. Defense Advanced Research Projects Agency. The Ubiquitous High-Performance Computing Progra

BOOM: Broadcast Optimizations for On-chip Meshes

Future many-core chips will require an on-chip network that can support broadcasts and multicasts at good power-performance. A vanilla on-chip network would send multiple unicast packets for each broadcast packet, resulting in latency, throughput and power overheads. Recent research in on-chip multicast support has proposed forking of broadcast/multicast packets within the network at the router buffers, but these techniques are far from ideal, since they increase buffer occupancy which lowers throughput, and packets incur delay and power penalties at each router. In this work, we analyze an ideal broadcast mesh; show the substantial gaps between state-of-the-art multicast NoCs and the ideal; then propose BOOM, which comprises a WHIRL routing protocol that ideally load balances broadcast traffic, a mXbar multicast crossbar circuit that enables multicast traversal at similar energy-delay as unicasts, and speculative bypassing of buffering for multicast flits. Together, they enable broadcast packets to approach the delay, energy, and throughput of the ideal fabric. Our simulations show BOOM realizing an average network latency that is 5% off ideal, attaining 96% of ideal throughput, with energy consumption that is 9% above ideal. Evaluations using synthetic traffic show BOOM achieving a latency reduction of 61%, throughput improvement of 63%, and buffer power reduction of 80% as compared to a baseline broadcast. Simulations with PARSEC benchmarks show BOOM reducing average request and network latency by 40% and 15% respectively

SWIFT: A Low-Power Network-On-Chip Implementing the Token Flow Control Router Architecture With Swing-Reduced Interconnects

A 64-bit, 8 × 8 mesh network-on-chip (NoC) is presented that uses both new architectural and circuit design techniques to improve on-chip network energy-efficiency, latency, and throughput. First, we propose token flow control, which enables bypassing of flit buffering in routers, thereby reducing buffer size and their power consumption. We also incorporate reduced-swing signaling in on-chip links and crossbars to minimize datapath interconnect energy. The 64-node NoC is experimentally validated with a 2 × 2 test chip in 90 nm, 1.2 V CMOS that incorporates traffic generators to emulate the traffic of the full network. Compared with a fully synthesized baseline 8 × 8 NoC architecture designed to meet the same peak throughput, the fabricated prototype reduces network latency by 20% under uniform random traffic, when both networks are run at their maximum operating frequencies. When operated at the same frequencies, the SWIFT NoC reduces network power by 38% and 25% at saturation and low loads, respectively