PADS Power Aware Distributed Systems Middleware Techniques and Tools USC Information Sciences Institute Brian Schott, Bob Parker UCLA Mani Srivastava Rockwell Science Center Charles Chien

PADS Project Q: How can you extend the dynamic power range of sensor networks from quiescent months of monitoring to frenetic minutes of activity? Architectural Approaches  Power Aware Research Platform Testbed  Deployable Power Aware Sensor Platform Middleware, Tools, and Techniques  Power Aware Resource Scheduling in RTOS  Techniques for Network-Wide Power Management Power Aware Algorithms  Multi-Resolution Distributed Algorithms UCLA

Introduction to UCLA PADS Team PI  Mani Srivastava Associate Professor in EE Department / Computer Engineering Area: wireless systems, networked embedded systems, low power systems Experience: Ph.D. Berkeley, 4+ years at Bell Labs Other faculty  Rajesh Gupta Associate Professor in CS Department at UCI (UCLA’s sister campus) Area: embedded and real-time systems, architecture, design tools Experience: Ph.D. Stanford, several years at Intel, UIUC-CS Students (planned)  Sung Park, Pavan Kumar, Paleologos Spanos, Vijay Raghunathan all are Computer Engineering graduate students

Planned Research Broad goals  Middleware techniques for “JIT” power through coordinated scheduling and power management of computing and communication resources locally at a sensor node (RTOS) as well as globally in a sensor network (protocols)  Tools for evaluating and designing the power management techniques  Target 10-30x gain in power efficiency Specific subtasks  Power management within a sensor node Power-aware RTOS scheduling under timing constraints Resource management with energy-speed and energy-accuracy control knobs Tools for RTOS power management evaluation, and power-aware kernel synthesis  Network-wide power management Network resource allocation for global power management Power-aware network protocols Hybrid sensor network simulation framework for power vs. quality evaluation of network level power management techniques and protocols  Power management with multimedia sensor data Power characterization, extension of PADS techniques to streaming multimedia  Integration with sensor nodes (Rockwell nodes, research platform)

Power Management Trade-offs in Sensor Networks Lifetime (power) Rapidity (latency -1 ) Quality (coverage, fidelity)

Power-aware Operation Intra-node hardware & circuits RTOS software Inter-node network protocols

Power Management in RTOS Traditional approaches  set voltage to match (average) sample rate  shutdown when idle and wake on demand to exploit e.g. predictive approach by Srivastava, Chandrakasan, and Brodersen Realities of sensor networks  Latencies are critical unlike DSP where only rate (sample period) matters deadlines important in protocols, target tracking  Tasks are dynamic cannot schedule the tasks statically Fortunately, hardware provides many “control knobs” for power- performance trade-off  CPUs with dynamic frequency/voltage, and shutdown mode  radios with multiple modes and symbol rate choices Potential for dynamic power management with power-quality trade-offs

Example: Fixed Priority Preemptive CPU Scheduling in RTOSs Consider task set (period, WCET, deadline)  {(10, 3, 10), (14, 7, 14)} CPU utilization = 3/10 + 7/14 = 80% Obvious power management strategies:  Shutdown when idle saves 20% power  Can we slow CPU by 20% (& reduce V) for more savings? NO, as deadlines will no longer be met  However, can slow by x 14/13 and lower voltage to still meet deadlines, and shutdown during idle time saves 22.5% in power Problem: uses WCET (worst case execution time)

Variation in Execution Times Significant variation in execution time of tasks  WCET:BCET often >> 1  e.g sensor processing time varies depending on target activity  e.g. compressed speech playout has different time for talkspurt vs. silence  e.g. on test run, MPEG decoder time range [0.003s, 0.15s] with average = 0.035s

Predictive Strategy for Exploiting Processing-time Variation Obvious: shut down or reduce voltage if task finishes earlier Even better: predict execution time of task instance and dynamically scale voltage even more aggressively  task-specific predictor to exploit history significant temporal correlation  but, some deadlines may be missed! leads to packet loss – another form of “noise”! Provides power-quality trade-off

Example #1 (Simulation)

Example #2 (Simulation)

Tool Framework for RTOS Power Management Evaluation Goals  Performance and power modeling of an RTOS environment that incorporate application-level contracts on power, timing, and functionality multiple power management policies and scheduling disciplines  Define suitable “contractual requirements” and ways to specify them.  Evaluate techniques for “admission control” by the RTOS  Generate RTOS kernels with application-specific power management Challenges  timing functionality and power performance are interrelated power management affects satisfiability of timing by scheduling and vice-versa  define “energy-speed” and “energy-accuracy” control variables in RTOS to navigate optimal scheduling and strategy  use mathematical programming approaches to optimize policy selection and scheduling

RTOS Power Analyzer Tool Build a power analyzer tool for determines power/performance feasibility for a given task scenario and application-level service contract (related to task timing, functionality, and energy budget)  initial work on bounds on improvement by different power management strategies for use by a RTOS in a decision procedure Inputs  task description and constraints prototype or automatic abstraction from routing protocols, … task deadline, response time, rate and interval separate constraints  a power management policy predictive, stochastic, adversarial  a scheduling discipline rate-monotonic, deadline-driven  a task level power model that allows accurate estimation of system-level speed versus power points Outputs  satisfiability of timing constraints: e.g., deadlines met or missed  an estimate of power savings

Power Management of Radios What is given or estimated?  Tx-Rx distance  Channel condition What does one control?  Radio modem settings transmit power, modulation scheme (type, symbol rate, bits/symbol)  Protocols and their parameters frame length, error control (coding, ARQ), routing: single-hop vs. multi-hop What do we get?  Quality: “useful” bit rate depends on raw bit rate, BER, protocol efficiency  Power: computing+communication energy spent at Tx and Rx per “useful” bit communicated Strategy: select modem and protocol parameters to minimize the power metric for a given quality level  power-quality trade-off  key problem: sender can’t wake up receiver

Example: Power-Rate Trade-off via Modulation At 0.001% BER and fixed transmission bandwidth with M-ary QAM

Example: Power-aware Frame Length Adaptation with TCP

Computation vs. Communication Trade-off in Error Control Code Rate Total Energy Code Rate Computation Energy Lowest energy for a given BER Communication Energy

Example: Coordinated Adaptation of FEC & Frame Length Fixed Code Route Packet Length (bytes) E n e r g y p e r U s e f u l B i t (  J / b i t ) Optimal Code Rate Packet Length (bytes) E n e r g y p e r U s e f u l B i t (  J / b i t ) 8x x

Envisioned MAC-level Power Management Strategy for Radio GIVEN - Estimated channel condition and Rx-Tx distance - Desired quality (latency, “useful” bits per second) - Radio energy consumption characteristics SELECT - Radio modem parameters modulation scheme, symbol rate, bits/symbol, spreading gain, transmit power level, carrier frequency - Protocol and protocol parameters frame length, error control (FEC, ARQ), # of hops MINIMIZE - computation + communication energy per “useful” bit

Leverage SensIT Research on Protocols Known results  power-aware adaptive link protocols optimize computation and communication energy spent per good application level bit distributed (Joules/bit)  multihop communication provides power & capacity benefits Traditional multihop routing is power unaware  focus on topology changes, and metrics such as shortest hop, shortest delay, link quality etc.  power wasted in quiescent state signaling using power-based routing metrics, and reactive protocols helps Our work in SensIT  coordinated routing and MAC large number of collisions with CSMA MAC during broadcasts used by routing protocols for probing  exploit path diversity, and node & traffic redundancy

Example: Exploiting Path Diversity & Node Redundancy Ideas : combine data from different nodes (Data Combining Entities), and distribute traffic over alternative paths (Spreading) increase network lifetime and coverage packet disperser and combiner entities works with variety of routing approaches Evaluation metrics time to breakdown # of depleted nodes RMS energy distribution Which nodes are important depends on future target traffic pattern and user movement traditional load balancing is based on only present activity goal is stochastic lifetime, but practical approaches need indirect measures

Energy Efficiency Impact An approximate way is a histogram area of histogram vs. shape of histogram but only approximate (can’t average over all futures) Possible metric to capture the essential histogram info: RMS of the histogram (measures total as well as spread) Energy used (%) Nodes (%) Energy used (%) Nodes (%) Without load spreading With load spreading

SensorSim Hybrid Simulator Motivation: study sensor network deployment, protocols, applications, and power-quality trade-offs at scale in a controlled setting Three key capabilities  Sensor and target modeling Target, sensor channel, and sensor transducer characteristics  Power modeling Power characterization via data from instrumented platforms Energy consumer models: radio, CPU, sensors Energy source models: batteries Power-quality trade-off analysis and visualization  Hybrid simulation selected nodes in a simulation can be “real” nodes  currently supports only higher layers in “real” nodes “real” applications can run on nodes in a simulation Current implementation based on ns simulator

SensorSim Architecture monitor and control hybrid network (local or remote) Simulation Machine Gateway Machine ns modified event scheduler V R V V V GUI app R real sensor apps on virtual sensor nodes gateway socket comm serial comm HS Interface Ethernet RS232 Proxies for real sensor nodes GUI Interface app

Sensor Node Model in SensorSim Node Function Model Network Layer Micro Sensor Node Applications Power Model (Energy Consumers and Providers) Battery Model Radio Model CPU Model Sensor #1 Model Sensor #2 Model MAC Layer Physical Layer Sensor Layer Wireless Channel Sensor Channel Network Protocol Stack Sensor Protocol Stack Middleware Physical Layer State Change Status Check

Battery Model Common battery model: bucket of constant energy Reality: delivered energy depends on how the battery is discharged  discharge rate (load current) C = k/I  where  up to 0.7  discharge profile and duty cycle  operating voltage and power level drained Appropriate protocols and power management strategy can lead to higher electrical work done for the same battery Microphone Model Geophone Model CPU Model Radio Model  Energy Provider Energy Consumers Battery Model Efficiency % Discharge Current Ratio

Radio Model Example Values: E elec = 50nJ/bit ε amp = 100pJ/bit/m 2 K bit packet E Tx,elec * k  amp * k * d  Transmit Electronics Transmit Amplifier E Tx (d) d K bit packet Receive Electronics E Rx E Rx,elec * k

Radio Power Management Example Using a 2Mbps WaveLAN NIC model in ns-2 Dynamic Source Routing (DSR) Case 1: node 0 transmits 512 byte packets every 2s to node 3 for 500s Case 2: nodes 0, 6, 1 continually transmit to nodes 3, 4, 2 MAC Layer is responsible for the power control Case 2 Case 1

Sample Power Management Strategy Transmit Receive Off Idle BZR event receive done transmit done Without Power Management Transmit Receive Sleep Off Idle transmit timeout(3 sec) BZR event transmit done BZR event receive done receive timeout With Power Management

Radio Power Management Simulation

Concept Demonstration using Initial Version of SensorSim SensorSim Workstation Gateway End User Station Location Coverage Target Reports Target Detectors