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Energy Management Issues in Wireless Computing and Networking Srijan Chakraborty Objective Reducing energy consumption of battery powered devices, e.g., Laptops and Handhelds, in wireless networks. Efficient runtime adaptation of application parameters. Exploiting mobility by movement prediction in a wireless ad hoc network. Presentation Outline Motivation Predicting Energy Consumption of MPEG Video Playback on Handhelds Introduction Experimental setup Experimental results Energy consumption models Summary Movement prediction Observation Power saving strategy System model Heuristics Simulation results Summary Conclusion Future work Motivation Wireless networks are getting popular. Increasing interest in mobile ad hoc networks Easy and low cost deployment Mobility No infrastructure Highly dynamic Problems Routing – nodes keep moving in and out of the network. Security – selfish, malicious, uncooperative nodes. Scalability. Limited battery life. Network communication – major energy drainer. For handhelds over 50% of the battery life can be consumed by network interface card! Improvements in battery technology - lifetime has increased. However, not to the extent to keep up with the increased energy requirement. Needs software level energy saving strategies. Related Work – Energy Saving Techniques Hardware level schemes Low power hardware design Dynamic voltage scaling Switching to power saving modes the whole device network interface card individual memory chips Transmission power control Software level schemes Computation offloading System redesign with energy metric Runtime adaptation of application parameters Energy aware routing protocols proactive/reactive location aware low cost spanning tree Predicting Energy Consumption of MPEG Video Playback on Handhelds Runtime adaptation of application parameters is promising Two extremes in adaptation react to low remaining energy conditions … by gracefully degrading application quality Complete system responsibility Complete Application responsibility A collaborative partnership between the operating system and the individual applications works best. Need to predict energy use as a function of independent controllable parameters. Target Application MPEG video playback decode and display Controllable parameters frame rate display size capture size frame type spatial resolution Experimental Setup Video Sequences Video Sequence Description Video-1 A rotating torus. Video-2 Beginning of Legendary Life. Video-3 Beginning of Bounce. Video-4 A person talking on a phone (from Bounce). Video-5 Captured from Bounce. Video-6 Captured from Legendary Life. Video-7 Two persons talking (from Legendary Life). Video-8 Captured from Bounce. Current Consumption by Display Size Energy Use by Frame Rate Frame rate (frames/s) Energy rate (Joules/sample period) Video-2 Video-3 Video-6 1.9 0.76 - - 2 - 0.89 0.89 2.8 - 1 - 2.9 0.95 - - 3 0.95 - 1.04 3.6 - 1.23 - 3.7 - - 1.2 3.8 - 1.22 - 4 1.11 - 1.21 4.5 - - 1.42 4.6 - 1.37 0 Energy Use by Frame Rate (Cont’d) Frame rate (frames/s) Energy rate (Joules/sample period) Video-2 Video-3 Video-6 4.7 - 1.37 1.42 4.8 1.2 1.38 - 5 - - 1.74 5.2 - - 1.70 5.3 - - 1.70 5,6 1.32 - - 5.7 1.33 - - 5.8 - 1.50 - 5.9 - 1.50 - 6.3 - 1.49 - Energy Consumption by Capture Size Capture size (pixel2) Energy used (Joules) Video-2 Video-5 Video-6 19200 436.25 391.04 - 76800 1481.09 1184.25 1706.06 84480 - - 1836.62 100800 1830.31 - - 115200 - - 2105.63 168960 - 2878.39 2967.74 172800 2192.26 2773.71 3148.15 153600 2171.47 2307.48 2782.37 230400 - 3200.81 3767.95 307200 - 3811.72 4477.85 345600 3144.51 4074.90 4855.33 Energy Consumption by Capture Bit Rate capture bit rate (MB/s) energy used (Joules) Video-4 Video-7 Video-8 0.4 - 653.58 747.57 1 1052.56 980.08 1043.91 2 - 1052.23 1166.01 2.99 - 1387.44 1635.93 4.02 1052.56 1463.74 1779.85 4.99 - 1775.91 - 6.01 - 1797.63 1994.32 7.04 - 1920.22 2066.08 8 1052.56 1996.98 2076.28 9.03 - 2052.46 2056.68 9.99 - - 1941.19 12.04 3146.58 - - 15 3176.30 - - Scatter Plot of I, P, B Energy Usage Energy Prediction Models Need to obtain quantitative prediction models from measurement data Run regression as function of relevant control parameters Find least-square best-fit polynomial increase polynomial degree until marginal improvement in sum of squared error is small Best-fit Polynomial for Capture Size Best-fit Polynomial for Frame Rate Best-fit Polynomial for Capture Bit Rate Summary of Energy Models Best fit Polynomial condition R E=4735.92029r+447.3r2 -33.9r3 d = 480 x 240, b = 2.4Mb/s 0.92 E = -131.13 + 0.0323d b = 2.4Mb/s, r varies with d 0.99 d = 480 x 240, r varies with b 0.95 E=627.07+359.92b31.98b2 +1.32b3 Summary Predictive energy models enable informed energy management For MPEG video playback, simple polynomial models possible (R value > 0.92) as function of several control parameters Models serve as building block in energy-aware handheld OS architecture Presentation Outline Motivation Predicting Energy Consumption of MPEG Video Playback on Handhelds Introduction Experimental setup Experimental results Energy consumption models Summary Movement prediction Observation Power saving strategy System model Heuristics Simulation results Summary Conclusion Future work Movement Prediction Observation: Reduced distance between communicating peers ⇒ Reduced transmission power requirement ⇒ Energy saving. Assuming network interface has transmission power control capability. Single hop communication – obvious Multi hop communication – expected Power Saving Strategy If likely to move closer to the target, postpone communication for a future time. Assuming application can tolerate some delay k. Needs movement prediction Based on movement history. Network Structure Mobile nodes are moving within a rectangular plane. We divide the network into virtual grids. Each grid has a unique grid ID. Assumptions Each node knows it's position – GPS. Each mobile host maintains a sequence of n previous grid IDs. Initial assumption – target is fixed. Every mobile node knows the target’s location. Relax the fixed target assumption – Both communicating peers are mobile. Mobility Model Defines a stochastic process which tells us how a mobile node moves in a network. Random waypoint mobility model 1. 2. 3. 4. 5. Wait for pause_time seconds Pick a random new destination Pick a random velocity Move steadily to the chosen destination Upon reaching the destination, repeat the steps 1 through 4 Regular waypoint mobility model Introduce regularity Home – work – home model with occasional diversions Choose new destination – not completely randomly Two parameters – Regularity r Periodicity T Terminology History of node h: Sh = {x1, x2, …, xn} A window of size l (for i ≤ n-l+1): W(i,i+l-1) = {xi, xi+1, …, xi+l-1} W(i,i+l-1) is a subsequence of Sh. Distance between two grids i and j: d(i,j). Binary Distance (BD) Heuristic Calculate the probability p that a mobile node will be in grid ID y within the next k time units as follows: p Pr(W (n 1, n k ) contains y ) (number of windows in S h of size k containing y ) /( n k 1) Communicate immediately if p is less than some probability threshold pth. Else, postpone communication. Problem With BD Heuristic A Too coarse granular idea of distance – Counts only when the communicating node is in the same grid as the target. A t Binary Markov Distance (BMD) Heuristic Based on order-m Markov model. Calculate the probability that a mobile node will be in grid ID y within the next k time units as follows: N Pr( xn 1 y ) i1 1 N i2 1 N Pr( xn 1 y | xn 1m i1 , xn 2m i2 ,, xn im ) im Problems: • Higher computational overhead. • Same coarse granularity problem as BD. Markov Distance (MD) Heuristic Let R be the set of all possible routes that can be taken by the mobile node in the next k time units Let R1, where R1 R, contain those routes in R that have at least one location closer to the target than the current distance. Then, we calculate the probability that a mobile node will move closer to the target as: p ( probabilit y of taking the route ) 1R1 1 ( probabilit y of taking the route ) R If p ≥ pth, then we postpone the communication, else we communicate immediately. • Higher computational overhead • Distinguishes the distance between the node and the target on a finer level MD Heuristic - Example Consider three possible paths of node A: • ρ1 moves closer to the target in the next two time steps. • ρ2 and ρ3 do not move closer to the target in the next two time steps. • If, these were the only options and A takes any of these paths with equal probability, then the probability that A will move closer to the target is: 1/3. 3 A 2 1 t Average Distance (AD) Heuristic Calculate the average distance between a mobile node and the target over all windows of size k in the mobile node's movement history as: avg n k 1 1 k j k 1 d ( x , y) j 1 i j i If the current distance between the mobile node and the target is greater than avg, then the mobile node decides to postpone the communication, or else it communicates immediately. • Less Computational overhead • Takes into consideration the actual distance Analogy With Secretary Problem Secretary problem: one must make an irrevocable choice from a number of applicants whose values are revealed only sequentially. Our problem: we must choose one time step when a node communicates and once it communicates it is done. Solutions to the secretary problem might help designing solutions to our problem. 37% Rule and The Least Distance (LD) Heuristic Best-choice(r) Algorithm: reject the first r-1 candidates. Then accept the next candidate whose relative rank is 1 among the candidates seen till now. Accepts the best candidate with probability 1/e ≈ 0.368. Optimal solution. Choose the time when the distance is the minimum seen till now. LD Heuristic: find Minimum as: d min Min d ( x, y) xS h Postpone communication if current distance is greater that dmin, else communicate immediately. Single Threshold Solution Select the first candidate whose value exceeds a pre-specified threshold value. Applicable only to the full information problem. Parameters can be estimated from partial observation. Average Distance heuristic – threshold is the average seen till now. One-bounce Rule Keep checking values as long as they go up. As soon as they go down we stop postponing any more and take the current value. postpone as long as the distance between the mobile host and the target is decreasing, and communicate as soon as the distance starts increasing. Ignores the history other than the last value. Use this idea along with AD heuristic. Use of One Bounce Rule If a node is moving away from the target, average keeps decreasing at each time step and finally we choose the worst alternative. A A t Solution: Directional Average Distance Heuristic • Take direction of movement into consideration. • If at any point of time, moving away from the target, communicate immediately. Moving Target Simple modifications to the heuristics proposed works for moving target. Assume a mobile host s with location history Ss = {x1, x2, …, xn} wants to communicate with node r with location history Sr = {y1, y2, …, yn}. MD heuristic: just define R and R1 with respect to Sr instead of y. AD heuristic: Define average as: avg n k 1 j 1 1 k j k 1 d (x , y ) i j i i LD heuristic: define minimum distance as: d min Min d ( x, y) xS h Preliminary Experiments Number of Grids: 3 x 3 Cost of single communication C(d) for distance d is d2. 10000 repetitions. Target Location: Randomly chosen for each run. Performance of BD Heuristic • Poor performance. Performance of BMD Heuristic Performance of MD Heuristic • With random waypoint mobility model. Performance of MD Heuristic • With regular waypoint mobility model. Performance of AD Heuristic Performance Comparison of BD, BMD, MD and AD. Simulation Experiments Network size: 1500m x 1500m Number of Grids: 3 x 3 Number of nodes: 20 Maximum speed: 10 m/s Simulation time: 20000 seconds Routing protocol: DSR Propagation model: Two-ray ground. Target Location: fixed at the center of the network. Performance of MD Heuristic for Varying Probability Threshold Performance of MD Heuristic for Varying Regularity Performance of AD Heuristic for Varying k Performance of AD and LD Heuristics for Varying Regularity Performance of LD Heuristic for Varying k Result for Mobile Target – Single hop Communication Result for Mobile Target – Multi hop Communication Observed Delay vs. Maximum Allowable Delay • We get higher energy saving by setting k higher, but without increasing the observed delay significantly. Energy Consumption Due to CPU Processing Comparison Among Heuristics Summary Our strategy predicts a good time for communication, when some amount of delay is tolerable. We postpone the communication until that point and then communicate. Simulation results show significant energy saving. Conclusion Wireless networking is rapidly emerging as the future communication technology The components of an ad hoc network are mostly battery-powered handheld devices. Limited battery life is an important issue in wireless networking. We address two issues: Predicting energy consumption as a function of adaptable parameters. We show that simple polynomial models can predict effectively. Exploiting node mobility in an ad hoc network to conserve energy. We can save more that 50% of the communication cost. Future Work Dynamic adaptation of system parameters. Location information for moving target in ad hoc networks. Considering transmission duration in predicting a good time for communication. Optimal way to divide the network into grids. Thank you!