Power Management Algorithms An effort to minimize Processor Temperature and Energy Consumption.

Slides:



Advertisements
Similar presentations
On the Complexity of Scheduling
Advertisements

S YSTEM -W IDE E NERGY M ANAGEMENT FOR R EAL -T IME T ASKS : L OWER B OUND AND A PPROXIMATION Xiliang Zhong and Cheng-Zhong Xu ICCAD 2006, ACM Trans. on.
CPE555A: Real-Time Embedded Systems
Real- time Dynamic Voltage Scaling for Low- Power Embedded Operating Systems Written by P. Pillai and K.G. Shin Presented by Gaurav Saxena CSE 666 – Real.
Mehdi Kargahi School of ECE University of Tehran
Greedy Algorithms Basic idea Connection to dynamic programming
Minimizing Expected Energy Consumption in Real-Time Systems through Dynamic Voltage Scaling Ruibin Xu, Daniel Mosse’, and Rami Melhem.
RUN: Optimal Multiprocessor Real-Time Scheduling via Reduction to Uniprocessor Paul Regnier † George Lima † Ernesto Massa † Greg Levin ‡ Scott Brandt ‡
Greedy Algorithms Basic idea Connection to dynamic programming Proof Techniques.
1 Better Scalable Algorithms for Broadcast Scheduling Ravishankar Krishnaswamy Carnegie Mellon University Joint work with Nikhil Bansal and Viswanath Nagarajan.
Ecole Polytechnique, Nov 7, Minimizing Total Completion Time Each job specified by  procesing time (length p j )  release time r j Goal: compute.
1 Ecole Polytechnque, Nov 7, 2007 Scheduling Unit Jobs to Maximize Throughput Jobs:  all have processing time (length) = 1  release time r j  deadline.
Investigating the Effect of Voltage- Switching on Low-Energy Task Scheduling in Hard Real-Time Systems Paper review Presented by Chung-Fu Kao.
Energy-Efficient Rate Scheduling in Wireless Links A Geometric Approach Yashar Ganjali High Performance Networking Group Stanford University
CSE 421 Algorithms Richard Anderson Lecture 6 Greedy Algorithms.
1 Scheduling on Heterogeneous Machines: Minimize Total Energy + Flowtime Ravishankar Krishnaswamy Carnegie Mellon University Joint work with Anupam Gupta.
System-Wide Energy Minimization for Real-Time Tasks: Lower Bound and Approximation Xiliang Zhong and Cheng-Zhong Xu Dept. of Electrical & Computer Engg.
Algorithmic problems in Scheduling jobs on Variable-speed processors Frances Yao City University of Hong Kong.
A Model for Minimizing Active Processor Time Jessica Chang Joint work with Hal Gabow and Samir Khuller.
Minimizing Flow Time on Multiple Machines Nikhil Bansal IBM Research, T.J. Watson.
Energy, Energy, Energy  Worldwide efforts to reduce energy consumption  People can conserve. Large percentage savings possible, but each individual has.
Optimal Fan Speed Control for Thermal Management of Servers UMass-Amherst Green Computing Seminar September 21 st, 2009.
University of Karlsruhe, System Architecture Group Balancing Power Consumption in Multiprocessor Systems Andreas Merkel Frank Bellosa System Architecture.
Packet Scheduling From Ion Stoica. 2 Packet Scheduling  Decide when and what packet to send on output link -Usually implemented at output interface 1.
Control and Optimization Meet the Smart Power Grid: Scheduling of Power Demands for Optimal Energy Management Authors: Iordanis Koutsopoulos Leandros Tassiulas.
VOLTAGE SCHEDULING HEURISTIC for REAL-TIME TASK GRAPHS D. Roychowdhury, I. Koren, C. M. Krishna University of Massachusetts, Amherst Y.-H. Lee Arizona.
Speed Scaling To Manage Temperature Nikhil Bansal IBM T.J. Watson Kirk Pruhs University of Pittsburgh.
Sensor-Based Fast Thermal Evaluation Model For Energy Efficient High-Performance Datacenters Q. Tang, T. Mukherjee, Sandeep K. S. Gupta Department of Computer.
Last Time Performance Analysis It’s all relative
Speed Scaling to Manage Energy and Temperature Nikhil Bansal (IBM Research) Tracy Kimbrel (IBM) and Kirk Pruhs (Univ. of Pittsburgh)
Low Power Design for Real-Time Systems Low power (energy) consumption is a key design for embedded systems Battery’s life during operation Reliability.
Approximation Algorithms for Task Allocation with QoS and Energy Considerations Bader N. Alahmad.
1 EE5900 Advanced Embedded System For Smart Infrastructure Energy Efficient Scheduling.
Dynamic Slack Reclamation with Procrastination Scheduling in Real- Time Embedded Systems Paper by Ravindra R. Jejurikar and Rajesh Gupta Presentation by.
1 Pruhs, Woeginger, Uthaisombut 2004  Qos Objective: Minimize total flow time  Flow time f i of a job i is completion time C i – r i  Power Objective:
1 Server Scheduling in the L p norm Nikhil Bansal (CMU) Kirk Pruhs (Univ. of Pittsburgh)
Scheduling policies for real- time embedded systems.
1 Distributed Energy-Efficient Scheduling for Data-Intensive Applications with Deadline Constraints on Data Grids Cong Liu and Xiao Qin Auburn University.
Parallel Processing Sharing the load. Inside a Processor Chip in Package Circuits Primarily Crystalline Silicon 1 mm – 25 mm on a side 100 million to.
Thermal-aware Issues in Computers IMPACT Lab. Part A Overview of Thermal-related Technologies.
Scheduling Periodic Real-Time Tasks with Heterogeneous Reward Requirements I-Hong Hou and P.R. Kumar 1 Presenter: Qixin Wang.
The 32nd IEEE Real-Time Systems Symposium Meeting End-to-End Deadlines through Distributed Local Deadline Assignment Shengyan Hong, Thidapat Chantem, X.
Hard Real-Time Scheduling for Low- Energy Using Stochastic Data and DVS Processors Flavius Gruian Department of Computer Science, Lund University Box 118.
Jennifer Campbell November 30,  Problem Statement and Motivation  Analysis of previous work  Simple - competitive strategy  Near optimal deterministic.
Special Class on Real-Time Systems
MULTICORE PROCESSOR TECHNOLOGY.  Introduction  history  Why multi-core ?  What do you mean by multicore?  Multi core architecture  Comparison of.
OPERATING SYSTEMS CS 3530 Summer 2014 Systems and Models Chapter 03.
Common Approaches to Real-Time Scheduling Clock-driven (time-driven) schedulers Priority-driven schedulers Examples of priority driven schedulers Effective.
ECE555 Topic Presentation Energy-efficient real-time scheduling Xing Fu 20 September 2008 Acknowledge Dr. Jian-Jia Chen from ETH providing PPT Slides for.
Multimedia Computing and Networking Jan Reduced Energy Decoding of MPEG Streams Malena Mesarina, HP Labs/UCLA CS Dept Yoshio Turner, HP Labs.
Xi He Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester, NY THERMAL-AWARE RESOURCE.
Thermal Management in Datacenters Ayan Banerjee. Thermal Management using task placement Tasks: Requires a certain number of servers (cores) for a specified.
Problems in Combinatorial Optimization. Linear Programming.
Determining Optimal Processor Speeds for Periodic Real-Time Tasks with Different Power Characteristics H. Aydın, R. Melhem, D. Mossé, P.M. Alvarez University.
CS203 – Advanced Computer Architecture
Distributed Process Scheduling- Real Time Scheduling Csc8320(Fall 2013)
Linear program Separation Oracle. Rounding We consider a single-machine scheduling problem, and see another way of rounding fractional solutions to integer.
Thermal-aware Task Placement in Data Centers (part 4)
Lecture 24: Process Scheduling Examples and for Real-time Systems
Flavius Gruian < >
Chapter 6: CPU Scheduling
Sanjoy Baruah The University of North Carolina at Chapel Hill
Jian-Jia Chen and Tei-Wei Kuo
Greedy Algorithms: Homework Scheduling and Optimal Caching
Richard Anderson Lecture 6 Greedy Algorithms
Lecture 18 CSE 331 Oct 9, 2017.
Richard Anderson Lecture 7 Greedy Algorithms
Lecture 19 CSE 331 Oct 10, 2016.
Richard Anderson Autumn 2015 Lecture 7
Richard Anderson Autumn 2019 Lecture 7
Presentation transcript:

Power Management Algorithms An effort to minimize Processor Temperature and Energy Consumption

Motivation  Microprocessor power consumption is increasing exponentially

Motivation  Battery capacity is increasing linearly  Expected battery life increase in the next 5 years: 30 to 40%  Chip manufacturers are close to “thermal wall”  Increase in speed  increase in heat generation  Expensive and noisy cooling systems required  Intel: Tejas and Jayhawk   Laptops may damage male fertility due to increased temperature (Reuters: December 9, 2004)

Motivation  Information Technology (IT) consumes about 8% of energy in US  Exponential growth  50% of energy consumption  Analysis from Intel: 25,000-square-foot server farm with approximately 8,000 servers consumes 2 megawatts -- 25% of the cost of such a facility

Processor Technologies for Power Management  Speed Scaling  Processor can operate on multiple speeds o Intel’s SpeedStep — 2 speeds o AMD’s PowerNow — 9 speeds o Intel’s Foxton technology — 64 speeds  Power Down  Processor can operate on multiple power levels o Can operate on any power level L 0, L 1, …, L n. o L n is normal state. L 0, …, L n-1 are idle states o It costs to bring back processor to L n

Relationship Between Speed and Energy  P = c V 2 s o Minimum voltage V required to run processor at speed s. V is roughly linear to s o Therefore, P = c s 3 o Generalize to P = s p, for some constant p ≥ 1  Energy = ∫ Time P dt  Speed goes up(down)  Energy consumption goes up (down)

Relationship Between Speed and Temperature  Key Assumption: fixed ambient temperature T a  First order approximation of temperature dT/dt = a P – b (T – T a ) = a P – b T  T = Temprature  t = time  P = supplied power  a,b some constants  For simplicity rescale so that T a = 0

Problem Formulation  Input: A collection of tasks, where task I has: o Release time r i when it arrives in the system o Deadline d i when it must finish by o Work requirement w i (number of cycles)  The processor must perform w i units of work between time r i and time d i o Preemption is allowed  Objectives o Minimize energy consumption o Minimize maximum temperature  For each time, the scheduler must specify both o Job Selection: which job to run  may assume Earliest Deadline First policy o Speed Setting: at what speed the processor should run at

Summary of Results

Offline YDS Algorithm (1995)  Repeat o Find the time interval I with maximum intensity  Intensity of time interval I = Σ w i / |I|  Where the sum is over tasks i with [r i,d i ] in I o During I  speed = to the intensity of I  Earliest Deadline First policy o Remove I and the jobs completed in I

YDS Example Release timedeadline time

YDS Example First Interval Intensity Second Interval Intensity = green work + blue work Length of solid green line

YDS Example  Final YDS schedule o Height = processor speed  YDS theorem: The YDS schedule is optimal for energy, or equivalently for temperature when b = 0. And YDS is optimal for maximum power, or equivalently when b = ∞. o Bansal, Pruhs: Consequence of KKT optimality  Bansal, Pruhs: The YDS is at worst 20-competitive with respect to temperature for all cooling parameters b

Why is YDS optimal?  Convex program o They are called KKT optimality conditions The problem has solution if these conditions hold:

Why is YDS optimal?  YDS as convex problem o Break time into intervals t 0,…t m at release times and deadlines o J(i): tasks feasibly executed in I i = [t i,t i+1 ] o W i,j for j in J(i): work done on j during [t i,t i+1 ] KKT optimality conditions hold It took 10 years to prove YDS’s optimality!!!

Online AVR Algorithm (1995)  Each job i has av. rate requirement or density avr i =w i /(d i – r i )  while(t < max d j ) o s(t) = Σavr j (t) o Apply Earliest deadline First policy  Yao, Demers, Schenker: 4 ≤ AVR ratio ≤ 8 with respect to energy  Bansal, Pruhs: AVR is not O(1)-competitive with respect to temperature AVR(t)

Online OA Algorithm (1995)  After each arrival o Recompute an optimal schedule (YDS alg.) consisting of  Newly arrived job j  Remaining portions of other jobs  Bansal, Pruhs: OA is not O(1)-competitive with respect to temperature

BKP Algorithm (2004)  Algorithm description Speed k(t) at time t = e * maximum over all t 2 > t of Σw i /(t 2 - t 1 ) o Sum is over jobs i with t 1 = et – (e-1)t 2 < r i < t and d i < t 2  Bansal, Pruhs: BKP is O(1)-competitive with respect to temperature tt2t2 riri didi didi t 1 = et – (e-1)t 2 current time Can be computed by an online algorithm

BKP example  Suppose e = 2.7  t =

BKP example  Suppose e = 2.7  t = 4 For t’ = 5 t 1 = et – (e – 1)t’ = 2.7*4 – (2.7 – 1)*5 = 10.8 – 8.5 =

BKP example  Suppose e = 2.7  t = 4 For t’ = 5 t 1 = et – (e – 1)t’ = 2.7*4 – (2.7 – 1)*5 = 10.8 – 8.5 = 2.3 w(t,t 1,t’) = w(4,2,5) =

BKP example  Suppose e = 2.7  t = 4 For t’ = 5 t 1 = et – (e – 1)t’ = 2.7*4 – (2.7 – 1)*5 = 10.8 – 8.5 = 2.3 w(t,t 1,t’) = w(4,2,5) = 4 w(t,t 1,t’) /e(t’-t) = w(4,2,5)/2.7(5-4) = 4/2.7 =

BKP example  Suppose e = 2.7  t = 4 For t’ = 6 t 1 = et – (e – 1)t’ = 2.7*4 – (2.7 – 1)*6 = 10.8 – 10.2 = 0 w(t,t 1,t’) = w(4,0,6) = = 12 w(t,t 1,t’) /e(t’-t) = w(4,0,6)/2.7*(6-4) = 12/5.4 =

BKP example  Suppose e = 2.7  t = 4 So t 2 = 6 s(4) = e*2.22 = 2.7 * 2.22 = 6  Bansal, Pruhs: BKP is O(1)-competitive with respect to temperature

Future Work  Combination of Speed Scaling and Power Down  What about multicore processors?  What about systems with rejuvinative sources (i.e. solar cells)?