How Can EDA Help Solve Challenges in Data Center Energy Efficiency?

Transcription

1 How Can EDA Help Solve Challenges in Data Center Energy Efficiency? Ayse K. Coskun Electrical and Computer Engineering Department Boston University June 5, 2016

2 Energy Efficiency of Computing Emerging applications in big data, cyberphysical systems, internet of things, cloud, etc.: Growing performance/watt demand Source: J. Koomey (Stanford, LBNL),

3 Energy Efficiency of Computing Data centers consume ~3-4% of US electricity (2011) IT is estimated to be responsible of 10% of world energy use (2013) Cutting 40% of server room energy waste could save businesses $3B annually (2013) Source: International Data Corporation (IDC) Energy-related costs are among the largest contributors to the total cost of ownership in data centers 3

4 Power Grid & Market Power supply = demand? ( => blackouts ) Renewable energy sources: intermittent Lack of reliable, large-scale, economical energy storage solutions Independent System Operator (ISO): Demand Response: Peak Shaving, Capacity Reserves (new) Credits provided to the participant who modulates its power consumption dynamically 4

5 Demand Side Data Centers Electricity: >3% of the overall consumption in the US [1] Power capping /management techniques Enable flexibility in power consumption Workload flexibility Data centers offer a unique opportunity for providing power capacity reserves. Benefits of Participation Help solve unstable renewable energy problem Provide additional reserves to accommodate other less flexible uses of electricity Achieve significant monetary savings 5 [1]: J. Koomey. Growth in Data Center Electricity Use 2005 to Oakland, CA: Analytics Press. August, 1, 2010.

6 Data Centers in the Smart Grid 6

7 Regulation Service (RS) Reserves Bidding: ( P, R) Price Settling: Get contract ISO: RS signal Data Center Regulation Typical PJM 150sec ramp rate (F) and 300sec ramp rate (S) regulation signal trajectories Error: P cap (t) = P + z(t)r e(t) = P real (t)- P cap (t) R ε(t) needs to be small: ε(t) > threshold => lose license Credit Earned E R Costs: P R Π E and Π R : market clearing prices Credits are reduced based on statistics of ε(t) 7

8 Data Center Model Server States: [ICCAD 13, ASPDAC 14, IGCC 14] Active: P server = P dyn + P static P dyn can be modulated by DVFS or CPU resource limits P dyn = k * RIPS Idle: P server = P static Sleep: P server = P sleep Constant low power, but resuming from sleep has time delay (t res ) and energy cost (E loss ) Servicing Model: Job arrival (Homogeneous jobs) * Queue FIFO Allocation Server 1 Server 2 Server i Server N Each server: 1 job at a time 8

9 [ICCAD 13, ASPDAC 14, IGCC 14] Dynamic Power Control Policy Server State Transition Rules [Gandhi IGCC12]: A server that has been in idle > t tout (timeout threshold): goes to sleep; When a new job arrives: select the server with the smallest current t idle (t) to activate; When we need to force servers to sleep: select the servers with the largest current t idle (t) to put to sleep. t idle (t): the time that a server has been in the idle state at time t. 9

10 QoS-feedback [ICCAD 13, ASPDAC 14, IGCC 14] Goal: Reduce energy consumption under QoS constraints Put all active servers at their maximal throughput (to reduce waste from idle power) P server, j = k j * RIPS j + P static Determine the minimal number of servers required at each time t, based on: the current length of queue the overall QoS performance till t SLA: (d,h), d = T real /T min N min = h(s(t)+ F(t)+Q(t))- S SLA(t)- F SLA (t) d If additional servers are required: wake them up Otherwise: apply server state transition rules for spare servers 10

11 [ICCAD 13, ASPDAC 14, IGCC 14] Regulation Service (RS) Case 1: P real (t) < P cap (t) 1. Active servers with P server < P max : P server P max ; 2. Existing waiting jobs and idle servers: activate idle servers P max ; 3. Sleeping servers: resume using server state transition rules. Do the above three steps in order until P real (t) = P cap (t). Case 2: P real (t) > P cap (t) 1. Active servers with P server < P max : P server -> P min ; 2. Active servers with P server = P max : P server -> P min ; 3. Idle servers: suspend using server state transition rules. Do the above three steps in order until P real (t) = P cap (t). P min set for guaranteeing QoS. 11

12 Server provisioning for a cluster [ASPDAC 14, IGCC 14] Goals: Minimize tracking error Reduce #transitions Reduce idle energy waste e(t) = P real (t)- P cap (t) R min J(x(t),u(t)) =a 1 P real (t)- P cap (t) +a 2 N tran (t)-a 3 N sleep (t)-a 4 N peak (t) u(t)îu(x(t)) Tracking Error Transition Energy Waste Static Energy Waste Probability Distribution of Power Tracking Error single server data center Probability Distribution of Servicing Time Degradation single server data center Regulation Reserves (R) /Avg. Power ( ): Single Server: 29.7% 100-server Data Center: 56.8% Power Tracking Error Servicing Time Degradation 12

13 Results [ICCAD 13, ASPDAC 14, IGCC 14] 13

14 Data Center Power Management Multi-level load queues Data center cooling control Performance, power & temperature models Workload arrivals Server power capping Efficient consolidation Racks P cooling P computing Sensor feedback Optimal control & allocation of power caps Regulation requests Load forecasting & bidding in the energy market Cooling control Data Center Multi-core server ISOs 14

15 Power Capping on Multicore Systems Goal: Adaptively control individual server to maximize performance within power cap Parallel Workloads Increasingly Prevalent Large Scale Computing System (several racks) Server (multicore processors) Individual Server Power Cap Allocated by a budgeting policy Maintain target power consumption Typically performed with DVFS Core 1 Thread 1 Core 3 Thread 3 Core 2 Thread 2 Core 4 Thread 4 Active Low-Power State 15

16 Power Capping on Multicore Systems Goal: Adaptively control individual server to maximize performance within power cap Parallel Workloads Increasingly Prevalent Large Scale Computing System (several racks) Server (multicore processors) Individual Server Power Cap Allocated by a budgeting policy Maintain target power consumption Typically performed with DVFS Core 1 Thread 1 Thread 3 Core 3 Core 2 Thread 2 Thread 4 Core 4 Active Low-Power State 16

17 Optimal DVFS + Thread Packing (Pack & Cap) [ICCAD 11, MICRO 11, IEEE Micro 12] blackscholes canneal fluidanmiate swaptions Φ(x 1 ) Φ(x 2 ) Φ(x 3 ) Φ(x 4 ) Φ(x 5 ) Φ(x 6 ) 1.60 GHz 1 core 105 W 50 s 110 W 50 s 105 W 50 s 110 W 50 s 110 W 50 s 105 W 50 s 2.00 GHz 1 core 115 W 45 s 120 W 45 s 115 W 45 s 120 W 45 s 120 W 45 s 115 W 45 s 2.67 GHz 1 core 125 W 40 s 130 W 40 s 125 W 40 s 130 W 40 s 130 W 40 s 125 W 40 s 1.60 GHz 2 cores 110 W 30 s 115 W 30 s 110 W 30 s 115 W 30 s 115 W 30 s 110 W 30 s Power Cap = 120 W 2.00 GHz 2 cores 120 W 25 s 125 W 25 s 120 W 25 s 125 W 25 s 125 W 25 s 120 W 25 s 2.67 GHz 2 cores 130 W 20 s 135 W 20 s 130 W 20 s 135 W 20 s 135 W 20 s 130 W 20 s 17

18 Optimal DVFS + Thread Packing (Pack & Cap) Sensor and counter inputs Statistical classifier to determine most relevant metrics (Logistic regression, L1 regularization, ) ϕ(x) Model Learning w [ICCAD 11, MICRO 11, IEEE Micro 12] y Model Parameters Optimal Setting Calculation Runtime Operation Φ(x 1 ) Φ(x 2 ) Φ(x 3 ) Φ(x 4 ) Φ(x 5 ) Φ(x 6 ) 1.60 GHz 1 core 105 W 50 s 110 W 50 s 105 W 50 s 110 W 50 s 110 W 50 s 105 W 50 s 2.00 GHz 1 core 115 W 45 s 120 W 45 s 115 W 45 s 120 W 45 s 120 W 45 s 115 W 45 s 2.67 GHz 1 core 125 W 40 s 130 W 40 s 125 W 40 s 130 W 40 s 130 W 40 s 125 W 40 s 1.60 GHz 2 cores 110 W 30 s 115 W 30 s 110 W 30 s 115 W 30 s 115 W 30 s 110 W 30 s Power Cap = 120 W 2.00 GHz 2 cores 120 W 25 s 125 W 25 s 120 W 25 s 125 W 25 s 125 W 25 s 120 W 25 s 2.67 GHz 2 cores 130 W 20 s 135 W 20 s 130 W 20 s 135 W 20 s 135 W 20 s 130 W 20 s Server Node ϕ(x) Model Query w Model Lookup optimal settings y C. Controller 18

19 Adherence to Power Caps [ICCAD 11, MICRO 11, IEEE Micro 12] Without a power meter: 0 W margin 82% adherence 5 W margin 96% adherence 10 W margin 99+% adherence Feedback-control using a power meter improves tracking ability. 19

20 Temperature vs. Leakage Power [Zapater, DATE 13], [Zapater, Trans. PDS 14] 20

21 Efficient Consolidation Multi-threading becoming more common (media processing, scientific apps, financial computing, etc.) Resource allocation per application / per VM a key factor in power control & efficient consolidation [Hankendi, IGCC 13, ISLPED 13] Cloud resources for HPC (among other traditional uses of the cloud) vcpu vcpu vcpu vcpu vcpu vcpu vcpu vcpu vcpu vcpu vcpu vcpu vcpu VM VM VM Virtualization Layer Virtualization Layer 21

22 Predicting Performance Scalability [Hankendi, IGCC 13, ISLPED 13] Estimate the CPU demand of VM: CPU demand=run%+ready% 97% accuracy for estimating the CPU demand of the applications Without requiring offline training 22

23 Adaptive Power Capping: vcap Power Cap Estimate CPU demands Compute R cap vcap -Check QoS and P cap violations -Set CPU limits [Hankendi, IGCC 13, ISLPED 13] QoS Req. VM Monitor Power readings Hypervisor C limit (VM n ) 23

24 Budgeting Computational vs. Cooling Power [Tuncer, ICCD 14] P0 server T crac, P cooling T max P compute P1 server P2 server 24

25 CoolBudget [ICCD 14] Offline system characterization Change T crac No Wait for workload change Model workload Budget P compute for a given T crac Best T crac found? Yes Set T crac and server powers Optimization 25

26 Computing Energy Efficiency Take-Aways 1. Complexity of the systems and the physical phenomena performance power energy cost Leakage cooling cost thermal hot spots and gradients Performance Reliability 26

27 Computing Energy Efficiency Take-Aways 1. Complexity of the systems and the physical phenomena 2. Time-varying and diverse application behavior 27

28 Computing Energy Efficiency Take-Aways 1. Complexity of the systems and the physical phenomena 2. Time-varying and diverse application behavior ISO 3. Changes in how cost is assessed e.g., integration with provider-side programs Data Center 28

29 Key Aspects of Data Center Efficiency Research Intra- & cross-layer optimization Application-awareness in all the layers Runtime learning and optimization capabilities Interactions of physical phenomena Strong interdisciplinary aspect: novel materials, architectures, energy markets, etc. 29

30 Performance and Energy Aware Computing Laboratory Current graduate students: Hao Chen, Fulya Kaplan, Tiansheng Zhang, Ozan Tuncer, Onur Sahin, Emre Ates, Onur Zungur, Yijia Zhang Collaborators: D. Atienza & Y. EPFL, J. UCM, J. M. UPM, C. Isci, S. IBM TJ Watson, T. Zurich, L. ETHZ/U. of Bologna, M. Caramanis, M. Herbordt, A. Joshi, J. Klamkin and Y. BU, K. Gross & K. Oracle, V. Leung, and A. Sandia Labs S. Brown University, D. UCSD. Postdoctoral researcher: Dr. Ata Turk Alumni: Dr. Jie Meng, Dr. Can Hankendi, Nathaniel Michener, Ann Lane, Katsu Kawakami, John Furst, Samuel Howes, Jon Bell, Benjamin Havey, Ryan Mullen Many masters students, especially: Dan Rossell, Charlie De Vivero Visitors: Dr. Marina Zapater, Dr. Andrea Bartolini Recent Funding: 30