Energy efficiency issues in distributed cyber physical systems
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Energy-efficiency issues in Distributed Cyber-Physical Systems . Tridib Mukherjee IMPACT Lab Arizona State University. Energy Management Principles. Reduce Wastage of Energy Holistic Resource Management Decentralized and Localized Algorithms Energy Scavenging Model-based Design.

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Energy efficiency issues in distributed cyber physical systems

Energy-efficiency issues in Distributed Cyber-Physical Systems

TridibMukherjee

IMPACT Lab

Arizona State University


Energy management principles

Energy Management Principles

  • Reduce Wastage of Energy

  • Holistic Resource Management

  • Decentralized and Localized Algorithms

  • Energy Scavenging

  • Model-based Design


Energy efficiency in data centers

Energy Efficiency in Data Centers


Typical layout of a datacenter

Typical layout of a datacenter

Temp

Heat Recirculation

Outlet

Outlet

Inlet

How can resource management decision be aware of the impact on the temperaturedistribution, coolingdemand and computingenergy requirement ?


Coordinated management architecture

Coordinated Management Architecture

Management Plane

Application

Synergistic Model-driven Proactive Data Center Management

Modeling

Plane

Workload Model

Power Model

Thermal Model

Cooling Model

Ambient

Sensor

Workload Manager

Workload Trace

Active Server Pool

Server Resource Utilization

Power Consumption

Temperature Information

Data

Collection

Plane

Cooling

Manager

Platform Power

Manager

Ethernet Network Plane

Server Rack

CRAC

Physical Plane

Data Flow

Control Flow

Room Roof

T. Mukherjee, A. Banerjee, G. Varasamopoulos, and S. K. S. Gupta, ‘Spatio-temporal Thermal-Aware Job Scheduling to Minimize Energy Consumption in Virtualized Heterogeneous Data Centers", Elsevier Journal on Computer Networks (ComNet), , Vol. 53, Issue 17, Pages 2888-2904, December, 2009.

Ambient Sensor Networks

Raised

Floor

Data Center


Thermal aware job scheduling problem

Thermal-aware Job Scheduling Problem

PROBLEM: Given a set of incoming jobs, find a job scheduling (i.e. job start times) and placement (i.e. server assignment) to minimize the total data center energy consumption subject to meeting of job deadlines (submitted times for execution) – requires 3D (job x server x time) decision-making.

Cooling Energy

Supply Temperature

Upper Bound

Computing Energy

Job Migration Overhead

Capacity Constraint: server assigned less server available

Server Required: Required no. of servers assigned for jobs

Deadline Constraint: job finish time less than deadline

Arrival Constraint: job start time later than arrival

T. Mukherjee, A. Banerjee, G. Varasamopoulos, and S. K. S. Gupta, ‘Spatio-temporal Thermal-Aware Job Scheduling to Minimize Energy Consumption in Virtualized Heterogeneous Data Centers", Elsevier Journal on Computer Networks (ComNet), , Vol. 53, Issue 17, Pages 2888-2904, December, 2009.


Thermal aware job scheduling algorithms

Thermal-aware Job Scheduling Algorithms

SCINT Algorithm: Heuristic solution (genetic algorithm)

  • Take a feasible solution and perform mutations until certain number of iterations.

  • Spreads the jobs over time while meeting the deadline.

  • Offline in nature requiring the job backlog information

    • Takes hours of operation.

EDF-LRH Algorithm: Tries to mimic the behavior of SCINT by spreading jobs using the Earliest Deadline First (EDF) scheduling approach.

  • Place jobs to servers contributing the Lowest Recirculated Heat (LRH)

  • Online in nature maintaining EDF job queues as and when jobs arrive

    • Takes milliseconds of operation.

FCFS Algorithm: Does not conventional temporal scheduling approach but uses thermal-aware job placement techniques for energy-savings.

  • Place jobs to servers contributing the Lowest Recirculated Heat (LRH)

    • Online in nature taking milliseconds of operations

T. Mukherjee, A. Banerjee, G. Varasamopoulos, and S. K. S. Gupta, ‘Spatio-temporal Thermal-Aware Job Scheduling to Minimize Energy Consumption in Virtualized Heterogeneous Data Centers", Elsevier Journal on Computer Networks (ComNet), , Vol. 53, Issue 17, Pages 2888-2904, December, 2009.


Power consumption over time

Power Consumption Over Time

Jobs get spread over time

(i.e. peak utilization is reduced)

by SCINT & EDF-LRH


Total energy consumption

Total Energy Consumption

  • SCINT saves up to 60% of energy consumption.

  • EDF-LRH mimics the behavior of SCINT specially for low average data center utilization.


Energy efficiency issues in distributed cyber physical systems

Requires

Thermostat

to be 18o C

Requires

Thermostat

to be 22o C

Job1

Thermostat = 18o C

(worst-case settings)

Requires

Thermostat

to be 20o C

Integrating Cooling Behavior in Choice of Servers (Spatial Scheduling)

Hot

server

CRAC

Requires

Thermostat

to be 18o C

Job2

Non-thermal-aware job management and cooling management

Requires

Thermostat

to be 22o C

Requires

Thermostat

to be 20o C

Thermostat = 18o C

(worst-case settings)

Job1

CRAC

Requires

Thermostat

to be 22o C

Requires

Thermostat

to be 18o C

Job2

Independent thermal-aware job management and cooling management

Requires

Thermostat

to be 20o C

Thermostat = 20o C

(dynamic settings)

Job1

CRAC

Requires

Thermostat

to be\22 C

Requires

Thermostat

to be 18o C

Requires

Thermostat

to be 22o C

Job2

Coordinated thermal-aware job management and cooling management


Energy efficiency issues in distributed cyber physical systems

Functional Architecture for Coordinated Job and Cooling Management

Temporal Job Scheduler

Job Management

Incoming

Jobs

Scheduled Job

Queue

Determine Thermostat Requirement

Spatial Job Scheduler

A. Banerjee, T. Mukherjee, G. Varasamopoulos, and S. K. S. Gupta, ‘‘Cooling-Aware and Thermal-Aware Workload Placement for Green HPC Data Centers", International Conference on Green Computing Conference (IGCC), Chicago, IL, August 2010.

Dynamically Set CRAC Thermostat

th

th

T

T

low

high

Highest Thermostat

Setting (HTS) Algorithm

HTS Server Ranking

A. Banerjee, T. Mukherjee, G. Varasamopoulos, and S. K. S. Gupta, ‘’Integrating Cooling Awareness with Thermal Aware Workload Placement for HPC Data Centers ", Elsevier Journal on Computer Networks (ComNet), Special Issue on Virtualized Data Centers, ACCEPTED (2011).

CRAC Unit

thermostat

setting

Computing Servers

Courtesy: www.liebert.com

Data Center


Important results

10

x 10

16

14

12

10

EDFHTS Idle Chassis Turned Off

8

EDFLRH Idle Chassis Turned Off

Energy Savings (Joules)

FCFSFF Idle Chassis Turned Off

6

EDFHTS

EDFLRH

4

FCFSHTS

2

0

5

40

80

Utilization (%)

Important Results

  • EDF-HTS achieve up to 16% energy savings over EDF-LRH

    • Changing CRAC mode can take time to take effect in room

    • Delay may be too much since redline temperature can be reached

  • Choice of algorithm depends on

    • Maximum energy savings under delay constraint


Preliminary software architecture

Preliminary Software Architecture

Presentation

Scheduling

Control

Access data from

the chassis level

sensors

Datacenter

Servers


Modularized implementation of thermal awareness in job scheduling

Modularized Implementation of Thermal Awareness in Job Scheduling

T. Mukherjee, Q. Tang, C. Ziesman, S. K. S. Gupta, and P. Cayton, ''Software Architecture for Dynamic Thermal Management in Datacenters",

In the International Conf. Communication System Software Middleware (COMSWARE), Bangalore, India, Jan 2007.


Energy efficiency in ad hoc networks

Energy Efficiency in Ad Hoc Networks


Mobile ad hoc networks manets

Mobile Ad hoc Networks (MANETs)

Network Model

  • mobile nodes (PDAs, laptops etc.)

  • multi-hop routes between nodes

  • no fixed infrastructure

Applications

  • Battlefield operations

  • Disaster Relief

  • Personal area networking

Multi-hop routes generated among nodes

Network Characteristics

  • Dynamic Topology

  • Constrained resources

    • battery power

B

C

A

C

A

B

D

D

Links formed and broken with mobility


Routing in manets

Reactive

  • Network divided in small zones.

  • Intra-region Proactive Routing.

  • Reactive Inter-region routing.

  • Balances Proactive & Reactive.

  • Scalable.

  • Latency higher than proactive.

  • Periodically maintainsroutes between every mobile node pair.

  • Predefined routes available

  • Low latency

  • Low scalability.

Hybrid

Proactive

Routing in MANETs

Routing

  • Routes NOT maintained.

  • Route established only if data to transmit.

  • High Scalability.

  • No pre-defined route.

  • High Latency.


Route maintenance

Energy Consumption

per bit transmitted

Beacon

Interval

Number

of Nodes

Average size

of beacon msg

Bits transmitted due to

beaconing per unit time

Route Broadcast

Interval

Bit transmitted per unit time

for periodic broadcast

Route Maintenance

E x N xlogN /β

  • Overhead

    • Periodic beacon messages for link state maintenance.

    • Periodic route update b’cast.

    • Triggered route update b’cast with each link change.

E x N2xlogN /φ

E x N2xlogN

for each triggered update

High Energy Overhead

in Maintenance Operations

Reduces Applicability

Low Scalability

Reduce maintenance operations and find

optimumβ & φ to minimize energy overhead.


Proactive protocol classification

PP+BTP

PP+BP

PP+BT

PP+B

Proactive

Proactive Protocol Classification

  • Research Goals:

    • Developing a PP+B type of protocol maintaining energy-efficient routes.

      • Uses Self-stabilization from Distributed Computing.

      • Improves Self-Stabilizing Shortest Path Spanning Tree (SS-SPST) for energy-efficiency.

    • Analytical Model for determining optimum β & φ for different proactive protocols.

Employs Beacons,

& Triggered Updates

Employs only

Beacons

Employs Beacons,

& Periodic Updates

Employs Beacons, Periodic, & Triggered Update

WRP, OLSR etc.

BFST, SS-SPST etc.

FSR, IARP etc.

DSDV, TBRPF etc.


Self stabilization in distributed computing

Self-stabilization in Distributed Computing

Topological Changes and Node Failures for MANETs.

Self-stabilizing distributed systems

  • Guarantee convergence to valid state through local actions in distributed nodes.

  • Ensure closure to remain in valid state until any fault occurs.

    Can adaptto topological changes

  • Is it feasible for routing in MANETs?

Fault

Closure

Invalid

State

Valid

State

Convergence

Local actions in distributed nodes.

Applied to Multicasting in MANETs


Self stabilizing multicast for manets

Self-stabilizing Multicast for MANETs

Multicast

source

Topological Change

  • Maintains source-based multi-cast tree.

  • Actions based on local information in the nodes and neighbors.

  • Pro-active neighbor monitoring through periodic beacon messages.

  • Neighbor check at each round (with at least one beacon reception from all the neighbors)

  • Execute actions only in case of changes in the neighborhood.

Convergence

Based on

Local actions

Problem–energy-efficiency

is not considered

Self-Stabilizing Shortest Path Spanning Tree (SS-SPST)


Energy aware self stabilizing protocol ss spst e

Energy Aware Self-Stabilizing Protocol (SS-SPST-E)

  • Actions at each node

  • (parent selection)

  • Identify potential parents.

  • Estimate additional cost after joining potential parent.

  • Select parent with minimum additional cost.

  • Change distance to root.

Loop Detected

E

Not in tree

F

A

B

D

C

X

AdditionalCost (B → X) = TB + R

AdditionalCost (A → X) = TA + 2R

Potential Parents of X

  • Action Triggers

  • Parent disconnection.

  • Parent additional cost not minimum.

  • Change in distance of parent to root.

Select Parentwith

minimum Additional Cost

Minimum overall

cost when parent

is locally selected

Execute action when

any action trigger is on

  • Tree validity– Tree will remainconnected

  • withno loops.


Ss spst e execution

SS-SPST-E Execution

Multicast

source

  • No multicast tree

    • parent of each node NULL.

    • hop distance from root of each node infinity.

    • cost of each nodeis Emax.

2

2

A

S

B

1

2

2

G

3

1

No potential parents for any node.

  • First Round – source (root) stabilizes

    • hop distance of root from itself is 0.

    • no additionalcost.

1

D

C

H

2

2

  • Second Round – neighbors of root stabilizes

    • hop distance of root’s neighbors is 1.

    • parent of root’s neighbors is root.

Potential parent forA, B, C, D, F={S}.

E

F

2

AdditionalCost (F → E) = TF + 2R

AdditionalCost (D → E) = TD + 3R

AdditionalCost (S → {A, B, C, D}) = Ts + 4R

AdditionalCost(D → E) = TD + 3R

  • And so on ……

Potential parent forE={D, F}.

AdditionalCost (S → F) = TS + 5R

AdditionalCost (C → F) = TC + 3R

AdditionalCost (S → F) = Ts + 5R

Potential parent forF= {S,C}.

  • Tolerance to topological changes.

  • Convergence- From any invalid state the total energy cost of the graph reduces afterevery roundtill all the nodes in the system are stabilized.

  • Proof - through induction on round #.

  • Closure:Once all the nodes are stabilized it stays there untilfurther faultsoccur.


Simulation results

Simulation Results


Proactive protocol classification1

PP+BTP

PP+BP

PP+BT

PP+B

Proactive

Proactive Protocol Classification

  • Research Goals:

    • Developing a PP+B type of protocol maintaining energy-efficient routes.

      • Uses Self-stabilization from Distributed Computing.

      • Improves Self-Stabilizing Shortest Path Spanning Tree (SS-SPST) for energy-efficiency.

    • Analytical Model for determining optimum β & φ for different proactive protocols.

Employs Beacons,

& Triggered Updates

Employs only

Beacons

Employs Beacons,

& Periodic Updates

Employs Beacons, Periodic, & Triggered Update

WRP, OLSR etc.

BFST, SS-SPST etc.

FSR, IARP etc.

DSDV, TBRPF etc.


Optimum tuning of route maintenance in ad hoc networks

Optimum Tuning of Route Maintenance in ad-hoc networks


Optimizations for different proactive protocols

Proactive

PP+BT

PP+BTP

PP+BP

PP+B

Employs Beacons,

& Triggered Updates

Employs Beacons,

& Periodic Updates

Employs Beacons, Periodic,

& Triggered Updates

Employs Beacons

WRP, OLSR etc.

BFST, SS-SPST etc.

FSR, IARP etc.

DSDV, TBRPF etc.

Single variable

Equating PDR

constraint gives

the result

Single variable

Equating PDR

constraint gives

the result

1st Derivative

Quadraticequation

1st Derivative

Quarticequation

Optimizations for different Proactive Protocols


Effect of optimization on dsdv

Effect of Optimization on DSDV

  • Balances energy-efficiency and reliability

  • Incorporates re-activity to traffic intensity in pro-active protocols

  • Increases protocol scalability


Application aware adaptive optimization sub layer

Application-aware Adaptive Optimization Sub-layer


Energy efficiency in body sensor networks

Energy Efficiency in Body Sensor Networks


Typical ban workload

Typical BAN Workload

  • Ayushman health monitoring application is considered as the workload

    • Ayushman has three phases of operation –

      • Sensing Phase – Sensing of physiological values (Plethysmogram signals) from the sensors and storing it in the local memory

      • Transmission Phase – Send the stored data to the base station in a single burst

      • Security Phase – Perform network wide key agreement for secure inter-sensor communication using Physiological value based Key Agreement Scheme (PKA) .

    • The Security phase occurs once in a day

    • The Sensing phase and Transmission phase alternate forming a sleep cycle

  • (the processor can sleep during sensing phase while it can be active during the transmission phase)

Ayushman Workload

Frequency Throttling during security phase

Sensing Phase

Enables Sleep Scheduling

Transmission Phase

Sleep Cycle

Sensor CPU Utilization

Security Phase

Time


Sustainability analysis results atom

Sustainability Analysis Results (Atom)

  • Four energy scavenging sources were considered

    • Body Heat, Ambulation, Respiration and Sun Light

  • The total energy obtained from any combination of scavenging sources can be higher

    • Higher number of nodes can be sustained

J. A. Paradiso and T. Starner. Energy scavenging for mobile and wirelesselectronics. Pervasive Computing, IEEE, 4(1):18–27, Jan.-March 2005.


Energy efficiency issues in distributed cyber physical systems

Vision for BAN Design Architecture

BAN Application

Analysis & Design Phase

Requirements

Analysis

Design

Modeling Phase

Thermal Interaction Model

Node Power Model

Workload Model

Available Energy Model

Profiling Phase

Scavenged Energy

Node Power Consumption

Physical Properties

Node Temperature

MAC Level Radio Sleep Scheduling

Management Plane

Processor Level Power Management

In-Network Processing

Physical

Plane


Energy efficiency issues in distributed cyber physical systems

Research Directions in Energy-efficient Systems

Awareness of

Scavenging and

Energy Storage

Awareness of Impact

on Green operation

and Sustainability

Awareness

of Workload

Sustainability Metrics

Holistic Management Algorithms

Internet-of-things

Experimental

Mobile Cloud

Benchmark Development

Performance Analysis

Novel Systems and Platforms

Specification Language

energy proportional

platform

Model-based

Code optimization

Design methodology

Safety

Tool Development

Equipment

Longevity

Reduce

Sustainability

Footprint

Safe and

Sustainable

Operations

Design space

exploration in

application

domain

Evaluate platforms

in design space to

identify gaps

Sustainability

under

Real-time

Requirements


Questions

Questions


Backup slides

Backup Slides


Introduction motivation

Introduction-Motivation

Projected Electricity Use of data centers\, 2007 to 2011

  • Emergence of cloud based services caused large growth in data centers

  • High magnitude of data center energy consumption

    • Internet users’ growth in the world from 2000-2009: 400% [http://www.internetworldstats.com/stats.htm]

    • Data center energy consumption grew 20-30% annually in 2006 and 2007

      [ Uptime Institute research]

  • Addressing energy saving for Data Centers

    • Thermal and Cooling awareness to improve energy consumption

Future energy use projection

- current efficiency trend

Historical energy use

[Source: EPA]

Typical data center energy end use

[Source: Department of energy]


Spatial job management job placement issues

Spatial job management (job placement) issues

Temporal job scheduling

determines the peak computing resource utilization leaving room for thermal-aware task placement

Peak air inlet temperaturedetermines upper bound toCRAC temperature setting

CRAC temperature settingdetermines it’s efficiency(Coefficient of Performance)

Task placement determines temperature distribution

Temperature distributiondetermines the equipmentpeak air inlet temperature

The lower the peak inlet temperature the higher the CRAC efficiency

Coefficient of Performance(source: HP)

bottomline

There is a task schedule & placement that minimizes the

energy (cooling + computing) consumption. Find it!


Energy management approaches

softwaredimension

Application

Thermal-aware

& cooling-aware

data centerjob scheduling

Thermal-aware VM

(middleware)

CPU Load balancing

O/S

Dynamic voltage scaling

Fan speed scaling

Dynamic frequency scaling

firmware

Circuitry redundancy

physicaldimension

Case/chassis

IC

room

Energy Management Approaches

Proactive

Solutions

Reactive

Solutions


Energy efficiency issues in distributed cyber physical systems

HPC resource management model

Heat recirculation contribution

Computing capabilities of machines

Computing power efficiency

Number of requests

Spatio-temporal Job Scheduling and Power Management

job id,

job data,

required no of servers,

expected execution times,

server preferences

decides on when and

in which servers to

assign the jobs so that

they complete within the

expected execution times

Time index (every 5 second)

Job arrival and execution over time @ ASU HPC data center

Job flow

Parameters

Control data

Load Dispatcher

On/Off Control

Server 1

Server 1

Server 2

Server 3

Server 3

Server N

Server N-1

……

Server N-1


Energy efficiency issues in distributed cyber physical systems

Resource Management in Internet data centers

Heat recirculation contribution

Computing capabilities of machines

Computing power efficiency

requests per

second

Number of requests

Server Provisioning Tier (Epochs)

decides on how many servers

required for an epoch

Workload Distribution Tier (Slots)

Time index (every 5 second)

HTTP requests over time, 1998 FIFA World Cup

Traffic flow

Parameters

Control data

Load Dispatcher

decides on which servers to

distribute the workload so that

they server utilization do not go

beyond a threshold to meet SLAs

On/Off Control

Server 1

Server 1

Server 2

Server 3

Server 3

Server N

Server N-1

……

Server N-1


Temporal job management issues in hpc data centers

Temporal Job Management Issues in HPC data centers

  • Peak utilization to be reduced to leave enough room for thermal-aware job placement

    • Trade-off with resource utilization

  • Job execution times are usually overestimated during submission

  • Jobs can be spread over time to reduce peak utilization

    • Trade-off with throughput & turn-around time.


Power model

system power (P)

Ppeak

Pidle=b

CPU utilization (U)

Power Model

  • Power Consumption is mainly affected by the CPU utilization

  • Power consumption is linear to the CPU utilization

P = aU+ b

T. Mukherjee, G. Varasamopoulos, S. K. S. Gupta, and S. Rungta, ‘’Measurement based Power Profiling of Data Center Equipment", Workshop on Green Computing (in conjunction with CLUSTER 2007), Austin, USA, Sept, 2007.


Linear thermal model

Linear Thermal Model

  • Heat Recirculation Coefficients

    • Analytical

    • Matrix-based

  • Properties of model

    • Granularity at air inlets

    • Assumes steadiness of air flow

P = a U + b

Tin

Tsup

D

P

+

×

=

Q. Tang, T. Mukherjee, S. K. S. Gupta, and P. Cayton,

''Sensor-based Fast Thermal Evaluation Model for

Energy-efficient High-performance Datacenters",

In the International Conf. Intelligent Sensing Info.

Proc. (ICISIP2006), Dec 2006.

Max(Tin) <= Tred

Tsup <= Tred – Max(DxP)

heat distribution

powervector

inlettemperatures

supplied airtemperatures


Hpc workload model conventional job scheduling

HPC Workload Model & Conventional Job Scheduling


Temporal scheduling of workload balancing utilization over time

Temporal Scheduling of Workload: Balancing Utilization Over Time


Energy efficiency issues in distributed cyber physical systems

Choice of Servers (Spatial Scheduling) based on Temperature

Job1

Hot

server

Job2

Non-thermal-aware job management

Job1

Job2

Thermal-aware job management


Thermal issues in data centers

Thermal issues in Data Centers

  • Heat recirculation

    • Hot air from the equipment air outlets is fed back to the equipment air inlets

  • Hot spots

    • Effect of Heat Recirculation

    • Areas in the data center with alarmingly high temperature

  • Consequence

    • Cooling has to be set very low to have allinlet temperatures in safe operating range

  • Solution

    • Jobs to be placed to minimize heat-recirculation

Courtesy: Intel Labs


Energy consumption

Energy Consumption

  • Total Power = Computing + Cooling Power

  • Cooling power depends on the computing power and the COP.

  • Energy consumption is the total power multiplied by the observed period of time.

Ptot = P + Pcooling

Ptot = P + P/COP(Tsup)

= P + P/COP(Tred – max(D x P))

E = Ptot x time


Instrumentation

Instrumentation

On-site

Set-up

Remote Power

Meter Reading

Chassis

NETWORK

DualCom

Power Meter

SNMP Control

Power Supply

(208 V)

T. Mukherjee, G. Varasamopoulos, S. K. S. Gupta, and Sanjay Rungta,

''Measurement based Power Profiling of Data Center Equipment”,

In the First International Worshop of Green Computing

(in conjunction with CLUSTER 2007), Austin, USA, Sept, 2007


Equipment power consumption

Equipment Power Consumption

Power Supply

Blade Server Power

Empty Chassis Power

Memory Power

Hard Disk Power

CPU Power


Applications

Applications

  • Gamut Benchmark

    • with different CPU Utilization & Disk I/O

  • Other Computing Applications

    • Matrix Multiplication

    • Convolution

    • Digital Image Filtering-5x5 matrix filter algorithm

    • with different input size

      • Fits Cache (Compute Intensive)

      • Fits Memory (Memory Intensive)

      • Virtual Page File Utilization (Disk Thrashing)

Power Profiling


Equipment details

Equipment Details

Power Profiling


Single server in chassis

Single Server In Chassis

Power Profiling


Simulated environment

Simulated Environment

  • Used Flometrics Flovent

  • Simulated a small scale data center

  • physical dimensions9.6m 8.4m 3.6m

  • two rows of industry standard 42U racks arranged

  • CRAC supply at 8 m3/s

  • There are 10 racks

    • 6 Dell PowerEdge1955

    • 4 Dell PowerEdge1955

    • each rack is equipped with 5 chassis

  • 1000 processors in data center.

    • 232KWatts at full utilization


Compared algorithms

Compared Algorithms

  • First Come First Serve (FCFS) with backfilling scheduling with first-fit placement

    • Most commonly used.

    • Maximizes utilization, throughput.

    • Minimizes average turnaround time.

    • NOT thermally-aware

  • Thermal-aware variations of FCFS with backfilling

    • LRH placement – place to servers with minimum heat recirculation.

    • XInt placement – determine correct portion of the tasks to be placed to the servers to minimize overall heat recirculation.

    • DOES NOT compromise throughput, utilization, and average turnaround time.


Energy consumption 40 average utilization

Energy Consumption (40% average utilization)


Energy consumption 80 average utilization

Energy Consumption (80% average utilization)


Modeling cooling behavior

supply airtemperature (Tsup)

Tthresholds (set points)

Input air temperature (Tsen)

Modeling Cooling Behavior

  • Two cooling power modes

    • low (preferred for energy eff.)

    • High

  • Two (programmable) set points

    • Low->high

    • High->low

  • Mode-switching delaytsw

  • Coefficient of performance depends on the current mode

mode

Tsup<= Tsen– Tdiff

low

high

Challenge is to set low->high

set point as high as possible.


System model 2

System model (2)

Tin≤Tred

Equip. 1

  • Models assumed

    • Cooling distribution matrix F

      • Diagonal matrix: fii: portion of cool air going to equipment i

    • Heat recirculation matrix D

      • dij: portion of heat going from equipment i to equipment j

  • Tin(t) = FTsup(t)+DP(t)≤Tred

    Tsup(t) ≤ F-1[Tred-D(aU(t)+ω) ]

d13

f1

d21

d12

d31

Equip. 2

CRAC

f2

f3

d32

d23

Equip. 3

Tsup(t) has to be dynamically adjusted in accordance to U(t) to match the Tred constraint

Highest thermostat setting, maxTsen, can be derived as:

maxTsen = Tsup + Tdiffm

- [temperature increase due to mode-switching delay]

Selecting/scheduling a different set of servers (i.e. changing a and ω) can change the requirements on Tsup.


Updating the linear thermal model

Updating the Linear Thermal Model

f1

  • Fraction of supply air goes to the inlet of each chassis

f3

Tin(t) = FTsup(t)+DP(t)≤Tred

Tsup(t) ≤ F-1[Tred-D(aU(t)+b) ]

f2

P = aU+ b

Highest thermostat setting, maxTsen, can be derived as:

maxTsen = Tsup + Tdiffm

- [temperature increase due to mode-switching delay]

F

Tin

Tsup

D

P

f1

f2

×

f3

+

×

=

fn

heat distribution

powervector

supplied airtemperatures

Diagonal matrix of supply air fraction

inlettemperatures


Energy efficiency in self stabilization

Energy-Efficiency in Self-stabilization


Energy consumption model

j

k

i

Ti

l

i

non-intended

neighbor

Ti reaches all nodes in range

Energy Consumption Model

Ci = Ti+NixR

Cost metric

for node i

Transmission energy of node i

Reception cost at all the neighbors

  • Variable through Power Control

  • One transmission reaches all in range

  • Reception energy at intended neighbors.

  • Overhearing energy at non-intended neighbors.

intended neighbor

No communication

schedule during

broadcastin random

access MAC

(e.g. 802.11).

Overhearing at j, k, and l

Ci = Ti + 7R

What is the additional cost if a node selects a parent?


Dependence of allowable route reconstruction delay on probability of packet loss

Dependence of Allowable Route Reconstruction Delay on Probability of Packet Loss

  • Derived through Packet Deliver Ratio Required

  • P = Probability of packet loss due to single link failure.

  • P =  xroute-reconstruction delay.

  • Packet Delivery Ratio = (1 - P)D.

  • (1 - P)D >= .

P

Function of link change and

application traffic distribution

Application reliability requirement

Window-of-opportunity

Controllability Condition: route-reconstruction delay <= [1 – 1/D] / 


Probability of packet loss p

Delay in route

reconstruction

Delay in route

reconstruction

Delay in route

reconstruction

Delay in route

reconstruction

Probability of Packet Loss (P)

  • CASE I: Link disconnection rate greater than traffic generation rate

    • Route-reconstruction delay MUST be less than consecutive link disconnections in the route.

    • P1 =  x route-reconstruction delay

  • CASE II: Link disconnection rate less than traffic generation rate

    • Route-reconstruction delay MUST be less than average interval between consecutive packets.

    • P2 =  x route-reconstruction delay

P=P1xprob of CASE I+P2xprob of CASE II

=xroute-reconstruction delay

PDR Constraint: route-reconstruction delay <= [1 – 1/D] / 


Optimization

Valid Route

Establishment

Link

Disconnection

Route-reconstruction delay

Beacons

Received

k

Beacons Not

Received

Optimization

  • Step 1: route-reconstruction delay in terms of βand φ.

  • Step 2: take the equality of the PDR constraint

    • optimum value at the boundary.

    • one variable represented in terms of other.

    • objective re-written as a convex function of one variable.

Worst case route-reconstruction delay = kβ + φ + end-to-end broadcast delay.

  • Step3: non-linear optimization of the objective

    • equate first order derivative to 0.

    • the resulting equation solved

    • second order derivative checked for +ve slope.


Proactive routing protocol classification and research contributions

Proactive

PP+BTP

PP+BP

PP+B

PP+BT

Proactive Routing Protocol Classification and Research Contributions

Contributions:

  • Analytical Model for determining optimum β & φ for different proactive protocols.1,2,3

  • Developing a PP+B type of protocol maintaining energy-efficient routes.

    • Improves Self-Stabilizing Shortest Path Spanning Tree (SS-SPST) for energy-efficiency. 4,5

Employs Beacons,

& Triggered Updates

Employs only

Beacons

Employs Beacons,

& Periodic Updates

Employs Beacons, Periodic, & Triggered Update

WRP, OLSR etc.

BFST, SS-SPST etc.

FSR, IARP etc.

DSDV, TBRPF etc.

1T. Mukherjee, S. K. S. Gupta, and G. Varasamopoulos, ''Energy Optimization for Proactive Unicast Route Maintenance in MANETs under End-to-End Reliability Requirements", In Elsevier Journal on Performance Evaluation, Vol. 66, Issue 3-5, Pages 141-157, Mar, 2009.

2T. Mukherjee, S. K. S. Gupta, and G. Varasamopoulos, ''Analytical Model for Optimizing Periodic Route Maintenance in Proactive Routing for MANETs", In the Proc of ACM MSWiM, Crete Island, Greece, Oct 2007. (one of the selected best papers)

3T. Mukherjee, S. K. S. Gupta, and G. Varasamopoulos, ''Application-Aware Adaptive Tuning of Proactive Routing Protocols for MANETs", Under review in Transactions on Autonomic and Adaptive Systems (TAAS).

4T. Mukherjee, G. Varasamopoulos, and S. K. S. Gupta, ''Self-Managing Energy-Efficient Multicast Support in MANETs under End-to-End Reliability Constraints", In Elsevier Journal on Computer Networks (ComNet), Vol. 53, Issue 10, Pages 1603-1627, July, 2009.

5T. Mukherjee, G. Sridharan, and S. K. S. Gupta, ''Energy-Aware Self-Stabilization in Mobile Ad Hoc Networks: A Multicasting Case Study", In the 21st IEEE Int'l Parallel and Distributed Processing Symposium (IPDPS), Long Beach, California, 26-30th March, 2007.


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