Distributed Energy-Efficient Scheduling
for Data-Intensive Applications with
Deadline Constraints on Data Grids
Cong Liu and Xiao Qin
Auburn University
1
Outline
 Introduction and Motivation
 System Model
 Algorithm
 Performance Analysis
 Summary
2
Introduction



3
Distributed scientific applications in many cases require
access to massive data sets.
In High Energy Physics (HEP) applications, for example, a
handful of experiments have started producing petabytes of
data per year for decades.
Data grids have served as a technology bridge between the
need to access extremely large data sets and the goal of
achieving high data transfer rates by providing
geographically distributed computing resources and largescale storage systems.
Introduction
 The Google Data Cluster
•31,654 machines
•63,184 CPUs
•126,368 Ghz of processing
power
•two identical buildings contain
about 100,000 square feet of data
center floor space
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Introduction
 Reliability
 Computing in high temperatures is more error-prone than
in an appropriate environment.
 Operational Cost
 A single 200-Watt server, such as the IBM 1U*300. The
energy bill for this single server would be $180/year.
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Introduction

A key factor in the process of scheduling dataintensive tasks is locations of input data sets
required by tasks.
 A straightforward strategy to enhance performance
of data-intensive applications on data grids is to
replicate popular data sets to multiple resource
sites.
 Offering higher data access speeds compared to
maintaining the data sets in a single site.
6
Drawbacks of Making Too Many Replicas

It is challenging to maintain consistency among
replicas in Data Grids.
 It is nontrivial to efficiently generate replicas of
massive data sets on the fly in Data Grids.
 A large number of data replicas can increase energy
dissipation in storage resources.
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Reduce Energy Consumption in Data Grids
Minimize electricity cost
• Improve system reliability
•
• How to reduce energy consumption in
Data Grids?
Energy-efficient scheduling algorithms for
applications running on data grids.
8
Goals of Scheduling

Tradeoffs between energy efficiency and highperformance for data-intensive applications.
 Integrate data placement strategies with task
scheduling
 Consider real-time requirements

How to achieve the goals?


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A Distributed Energy-Efficient Scheduler called DEES
Three key components: energy-aware ranking,
performance-aware scheduling, and energy-aware
dispatching.
Design Goals of DEES

Maximize the number of tasks completed before
their corresponding deadlines
 Replicate data and place replicas in an energyefficient way
 Dispatches real-time tasks to peer computing sites,
considering three factors:



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Computational capacities of peer computing sites,
Energy consumption introduced by tasks, and
Data location.
Features of DEES

High scalability
 Require no full knowledge of workload conditions
of all the computing sites in a data grid.
 One must consider that obtaining full knowledge of
the state of the grid is a difficult task.
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Key Ideas
 High-priority tasks are scheduled first in order to meet their
deadlines.
 Explore slacks: low-priority tasks can have their deadlines
guaranteed.
 The dynamic voltage scaling (DVS) technique is used to
reduce energy consumption by exploiting available slacks
and adjusting appropriate voltage levels accordingly.
12
Dynamic Voltage Scaling
 A effective technique for reducing energy
consumption by adjusting the clock speed and
supply voltage dynamically.
 Energy dissipation per CPU cycle is proportional to
v2
 Processor energy can be saved by reducing CPU
voltages while running it at a slower speed.
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Design Ideas
 Two types of tasks: hard real-time tasks and soft
real-time tasks.
 Prioritize hard real-time tasks but create slacks by
delaying their executions till the latest moment.
 After a schedule is made, the processor voltage is
adjusted to the lowest possible level on a task-bytask basis at each scheduling point.
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System Model

Geographically distributed sites are interconnected through a
WAN.
 Each site consists of storage resources, computing resources, and
a ticket server.
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Energy Consumption Model
 Consider energy consumption of executing tasks, making
data replicas, and communicating.
 The total energy consumption of a data grid, Etotal can be
expressed as:
Etotal  Ecomp  Ecomm  Erep
where Ecomp is the total energy consumption of computing
resources, Ecomm is the total energy consumption of
communication, and Erep is the total energy consumption of
replicating data.
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Four Cases of Energy Consumption




Case 1: Local execution and local data
Case 2: Local execution and remote data
Case 3: Remote execution and same remote data
Case 4: Remote execution and different remote data
Ei ,k ,v  Eic,k ,v  Eitd, j ,o ,v  Eitt,u ,v  Eir, ,jw,o ,v
 Eic,k ,v  0  0  0
 c
td
r ,w
E

E

0

E
 i , k ,v
i , j ,o ,v
i , j ,o ,v
 c
tt
E

0

E
 i , k ,v
i ,u , v  0
 c
td
tt
r ,w
E

E

E

E
i , j ,o ,v
i ,u , v
i , j ,o ,v
 i , k ,v
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if S1
if S 2
if S 3
if S 4
If data is not locally available, then?

Executing a task at a site where its data is located:



Compared to the local execution and remote data
scenario, executing the task at a remote site where
data is located is still more energy efficient if

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Energy efficient
No data transfer and no replication cost
task’s input data set is larger than its execution code size.
Algorithm Components
 DEES is composed of
 Ranking
 Scheduling
 Dispatching
 Goals:
 Maximize the number of tasks meeting deadlines
 Minimize energy consumption
 Improve scalability
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Task Grouping
 Task Grouping:
 Tasks requiring the same data are grouped together.
 The task group whose data resides in the local site, called
local task group, is ranked first.
 Other task groups are ranked in descending order,
according to the number of tasks in the task group.
 Considering Real-Time Requirements:
 Within each group, tasks are ordered by increasing
deadline.
 Thus, tasks with shorter deadlines are scheduled sooner.
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DEES Scheduling

DEES schedules tasks on a group basis.
 A local task group is scheduled first. In order to
schedule task ti on site su, DEES selects machine mk
at su that can complete ti within its deadline and
provide the minimum completion time.
 After processing all tasks, remaining unscheduled
tasks will be dispatched to remote sites.
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Dispatching

Dispatching: To delivers tasks within each task
group to data sites.
 For task group gj whose data site is so, scheduling
decisions are made by so’s scheduler based on its
local resource status and task information of gj.
 If so cannot schedule all tasks in gj, then
unscheduled tasks are dispatched to so’s immediate
neighbors using tickets in a breadth-first manner.
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Energy-Aware Ranking

To make tradeoffs between energy efficiency and real-time
performance, we propose a ranking system to rank so’s
neighbors.
rank ( g i , sv , so )    n   
1
i ,o , v
i ,o ,v
i , n ,v
Erep
 ( Ecomm
 Ecomp
)/n
where n is the number of tasks in gj that can be scheduled on sv, ε is a
coefficient concerning the task deadline, μ is a coefficient concerning
energy saving.
 Energy consumed to replicate gi’s data from so to sv,
 Energy consumed to transfer gi’s data from so to sv,
 Energy consumed to execute these n tasks at sv.
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Dispatching: Energy Efficiency vs. real-time
rank ( g i , sv , so )    n   



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1
E
i ,o , v
rep
 (E
i ,o ,v
comm
E
i , n ,v
comp
)/n
ε and μ: To manage the two conflicting goals of saving
energy and meeting deadlines.
For mission-critical tasks: ε is set to 1 and μ is set to 0,
which means the neighbor that can schedule more tasks is
given preference.
For energy efficiency: ε is set to 0 and μ is set to 1. Thus,
the neighbor that consumes the least amount of energy will
be considered first.
Simulation Parameters
Parameter
Number of jobs
Number of sites
Site processing speed
Number of datasets
Task execution time
range (Uniform
distribution)
Size of datasets
Dataset popularity
distribution
Dataset popularity
threshold
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Value(fixed)-(varied)
(9600)-(1600,3200,6400,9600
12800,16000,19200, 22400)
(32)8*8 nodes
(200)-(100,200,400)
(1,500) second
(500-800MB
short
jobs,
800MB-1GB medium jobs, 12GB long jobs)-(500MB-2GB)
(Uniform)-(Uniform, Normal,
Geometric)
(2)-(2,4,6,8,10)
Performance Analysis

Compared DEES with an effective scheduling
algorithm - Close-to-Files.
 Features of the Close-to-Files algorithm:



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Good performance since Close-to-File takes data locality
into account.
It schedules a task to its data site to decrease the amount
of data transfer.
Scheduling overhead is high: It is an exhaustive
algorithm that searches across all combinations of
computing and data sites to find a result with the
minimum computation and data transmission cost.
Performance Metrics

The Guarantee Ratio
Ns
Guarantee Ratio 
N total

Normalized Average Energy Consumption and
Total Energy Consumption are used as the
performance metrics in the evaluation.
Etotal
Normalized Average Energy Consumption 
Ns
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Real-Time Performance
Fig. 5. Guarantee Ratio by ranking coefficients
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Energy Consumption
Fig. 6. Normalized Average Energy Consumption by
ranking coefficients
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Performance
Fig. 7. Guarantee Ratio by task loads
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Energy Consumption
Fig. 8. Normalized Average Energy Consumption
by task loads
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Summary




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An energy efficient algorithm to schedule real-time tasks
with data access requirements on data grids.
By reducing the amount of data replication and task
transfers, the proposed algorithm effectively saves energy.
Distributed since it does not need knowledge of the
complete state of the grid.
Detailed simulations demonstrate that DEES significantly
reduces the energy consumption while increasing the
Guarantee Ratio.
Questions
 Xiao Qin
 http://www.eng.auburn.edu/~xqin
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