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Distributed Energy-Efficient Scheduling for Data-Intensive Applications with Deadline Constraints on Data Grids

Cong Liu and Xiao Qin

Auburn University

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Outline

Introduction and MotivationSystem ModelAlgorithmPerformance AnalysisSummary

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Introduction

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 large-scale storage systems.

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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 data-intensive 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.

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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.

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Goals of Scheduling

Tradeoffs between energy efficiency and high-performance for data-intensive applications.

Integrate data placement strategies with task scheduling

Consider real-time requirements

How to achieve the goals? A Distributed Energy-Efficient Scheduler called DEES Three key components: energy-aware ranking,

performance-aware scheduling, and energy-aware dispatching.

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Design Goals of DEES

Maximize the number of tasks completed before their corresponding deadlines

Replicate data and place replicas in an energy-efficient way

Dispatches real-time tasks to peer computing sites, considering three factors: Computational capacities of peer computing sites, Energy consumption introduced by tasks, and Data location.

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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.

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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-by-task 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:

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.

repcommcomptotal EEEE

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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

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If data is not locally available, then?

Executing a task at a site where its data is located: Energy efficient No data transfer and no replication cost

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 task’s input data set is larger than its execution code

size.

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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.

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

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.

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Simulation Parameters

Parameter Value(fixed)-(varied)

Number of jobs (9600)-(1600,3200,6400,9600 12800,16000,19200, 22400)

Number of sites (32)- Site processing speed 8*8 nodes Number of datasets (200)-(100,200,400) Task execution time range (Uniform distribution)

(1,500) second

Size of datasets (500-800MB short jobs, 800MB-1GB medium jobs, 1-2GB long jobs)-(500MB-2GB)

Dataset popularity distribution

(Uniform)-(Uniform, Normal, Geometric)

Dataset popularity threshold

(2)-(2,4,6,8,10)

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Performance Analysis

Compared DEES with an effective scheduling algorithm - Close-to-Files.

Features of the Close-to-Files algorithm: 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.

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Performance Metrics

The Guarantee Ratio

Normalized Average Energy Consumption and Total Energy Consumption are used as the performance metrics in the evaluation.

total

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NRatioGuarantee

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total

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EnConsumptioEnergyAverageNormalized

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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 Consumptionby task loads

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Summary

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.

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Questions

Xiao Qinhttp://www.eng.auburn.edu/~xqin