Dynamic Resource Allocation for Shared Data Centers Using Online Measurements

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Dynamic Resource Allocation for Shared Data
Centers Using Online Measurements

• Abhishek Chandra
• Weibo Gong
• Prashant Shenoy
• UMASS Amherst
• http//lass.cs.umass.edu/projects/shop

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Motivation

• Data Centers
• Server farms
• Rent computing and storage resources to
  applications
• Revenue for meeting QoS guarantees

• Goals
• Satisfy application QoS guarantees
• Maximize resource utilization of platform
• Robustness against Slashdot effects

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Dynamic Resource Allocation

• Periodically re-allocate resources among
  applications
• Estimate resource requirements for near future
• Challenges
• Reallocation at short time-scales
• No prior workload profiling/knowledge
• Low overhead
• Approach Online Measurement-based Allocation

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Talk Outline

• Motivation
• System Model
• Dynamic Allocation Techniques
• Experimental Results
• Conclusions

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Resource Model
Resource
GPS

• Queuing System
• Generalized Processor Sharing (GPS) scheduler
• Request classes
• Different arrival processes, service time
  distributions
• QoS Goal Mean Response Time

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Dynamic Resource Allocation
















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Dynamic Resource Allocation


APPLICATION MODELS










ALLOCATOR




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Monitoring

• Measure system and application metrics
• Queue lengths
• Request response times
• Monitoring windows

Time
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Prediction

• Short-term prediction of workload characteristics
• Request arrival rate
• Average service time
• Use history of measured system metrics

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Prediction Accuracy
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Dynamic Resource Allocation


PREDICTOR










MONITOR
ALLOCATOR




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Measurement-based Model

• Goal Relate QoS metric to resource requirement
• Idea Model parameterized by online measurements
• Advantages
• Parameters do not need to be computed
• Allow adaptation to dynamic workload
• Proposed Transient Queuing System Description

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Transient Queuing Model

• Transient queuing behavior over adaptation window
• Relation between mean response time T and
  application share w
• Littles Law
• Relation is parameterized by the measured
  workload
• Arrival rate ? and mean service time s

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Resource Allocation Utility Model
u1
Optimization
u2

• Discontent function Measures the QoS violations
  of an application
• Constrained Optimization problem

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Constrained Optimization Formulation

• Non-linear Optimization Problem

• Solved using Lagrange multiplier method

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Talk Outline

• Motivation
• System Model
• Dynamic Allocation Techniques
• Experimental Results
• Conclusions

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Experimental Setup

• Simulation experiments
• Soccer World Cup98 Traces
• Results based on a 24-hour portion of the trace
• 755,000 requests
• Mean req rate 8.7 req/sec
• Mean req size 8.47 KB

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Adaptation to Transient Overloads
Shares adapt to changing workload characteristics
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Adaptation System Discontent
GPS without reallocation
GPS with reallocation
System Discontent is lowered substantially
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Conclusions

• Dynamic Resource Allocation needed for data
  centers
• Measurement-based allocation
• Monitoring and Prediction gather online state
• Use this state for application modeling and
  allocation
• Future Work
• Prediction policies
• Utility functions
• http//lass.cs.umass.edu/projects/shop

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Related Work

• Prediction
• Statistical Prediction Models Zhang00
• Application Models
• Queuing-Theoretic Models Carlstrom02,Liu01
• Control-Theoretic Models Abdelzaher02,Lu01
• Data Centers
• MUSE Chase01
• COD Moore02
• Oceano Appleby01