Dynamic Resource Allocation for Shared Data Centers Using Online Measurements

By- Abhishek Chandra, Weibo Gong and Prashant Shenoy

Overview Outline Motivation

System Model

Dynamic Allocation Techniques

Experimental Results

Conclusions

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

Cluster of servers – Dedicated or Shared Static Allocation is problematic

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

Research Contribution Generalized processor sharing (GPS)

Time domain queuing model & Non-linear optimization technique

Prediction algorithm

Synthetic Workloads & Real Web Traces

Problem Formulation Resource Model

Queue are assumed to be served in FIFO order and the resource capacity C is shared among the queues using GPS

Queue is assigned a weight

Allocated a resource share in proportion to its weight.

GPS Scheduler

Problem Definition

If denotes the target response time of application and is its observed mean response time, then the application should be allocated a share , such that .

The discontent of an application grows as its response time deviates from the target di. This discontent function can be represented as follows

System goal then is to assign a share to each application such that the total system-wide discontent,

i.e., the quantity is minimized.

Dynamic Resource Allocation

Monitoring Measure system and application metrics

Queue lengths Request response times

Monitoring windows

AdaptationWindowHistoryMeasurement

Interval

Time

Allocating Invoked periodically to dynamically partition the resource

capacity among the various applications running on the shared server.

Resource Model Types

Time-domain Queuing Model

Online optimization-based Model

Time Domain Queuing Model Transient queuing behavior over adaptation window

The request service rate is

Relation between mean response time T¯ and application share. Average response time in near future:

Relation is parameterized by the measured workload Arrival rate λ and mean service time s¯

Optimization-based Resource Allocation

Discontent function

Non-linear Optimization Problem:

Solved using Lagrange multiplier method

Prediction Short-term prediction of workload characteristics

Request arrival process Service demand distribution

Use history of measured system metrics

Prediction Techniques Estimating the Arrival Rate

Accurate estimate of allows the time domain queuing model to estimate the average queue length for the next adaptation window.

We represent Ai at any time by the sequence of values from the measurement history. To predict , model using the AR(1), a sample value of

Ai is estimated as Estimating the Service Demand

Computes the probability distribution of the per-request service demands

Mean of the distribution is used to represent the service demand of application requests

Measuring the Queue Length Monitoring module records the no. of outstanding

requests at the beginning of each adaptation window.

Experiments Soccer World Cup’98 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

Experiments Evaluation Synthetic Web Workload

Comparison of static and dynamic resource allocations for a synthetic web workload

Trace-driven Web Workloads

Comparison of static and dynamic resource allocations in the presence of heavy-tailed request sizes and varying arrival rates.

Adaptation to Transient Overloads

The workload and the resulting allocations in the presence of varying arrival rates and varying request sizes

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

Results showed that these techniques can judiciously allocate system resources, especially under transient overload conditions

Thank You