Multi-Class Latency Bounded Web Services Vikram Kanodia and Edward Knightly Rice Networks Group

Multi-Class Latency Bounded Web Services

Vikram Kanodia and Edward Knightly

Rice Networks Group

http://www.ece.rice.edu/networks

Vikram Kanodia 2

Motivation

Poor end-to-end performance of web traffic. Excessive latencies due to overloaded

servers a dominant factor.

Present day web servers provide only FCFS.

Need Mechanisms to: Reduce server latency; and Control server latency.

Vikram Kanodia 3

Web Hosting Example

A A A B B B B B

B .C O M

A .C O M

Vikram Kanodia 4

Steps Towards Web QoS - 1

SBAC – Session Based Admission Control [CherkPhaal99]. Blocks sessions if load above a certain threshold.

Pros: Prevents server from going into overload.

Cons: Only ensures better service to all admitted

requests. Cannot ensure that requested service is met.

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Steps Towards Web QoS - 2

Operating system hooks: Mechanisms to support resource reservation

among different domains at OS level. Resource Containers [BangDrsch99]. Eclipse/BSD operating system [Silber99].

Prioritizing incoming requests provides class differentiation [BhattiFried99].

Distributed server architecture for better throughput [Vpai98].

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What is Lacking ?

No mechanism to meet a requests’ targeted delay.

No class based service model: Multiple user classes. Each class has a different response time target. All classes contending for the same resource.

No means of statistically quantifying the service received .

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Key Challenges

Net service rate is a complex, unknown function of CPU / disk/ cache behavior.

Very difficult to model a requests’ service demand in terms of low level system resources.

Interaction between requests belonging to different classes difficult to predict a priori.

All present day web QoS schemes coupled tightly with server architecture .

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System Model

L IS T E N Q U E U E A D M IS S IO NC O N TR O L

M EA S UR EM ENT

S C H E D U L IN G

B A CK E N DN O D E S

IN C O M IN GR E Q U E S T S

D R O P P E DR E Q U E S T S

B L O C K E DR E Q U E S T S

S E R V E DR E Q U E S T S

F R O N T E N D

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First Cut: Baseline Scheme

Latency targeted service model: Single user class with a targeted delay to be

met by some percentage of all serviced requests.

Goals: Illustrate an abstraction of the server

resources into a simple queuing model. Highlight key issues for managing multi-

class web services. Use for experimental comparisons.

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Baseline Scheme: Problem formulation

Assumption: Stationary and homogeneous arrivals.

Some maximum service rate which satisfies QoS requirements. All arrival greater than the maximum

service rate need to be be blocked.

How to determine the maximum service rate ?

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Model for Baseline Scheme

A D M I S S I O NC O N T R O L F O R

C L A S S i

i i

i i

a i , d i

j j

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Baseline Scheme: M/M/1 model

Approximate a class’ service by an M/M/1 queue with an unknown service rate. Abstracts the low level server resources into

a virtual server.

Unknown Service rate is given by:

d

1

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Baseline Scheme: Admission Control

A new request leads to an increase in load to ’. Delay violation probability under load ’:

If P( D > d*) is greater than the targeted fraction of requests meeting the delay target , block the new request.

))' (*exp(*)( ddDP

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Limitations of Baseline Scheme

No support for multiple service classes M/M/1 models each class as independent of

other classes. Cannot capture inter class interference.

Assumption of independent and exponentially distributed service times is faulty. Does not account for highly variable service

time. Ignores temporal correlation among different

requests for the same document.

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Solution

LMAC : Latency Targeted Multi-Class Admission Control

Service model: A minimum fraction of accepted requests

will be serviced within the class delay target. Mechanism to characterize and control inter-

class relationships. Decouples access control from actual server.

architecture or the operating system.

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Our Technique: Envelopes

Envelopes: arrival/service rates over intervals of time.

Deterministic [Cruz95] and statistical [QK99,CK00] envelopes are used to manage network QoS.

Envelopes represent net service received in the presence of other concurrent requests being processed by the server at the same time.

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What do Envelopes Buy Us ?

A general yet accurate way of describing a class’ service and demand.

A higher level of abstraction of low level system resources.

Capture effects of temporal correlation and high variability in requests and server latencies.

Model relationship among different user classes in a tractable manner.

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Measured Based Service Envelope

Envelope is service received versus interval length when backlogged.

Given the number of concurrently backlogged requests:

Compute the request latency mean and variance.

Use gaussian approximation to get the targeted percentile delay.

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Model for LMAC

A D M I S S I O NC O N T R O L F O R

C L A S S i

i

i i

a1 ,a2 ,.....,a n s1 ,s2 ,.....,sn

i

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LMAC Algorithm

Ensure that a arrival maintains the latency target of its own class Maintain a maximum horizontal distance

between the requests and service envelopes less than the targeted latency.

How to ensure that the service of other classes is not disrupted ?

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LMAC Algorithm (cont.)

To ensure that other classes do not suffer:

Assume that the new arrival has strict priority over all other requests.

This is a worst case assumption.

For all other classes, the request workload remains the same, but there is a reduction in service.

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Simulation Details

Simulations performed using a simulator which approximates the behavior of OS management for CPU, disk, caching etc.

Use a trace generated from the CS departmental server logs at Rice University.

Assume arrival rate is poisson with a given mean rate.

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Experiment 1

Targeted delay of 1 second for 95 percentile of all admitted requests.

Demonstrates overload protection properties similar to SBAC.

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Experiment 2

Single class-single node case.

Baseline scheme does meet its delay target, but is too conservative.

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Multi-Class Performance

In the absence of any server level support : Performance of each class bounded by the

most stringent class.

To investigate a true multi-class scenario: Devise an artificial resource allocation

policy.

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Experiment 3: Setup

IS O L A TIO NF R O NT

END

BAC KEND

BAC KEND

A

B

A

B

B E X P L O ITSIN TE R -C L A S SG A IN S

F R O NTEND

BAC KEND

BAC KEND

A

B

B

A + B

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Experiment 3 (cont.)

Class A: Arrival rate 300 reqs/sec, target delay .5 sec

Class B: Arrival rate 200 reqs/sec, target delay 1 sec

Class IsolationMulti-class with

SharingThroughp

ut(reqs/sec)

Delay(sec)

Throughput(reqs/sec)

Delay(sec)

A 147 .467 141 .501

B 92 .912 145 .935

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Conclusions

Scheme to ensure that a minimum fraction of all accepted requests meet latency targets.

A way to model system resources into a high level server: Makes our approach general and

independent of OS/ server architecture.

Ability to exploit additional features within the server architecture for higher utilization.

Vikram Kanodia 29

Future Work

Address Heterogeneous Content Content with different service demands , e.g

dynamic content.

Perform experiments with additional traces.

Incorporate LMAC into a real server and test its performance.