- dynamic request allocation and scheduling for context aware...

Dynamic request allocation and scheduling forcontext aware applications subject to a percentile

response time SLA in a distributed cloud

Keerthana Boloor∗, Rada Chirkova?�, Tiia Salo�

and Yannis Viniotis∗�

∗Department of Electrical and Computer Engineering?Department of Computer Science

North Carolina State University

�IBM Software GroupResearch Triangle Park

1 / 17Cloudcom 2010, Indianapolis, Indiana, USA

Agenda

Agenda

Problem description

Dynamic request allocation and scheduling scheme

Comparison with static allocation and FIFO/WeightedRound Robin scheduling scheme

Conclusion


Problem description

Problem description

More web applications are designed to be context aware.

Most context aware applications are built on SOAprinciples.

Cloud computing systems - the most preferred platformfor deployment.

Service Level Agreements (SLA) - terms of service andpricing model.

What is this presentation about?


Problem description Geographically distributed cloud computing system

Geographically distributed cloud computing system

Clients

Data center hosting K context-aware

applications


applications


applications


applications


Problem description Context aware applications

SOA based context aware application

Contextaware SOA applications

End servers

Contextdata stores

Gateway

2. Client request allocated to and scheduled at end-server

3. Load required service-endpoint 4. Load required

contextdata

DATA CENTER

1. Client request with context-id

InternetUpdates to contexts at

contextdata stores


Problem description Model of an end-server

An end-server serving multiple user classes

Server ‘j’ at data

center ‘i’

Class 1

Class 2

Class K

Each context aware application services multiple classes of users

Each user class is guaranteed different quality of service based on

economic considerations

SLA specifies different service levels and service charges for the

different user classes


Problem description Percentile Service Level Agreements

Percentile Service Level Agreements

P

X 100

Profit

Conformance(%)

0

X% - the fraction of requests of a particular user class which need to have a response

time less than r seconds

$P - The profit charged by the cloud, if the percentile of requests that have response

time less than r seconds is greater than or equal to X%


Problem description Problem statement

Problem statement

Allocate and schedule service requests locally at theend-servers so as to globally:

max∑

1≤j≤K

profitj (1)

where profitj is the profit charged for conformance of therequests from users of class j .

This problem is NP-hard!!


Solution Management scheme description

Heuristic-based data-oriented request management scheme

Periodic allocation and adaptation at each datacenter.

00:00 06:00

Allocation phase

Allocation phase

Adaptation phase

Adaptation phase

Observation interval (T)

subinterval

Allocation phase

Allocation phase

Allocation phase

Adaptation phase

Adaptation phase

Adaptation phase

Adaptation phase

Datacenters exchange conformance levels.

Allocation phase

Rank-based request allocation and gi-FIFO scheduling.

Aim at increasing global profit.


Solution Rank-based allocation and gi-FIFO scheduling

Rank-based allocation and gi-FIFO scheduling

Profit-score calculation

Profit: pk

Required global conformance: ck

Current global conformance: cck

If cck < ck

Profit-score = pk/(ck − cck )

Else

Profit-score = 00 10 20 30 40 50 60 70 80 90 100

0

500

1000

1500

2000

Current conformance of class 1 (%)

Pro

fit−

scor

e as

sign

ed to

eac

h ar

rivin

g re

ques

t of c

lass

1 (

$)

Class 1 SLA − Profit of 2000$ on conformance of 75%



Rank-based request allocation

1 Query hash-based lookup table ([context-id,machine-id] or [service-id,machine-id])

2 Rank-based compatibility test

1 The arriving request is assigned a rank based on its profit-score and deadline.

2 Does the arriving request meet its deadline? - Machine compatible!!!

3 Compatible machine not found? - Choose least loaded closest to context DB



gi-FIFO scheduling

Choose the request of user class with the highest current profit-score

Choose one with maximum waiting time but which results in a response time less than

or equal to r

If no such request exists, choose the request with higher waiting time resulting in a response time greater than r

gi-FIFO has been proven to be the most suitable for percentile SLAs for a single server

serving multiple classes.


Evaluation

Evaluation

Dynamic scheme vs static schemes

5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

11000

Request rate

Pro

fit in

curr

ed (

$)

Dynamic rank based allocation with gi−FIFO scheduling

Static allocation with WRR scheduling

Static allocation with FIFO scheduling


Evaluation

Dynamic rank based allocation vs static allocation scheme

0 50 100 1500

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

11000

Request rate

Pro

fit in

curr

ed (

$)

Static allocation with gi−FIFO scheduling

Dynamic rank based allocation with gi−FIFO scheduling


Evaluation

Variation in subinterval length

0 50 100 150 200 250 300 350 400 450 5000

2000

4000

6000

8000

10000

12000

14000

16000

18000

Subinterval period

Pro

fit o

bta

ine

d($

)

Uniform distribution of classes, stringent SLAUniform distribution of classes, relaxed SLANon−uniform distribution of classes, stringent SLANon−uniform distribution of classes, relaxed SLA

Variation in context update interval

0 20 40 60 80 100 120 140 160 180 2000

2000

4000

6000

8000

10000

12000

14000

16000

18000

Contextdata update intervalP

rofit

obta

ined (

$)

Low contextdata load times

High contextdata load times

Medium Contextdata load times


Conclusion

Conclusion

Identified the need for dynamic request scheduling and allocation for context aware

applications in a distributed cloud.

Proposed a novel rank-based request allocation and gi-FIFO scheduling scheme for

managing percentile SLAs with an aim to maximize profit obtained by the cloud.


Questions??


- dynamic request allocation and scheduling for context aware...

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