ClouDiA: A Deployment Advisor for Public Clouds

Tao Zou, Ronan Le Bras, Marcos Vaz Salles*, Alan Demers, Johannes Gehrke

Cornell University*University of Copenhagen (DIKU)

Instance Allocation in Public CloudsCloud Provider’s View Cloud Tenant’s View

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Instance Allocation in Public Clouds

……

TOR TOR TOR

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Sub-Aggregation ……

Aggregation

CoreCloud Provider’s View

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• Mean Latency Heterogeneity in EC2

• Mean Latency Stability in EC2

Network Latencies in Public Clouds

Challenge: Can this be done out-of-the-box?(i.e. no changes to the cloud or the application)

Challenge: How to avoid long links?

Examples of Latency Sensitive ApplicationsScientific Simulation Key-value Store

Search Aggregation Service Pipelines

(time-to-solution) (response time)

(response time) (response time)

Communication Graphs

Longest Link

Longest Path

Opportunity: Communication graphs are not complete.

A careful logical to physical mapping can help!

Opportunity: Gamble with over-allocation.

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Architecture of ClouDiA

Allocate Instances (+ Extra Instances)

Get Measurements

Search Mapping

Deployment Plan

Terminate Extra Instances

Communication Graph

Objectives

Start Application

ClouDiA Public Cloud Tenant

Measuring Network Distance• Goal: obtain a reliable estimate of link costs

• Approximations that are easy to obtain – Hop counts– Length of Common IP Prefix

• Accurate distance: pair-wise network latencies– Large number of measurements for each pair

• To observe enough latency jitters – Interferences at end points

• Heavily application and network dependent • Can’t model exactly measure without interference

do not work

Measuring Network Latencies • Without interference• Most efficient method: “staged”

Stage i

Stage i+1

No concurrent send/recv at end points Parallelism with minimal coordination

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Architecture of ClouDiA

Allocate Instances (+ Extra Instances)

Get Measurements

Search Mapping

Deployment Plan

Terminate Extra Instances

Communication Graph

Objectives

Start Application

ClouDiA Public Cloud Tenant

Search Mapping (Longest Link)• NP-Hard

– To find a solution of cost – To find a solution of cost – To find a solution of cost

• Goal: find a “good” solution within timeout

• Two formulations:– Mixed-Integer Programming

• O() boolean variables– Constraint Programming (*)

• O() integer variables• an objective cost has to be given a priori

Hard to approximate

Searching using Constraint Programming

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Give an objective c:1. Remove all links with cost > c2. Find a subgraph isomorphism

k=4

• A lot of distinct latency values– Bi-section search? finding “no solution” takes time– k-means clustering? works well with proper k

Give an objective c:1. Remove all links with cost > c2. Find a subgraph isomorphism

TimeoutTimeoutcost

Experimental Settings

• 100 to 150 m1.large instances in EC2

• IBM ILOG CPLEX Optimizer/CP Optimizer– Multi-core, a single machine

• Three workloads:– Behavioral simulation – Synthetic aggregation query– Key-value store

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Effect of Over-Allocation

• 100 instances + 10%-50% over-allocation• Get Measurements + Search Deployment < 10 minutes

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• 10% over-allocation

Overall Improvement

Thank you!

• ClouDiA is a deployment advisor for public clouds– Out-of-the-box– 15%-55% time reduction for latency sensitive

applications

• Could be adopted by cloud services providers– With API changes

Conclusion