Capsule Placement in the Service Platform

Bhuvan Urgaonkar

Timothy Roscoe

Systems Group, Sprint ATL

Service Platform: an overview

Processors

High speed interconnect

Internet

Management/Control Unit

Service Platform: Goals

• Sell the platform’s resources

• Manage the resources efficiently

• Provide performance guarantees to customers

• Start or stop services within minutes

Services and Capsules

• Services: – web/game/streaming servers– service provider pays the platform

• Capsules– Def: Component of a service that should run on

a single node– e.g.: consider a replicated web server

Nucleus

• Node specific control/management software:– Capsule creation, destruction– Health information (process liveness)– Resource parameters (memory, CPU, network

bandwidth etc.)

Control Plane

• Capsule Placement

• Flow Placement

• Node, network, service monitoring

• Deployed Service Database

• Billing

Outline of this talk

• Service Platform: an overview

• Quality of Service

• Capsule Placement

• Design of the Placement unit

• Conclusions and future work

QoS Representations

• Application level– e.g., 50 transactions per sec

• Contract level– e.g., “something like a 300 MHz Pentium II”

• Platform level– e.g., ?

• Node level– e.g., weights, priorities etc.

Translation between QoS levels

• Application level => Contract level– Application specific, customer’s problem

• Contract level => Platform level– More a business problem

• Platform level => Node level– OS dependent

Capsule Placement: Desirables

• Maximize revenue!

• Aware of the “importance” of services.

• Overbooking.

• Exploit known workload characteristics.

• Adapt to changes in workload?

• Fast.

Stages in hosting a service

• Requirement specification

• Placement

• Deployment

• Activation

Requirement Specification

• Contract level representation– Many possibilities: 300 MHz PII, best effort or a CPU

instruction token bucket.

• Platform level representation – Must be uniform across the platform.

– (rate, burst, ovb tolerance, arch, OS)

Translation to Node level

• Reservation based scheduler– map (rate, burst) to (period, slice)– bigger burst => bigger period

• Proportional share scheduler– burst ?– weight in proportion to rate

• Priority based scheduler– no easy mapping

Placement

• Find the set of feasible nodes– Compatible architecture and OS– No overbooking tolerances violated

• Pick one node from this set– Best Fit– Worst Fit– Random Select– Close Overbooking

Placement: Example

capsules

nodes

a b c

N1 N4N3N2

One possible placement: (a, N1), (b, N2), (c, N3)

1030 20

30 1020 10

Deployment and Activation

• Deployment: The process of preparing a capsule for execution on a node.– Why ?

• e.g., need to download some files before starting

– the control plane sends all information to deploy the capsule

• Activation: Starting a deployed service

Capsule State Diagram

deployedundeployed

deploying

undeploying

activating

deactivating

active

Example Message Exchange

Control Plane Nucleus

deployed svc cap

state svc cap deployed

deployed svc cap

Instruct nucleus to deploy a capsule, start timer

No response! Send again

Starts deploying the capsule

Still deploying

Done deploying, send status messageDeployed before

timeout, instruct nucleus to activate

activated svc cap Starts activating the capsule

. . .

Placement Unit Architecture

Listen for new requests

Event Queue Message Queue

Dispatch Events Listen to nuclei

Events due to new requestsEvents due to msgs from nuclei Messages from nuclei

Database Consistency

• Transactions and exceptions– e.g:

try: transaction_begin ()

deploy_service (svc): transaction_commit () except: transaction_abort ()

Performance

• Time to compute placement: 1-2 sec => time to deploy usually much larger

• Comparison of heuristics– experiments with following workloads

• 1-3 capsules, CPU requirement 0-10%, wide range of overbooking tolerances

– Random Select admitted most # services, Best Fit admitted least

– But … more investigation needed

Summary

• QoS representation for CPU requirements of services.

• Implementation of placement unit.

• Some simple experiments to deploy and activate services.

Unfinished ...

• Experiments:– heuristics better suited to specific workloads.– Scalability and efficiency of the system.

• Integration of placement unit with rest of the Control Plane

• Handling various failures

• Extend to multiple resources - much harder than a single resource!