Optimized Distributed Data Sharing Substrate in Multi-Core...

Optimized Distributed Data Sharing Substrate

in Multi-Core Commodity Clusters: A

Comprehensive Study with Applications

K. Vaidyanathan, P. Lai, S. Narravula and D. K. Panda

Network Based Computing Laboratory (NBCL)

The Ohio State University

Presentation Outline

• Introduction and Motivation

• Distributed Data Sharing Substrate

• Proposed Design Optimizations

• Experimental Results

• Conclusions and Future Work

Introduction and Motivation

• Interactive data-driven applications

– Stock trading, airline tickets, medical imaging, online auction, online

banking, web streaming, …

– Ability to interact, synthesize and visualize data

• Datacenters enable such capabilities

– Processes data and reply to client queries

– Common and increasing in size (IBM, Amazon, Google)

• Datacenters unable to meet increasing client demands

Stock markets Airline industries Medical imaging Online auction

Proxy/Web Server

(Apache, STORM)

Application Server

(PHP, CGI)

Storage

Tier 0 Tier 1 Tier 2

Database Server

(MySQL, DB2)

Resource monitoring (Ganglia), resource mgmt (IBM WebSphere), caching

Datacenter Architecture

WANWAN

Clients

• Applications host web content online

• Services improve performance and scalability

• State sharing is common in applications and services– Communicate and synchronize (intra-node, intra-tier and inter-tier)

More Computation and CommunicationRequirements

State Sharing in Datacenters

ProxyServer

Tier 0

Application

Server

Tier 1

Memory copies

Apache Network

Memory copies

STORM Network

Memory copies

Caching Network

Memory copies

Res Mgmt Network

IPC

Apache Caching

IPC

STORM A STORM B

IPC

Servlets App

IPC

Apache Res Mgmt

Intra-Node State SharingIntra-Tier State SharingInter-Tier State Sharing

Resourceadaptation

Systemstate

Resourceadaptation

Systemstate

Caching Data

Caching Data

Resourcemonitoring

Tier 1load

Loadbalancing

Tier 1load

Resourceadaptation

Tier 1load

Resourceadaptation

Tier 1load

State Sharing in Datacenters…

• Several applications employ

their own

– data management protocols

– maintain versions of stored data

– synchronization primitives

• Datacenter Services

frequently exchange

– System load, system state, locks

– Cached data

Issues

• Ad-hoc messaging protocols for exchanging data/resource

• Same data/resource at multiple places (e.g., load information, data)

• Protocols used are typically TCP/IP, IPC mechanisms, memory copies, etc

• Performance may depend on the back-end load

• Scalability issues

High-Performance Networks

• InfiniBand, 10 Gigabit Ethernet

• High-Performance– Low latency (< 1 usecs) and high bandwidth (> 32 Gbps with

QDR adapters)

• Novel features– One-sided RDMA and atomics, multicast, QoS

• OpenFabrics alliance (http://www.openfabrics.org/)– Common stack for several networks including iWARP

(LAN/WAN)

Datacenter Research at OSU

ReconfigurationResource

Monitoring

Soft Shared State Lock ManagerGlobal Memory

Aggregator

Distributed Data/Resource Sharing Substrate

High-Performance Networks (InfiniBand, iWARP 10GigE)

Existing Datacenter Components

Multicast

Advanced System Services

ActiveCaching

CooperativeCaching

Dynamic Content Caching

ActiveCaching

CooperativeCaching


Monitoring

Active Resource Adaptation


Monitoring

RDMA Atomics

High-speedNetworks

Advanced CommunicationProtocols and SubsystemsSockets Direct Protocol

QoS&

AdmissionControl

AdvancedService

Primitives

Distributed Data/Resource Sharing Substrate

Soft Shared State Lock ManagerGlobal Memory

Aggregator

Existing Datacenter Components

Datacenter Homepage: http://nowlab.cse.ohio-state.edu/projects/data-centers/

Distributed Data Sharing Substrate

Load InfoSystem State

Meta-dataData

Datacenter

Application

Datacenter

Application

Datacenter

Services

Datacenter

Application

Datacenter

Application

Datacenter

Services

Get

Get

Get

Put

Put

Put

Multicore Architectures

• Increased cores per-chip

– More parallelism available

• Intel, AMD

– Dual-core, quad-core

– 80-core systems are currently built

• Significant benefits for datacenters

– Applications are multi-threaded in nature

– Design Optimizations in state sharing mechanisms

– Opportunities for dedicating one or more cores

Future multicore systems

Objective

• Can we enhance the distributed data sharing

substrate using the features of multicore

architectures by dedicating one or more of

the cores?

• How do these enhancements help in

improving the overall performance with

datacenter applications and services?

Distributed Data Sharing Substrate

• Use of a common service thread to get access to the shared state

• Applications get shared state information using the service thread

• Several design optimizations in communicating with the service thread– Message Queues (MQ-DDSS)

– Memory mapped queues for request (RMQ-DDSS)

– Memory mapped queues for request and response (RCQ-DDSS)

Message Queue-based DDSS (MQ-DDSS)

NIC

Produce

Produce

Consume

Consume

Request

Queue

Completion

Queue

Kernel Message Queues

Application

Threads

Service

Thread

User Space

Kernel Space

IPC_SendIPC_Recv IPC_Send

IPC_Recv

Interrupt

Event

Kernel

Thread

Kernel Involvement

Message Queue-based DDSS

• Kernel involvement– IPC Send and Receive operations

– Communication Progress

• Limitations– Several context-switches

– Interrupt overheads

Request/Message Queue-based

DDSS (RMQ-DDSS)

NIC

Produce

Produce

Consume

Consume

Request

Queue

CompletionQueue

Kernel Message Queues

ApplicationThreads

Service

Thread

User Space

Kernel Space

IPC_Recv IPC_Send

Kernel Involvement

Produce Consume

RequestQueue

Request/Completion Queue-based

DDSS (RCQ-DDSS)

NIC

Produce

Produce

Consume

Consume

Request

Queue

CompletionQueue

ApplicationThreads

Service

Thread

User Space

Kernel SpaceNo Kernel Involvement

Produce Consume

RequestQueue

Produce

Consume

CompletionQueue

RMQ-DDSS and RCQ-DDSS Schemes

• RMQ-DDSS scheme+ Lesser number of interrupts and context-switches compared to

MQ-DDSS

+ Improvement in response time as request is sent via memory mapped queues

– May occupy significant CPU

• RCQ-DDSS scheme+ Avoids kernel involvement

+ Significant improvement in response time as request and response are sent via memory mapped queues

– May occupy more CPU as compared to RMQ-DDSS - apps & service thread need to poll on the completion queue

Experimental Testbed

• InfiniBand experiments– 560-core cluster consisting of 70 compute nodes with dual 2.33

GHz Intel Xeon quad-core processors

– Mellanox MT25208 dual port HCA

• 10-Gigabit experiments– Intel dual quad-core Xeon 3.0 GHz, 512 MB memory

– Chelsio T3B 10 GigE PCI-Express adapters

• OpenFabrics stack– OFED 1.2

• Experimental outline– Microbenchmarks (performance and scalability)

– Application performance (R-Trees, B-Trees, STORM, checkpointing)

– Dedicating cores for datacenter services (resource monitoring)

Basic Performance of DDSS

0

10

20

30

40

50

60

70

80

1 4 16 64 256 1K 4K 16K

Message Size (bytes)

Late

ncy (usecs)

RCQ-DDSS RMQ-DDSS MQ-DDSS

• RCQ-DDSS scales with increasing client threads

• RCQ-DDSS performs better than RMQ-DDSS and MQ-DDSS

0

10

20

30

40

50

60

70

1 2 3 4 5 6 7

Number of Client Threads

IPC

La

ten

cy

(u

se

cs

)


0

10

20

30

40

50

60

1 4 16 64 256 1K 4K 16K

Message Size (bytes)

Late

ncy (usecs)


InfiniBand

10-Gigabit Ethernet

DDSS Scalability

0

50

100

150

200

250

1 2 4 8 16 32 64 128 256 512


Late

ncy (

usecs)


• Hybrid approach is required for scalability with large number of threads

• DDSS scales when keys are distributed

0

1

10

100

1000

10000

100000

1 2 4 8 16 32 64 128

256

512


IPC

Late

ncy (

usecs)


0200400600800

1000120014001600

1 2 4 8 16 32 64 128

256

512


Late

ncy (

usecs)


Keys are on a single node

Keys are distributed

Performance with R-Trees, B-Trees, STORM

0

5

10

15

20

25

20% 40% 60% 80% 100%

Records Accessed

Tim

e (

msecs)

BTREE-RCQ-SS BTREE-RMQ-SS

BTREE-MQ-SS BTREE

0

20

40

60

80

20% 40% 60% 80% 100%

Records Accessed

Tim

e (

msecs)

RTREE-RCQ-SS RRTEE-RMQ-SS

RTREE-MQ-SS RTREE

02000

40006000

800010000

1200014000

10K 100K 1000K

Number of Records

Tim

e (

msecs)

STORM-RCQ-SS STORM-RMQ-SS

STORM-MQ-SS STORM

• MQ-SS shows significant improvement compared to

traditional implementations but

RCQ-SS shows marginal improvements compared to MQ-SS

Data Sharing Performance in Applications

1

10

100

1000

10000

20% 40% 60% 80% 100%

Records Accessed

Tim

e (

usecs)

BTREE-RCQ-DDSS BTREE-RMQ-DDSS

BTREE-MQ-DDSS BTREE

• RCQ-DDSS shows significant improvement as compared to RMQ-DDSS and MQ-DDSS

1

10

100

1000

10000

100000

20% 40% 60% 80% 100%

Records Accessed

Tim

e (

usecs)

RTREE-RCQ-DDSS RRTEE-RMQ-DDSS

RTREE-MQ-DDSS RTREE

1

10

100

1000

10000

1K 10K 100K 1000K

Number of Records

Tim

e (m

illiseconds)

STORM-RCQ-DDSS STORM-RMQ-DDSS

STORM-MQ-DDSS STORM

Performance with checkpointing

0

20000

40000

60000

80000

100000

120000

140000

160000

1 4 16 64 256 1024 4096


Late

ncy (usecs)


• Hybrid approach is required for scalability with large number of threads

1

10

100

1000

10000

100000

1 2 4 8 16 32 64


Execu

tio

n T

ime (

usecs)


1

10

100

1000

10000

1 4 16 64 256


Execu

tio

n T

ime (

usecs)


Clients on diff node (non-distributed)

Clients on diff node (non-distributed)

Clients on single node (non-distributed)

Performance with Dedicated Cores

• Dedicating a core for resource monitoring can avoid up

to 50% degradation in client response time

0 200 400 600 800 1000800

1000

1200

1400

1600

1800

2000

Iterations

La

ten

cy(M

icro

se

co

nd

s)

4Servers8Servers16Servers32Servers

0 200 400 600 800 1000800

1000

1200

1400

1600

1800

2000

Iterations

Late

ncy(M

icro

seconds)

4Servers8Servers16Servers32Servers

Conclusions & Future Work

• Proposed multicore optimizations for

distributed data sharing substrate

• Evaluations with several applications shows

significant improvement

• Showed the benefits of dedicating cores for

services in datacenters

• Future work on dedicating other datacenter

services, datacenter-specific operations

Web Pointers

Datacenter Homepage: http://nowlab.cse.ohio-state.edu/projects/data-centers/

Emails: {vaidyana, laipi, narravul, panda}@cse.ohio-state.edu

NBC-LAB

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