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Adaptive Storage Management for Modern Data Centers

Imranul Hoque

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Applications in Modern Data Centers

More than 20 PB of data (> 260 billion files) just in the photo app.

More than 500 million active users.

Over 2.5 million websites have integrated with Facebook.

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Application Characteristics

• Type of data– Networked data (integration between content and

social sites)• Volume of data/scale of system– TB to PB (photos, videos, news articles, etc.)

• End-user performance requirement– Low latency (real time web)

Traditional storage systems have failed to satisfy these requirements.

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New Classes of Storage SystemsApplication requirements

Why traditional storage systems have failed?

New storage system

Networked data Representation Join (e.g., friends of friends)

Graph databaseExample: Twitter uses FlockDB

Large data volume and massive scale of system

Consistency vs. (Performance and Availability)

(Small but frequent r/w and batch transactions with rare write) vs. Heavy read/write workload

Key-value storageExample: Digg uses Cassandra

Low latency and high throughput

Disk is bottleneck In-memory storageExample: Craigslist uses Redis

Absence of adaptive techniques for storage management cause these new storage systems to perform sub-optimally.

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Adaptive Storage ManagementNew storage system Use cases Scope of performance

improvementGraph database Online social network

Amazon style recommendation engine

Hierarchical data set

Data layout on disk

Key-value storage Status update Image tags Click stream

Load distribution across servers

In-memory storage Status update Financial tick data Online gaming stats

Construction of working set

Bondhu

Shomota

Sreeti

How the adaptive techniques should be designed?

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Hypothesis Statement

“Adaptive techniques which leverage the underlying heterogeneity of the system as a first class citizen, can improve the performance of these new classes of storage systems significantly.”

Leverage heterogeneity = exploit heterogeneity + mitigate heterogeneity.

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Bondhu: Leveraging Heterogeneity

Placement techniques

Social Graph Hard Disk Drive

Exploit heterogeneity in the social graph to make better data placement decisions.

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Shomota: Leveraging Heterogeneity

Server 1 Server 2 Server 3 Server 4

SATTUE

SUNMON

WED THU

FRI

MON TUE WED THU FRI SAT SUN

Tablets Table

Mitigate load heterogeneity across servers to alleviate hot spot via adaptive load balancing techniques.

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Sreeti: Leveraging Heterogeneity

Swapping strategy

Prefetching strategy

Exploit heterogeneity in user access patterns to design prefetching and swapping techniques for better performance.

Main memory Persistent storageUsers

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ContributionProject name

Storage type

Heterogeneity leveraged

Exploit/mitigate

Techniques proposed

Status

Bondhu Graph Social graph Exploit Data layout on disk

Mature

Shomota Key-value Load Mitigate Load balancing across servers

On-going

Sreeti In-memory Request pattern

Exploit Pre-fetching and swapping

Future work

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Bondhu: A Social Network-Aware Disk Manager for Graph Databases

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Visualization of Blocks Accessed

• Facebook New Orleans network [Viswanath2009]

• Neo4j graph database– 400 KB blocks per user

• blktrace tool to trace blocks

getProperty()Property

Property

Property

Scattered disk access

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Sequential vs. Random Access

• How bad is random access?• fio benchmarking tool

Disk layout techniques might improve performance significantly.

70 vs. 0.7 MB/s

150 vs. 1 MB/s

98 vs. 0.8 MB/s

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Social Network-Aware Disk Manager

• Approaches in other systems– Popularity-based approach: multimedia file system

[Wong1983]

– Tracking block access patterns [Li2004][Bhadkamkar2009]

• Properties of online social networks [Misolve2007]

– Strong community structure– Small world phenomenon

• Exploit heterogeneity in social graph– Keep related users’ data close by on disk– Reduce seek time, rotational latency, # of seeks

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The Bondhu System

• Novel framework for disk layout algorithms– Based on community detection

• Integration into Neo4j, a widely-used open source graph database

• Experimentation using real social graph– Response time improvement by 48% compared to

the default Neo4j layout

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Problem Definition

• Logical block addressing scheme (LBA)– One-dimensional representation of disk

L1 L2 L3 L4 L5 L6 L7

V1 V2 V3 V4 V5 V6 V7

V5 V1 V3 V2 V4 V6 V7

Cost of layout = 18 Cost of layout = 14

NP-hard problem: fast multi-level heuristic approach

V1

V5

V2

V3

V4

V6

V7

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Disk Layout Algorithm

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Community Detection Module

• Goal: organize the users of the social graph into clusters

• Based on community detection algorithms– Graph partition driven (ParCom) [Karypis1998]

– Modularity optimization driven (ModCom) [Blondel2008]

V1

V5

V2

V3

V4

V6

V7

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Intra-community Layout Module

V1

V5

V2

V3

V4

V6

V7

1

1 1

1

1

1

1

1

V2 V1 V3 V5

V3

V1

2

2VC V5

V2

V7 V6 V4

• Goal: create a disk layout for each community

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Inter-community Layout ModuleV1

V5

V2

V3

V4

V6

V7

VA VB

2

VA VB

V2 V1 V3 V5V7 V6 V4

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Modeling OSN Dynamics

• Uniform Model– Assign equal weight

• Preferential Model– Weight of edge (Vi, Vj) edge degree of V∝ j

– We use [edge_degree(Vi) + edge_degree(Vj)]/2

• Overlap Model– Weight proportional to # of common friends– We use (c + 1), c = # of common friends

V1

V5

V2

V3

V4

V6

V7

degree = 4

degree= 3

3.52

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Implementation and Evaluation

• Modified PropertyStore of Neo4j• Facebook New Orleans network [Viswanath2009]

– 63731 users– 817090 links– Assign weights according to uniform, preferential, and overlap models

• Workload: sample social network application– ‘List all friends’ operation– 1500 random users, 6 times/user

• Metrics– Cost (defined earlier)– Response time = time to fetch data blocks from all friends of a random

user

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Visualization using Bondhu

Default layout: Scattered disk access

Bondhu: Clustered disk access

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Effect of Block Size

Caching disabled Caching enabled

• File system reads data in chunks of 4KB– (4B, 40B, 400B) block = (1024, 102, 10) users data

• Default layout– 10x decrease in expected # of friends, when block size

increases from 4B to 40B to 400B • Bondhu layout

– 4B to 40B not much decrease in expected # of friends– 40B to 400B rapid decrease in expected # of friends

Cached in memory

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Response Time Metric vs. Cost Metric

Improvement in response time is due to better placement decisions.

Block size: 40 B Block size: 400 KB

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Effect of Different Models

• Model = {preferential, overlap, uniform, default}• Workload = {random, preferential, overlap}– 1000 random users, 1000 requests, 10 measurements

Little added benefit

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Effect of OSN EvolutionStill better than the default layout by 72%

33% less nodes

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Summary

• Adaptive disk layout techniques– Exploit heterogeneity of social graph (community

structure)• Implementation in Neo4j graph database• Extensive trace-driven experimentation• 48% improvement in median response time• Low additional benefit using complex models• Infrequent re-organization

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Shomota: An Adaptive Load Balancer for Distributed Key-Value Storage Systems

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Load Heterogeneity in K-V Storage

• Hash partitioned vs. range partitioned– Range partitioned data ensures efficient range scan/search– Hash partitioned data helps even distribution

• Uneven space distribution due to range partitioning– Solution: partition the tablets and move them around

• Few number of very popular records

Server 1 Server 2 Server 3 Server 4

SATTUE

SUNMON

WED THU

FRI

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The Shomota System

• Mitigate load heterogeneity• Algorithms for solving the load balancing problem– Load = space, bandwidth– Evenly distribute the spare capacity– Distributed algorithm, not a centralized one– Reduce the number of moves

• Previous solutions:– One dimensional/key-space redistribution/bulk loading

[Stoica2001, Byers2003, Karger2004, Rao2003, Godfrey2005, Silberstein2008]

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System Modeling and Assumptions

Table

Tablet

Tablet

Tablet

Server A

Server B

Server C

B1, S1

B2, S2

B3, S3

BA, SA

BB, SB

BC, SC1. <= 0.01 in both dimensions2. # of tablets >> # of nodes

B1, S1

B4, S4

B5, S5

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System State

B

STarget Zone:

helps achieve convergence

Target Point

Goal: Move tablets around so that every server is within the target zone

A given server

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Load Balancing Algorithms

• Phase 1:– Global averaging phase– Variance of the approximation of the average decreases

exponentially fast [Kempe2003]

• Phase 2:– Gossip phase– Point selection strategy

• Midpoint strategy • Greedy strategy

– Tablet transfer strategy• Move to the selected point with minimum cost (space transferred)

Phase 1 Phase 2 Phase 1 Phase 2

t

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Summary

• Distributed load balancing techniques for key-value storage system– Mitigates both space and throughput

heterogeneity across servers– PeerSim-based simulation– Integration in Voldemort (on-going)– Simulation results exhibit fast convergence while

keeping the data movements at a low level

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Sreeti: Access Pattern-Aware Memory Management

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In-memory Storage System

• Growth in Internet population– Search engine, social networking, blogging, e-commerce,

media sharing• User expectation– Fast response time + high availability

• Serving large number of users at real-time– Option 1: SSD– Option 2: memory

• Emerging trends– Memory caching system: Memcached– In-memory storage system: Redis, VoltDB, etc.

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Motivation

• Assumption in existing in-memory storage systems– Enough RAM to fit all data in memory

• Counter example:– Values associated with keys are large

• Approach taken by existing systems:– Redis, Memcached: use LRU for swapping

Performance of in-memory storage systems can be improved further if heterogeneity in user request-pattern is leveraged.

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Proposal for Sreeti System

• Adaptive techniques for prefetching, caching, and swapping– Exploit heterogeneity in user request-patterns

Swap

Fetch

Users Application Main Memory Persistent Storage

Associative rule mining

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Hypothesis Statement

• “Adaptive techniques which leverage the underlying heterogeneity of the system as a first class citizen, can improve the performance of these new classes of storage systems significantly.”

Disk layoutLoad balancing

Prefetching, caching, swapping

ExploitMitigate

Graph databaseKey-value storage

In-memory storage

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SummaryProject name

Storage type Heterogeneity leveraged

Exploit/mitigate

Techniques proposed

Bondhu Graph Social graph Exploit Data layout on disk

Shomota Key-value Load Mitigate Load balancing across servers

Sreeti In-memory Request pattern Exploit Pre-fetching and swapping