Elimination Service for Enterprises Ram Ramjee Microsoft Research - - PowerPoint PPT Presentation

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Elimination Service for Enterprises Ram Ramjee Microsoft Research - - PowerPoint PPT Presentation

Dec 07, 2023 165 likes •418 views

EndRE: An End-System Redundancy Elimination Service for Enterprises Ram Ramjee Microsoft Research India Bhavish Aggarwal^, Aditya Akella*, Ashok Anand*, Athula Balachandran~, Pushkar Chitnis^, Chitra Muthukrishnan*, and George Varghese# ^:

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SLIDE 1

EndRE: An End-System Redundancy Elimination Service for Enterprises

Ram Ramjee Microsoft Research India Bhavish Aggarwal^, Aditya Akella*, Ashok Anand*, Athula Balachandran~, Pushkar Chitnis^, Chitra Muthukrishnan*, and George Varghese#

^: Microsoft Research India *: University of Wisconsin-Madison ~: CMU #: University of California, San Diego

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SLIDE 2
  • Large enterprises have a global footprint
  • Data centers consolidated to save management cost
  • Diminished performance due to Wide Area Network

(WAN) bandwidth and latency constraints

Enterprise Dilemma

2

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SLIDE 3

Middlebox-based WAN Optimizers

  • Protocol independent redundancy elimination using

synchronized in-memory caches at two ends [Spring & Wetherall, Sigcomm 2000]

  • Disk-based caches for large static objects
  • Current leaders: RiverBed, Juniper, Cisco,…
  • Annual revenue > $1Billion
  • Are middleboxes the right approach for enterprises?

Enterprise Data Center

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Synchronized packet caches

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SLIDE 4

Issues with Middleboxes

  • 1. End-to-end security and encryption

– Either no RE or require key sharing

  • 2. Resource-constrained mobile smartphones

– No RE on the bandwidth limited 2.5/3G wireless link

  • 3. Cost

4

Data Center Enterprise

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SLIDE 5

End-to-End RE: Benefits

  • 1. RE before encrypt  End-to-end security
  • 2. RE on mobiles  Bandwidth savings over wireless
  • 3. Bandwidth savings + simple decode  Energy gains
  • 4. Operate above TCP  Latency gains

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Enterprise Data Center

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SLIDE 6

Enterprise Data Center

Our Contributions

  • 1. EndRE Design

– New SAMPLEBYTE fingerprinting for fast processing: 10X speedup – Optimized data structures for reducing memory overhead by 33-75%

  • 2. Evaluation of benefits

– Analysis using 6TB of packet traces from 11 sites over 44 days – Small-scale deployment

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SLIDE 7

Outline

  • Overview
  • Design of EndRE
  • EndRE costs and benefits
  • Summary

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SLIDE 8

EndRE: Design Goals

  • Opportunistic use of limited end host resources
  • 1. Fast and adaptive RE processing

– Lightweight and tunable depending on server load

  • 2. Parsimonious memory usage

– Data structure and design optimizations to reduce memory overhead

  • 3. Asymmetric

– Simple client decoding

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SLIDE 9

Redundancy Elimination: Overview

Bandwidth constrained link

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Packet cache (Synchronized circular buffer) Fingerprinting hash-table lookups pointer lookups

Need to quickly identify repeated content (≈32 bytes)

– Identifying all matches (optimal) impractical – Sampling-based approach necessary but comes at the cost of missed redundancy identification

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SLIDE 10

Redundancy Elimination: Overview

Bandwidth constrained link

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Packet cache (Synchronized circular buffer) Fingerprinting hash-table lookups

1. Fingerprinting – Generate representative fingerprints of packet – New SAMPLEBYTE fingerprinting algorithm

  • 2. Matching & Encoding

– Lookup fingerprints in a hash-table of cache fingerprints – Max-Match: Byte-by-byte comparison between cache & packet – Chunk-Match: Full chunk matches (see paper) – Encode matched region with (position, length) tuples

pointer lookups

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SLIDE 11
  • 1. Fingerprinting: MODP

Packet payload Window

Rabin fingerprinting Value sampling: sample those fingerprints whose value is 0 mod p

  • Compute fingerprints based on content [Spring & Wetherall]

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+ Robust to small changes in content  better bandwidth savings – Rabin hashes expensive and not adaptive  lower speed

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SLIDE 12
  • 1. Fingerprinting: FIXED

Choose marker every p bytes

  • Fingerprints chosen at fixed intervals by position in the packet

+ Simple selection criteria and tunable  fast and adaptive – A small insertion/deletion in content will result in failure in detecting redundancy  lower bandwidth savings

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01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17

Hash w-byte Hash w-byte Hash w-byte

Fingerprints

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SLIDE 13
  • 1. Fingerprinting: SAMPLEBYTE

Choose marker if F(singlebyte) = 1; e.g., F(0) = 1, F(5) =1 Once chosen, skip p/2 bytes

  • Can we get the speed/adaptability of FIXED and the robustness of

MODP?

  • F(singlebyte) derived from training data using a greedy strategy

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7 4 6 0 0 0 8 5 0 1 1 5 0 6 7 0 0

Hash w-byte Hash w-byte Hash w-byte

Fingerprints + Content-based  bandwidth savings close to MODP? + Simple selection & tunable skipping  speed/adaptability of FIXED?

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SLIDE 14
  • 2. Matching & Encoding: Max-Match
  • Approach used in

Spring & Wetherall

– Meta data overhead is 67% of cache size

  • Collisions are not costly

– Simple hash function – Overwrite hash table – No deletion

  • Don’t store fingerprints!

– Use the table index to implicitly represent part/all of fingerprint

  • Meta data overhead is

6-12% of cache size

  • 2. Lookup in Fingerprint hash table
  • 1. Compute fingerprints
  • ver fixed windows

(e.g., 32bytes) Fingerprint hash table Packet Cache fingerprint payload

  • 3. In case of match,

expand region

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SLIDE 15

Outline

  • Overview
  • Design of EndRE
  • EndRE costs and benefits
  • Summary

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SLIDE 16

Fingerprinting Algorithms: Comparison

1 2 3 4 5 6 7 8 32 64 128 256 512

Speed (Gbps) Sampling period (p)

SAMPLEBYTE MODP FIXED

5-10X

10 12 14 16 18 20 22 24 26 32 64 128 256 512

Bandwidth savings (%) Sampling period (p)

SAMPLEBYTE MODP FIXED

  • SAMPLEBYTE delivers bandwidth savings similar to MODP

while operating at speeds similar to FIXED

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SLIDE 17

EndRE Memory Requirements: 44-day 11-enterprise Trace Analysis

10 20 30 40 50 60 70 80 90 100 100 200 300

% of Clients

Maximum Cache Size at Client (MB) 10 20 30 40 50 60 70 80 90 100 1000 2000

% of Servers

Maximum Cache Size at Server (MB)

  • Median/Max memory requirement at Client is 60/360MB
  • Memory requirement at server tunable, at cost of reduced savings

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SLIDE 18

Implementation

EndRE Callout

Other Callout modules user kernel WFP Filtering Engine Base Filtering Engine (BFE)

WFP APIs

Network Layer Transport Layer Forward Layer IPsec Stream Layer TDI/WSK ALE

HTTP SMB OTHERS EndRE Stream Layer Filter WFP APIs EndRE Management

ADD CALLOUT ADD FILTER

  • EndRE pilot deployment on laptops/desktops over one week with 11

users for HTTP traffic (1.7GB) delivered bandwidth savings of 31%

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SLIDE 19

Bandwidth Savings (~2 weeks)

  • EndRE delivers average bandwidth savings of 26-34%, a

significant portion of the 39-41% savings of middlebox

Enterprise Site Trace Size (GB) Middle (2GB) % savings EndRE (1-10 MB) % savings Middle + large-files %savings EndRE + large-files % savings 1. 173 71 47 72 56 2. 8 33 24 33 33 3. 71 34 26 35 32 4. 58 45 24 47 30 5. 69 39 27 42 37 6. 80 34 22 36 28 7. 80 31 26 33 33 8. 142 34 22 40 30 9. 198 44 16 46 26 10. 117 27 21 30 30 Avg/Site 100 39 26 41 34

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SLIDE 20

Mobile Smartphone Server

Energy Savings

None ZLIB (LZ) EndRE Energy uAh Energy % savings Bandwidth % savings Energy %savings Bandwidth %savings Trace Packet 32KB Packet 32KB Packet Packet A 2038

  • 11

42 26 44 25 29 B 1496

  • 11

68 41 75 70 76

  • ZLIB works well for large chunk sizes but on a packet-by-packet

basis may result in increased energy consumption

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SLIDE 21

Mobile Smartphone Server

Energy Savings

None ZLIB (LZ) EndRE Energy uAh Energy % savings Bandwidth % savings Energy %savings Bandwidth %savings Trace Packet 32KB Packet 32KB Packet Packet A 2038

  • 11

42 26 44 25 29 B 1496

  • 11

68 41 75 70 76

  • EndRE’s bandwidth savings translate into equivalent

savings in energy with no additional latency

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SLIDE 22

Related work

  • Static content (e.g., large files)

– Host: Disk De-Duplication – Client and Server: LBFS (SOSP’01), RSYNC/RDC – Peer-to-Peer: DOT(NSDI’06), SET (NSDI’07), BranchCache in Win7

  • Dynamic content

– Middlebox – Spring & Wetherall (SIGCOMM’00) – Products from Riverbed, Cisco, Juniper, etc.

  • New architectures

– Packet Caches: RE in routers (SIGCOMM’08) – Ditto: RE in wireless mesh networks (MobiCom’08)

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SLIDE 23

Summary

  • 1. EndRE

– SAMPLEBYTE fingerprinting algorithm supports processing speeds of 1.5-4Gbps/core – Data structure optimizations reduce server memory requirement by 33-75%

  • 2. Costs

– Client processing negligible; Server processing is load adaptive; – Median client requires only 60MB of memory; Server up to 2GB

  • 3. Benefits

– Avg. bandwidth savings of 26-34% – Bandwidth savings equivalent energy savings on smartphones

  • EndRE is a promising alternative to WAN optimizers
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SLIDE 24

Questions?