© 2010 Cisco and/or its affiliates. All rights reserved. 1 © 2010 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 1

Gary Berger Technical Leader, Engineering Office of the CTO

May 17, 2012

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Technical Leader, Office of the CTO Data Center Business Unit

•  22 Years Infrastructure Architecture and Platform Development

•  Performance and Capacity Planning •  Data Center Design •  Protocol Architecture •  Application Design and Scalability •  Software Defined Networking

@gbatcisco garyberger.net

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•  Partnering since 2008

•  Advanced integration with Cisco Unified Compute System

•  OpenStack Integration (Nova, Quantum)

•  “Cloud in a Box” - High performance scaling to 1TB and 40 Cores.

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1.  Compute Intensive •  Low number of tasks and small input size •  This includes MPI workloads familiar in HPC applications.

2.  Data Analytics •  Larger data sizes familiar to Map/Reduce programming

model

3.  Loosely Coupled •  Modest data size but increasing the number of tasks •  Indicative of data-grid applications and HTC which are

bounded by memory capacity but also can be bounded by local disk I/O

4.  Data Intensive •  Many tasks and large datasets. •  Formidable challenge for networks with dense matrix •  Categorized as Many Task Computing (MTC)

Data Size compared to Task Rate

AnalyticsData Intensive

Loosely Coupled

ComputeIntensive

1 1K 1M

Number of Tasks

Low

Med

High

Data Size

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•  Current Internet Trends

•  Quick historical perspective and state of the “cloud”

•  Data Center as a Business Archetypes

•  Mechanical Sympathy

•  Real World Challenges

•  Service Centric Networking

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•  +150M Active Users •  +340M Tweets per day

•  4B videos view/day •  800M visitors/mnth •  60H uploaded/min

•  +900M Users •  3.2B Likes/Comments/day •  +300M photos uploaded/day •  125B Friendships

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0

2

4

6

8

10

12

2011 2012 2013 2014 2015 2016

Mobile Data Traffic (Exabytes/Month)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

100%

Operator A Operator B Operator C Operator D

Mobile Data Transfer Distribution

Other Web Video

Source: Cisco VNI Mobile 2012 Source: ByteMobile Mobile Analytics Report 2012

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Unique problems that Cloudfy solves

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Alan Turing

June 1912 - June 1954

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•  Time shared system

•  Explicit control •  Restricted scope •  Tightly Coupled •  Vertically

Integrated

Database Centric Client Centric Service Centric Host Centric

•  Desktop applications

•  Centralized File & Print

•  Many dependencies •  Low network

utilization

•  Evolution of Client/Server

•  4GL Programming •  Stored Procedures •  Vertically Integrated •  Proprietary

“Technical Debt”

•  Loosely coupled components

•  Web based interactions

•  Almost Infinite Scalability

•  Global scope •  App driven

operational integrity

Web Centric

•  Normalized Presentation Layer

•  Ubiquitous Access •  Ubiquitous API •  Self-Described Data

“New Economy”

Sparse to Dense

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ZCloud

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Geographic Reach

New Sources Of Data

Capex Controls

Market Expansion

Your Business

Service Monetization

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“Until now, cloud computing has been mostly about the distribution of applications” “The next wave of cloud computing will enable the sharing of the environment to run those applications.” “You will be able to take advantage of what we had to build in order to create those applications” Ben Fried, CIO Google 2012

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Heterogeneous Multi-Tenant •  Highly virtualized •  Leverage compute arbitrage and

SPOT market •  Benefits from a mixture of customer

market segments to randomize demand

•  Complex engineering due to overlapping naming/addressing

•  Complex operations due to uncoordinated modifications, interference due to competing access to shared resources

•  Enhanced focus on security and isolation

Examples: Amazon EC2, Rackspace, etc..).

Homogenous Web Scale •  Highly distributed •  Leverages scale-out/parallel

application design •  Minimizes heterogeneous applications

by providing higher level services and common resources management

•  Enhanced focus on cost and efficiency due to large population.

•  Operational separation of code, data, configuration and policy

Examples: Google, MSFT, Facebook, Yahoo

Unified Multi-Service •  Highly flexible •  Incorporates qualities of both HMT and

HWS •  Purpose built to remove infrastructure

barriers to application development •  Manages resources more efficiently by

controlling allocation via higher-level platform services

•  Provides best ROI and flexibility through common abstraction libraries and runtimes

•  “Its all about the app” •  Operations as a Service

Examples: Amazon (DDB, EMR), RHEL OpenShift, MSFT Azure, VMForce

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I/O Wall App Memory Wall

Having an understanding of the underlying architecture and behavior in order to build better systems.

Power Wall

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Serialized Contention starts to dominate (i.e. locking)

Linear Growth (Scale-Up/In)

Coherency starts to force retrograde behavior O(N^2)

Amdahl

C(p) = p1+α (p −1)+ β p(p −1)

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Presentation Tier

App Logic Data

Increased Delay/Limited Scalability

Network

Firewall

Load Balancer

Web

Firewall

Load Balancer

App

Firewall

Load Balancer

DBA Network

Network

Network

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App Services

Caching &

Presentation

Data Services

Recipe

Cluster Manager

SDN Controller

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application { name : myApp tenantID: tenantID service { compute { template: ucs_small_linux } network { template: publish_subscribe } storage { template: cache_persistant }

}

}

network{ name: publish_subscribe

qos: best_effort isolation: per_domain encryption: true msgPattern: pubsub

} storage {

name= cache_persistent cache { capacity: 5G

evictionPolicy: LRU } persistence{ block: 10TB file: extfs RAID: 10 }

}

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•  Effective Resource Sharing •  Further away from the metal, the harder it is to understand (non-deterministic performance) •  Contention grows while accessing shared resources •  What instruments to collect analyze and model

•  Programming Languages •  Generally languages are insufficient for building large applications (lack of procedures in JAVA, lack of encapsulation in

Python, etc.) •  Concurrency is still extremely difficult and hard to reason about (trend towards functional reactive programing) •  Throw away code

•  Network Scalability •  Segmentation and Isolation •  Address Learning •  Application aware •  Programmatic Interfaces

•  Security •  In-flight/At-Rest encryption •  Proper tradeoff between performance and privacy •  Rat-Hole because of lack of tools, developer education and highly incentivized and motivated hacker community

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Thank you.