• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
The DevOps PaaS Infusion - May meetup
 

The DevOps PaaS Infusion - May meetup

on

  • 359 views

 

Statistics

Views

Total Views
359
Views on SlideShare
355
Embed Views
4

Actions

Likes
0
Downloads
12
Comments
0

1 Embed 4

http://azureinsider 4

Accessibility

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    The DevOps PaaS Infusion - May meetup The DevOps PaaS Infusion - May meetup Presentation Transcript

    • Gary BergerTechnical Leader, Engineering Office of the CTOMay 17, 2012© 2010 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 1
    • 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© 2010 Cisco and/or its affiliates. All rights reserved. 2
    • •  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.© 2010 Cisco and/or its affiliates. All rights reserved. 3
    • Data Size compared to Task Rate1.  Compute Intensive •  Low number of tasks and small input size Data Size •  This includes MPI workloads familiar in HPC applications. High2.  Data Analytics •  Larger data sizes familiar to Map/Reduce programming model Analytics3.  Loosely Coupled Data Intensive Med •  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 Compute Intensive local disk I/O Loosely Coupled4.  Data Intensive Low •  Many tasks and large datasets. •  Formidable challenge for networks with dense matrix 1 1K 1M •  Categorized as Many Task Computing (MTC) Number of Tasks© 2010 Cisco and/or its affiliates. All rights reserved. 4
    • •  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© 2010 Cisco and/or its affiliates. All rights reserved. 5
    • •  +900M Users •  +150M Active Users •  4B videos view/day •  3.2B Likes/Comments/day •  +340M Tweets per day •  800M visitors/mnth •  +300M photos uploaded/day •  60H uploaded/min •  125B Friendships© 2010 Cisco and/or its affiliates. All rights reserved. 6
    • Mobile Data Traffic Mobile Data Transfer Distribution (Exabytes/Month) 100% 12 90% 80% 10 70% 8 60% Other 6 50% Web 40% 4 Video 30% 2 20% 0 10% 2011 2012 2013 2014 2015 2016 0% Operator A Operator B Operator C Operator D Source: Cisco VNI Mobile 2012 Source: ByteMobile Mobile Analytics Report 2012© 2010 Cisco and/or its affiliates. All rights reserved. 7
    • Unique problems that Cloudfy solves© 2010 Cisco and/or its affiliates. All rights reserved. 8
    • Alan Turing June 1912 - June 1954© 2010 Cisco and/or its affiliates. All rights reserved. 9
    • Host Centric Client Centric Database Centric Web Centric Service Centric “Technical Debt” “New Economy”•  Time shared •  Desktop •  Evolution of Client/ •  Normalized •  Loosely coupled system applications Server Presentation Layer components•  Explicit control •  Centralized File & •  4GL Programming •  Ubiquitous Access •  Web based•  Restricted scope Print •  Stored Procedures •  Ubiquitous API interactions•  Tightly Coupled •  Many dependencies •  Vertically Integrated •  Self-Described Data •  Almost Infinite•  Vertically •  Low network •  Proprietary Scalability Integrated utilization •  Global scope •  App driven Sparse to Dense operational integrity© 2010 Cisco and/or its affiliates. All rights reserved. 10
    • © 2010 Cisco and/or its affiliates. All rights reserved. 11
    • ZCloud© 2010 Cisco and/or its affiliates. All rights reserved. 12
    • Geographic Market Expansion Reach Your Business Service New Sources Monetization Of Data Capex Controls© 2010 Cisco and/or its affiliates. All rights reserved. 13
    • © 2010 Cisco and/or its affiliates. All rights reserved. 14
    • “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© 2010 Cisco and/or its affiliates. All rights reserved. 15
    • © 2010 Cisco and/or its affiliates. All rights reserved. 16
    • Homogenous Web Scale Heterogeneous Multi-Tenant Unified Multi-Service •  Highly distributed •  Highly virtualized •  Highly flexible •  Leverages scale-out/parallel •  Leverage compute arbitrage and •  Incorporates qualities of both HMT and application design SPOT market HWS •  Minimizes heterogeneous applications •  Benefits from a mixture of customer •  Purpose built to remove infrastructure by providing higher level services and market segments to randomize barriers to application development common resources management demand •  Manages resources more efficiently by •  Enhanced focus on cost and efficiency •  Complex engineering due to controlling allocation via higher-level due to large population. overlapping naming/addressing platform services •  Operational separation of code, data, •  Complex operations due to •  Provides best ROI and flexibility configuration and policy uncoordinated modifications, through common abstraction libraries interference due to competing access and runtimes to shared resources •  “Its all about the app” •  Enhanced focus on security and •  Operations as a Service isolation Examples: Google, MSFT, Facebook, Examples: Amazon EC2, Rackspace, Examples: Amazon (DDB, EMR), RHEL Yahoo etc..). OpenShift, MSFT Azure, VMForce© 2010 Cisco and/or its affiliates. All rights reserved. 17
    • Having an understanding of the underlying architecture and behavior in order to buildbetter systems. Power Wall I/O Wall App Memory Wall© 2010 Cisco and/or its affiliates. All rights reserved. 18
    • Coherency starts to force retrograde behavior O(N^2)Serialized Contentionstarts to dominate (i.e.locking) AmdahlLinear Growth p(Scale-Up/In) C( p) = 1 + α ( p −1) + β p( p −1)© 2010 Cisco and/or its affiliates. All rights reserved. 19
    • Load Balancer Load Load Web Balancer Firewall Network Balancer Network Network Network Firewall Firewall DBA Presentation App App Tier Logic Data Increased Delay/Limited Scalability© 2010 Cisco and/or its affiliates. All rights reserved. 20
    • Cluster Manager Recipe Caching App Data & Services Services SDN Controller Presentation© 2010 Cisco and/or its affiliates. All rights reserved. 21
    • network{ name: publish_subscribeapplication { qos: best_effort name : myApp isolation: per_domain tenantID: tenantID encryption: true service { msgPattern: pubsub compute { } template: ucs_small_linux storage { } name= cache_persistent network { cache { template: publish_subscribe capacity: 5G } evictionPolicy: LRU storage { } template: cache_persistant persistence{ } block: 10TB file: extfs } RAID: 10} } } © 2010 Cisco and/or its affiliates. All rights reserved. 22
    • •  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© 2010 Cisco and/or its affiliates. All rights reserved. 23
    • Thank you.© 2010 Cisco and/or its affiliates. All rights reserved. 24