Appscale at CLOUDCOMP '09
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Appscale at CLOUDCOMP '09



These are the slides from my presentation at CLOUDCOMP 2009 on AppScale, an open source platform for running Google App Engine apps on. See our project home page at or our ...

These are the slides from my presentation at CLOUDCOMP 2009 on AppScale, an open source platform for running Google App Engine apps on. See our project home page at or our code page at



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Appscale at CLOUDCOMP '09 Appscale at CLOUDCOMP '09 Presentation Transcript

  • Scalable and Open AppEngine Development and Deployment Navraj Chohan Chris Bunch Sydney Pang Chandra Krintz Nagy Mostafa Sunil Soman Rich Wolski
  • from_lamp_to_leap_and_beyond.php
  • Terminology Software-as-a-Service (SaaS) e.g., SalesForce, Gmail Provides remote application access Platform-as-a-Service (PaaS) e.g., Google App Engine Provides scalable runtime stack Infrastructure-as-a-Service (IaaS) e.g., Amazon Web Services Provides full system images
  • •  Open-source, Platform-as-a-Service for research and engineering of cloud computing components, applications, and services •  Automated deployment of applications to high- performance databases •  Fine grain control over application environment •  Google App Engine apps hosting on your cluster –  Real applications –  Familiar API (that is extensible for lock-in avoidance) –  Your data and code on your resources
  • From Google App Engine (GAE) to AppScale •  GAE Application Programming Interface –  Datastore (get/put) –  Memcache –  URL Fetching –  Mail –  Images –  Authentication •  Write Python/Java GAE app –  Use SDK locally to test and generate indexes •  APIs implemented as non-scalable, simple versions
  • From Google App Engine (GAE) to AppScale •  GAE Application Programming Interface –  Datastore (get/put) BigTable –  Memcache Memcached –  URL Fetching –  Mail GMail –  Images –  Authentication Google Accounts •  Write Python/Java GAE app –  Use SDK locally to test and generate indexes •  APIs implemented as non-scalable, simple versions –  Upload to Google resources •  Highly scalable API implementation
  • Sandboxed Runtime •  Restricted subset of library calls •  No reading/writing from/to file system •  Data persistence only via get/put interface •  Computation bounded: 30 secs per request •  Access web services over via HTTP / HTTPS only (ports 80 and 443)
  • Recent GAE Additions •  Python and JVM SDKs –  JRuby, Clojure, etc. available through Java •  Task Queue, Cron, XMPP APIs •  New SLAs for paying customers –  $0.10 per CPU core hour –  $0.10 per GB bandwidth in –  $0.12 per GB bandwidth out –  $0.15 per GB data stored per month
  • Protocol Buffers •  Google App Engine’s internal data format –  And AppScale’s •  Similar to C-style structs: message Person { required int32 id = 1; optional string name = 2; }
  • From Google App Engine (GAE) to AppScale •  AppScale extends the GAE SDK –  Replaces the simple, non-scalable API implementation with pluggable, distributed, scalable components •  Using open-source solutions as available/possible •  Communication over SSL •  Available as source and as system image –  Each instance can implement any component •  Self configuring as part of AppScale cloud deployment –  Deploys over •  Virtual machine monitors (Xen, KVM) •  Infrastructure (IaaS) cloud layers
  • IaaS Cloud Systems •  Amazon Web Services (AWS) –  Elastic Compute Cloud (EC2), Persistent Storage (S3, EBS) –  For-fee, as negotiated in SLA (CPU, network, storage) –  Vast resources available •  Users access small (opaque) subset, can scale-out •  Eucalyptus –  Open source implementation of the AWS APIs –  Inspiration for AppScale – familiar, widely-used API implementation for execution on your cluster •  Limited only by the hardware you have available
  • Differences in AppScale Deployment Options •  Xen / KVM: –  Static deployment •  Can use as many nodes as are manually configured •  Eucalyptus / EC2 –  Dynamic deployment •  Can use as many nodes as the system can support (or pay for for EC2 deployment) –  As part of ongoing/future work: support for dynamic scaling •  Front-end (user-facing) & back-end (data managment & computation) •  SLA renegotiation
  • AppScale System Layout •  AppLoadBalancer (ALB) •  AppServer (AS) •  Database Master/Slave/Peer (DB M/S/P) GAE App AppScale DB M/P Developer tools ALB (AppScale Admin) App DB S/P Controller GAE App GAE App GAE App AS Users Users Users HTTPS
  • AppController (AC) •  SOAP Server written in Ruby –  Runs on all nodes •  Middleware layer •  Controls and sets up a node for use –  Sets up configuration files (data replication) –  Sets up firewall for security •  Master AC “heartbeats” all other nodes –  Collects performance info as well
  • AppLoadBalancer (ALB) •  Ruby on Rails application •  Handles authentication and routing of users to AppServers •  Three copies are deployed via Mongrel –  Load balanced via nginx
  • Database Management •  Five databases currently available: –  HBase, Hypertable: Master / Slave –  Cassandra, Voldemort: Peer / Peer –  Clustered MySQL: Relational •  Two main components –  Protocol Buffer Server: Data access / storage –  User / App Server: Authentication
  • AppServer (AS) •  Modified Google App Engine SDK •  App requests internally are Protocol Buffers –  Forwards requests to PB Server •  Minimal request set: –  Put(id) –  Get(id) –  Query: Equivalent to get_all_in_table –  Delete(id) –  Count: Total number of items in database –  GetSchema
  • AppScale Tools •  Ruby scripts that initiate AppScale deployment –  Initializes the first AppController for use –  Uploads AppEngine app •  Conceptually similar to Amazon AWS EC2 tools –  describe-instances –  upload-app: Introduce additional apps –  terminate-instances
  • Fault Tolerance •  System can survive the following failures: –  AppServer failure –  Database Slave failure –  Database Peer failure –  AppLoadBalancer failure * –  AppController failure *
  • Testing Methodology •  Load testing done via the Grinder •  Test specifics: –  Initially 3 users –  3 users added every 5 seconds –  Done until 160 seconds have passed •  Each user navigates the page, performs some scripted action •  Measured total transactions performed and average response time
  • AppScale Evaluation Cluster •  Three Grinder nodes, four AppScale nodes –  One master, three slaves –  Virtualized via Xen –  Database: HBase (3x replication) 64 MB HDFS blocks •  PBServer via Thrift; stores entire protocol buffers •  Hardware –  Quad-core 2.66 GHz machines –  8 GB of RAM –  Connected via Gigabit Ethernet
  • Applications Tested •  Tasks - a to-do list –  Read and write intensive (44 transactions per user) •  Cccwiki – allows users to edit web pages –  Read intensive, updates only (74 transactions per user) •  Guestbook – allows users to post messages –  Retrieves ten most recent posts only (9 transactions per user) •  Shell – provides an interactive Python shell –  Compute intensive (14 transactions per user)
  • Transactions per App
  • App Response Time
  • Comparison with Google
  • Room for Improvement •  Current bottlenecks: –  Queries perform filtering server-side –  Filtering is done outside of the DB –  AppEngine, PB Server are single-threaded –  Entry point to some DBs is single-threaded •  Future work will address these problems –  Will also compare performance across DBs –  e.g., BigTable-like DBs vs. P2P DBs
  • Related Work •  AppDrop –  Proof-of-concept Rails app •  TyphoonAE –  Relatively new (alpha release) –  Runs MongoDB only •  Microsoft Azure –  Uses .NET as the platform –  Has a similar pricing model to AppEngine
  • AppScale Recap •  Distributed, multi-component system –  Deployed as a single system image (self configuring) •  Static deployment over Xen/KVM •  Dynamic deployment over Eucalyptus/EC2 •  Databases supported: –  HBase, Hypertable, MySQL, Cassandra, Voldemort •  Fault-tolerant
  • AppScale Recap •  Open cloud research platform –  International user community •  Goals –  Easy to use and extend –  Automatic deployment of PaaS cloud and GAE apps on resources other than Google’s –  Support real applications and users •  Experimentation and testing in real environments •  Current performance results are a baseline
  • Performance Improvements •  AppEngine now multi-process, load balanced •  PB Server now multi-threaded •  Storing data like Google for HBase and Hypertable –  Three tables: Reference, Sort Ascending, Sort Descending
  • Future Work •  Expand out of the web services domain –  Investigating opportunities in streaming –  Integrated MapReduce support for high- performance computing (HPC) –  Co-locate AppEngines and use shared memory •  Additional databases: –  MongoDB, Scalaris, CouchDB
  • Thanks! •  To the AppScale team! –  Co-lead Navraj Chohan –  Advisor Prof. Chandra Krintz •  To the open-source community •  To Google, NSF, and IBM for financial support •  To you all for coming out today •  Check us out on the web: –