appscale: open-source platform-level cloud computing

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1 : open-source platform-level cloud computing I2 Joint Techs February 2 nd, 2010 Chandra Krintz Computer Science Dept. Univ. of California, Santa Barbara

2 cloud computing Remote access to distributed and shared cluster resources Potentially owned by someone else (e.g. Amazon, Google, ) Users rent access to vast resources Advertised service-level-agreements (SLAs) Resources are opaque and isolated Highly scalable, fault tolerant Service-oriented, utility computing Relies on OS, network, and storage virtualization SLAs Virtualization Web Services

3 cloud computing 3 types: as-a-service (aas) Infrastructure: Amazon Web Services (EC2, S3, EBS) Virtualized, isolated (CPU, Network, Storage) systems on which users execute entire runtime stacks Fully customer self-service Open APIs (IaaS standard), scalable services Platform: Google App Engine, Microsoft Azure Scalable program-level abstractions via well-defined interfaces Enable construction of network-accessible applications Process-level (sandbox) isolation, complete software stack Software: Salesforce.com Applications provided to thin clients over a network Customizable

4 an opening in the clouds Open-source cloud computing systems from the UCSB Computer Science Department Goal: Bring popular cloud fabrics to on-premise clusters that are easy to use and are transparent To facilitate investigation of Energy-efficient cloud computing Services, underlying device technology, support technologies Customization (availability, performance, application behavior) Hybrid cloud solutions (public and on-premise)

5 an opening in the clouds Open-source cloud computing systems from the UCSB Computer Science Department Goal: Bring popular cloud fabrics to on-premise clusters that are easy to use and are transparent To facilitate investigation of Energy-efficient cloud computing Services, underlying device technology, support technologies Customization (availability, performance, application behavior) Hybrid cloud solutions (public and on-premise) By emulating key cloud layers from the commercial sector Engender user community, access to real applications/users Leverage extant software technologies Not a replacement technology for any Public Cloud service

6 cloud computing from UCSB IaaS: Open-source implementation of all AWS APIs Robust, highly-available, scalable emulation Cluster/data center support over Xen, KVM, VMWare PaaS: Open-source implementation of Google App Engine APIs Pluggable (services), scalable, fault tolerant Runs over virtualization or IaaS layer: AWS, Eucalyptus

7 google app engine private, enterprise data SDC Google App Engine (GAE) MyApp.appspot.com GAE Application (Python, Java) Administrator Console Images Cron Memcache URL Fetch Services Tasks Blob store Users IM Mail Protobuf Data APIs Data Store

8 google app engine: the sdk Google App Engine (GAE) Images python2.5 dev_appserver.py port=8181 MyApp URL Fetch GAE Application (Python, Java) Blob Users Mail IM Tasks Cron Store Mem Cache Data store Open-source Google App Engine Software Development Kit (SDK)

9 google app engine: run/test locally Google App Engine (GAE) Images framework lib python2.5 dev_appserver.py port=8181 MyApp URL Fetch curl /wget GAE Application (Python, Java) Blob Users Mail IM Store Tasks no auth sendon mail console on console Cron on console Mem Cache Data store Open-source Google App Engine Software Development Kit (SDK) = simulation of actual API functionality using localhost (flat file, in-memory hash (Memcache))

10 google app engine: upload to google private, enterprise data SDC GAE app users via the Internet Google App Engine (GAE) Images URL Fetch appcfg.py update MyApp/ GAE Application (Python, Java) Blob Users Mail IM Tasks Cron Store Mem Cache Data store Administrator Console MyApp.appspot.com Free w/ quotas Pay for additional scale: CPU, BW, s, data BigTable Automatic scaling High availability

11 sandbox restrictions Google App Engine (GAE) MyApp.appspot.com GAE Application (Python, Java) Pure Python or Java, white list of library calls to framework No thread/subprocess spawning, system calls No writes to file system, reads only to static files uploaded w/app Storage using key-value, schema-free datastore (Bigtable-based) HTTP/S communication only, CGI to handle page requests Limit on number of datastore elements accessed per request Limit on response duration, task frequency, request rate Enforced quotas (BW, CPU, requests/s, files, app size, ) Other things to consider Your code and data on Google resources APIs customized for MVC applications Other application domains not supported

12 from gae to GAE SDK extensions Data store Mem Cache Tasks Pluggable using open-source distributed database technologies HBase, Hypertable, Cassandra, Voldemort, MongoDB, MemcacheDB, MySQL MemcacheD library (Python and Java) From console or as background thread (automatically) Interface to Hadoop (MapReduce) Multi-language support: Python, Java, Ruby, Perl, soon: X10 Cron Users Translator to Linux Cron job, similar to Tasks Pluggable: built-in cloud-wide authentication via Rails, support for Eucalyptus and EC2 credentials

13 Distributed system with four key components AppLoadBalancer (ALB) Database Master/Peer (DB M/P) AppServer (AS) Services GAE App Developer (AppScale Admin) GAE GAE App GAE App Users Users ALB AS Database Slave/Peer (DB S/P) Automatic deployment, database replication, node & front-end scaling Over Eucalyptus, EC2, and virtualization (Xen, KVM) System-wide performance/availability monitoring, user/admin console AppScale Cloud Tasks DB M/P (e.g. Map Reduce) DB S/P App Controller HTTPS AppScale tools

14 Implements every AppScale component Can instantiate as a particular role (ALB, AS, DB) Can change functionality and instantiate itself as another AppScale tools deploy/control cloud GAE App Developer (AppScale Admin) GAE GAE App GAE App Users Users AppScale Cloud ALB AS DB M/P DB S/P Tasks (e.g. Map Reduce) App Controller HTTPS AppScale tools

15 performance 2 VCPUs 2.83GHz, 4GB RAM, 16GB disk Average Time to Query a Database of Size HBase (1 accessor) MongoDB (1 accessor) MemcacheDB (1 accessor) Google (1 accessor) HBase (3 accessors) MongoDB (3 accessors) MemcacheDB (3 accessors) Google (3 accessors) 5 Query Time [s] Number of Nodes

16 projects: Open-source community management Bug fixes, feature additions, releases, user support Research (currently only internally available) Automatic scaling of load, demand, other metrics Scheduling and load balancing of apps, tasks, components Hybrid cloud solutions (public/private, multi-zone) Tunable fault-tolerance and availability Efficient communication across isolation boundaries Alternative application domains (streaming, HPC) Distributed profiling/sampling, feedback-driven optimization Paas/IaaS integration and co-operation Customized, dynamic/adaptive SLAs Platform-aware resource scheduling, isolation, provisioning

17 projects: Open-source community management Bug fixes, feature additions, releases, user support Research (currently only internally available) Automatic scaling of load, demand, other metrics Scheduling and load balancing of apps, tasks, components Hybrid cloud solutions (public/private, multi-zone) Tunable fault-tolerance and availability Efficient communication across isolation boundaries Alternative application domains (streaming, HPC) Distributed profiling/sampling, feedback-driven optimization Paas/IaaS integration and co-operation Customized, dynamic/adaptive SLAs Platform-aware resource scheduling, isolation, provisioning

18 Thanks! Leads: Chris Bunch, Navraj Chohan Development and research team: Jovan Chohan, Nupur Garg, Matt Hubert, Jonathan Kupferman, Puneet Lakhina, Yiming Li, Nagy Mostafa, Yoshihide Nomura (Fujitsu), Michal Weigel Support NSF, Google, IBM Research

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