11-05-2012, 03:07 PM
Cloud Technologies and Parallel Programming Frameworks for Scientific Applications
thilina_oral_qual_v3_small(1).pptx (Size: 1.76 MB / Downloads: 69)
Trends
Massive data
Thousands to millions of cores
Consolidated data centers
Shift from clock rate battle to multicore to many core…
Cheap hardware
Failures are the norm
VM based systems
Making accessible (Easy to use)
More people requiring large scale data processing
Shift from academia to industry..
Moving towards..
Computing Clouds
Cloud Infrastructure Services
Cloud infrastructure software
Distributed File Systems
HDFS, etc..
Distributed Key-Value stores
Data intensive parallel application frameworks
MapReduce
High level languages
Science in the clouds
Virtualization
Goals
Server consolidation
Co-located hosting & on demand provisioning
Secure platforms (eg: sandboxing)
Application mobility & server migration
Multiple execution environments
Saved images and Appliances, etc
Different virtualization techniques
User mode Linux
Pure virtualization (eg:Vmware)
Hard till processor came up with virtualization extensions (hardware assisted virtualization)
Para virtualization (eg: Xen)
Modified guest OS’s
Programming language virtual machines
Cloud Computing
On demand computational services over web
Spiky compute needs of the scientists
Horizontal scaling with no additional cost
Increased throughput
Public Clouds
Amazon Web Services, Windows Azure, Google AppEngine, …
Private Cloud Infrastructure Software
Eucalyptus, Nimbus, OpenNebula