Status, trends and challenges
in cloud and multi-cloud
service provisioning
Erik Elmroth
Department of Computing Science & HPC2N
Umeå University
From where I view the clouds –
Three main projects (FP7 IPs)
Introduced federated clouds.
Among EUs first major cloud project.
Optimized cloud services over the complete
lifecycle. Non-functional aspects.
Pioneering federated storage clouds. Raised
abstraction level. Media- & telecom apps.
Critical performance requirements
- to be cost-efficiently met
Extremely rapid growth (from global scale)
– YouTube (16 months) 100 mil/movies per day, 20
mil. unique users per month
– AppStore (19 months): Over 100000 Iphone
programs, over 3 billion downloads
Regular/planned peaks
– Banks, tax filing
– Market campaign effects
Unexpected peaks
– News related video streaming
– Stock trading peaks at financial crises
Regional aspects in usage patters
– Regional concerns (news, events, etc)
– Time-dependent usage-patterns
Critical performance requirements
- to be cost-efficiently met
Extremely rapid growth (from global scale)
– YouTube (16 months) 100 mil/movies per day, 20
mil. unique users per month
– AppStore (19 months): Over 100000 Iphone
programs, over 3 billion downloads
Regular/planned peaks
– Banks, tax filing
– Market campaign effects
Unexpected peaks
– New related video streaming
– Stock trading peaks at financial crises
Regional aspects in usage patters
– Regional concerns (news, events, etc)
– Time-dependent usage-patterns
E ri k E lmro th e lmro th @cs .u mu .s e E ri k E lmro th e lmro th @cs .u mu .s e
New data center challenges
Traditionally: managed peak loads by hosting ”too
much” hardware
Requirements for an elastic
data center infrastructure
Today’s Clouds provide partial solutions
Google @ The Dulles, ORNIST definition of cloud computing
3 service models • Software-as-a-Service • Plattform-as-a-Service • Infrastructure-as-a-Service 5 characteristics • On-demand self-service• Broad network access
• Resource pooling • Rapid elasticity • Measured service 4 deployment models • Public • Private • Community • Hybrid
National Institute of
Standards and Technology
How?
Virtual machines
-Abstracts hardware Grid technology
-Distributed virtual resource
Business Service Management
- Dynamic SLA management Autonomic systems
Next Generation Clouds
What
?
• Large-scale IT capacity
• Compute + storage + network
• Automatically increase, decrease & migrate
E ri k E lmro th e lmro th @cs .u mu .s e E ri k E lmro th e lmro th @cs .u mu .s e Multi-clouds Federated Clouds Infrastructure Provider Bursted Private Clouds
T
HREE
B
ASIC
S
CENARIOS
Infrastructure Provider Infrastructure Provider Infrastructure Provider Infrastructure Provider Service Provider Broker Infrastructure Provider Service Provider Service Provider Infrastructure Provider Private infra-structureSome trends
Service types
• Infrastructure platform & software Infrastructure types
• Compute or storage unified capacity management Abstraction level
• Focus on data, not storage
• Focus on work done, not computers Non-functional aspects
• Service qualities. Not what can be done but how good it can be done Location
• For performance, legal, trust or economical reasons Systems scale
• Autonomous systems Information scale
• Data & metadata. User, services and systems data Security & Trust
• The most common areas for questions and concerns
E ri k E lmro th e lmro th @cs .u mu .s e
E ri k E lmro th e lmro th @cs .u mu .s e
Senior researchers Project admin.
Erik Elmroth, Professor Francisco Hernandez, Assistant Professor Johan Tordsson, Assistant Professor Lei Xu,
Post Doc P-O Östberg Post Doc Lennart Edblom,
Senior lecturer PhD students Ahmed Ali-Eldin Daniel Henriksson Ewnetu Bayuh Lakew Wubin Li Mina Sedaghat Petter Svärd
Systems developers/systems experts
Tomas Forsman, Systems expert Peter Gardfjäll, Systems Developer Sebastian Gröhn, Research assistant Anders Häggström, Research assistant Lars Larsson, Systems Developer
cloudresearch.se
• So far, lots of focus on compute clouds
– Data is everywhere and growing
– Data is key to cloud management
• Unified management view for compute, storage and
network
• Larger systems
• More data
• Locality awareness
• More focus on self-managed systems
• Split-deployment
– dependencies
– interoperability
• Managing information
• Information for management
• Managing capacity (resources)
• Managing services
• Virtualizing resources & abstracting storage
E ri k E lmro th e lmro th @cs .u mu .s e E ri k E lmro th e lmro th @cs .u mu .s e
• Nåt om “from …. amazon-type… to
large-scale distributed …. data + storage+
E ri k E lmro th e lmro th @cs .u mu .s e E ri k E lmro th e lmro th @cs .u mu .s e
• Nån typ av allmän intro till vad cloud är
– Kanske med lite om roller, virtualisering, data
centers
• Management with respect to non-functional
apsects/requirements
– Få med alla möjligt OPTIMIS-aspekter
– TREC
• Managing capacity (resources)
• Managing services
• Virtualizing resources & abstracting storage
E ri k E lmro th e lmro th @cs .u mu .s e E ri k E lmro th e lmro th @cs .u mu .s e
Cloud Resource
M
anagement
What?
• Compute + storage + network
• Low and high level management
For whom?
• Service providers
• Infrastructure providers
How?
• Single abstraction – multiple use (scenarios)
• General tools for key functionality
Example (low level management):
Elasticity- & access control
Elasticity control
• Control the system’s handling peaks & lows
• Inceasing ability to meet SLAs • Reduces resource consumption
Access control
• Overbooking of elastic services• Access control quality directly determines income and SLA
E ri k E lmro th e lmro th @cs .u mu .s e
Holistic cloud management
Algorithms
Policies Algorithms Policies Algorithms Policies Algorithms Policies Algorithms Policies
Business Level Objectives Management constraints
Algorithms Policies
Live migration
“Transfer a VM from one host to another
without disrupting services.”
Improves:
•
Resource utilization
•
Reliability
•
Flexibility
•
Enables cloud
management!
UMU:s research: better algorithms
for live migration
• Caching, compression
