QoS Resource Management
for Cloud Federations
Gaetano F. Anastasi
National Council of Research (CNR), Pisa, Italy
Pisa, June 16th, 2014
gaetano.anastasi@isti.cnr.it
QoS Management for Cloud Federations
1/13
1 QoS Resource Management
2 Conclusion and Future work
gaetano.anastasi@isti.cnr.it
QoS Management for Cloud Federations
2/13
Contrail Resource Mgmt.
• Contrail Federation sits in the middle
- serving the users
- exploit efficiently the providers
• Must operate a trade-off between two
apparent discording objectives
- optimize for users (e.g., minimize cost)
- optimize for providers (e.g., ensuring fairness
in providers’ revenues)
• Federation is an autonomous entity
- directly corresponds for providers’ violations
gaetano.anastasi@isti.cnr.it
QoS Management for Cloud Federations
3/13
Allocation Problem (1/5)
Common Cloud Approach
• bin-packing
• performance corrective actions
(e.g. VM migration host-basis)
Federation Issue
• migration across providers is very
expensive
- increasing down-time (provider negotiation,
image transfer time, etc.)
• smart allocation to avoid migration
gaetano.anastasi@isti.cnr.it
QoS Management for Cloud Federations
4/13
Allocation Problem (2/5)
Problem Formulation
• Allocating N services on M providers
• Respecting QoS constraints from user
(e.g. dedicated bandwidth, bounded
response time, provider location, etc.)
• Input: app reqs and provider characteristics
• Objectives
- Minimizing user criteria (e.g. cost)
- Maximize federation revenue (minimizing risk
of paying penalties)
gaetano.anastasi@isti.cnr.it
QoS Management for Cloud Federations
5/13
Allocation Problem (3/5)
Broker case
• Sub-problem of the
federation allocation
• Optimization on cost
• Loosely-coupled architecture
(cannot provide additional
guarantees)
• Can mitigate vendor lock-in
gaetano.anastasi@isti.cnr.it
QoS Management for Cloud Federations
6/13
Allocation Problem (4/5)
QBrokage
• Genetic approach
Table: Lock-in Degree
• QoS = cost, ram,
storage, location
• Support multiple
provider cost models
• Flexible to plug-in
• Scalable (250 ms
increment on m-time
from 50 to 500 P)
gaetano.anastasi@isti.cnr.it
QBrokage
Naive
P
Cost (P)
Cost (P)
50
100
150
200
0.082 (1.5)
0.078 (2.35)
0.082 (3.20)
0.075 (3.50)
0.082 (1.0)
0.077 (1.0)
0.080 (1.0)
0.073 (1.0)
QoS Management for Cloud Federations
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Allocation Problem (5/5)
Addressing Federation Issues
• Resource cost model that consider
providers’ reputation
- federation directly corresponds penalties
- acquiring resources from bad providers may
cost more at the end
• Monitoring applications and SLA violations
- updating reputation and costs per provider
• Dynamic provider reputation
- transient overloads must be considered
- avoiding fluctuations
gaetano.anastasi@isti.cnr.it
QoS Management for Cloud Federations
8/13
1 QoS Resource Management
2 Conclusion and Future work
gaetano.anastasi@isti.cnr.it
QoS Management for Cloud Federations
9/13
PaaS Federation (1/2)
Today
• libcloud drivers for the Contrail Federation
• PaaS layers can run transparently on top of
the federation
Tomorrow
• Towards a PaaS Federation
• Third-party layers can benefit from quantity
and quality of resources
• Reusing existing federated mechanisms
gaetano.anastasi@isti.cnr.it
QoS Management for Cloud Federations
10/13
PaaS Federation (2/2)
Translating PaaS requirements
• To address heterogeneity of providers
• Enforce high-level QoS guarantees
Enforcement strategies
• VM Elasticity
- Increasing/decreasing VM number
- Less precise but widely supported
• Resource scaling
- Increasing virtual resource quantity
- Require underlying support
gaetano.anastasi@isti.cnr.it
QoS Management for Cloud Federations
11/13
Conclusion
• Inter-Cloud Computing brings benefits in
terms of interoperability and dependability
• ...but the complexity of management is
increased and poses many challenges
• Cloud Federations cannot scale if
relying on ad-hoc methods
• Leveraging other research fields
- Cloud Control
- Real-time techniques
- Cognitive heuristics
gaetano.anastasi@isti.cnr.it
QoS Management for Cloud Federations
12/13
Contribution
Gaetano F. Anastasi, Emanuele Carlini and Patrizio Dazzi
Smart cloud federation simulations with CloudSim
In Proceedings of the first ACM workshop on Optimization techniques for resources
management in clouds, ORMaCloud ’13 , June 2013
https://github.com/ecarlini/smartfed/
Gaetano F. Anastasi, Emanuele Carlini, Massimo Coppola, Patrizio Dazzi, Aliaksandr Lazouski,
Fabio Martinelli, Gaetano Mancini and Paolo Mori
Usage Control in Cloud Federations
In Proceedings of the IEEE International Conference on Cloud Engineering, IC2E 2014 , March
2014
Gaetano F. Anastasi, Emanuele Carlini, Massimo Coppola and Patrizio Dazzi
QBROKAGE: A Genetic Approach for QoS Cloud Brokering
In Proceedings of the 7th IEEE International Conference on Cloud, CLOUD 2014 (To appear),
June 2014.
´ Patrizio Dazzi, Alberto Gotta, Matteo Mordacchini, Andrea
Gaetano F. Anastasi, Pietro Cassara,
Passarella
A Hybrid Cross-Entropy Cognitive-based Algorithm for Resource Allocation in Cloud
Environments
In Proceedings of the 8th IEEE International Conference on Self-Adaptive and Self-Organizing
Systems, SASO 2014 (Under revision), June 2014.
Gaetano F. Anastasi, Emanuele Carlini, Massimo Coppola, Patrizio Dazzi and Marco Distefano
An OVF Toolkit Supporting Inter-Cloud Application Splitting
In Proceedings of the IEEE International Conference on Cloud Networking, Cloudnet 2014
(Under revision), June 2014.
gaetano.anastasi@isti.cnr.it
QoS Management for Cloud Federations
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