Placement (Scheduling)
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Optimal mapping of VMs
–
–
to physical hosts in a data center (cloud)
across multiple clouds
•
•
•
•
When?
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•
Federation and bursting
Multi-cloud service deployment
Third-party broker scenarios
Admission of new service, upon elasticity,
hardware failure, periodically
Optimal?
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Service Provider perspective
•
–
Performance (hosts, VMs), cost, guarantees, nonfunctional criteria (location, isolation, trust, risk, ecoefficiency, etc.)
Infrastructure Provider perspective
•
Provisioning cost, consolidation, isolation, SLA
violations, etc.
1
Placement (cont.)
•
Further considerations
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–
–
–
–
–
–
–
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Historical performance data
Benchmarking and application profiling
Co-location and (anti)affinity
End-user location
Data constraints (legislations)
Federation (lack of control over remote
resources)
Dynamicity - providers, prices, performance,
workloads, etc. change over time
(Live) Migration overhead
(end-user) SLAs – perspectives
1.
2.
3.
All management actions are SLA-driven
Placement = SLA refinement
SLAs are just another criteria
2
Sense
Example Approach
•
Act
Plan
Combinatorial optimization formulations
Packing formulations for data centers (MMKP)
–
•
•
•
0-1 integer programming (assignment
problems) for multi and federated clouds
–
•
•
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Multi-dimensional (CPU, memory, disk, network),
multi-choice (many physical hosts) Knapsack Problems
Policies for load balancing, power saving
(consolidation), SLA protection
Scalability improvements (fractional 2-approximation)
Optimize service performance and/or cost, with service
layout (load balancing), budget, VM configuration, etc.
as constraints.
Model uncertainty (changing conditions in providers,
offers, performance, etc.) and migration overhead
Approximations (greedy heuristics) for
scalability
Placement - Experiences
•
Reservoir (and IBM SUR grant)
–
•
OPTIMIS
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•
Bursting and Federated/Multi-cloud deployment
based on functional and non-functional criteria
(trust, risk, eco-efficiency, cost)
Vision Cloud
–
•
Placement optimization within clouds and across
federated clouds. SLA protection and/or load
balancing, consolidation, revenue maximization
Placement of compute close to data
Various Grid research projects
–
QoS, SLA management, advance reservations,
co-allocation, fair-share scheduling, job
management, performance predictions, etc
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Outlook and perspectives
• Placement of services (that use compute,
data, and networking)
– Compute, data, and/or network intense
– Network aware vs. managed networks
• Holistic view of placement problems for all
cloud architectures
• Interactions with related problems
– Time perspective (short - long)
• Placement and admission control
– Abstraction level (low - high)
• Placement and governance
Selected references
•
D. Breitgand, A. Marashini, and J. Tordsson. Policy-Driven
Service Placement Optimization in Federated Clouds, IBM
Haifa Labs technical report H-0299, 2011
•
J. Tordsson, R.S. Montero, R.M. Vozmediano, and I.M.
Llorente. Cloud brokering mechanisms for optimized
placement of virtual machines across multiple providers.
Future Generation Computer Systems, 2011, accepted.
•
B. Rochwerger, J. Tordsson, C. Ragusa, D. Breitgand, S.
Clayman, A. Epstein, D. Hadas, E. Levy, I. Loy, A.
Maraschini, P. Massonet, H. Munoz, K. Nagin, G. Toffetti,
and M. Villari. Reservoir - when one cloud is not enough,
IEEE Computer 2011, accepted.
•
W. Li, J. Tordsson, and E. Elmroth. Modelling for Dynamic
Cloud Scheduling via Migration of Virtual Machines
(tentative), in preparation, 2011
•
P-O Östberg, Virtual Infrastructures for Computational
Science, PhD thesis, 2011
•
J. Tordsson. Portable Tools for Interoperable Grids, PhD
thesis, 2009
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