COST IC804 – IC805 Joint meeting, February 7-8 2013 Jorge G. Barbosa , Altino M. Sampaio, Hamid Harabnejad Universidade do Porto, Faculdade de Engenharia, LIACC Porto, Portugal, [email protected]Experiments on cost/power and failure aware scheduling for clouds and grids
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Jorge G. Barbosa , Altino M. Sampaio , Hamid Harabnejad
Experiments on cost/power and failure aware scheduling for clouds and grids. Jorge G. Barbosa , Altino M. Sampaio , Hamid Harabnejad Universidade do Porto, Faculdade de Engenharia, LIACC Porto, Portugal, [email protected] . Outline. - PowerPoint PPT Presentation
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COST IC804 – IC805 Joint meeting, February 7-8 2013
Jorge G. Barbosa, Altino M. Sampaio, Hamid Harabnejad
Universidade do Porto, Faculdade de Engenharia, LIACCPorto, Portugal, [email protected]
Experiments on cost/power and failure aware scheduling for clouds and grids
Characteristics Dependability of the infrastructure
Distributed systems continue to grow in scale and in complexity Failures become norms, which can lead to violation of the negotiated SLAs Mean Time Between Failures (MTBF) would be 1.25h on a petaflop system(1)
Energy consumption The main part of energy consumption is determined by the CPU Energy consumption dominates the operational costs
(1) S. Fu, "Failure-aware resource management for high-availability computing clusters with distributed virtual machines," Journal of Parallel and Distributed Computing, vol. 70, April 2010, pp. 384-393, doi: 10.1016/j.jpdc.2010.01.002.
(1) Optimistic Best-Fit (OBFIT) algorithm- Selects the PM with minimum weighted available capacity and reliability.
(2) Pessimistic Best-Fit (PBFIT) algorithm - Selects also unreliable PMs in order to increase the job completion rate. - Selects the unreliable PM p with capacity Cp such that Cavg + Cp results in the minimum required capacity
Cavg average capacity from reliable PMs.
Dynamic allocation of VMs, considering PMs’ reliability Based in a failure predictor tool with 76.5% of accuracy
Proposed architecture for reconfigurable distributed VM (1)
Approach Multi-objective scheduling algorithms are addressed in three ways:
1- Finding the pareto optimal solutions, and let the user select the best solution.
2- Combination of the two functions in a single objective function.
3- Bicriteria scheduling which the user specifies a limitation for one criterion (power or budget constraints), and the algorithm tries to optimize the other criterion under this constraint.
Users’ jobs A job is a set of independent tasks A task runs in a single VM, which CPU-intensive workload is known Number of tasks per job and tasks deadlines are defined by user
IntroductionTarget platform: - Utility Grids that are maintained and managed by a service provider. - Based on user requirements, the provider finds a scheduling that meets user constrains.
In utility Grids, other QoS attributes than execution time, like economical cost or deadline, may be considered. It is a multi-objective problem.
Multi-objective scheduling algorithms are addressed in three ways:1- Finding the pareto optimal solutions, and let the user select the best solution;2- Combination of the two functions in a single objective function;3- Bicriteria scheduling which the user specifies a limitation for one criterion (power or budget constraints), and the algorithm tries to optimize the othercriterion under this constraint.