# Adaptive Scheduling on Power-Aware Managed Data-Centers using Machine Learning

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7 Parameters Method Power (Kw) Migs Profit AvgQoS MaxQoS MinQoS UsedCPU UsedHosts FirstFit Power Cost: 0.09 e/kwh λ-rr Job Revenue: 0.17 e Desc. BestFit MaxRT: 2 RT 0 ILP Solver FirstFit Power Cost: 0.45 e/kwh λ-rr Job Revenue: 0.17 e Desc. BestFit MaxRT: 2 RT 0 ILP Solver FirstFit Power Cost: 0.09 e/kwh λ-rr Job Revenue: 0.17 e Desc. BestFit MaxRT: 10 RT 0 ILP Solver Table II SCHEDULING COMPARATIVE BETWEEN TECHNIQUES Euro/Kwh Power (Kw) Migs Profit (euro) AvgQoS MaxQoS MinQoS TimeSpent Gap LB UsedCPU UsedHosts Parameters: Power Cost = variable, Job Revenue = 0.17e, MaxRT = 2 RT 0 Table III SCHEDULING ILP SOLVER WITH DIFFERENT ELECTRIC COSTS Figure 1. Power and SLA Comparative on the Schedulers more powerful machines or more of them and use content distribution mechanisms. D. Reducing migrations Once seen that the model is able to schedule following a set of policies depending on the values for each involved factor (service level agreement fulfillment or power cost) using the exhaustive or the approximate algorithms, a problem that could arise in a real system is the amount of migrations done. Some migration processes can be expensive in time or produce small downtimes in the migrated service, and so they should be avoided if possible. However, the model presented here does not care about the number of migrations, as the solution found may by chance turn the previous schedule upside down - even if a conservative solution exists with exactly the same profit! As a proof of concept, suppose that migrating a VM can last up to 5 minutes, and in this time the service may stop responding. The SLA fulfillment would then be 0 for this interval. A way to maximize the profit considering that each migration can imply zero revenue during X minutes is to add a penalty for each migration up to the revenue of the given job in the migration time. E.g. when a job with an income of 0.17 euros/hour is migrated (max 5 minutes), the service provider grants the customer a discount of euros for it, covering a priori the chance of service degradation during migration. With this idea, we modify the model as shown next. Note that the constraint is linear as one of the arguments of the xor is a parameter, not a variable. Maximize: P rofit = P rofit f penalty (Migrations(Job i )) Parameters: oldsched[hosts, Jobs], as the last schedule for current jobs Subject To: Migrations = 1 2 h,j oldsched(schedule[h, j] oldsched[h, j]) Table IV shows the results for the migration-aware versions of the exhaustive solver and BestFit. Comparing with Table I, it can be seen that migrations are drastically reduced, with only a small profit loss. Table V shows that applying the new policy, the sensitivity to job price and power costs still applies. V. CONCLUSIONS AND FUTURE WORK Nowadays optimizing the management of data-centers to make them efficient, not only in economic values but also in power consumption, requires the automation of several systems such as job scheduling and resource management. And automation needs knowledge about the system to act in an intelligent way. As shown here machine learning can provide this knowledge and intelligence, as well as adaptivity. Compared to previous works, we have shown here that it is possible to model jobs and system behaviors towards resources

8 Method Power (w) Migs Profit (euro) AvgQoS MaxQoS MinQos UsedCPU UsedHosts Descending BestFit ILP Solver Parameters: Power Cost = 0.09 euro/kwh, Job Revenue = 0.17 euro, MaxRT = 2 RT 0, Migration Penalty = euro Table IV SCHEDULING COMPARATIVE BETWEEN TECHNIQUES APPLYING MIGRATION PENALTIES Euro/Kwh Power (w) Migs Profit (euro) AvgQoS MaxQoS MinQoS TimeSpent Gap LB UsedCPU UsedHosts Parameters: Power Cost = variable, Job Revenue = 0.17 euro, MaxRT = 2 RT 0, Migration Penalty = euro Table V SCHEDULING ILP SOLVER WITH DIFFERENT ELECTRIC COSTS AND MIGRATION PENALTY and SLA metrics in an automatic manner through machine learning, and apply them to full-scale data-center models, letting schedulers more adjusted estimation functions a priori, to make better their decisions for each system and kind of job. Also, by focusing the data-center model towards factors to be optimized (economics, service quality to clients, and energy-related ones), a mathematical model can be built and solved by different methods. Allocation problems like the presented job host scheduling are NP-hard problems, so solving them with exact methods can be intractable given (not so) high sizes or problem dimensions. Experiments have shown that our model can find optimal solutions according to the introduced policies, but paying a high cost in computation time. For this reason, heuristics and approximate algorithms can be applied over the same model, obtaining, in the case of the Best Fit, solutions enough close to the optimal in extremely low time. Future work includes examining scenarios where the CPU and Memory are not the only critical resources, so the ML methods have to deal with a more complex space of attributes, identifying different situations. The effects of the memory behavior of web server platforms will be studied to model and predict that factors affecting the memory occupation. Complementing the learned models revealing new hidden factors will make the model easier to handle and solve. Finally, new heuristics and approx. algorithms will be studied to solve the ILP model without applying solvers with high cost in time. ACKNOWLEDGMENTS Thanks to Í.Goiri, F.Julià, R.Nou, J.O.Fitó and J.Guitart from UPC-BSC for lending us their testbed workbenches in order to evaluate our methods. This work has been supported by the Ministry of Science of Spain under contract TIN C REFERENCES [1] Europe s energy portal. [2] J. Berral, R. Gavaldà, and J. Torres. An Integer Linear Programming Representation for DataCenter Power-Aware Management, detallat.php?id=1096. [3] J. Berral, R. Gavaldà, and J. Torres. Li-BCN Workload 2010, jlberral/documents/libcn-2011.pdf. [4] J. Berral, Í. Goiri, R. Nou, F. Julià, J. Guitart, R. Gavalda, and J. 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