Approximation algorithms
Managing energy and server resources in hosting centers
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Introduction to Algorithms
Dynamic power management using machine learning
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
Queue - Virtualization
A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation
ICAC '06 Proceedings of the 2006 IEEE International Conference on Autonomic Computing
GreenCloud: a new architecture for green data center
ICAC-INDST '09 Proceedings of the 6th international conference industry session on Autonomic computing and communications industry session
NCA '09 Proceedings of the 2009 Eighth IEEE International Symposium on Network Computing and Applications
VPM tokens: virtual machine-aware power budgeting in datacenters
Cluster Computing
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Adaptive power management using reinforcement learning
Proceedings of the 2009 International Conference on Computer-Aided Design
Optimal sleep patterns for serving delay-tolerant jobs
Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking
Towards energy-aware scheduling in data centers using machine learning
Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking
SLA-driven Elastic Cloud Hosting Provider
PDP '10 Proceedings of the 2010 18th Euromicro Conference on Parallel, Distributed and Network-based Processing
Characterizing Cloud Federation for Enhancing Providers' Profit
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
Energy-Aware Scheduling in Virtualized Datacenters
CLUSTER '10 Proceedings of the 2010 IEEE International Conference on Cluster Computing
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Energy-related costs have become one of the major economic factors in IT data-centers, and companies and the research community are currently working on new efficient power-aware resource management strategies, also known as "Green IT". Here we propose a framework for autonomic scheduling of tasks and web-services on cloud environments, optimizing the profit taking into account revenue for task execution minus penalties for service-level agreement violations, minus power consumption cost. The principal contribution is the combination of consolidation and virtualization technologies, mathematical optimization methods, and machine learning techniques. The data-center infrastructure, tasks to execute, and desired profit are casted as a mathematical programming model, which can then be solved in different ways to find good task scheduling. We use an exact solver based on mixed linear programming as a proof of concept but, since it is an NP-complete problem, we show that approximate solvers provide valid alternatives for finding approximately optimal schedules. The machine learning is used to estimate the initially unknown parameters of the mathematical model. In particular, we need to predict a priori resource usage (such as CPU consumption) by different tasks under current workloads, and estimate task service-level-agreement (such as response time) given workload features, host characteristics, and contention among tasks in the same host. Experiments show that machine learning algorithms can predict system behavior with acceptable accuracy, and that their combination with the exact or approximate schedulers manages to allocate tasks to hosts striking a balance between revenue for executed tasks, quality of service, and power consumption.