A game theoretic formulation of the service provisioning problem in cloud systems
Proceedings of the 20th international conference on World wide web
Energy-aware capacity scaling in virtualized environments with performance guarantees
Performance Evaluation
Self-management of applications QoS for energy optimization in datacenters
Green Computing Middleware on Proceedings of the 2nd International Workshop
An adaptive model-free resource and power management approach for multi-tier cloud environments
Journal of Systems and Software
Dual time-scale distributed capacity allocation and load redirect algorithms for cloud systems
Journal of Parallel and Distributed Computing
SLA-based Optimization of Power and Migration Cost in Cloud Computing
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
Self-management of cloud applications and infrastructure for energy optimization
ACM SIGOPS Operating Systems Review
Automatic virtual machine clustering based on bhattacharyya distance for multi-cloud systems
Proceedings of the 2013 international workshop on Multi-cloud applications and federated clouds
ACRA: a unified admission control and resource allocation framework for virtualized environments
Proceedings of the 8th International Conference on Network and Service Management
Efficient optimization of software performance models via parameter-space pruning
Proceedings of the 5th ACM/SPEC international conference on Performance engineering
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With the increase of energy consumption associated with IT infrastructures, energy management is becoming a priority in the design and operation of complex service-based systems. At the same time, service providers need to comply with Service Level Agreement (SLA) contracts which determine the revenues and penalties on the basis of the achieved performance level. This paper focuses on the resource allocation problem in multitier virtualized systems with the goal of maximizing the SLAs revenue while minimizing energy costs. The main novelty of our approach is to address—in a unifying framework—service centers resource management by exploiting as actuation mechanisms allocation of virtual machines (VMs) to servers, load balancing, capacity allocation, server power state tuning, and dynamic voltage/frequency scaling. Resource management is modeled as an NP-hard mixed integer nonlinear programming problem, and solved by a local search procedure. To validate its effectiveness, the proposed model is compared to top-performing state-of-the-art techniques. The evaluation is based on simulation and on real experiments performed in a prototype environment. Synthetic as well as realistic workloads and a number of different scenarios of interest are considered. Results show that we are able to yield significant revenue gains for the provider when compared to alternative methods (up to 45 percent). Moreover, solutions are robust to service time and workload variations.