Context: In recent years, Cloud computing has gained a wide range of attention in both industry and academics as Cloud services offer pay-per-use model, due to increase in need of factors like reliability and computing results with immense growth in Cloud-based companies along with a continuous expansion of their scale. However, the rise in Cloud computing users can cause a negative impact on energy consumption in the Cloud data centers as they consume huge amount of overall energy. In order to minimize the energy consumption in virtual datacenters, researchers proposed various energy efficient resources management strategies. Virtual Machine dynamic Consolidation is one of the prominent technique and an active research area in recent time, used to improve resource utilization and minimize the electric power consumption of a data center. This technique monitors the data centers utilization, identify overloaded, and underloaded hosts then migrate few/all Virtual Machines (VMs) to other suitable hosts using Virtual Machine selection and Virtual Machine placement, and switch underloaded hosts to sleep mode.   Objectives: Objective of this study is to define and implement new energy-aware heuristic algorithms to save energy consumption in Cloud data centers and show the best-resulted algorithm then compare performances of proposed heuristic algorithms with old heuristics.   Methods: Initially, a literature review is conducted to identify and obtain knowledge about the adaptive heuristic algorithms proposed previously for energy-aware VM Consolidation, and find the metrics to measure the performance of heuristic algorithms. Based on this knowledge, for our thesis we have proposed 32 combinations of novel adaptive heuristics for host overload detection (8) and VM selection algorithms (4), one host underload detection and two adaptive heuristic for VM placement algorithms which helps in minimizing both energy consumption and reducing overall Service Level Agreement (SLA) violation of Cloud data center. Further, an experiment is conducted to measure the performances of all proposed heuristic algorithms. We have used the CloudSim simulation toolkit for the modeling, simulation, and implementation of proposed heuristics. We have evaluated the proposed algorithms using PlanetLab VMs real workload traces.   Results: The results were measured using metrics energy consumption of data center (power model), Performance Degradation due to Migration (PDM), Service Level Agreement violation Time per Active Host (SLATAH), Service Level Agreement Violation (SLAV = PDM . SLATAH) and, Energy consumption and Service level agreement Violation (ESV).  Here for all four categories of VM Consolidation, we have compared the performances of proposed heuristics with each other and presented the best heuristic algorithm proposed in each category. We have also compared the performances of proposed heuristic algorithms with existing heuristics which are identified in the literature and presented the number of newly proposed algorithms work efficiently than existing algorithms. This comparative analysis is done using T-test and Cohen's d effect size.   From the comparison results of all proposed algorithms, we have concluded that Mean absolute Deviation around median (MADmedain) host overload detection algorithm equipped with Maximum requested RAM VM selection (MaxR) using Modified First Fit Decreasing VM placement (MFFD), and Standard Deviation (STD) host overload detection algorithm equipped with Maximum requested RAM VM selection (MaxR) using Modified Last Fit decreasing VM placement (MLFD) respectively performed better than other 31 combinations of proposed overload detection and VM selection heuristic algorithms, with regards to Energy consumption and Service level agreement Violation (ESV). However, from the comparative study between existing and proposed algorithms, 23 and 21 combinations of proposed host overload detection and VM selection algorithms using MFFD and MLFD VM placements respectively performed efficiently compared to existing (baseline) heuristic algorithms considered for this study.   Conclusions: This thesis presents novel proposed heuristic algorithms that are useful for minimization of both energy consumption and Service Level Agreement Violation in virtual datacenters. It presents new 23 combinations of proposed host overloading detection and VM selection algorithms using MFFD VM placement and 21 combinations of proposed host overloading detection and VM selection algorithms using MLFD VM placement, which consumes the minimum amount of energy with minimal SLA violation compared to the existing algorithms. It gives scope for future researchers related to improving resource utilization and minimizing the electric power consumption of a data center. This study can be extended in further by implementing the work on other Cloud software platforms and developing much more efficient algorithms for all four categories of VM consolidation.

Submitted by Erika Demachki (erikademachki@gmail.com) on 2015-02-05T19:59:39Z No. of bitstreams: 2 Dissertação - Pedro Henrique Pires de Castro - 2014.pdf: 1908182 bytes, checksum: edac87bddd8346a2bcce5d9b5f00301d (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Erika Demachki (erikademachki@gmail.com) on 2015-02-05T20:00:48Z (GMT) No. of bitstreams: 2 Dissertação - Pedro Henrique Pires de Castro - 2014.pdf: 1908182 bytes, checksum: edac87bddd8346a2bcce5d9b5f00301d (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2015-02-05T20:00:48Z (GMT). No. of bitstreams: 2 Dissertação - Pedro Henrique Pires de Castro - 2014.pdf: 1908182 bytes, checksum: edac87bddd8346a2bcce5d9b5f00301d (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2014-08-04 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Cloud computing is being consolidated as a new distributed systems paradigm, offering computing resources in a virtualized way and with unprecedented levels of flexibility, reliability, and scalability. Unfortunately, the benefits of cloud computing come at a high cost with regard to energy, mainly because of one of its core enablers, the data center. There are a number of proposals that seek to enhance energy efficiency in data centers. However, most of them focus only on the energy consumed by CPU and ignore the remaining hardware, e.g., RAM. In this work, we show the considerable impact that RAM can have on total energy consumption, particularly in servers with large amounts of this memory. We also propose three new approaches for dynamic consolidation of virtual machines (VMs) that take into account both CPU and RAM usage. We have implemented and evaluated our proposals in the CloudSim simulator using real-world traces and compared the results with state-of-the-art solutions. By adopting a wider view of the system, our proposals are able to reduce not only energy consumption but also the number of SLA violations, i.e., they provide a better service at a lower cost. / A computação em nuvem tem levado os sistemas distribuídos a um novo patamar, oferecendo recursos computacionais de forma virtualizada, flexível, robusta e escalar. Essas vantagens, no entanto, surgem juntamente com um alto consumo de energia nos centros de dados, ambientes que podem ter até centenas de milhares de servidores. Existem muitas propostas para alcançar eficiência energética em centros de dados para computação em nuvem. Entretanto, muitas propostas consideram apenas o consumo proveniente do uso de CPU e ignoram os demais componentes de hardware, e.g., RAM. Neste trabalho, mostramos o impacto considerável que RAM pode ter sobre o consumo total de energia, principalmente em servidores com grandes quantidades dessa memória. Também propomos três novas abordagens para consolidação dinâmica de máquinas virtuais, levando em conta tanto o consumo de CPU quanto de RAM. Nossas propostas foram implementadas e avaliadas no simulador CloudSim utilizando cargas de trabalho do mundo real. Os resultados foram comparados com soluções do estado-da-arte. Pela adoção de uma visão mais ampla do sistema, nossas propostas não apenas são capazes de reduzir o consumo de energia como também reduzem violações de SLA, i.e., proveem um serviço melhor a um custo mais baixo.