Energy and Performance: Management of Virtual Machines: Provisioning, Placement, and Consolidation

Cloud computing is a new computing paradigm that offers scalable storage and compute resources to users on demand through Internet. Public cloud providers operate large-scale data centers around the world to handle a large number of users request. However, data centers consume an immense amount of electrical energy that can lead to high operating costs and carbon emissions. One of the most common and effective method in order to reduce energy consumption is Dynamic Virtual Machines Consolidation (DVMC) enabled by the virtualization technology. DVMC dynamically consolidates Virtual Machines (VMs) into the minimum number of active servers and then switches the idle servers into a power-saving mode to save energy. However, maintaining the desired level of Quality-of-Service (QoS) between data centers and their users is critical for satisfying users’ expectations concerning performance. Therefore, the main challenge is to minimize the data center energy consumption while maintaining the required QoS. This thesis address this challenge by presenting novel DVMC approaches to reduce the energy consumption of data centers and improve resource utilization under workload independent quality of service constraints. These approaches can be divided into three main categories: heuristic, meta-heuristic and machine learning. Our first contribution is a heuristic algorithm for solving the DVMC problem. The algorithm uses a linear regression-based prediction model to detect over-loaded servers based on the historical utilization data. Then it migrates some VMs from the over-loaded servers to avoid further performance degradations. Moreover, our algorithm consolidates VMs on fewer number of server for energy saving. The second and third contributions are two novel DVMC algorithms based on the Reinforcement Learning (RL) approach. RL is interesting for highly adaptive and autonomous management in dynamic environments. For this reason, we use RL to solve two main sub-problems in VM consolidation. The first sub-problem is the server power mode detection (sleep or active). The second sub-problem is to find an effective solution for server status detection (overloaded or non-overloaded). The fourth contribution of this thesis is an online optimization meta-heuristic algorithm called Ant Colony System-based Placement Optimization (ACS-PO). ACS is a suitable approach for VM consolidation due to the ease of parallelization, that it is close to the optimal solution, and its polynomial worst-case time complexity. The simulation results show that ACS-PO provides substantial improvement over other heuristic algorithms in reducing energy consumption, the number of VM migrations, and performance degradations. Our fifth contribution is a Hierarchical VM management (HiVM) architecture based on a three-tier data center topology which is very common use in data centers. HiVM has the ability to scale across many thousands of servers with energy efficiency. Our sixth contribution is a Utilization Prediction-aware Best Fit Decreasing (UP-BFD) algorithm. UP-BFD can avoid SLA violations and needless migrations by taking into consideration the current and predicted future resource requirements for allocation, consolidation, and placement of VMs. Finally, the seventh and the last contribution is a novel Self-Adaptive Resource Management System (SARMS) in data centers. To achieve scalability, SARMS uses a hierarchical architecture that is partially inspired from HiVM. Moreover, SARMS provides self-adaptive ability for resource management by dynamically adjusting the utilization thresholds for each server in data centers.Siirretty Doriast

Energy and Performance Management of Virtual Machines: Provisioning, Placement and Consolidation

Cloud computing is a new computing paradigm that offers scalable storage and compute resources to users on demand through Internet. Public cloud providers operate large-scale data centers around the world to handle a large number of users request. However, data centers consume an immense amount of electrical energy that can lead to high operating costs and carbon emissions. One of the most common and effective method in order to reduce energy consumption is Dynamic Virtual Machines Consolidation (DVMC) enabled by the virtualization technology. DVMC dynamically consolidates Virtual Machines (VMs) into the minimum number of active servers and then switches the idle servers into a power-saving mode to save energy. Ho- wever, maintaining the desired level of Quality-of-Service (QoS) between data centers and their users is critical for satisfying users’ expectations con- cerning performance. Therefore, the main challenge is to minimize the data center energy consumption while maintaining the required QoS. This thesis address this challenge by presenting novel DVMC approaches to reduce the energy consumption of data centers and improve resource utili- zation under workload independent quality of service constraints. These ap- proaches can be divided into three main categories: heuristic, meta-heuristic and machine learning. Our first contribution is a heuristic algorithm for solving the DVMC problem. The algorithm uses a linear regression-based prediction model to detect over-loaded servers based on the historical utilization data. Then it migrates some VMs from the over-loaded servers to avoid further performan- ce degradations. Moreover, our algorithm consolidates VMs on fewer number of server for energy saving. The second and third contributions are two novel DVMC algorithms based on the Reinforcement Learning (RL) approach. RL is interesting for highly adaptive and autonomous management in dynamic environments. For this reason, we use RL to solve two main sub-problems in VM consolidation. The first sub-problem is the server power mode detection (sleep or active). The second sub-problem is to find an effective solution for server status detection (overloaded or non-overloaded). The fourth con- tribution of this thesis is an online optimization meta-heuristic algorithm called Ant Colony System-based Placement Optimization (ACS-PO). ACS is a suitable approach for VM consolidation due to the ease of parallelization, that it is close to the optimal solution, and its polynomial worst-case time complexity. The simulation results show that ACS-PO provides substantial improvement over other heuristic algorithms in reducing energy consump- tion, the number of VM migrations, and performance degradations. Our fifth contribution is a Hierarchical VM management (HiVM) archi- tecture based on a three-tier data center topology which is very common use in data centers. HiVM has the ability to scale across many thousands of ser- vers with energy efficiency. Our sixth contribution is a Utilization Prediction- aware Best Fit Decreasing (UP-BFD) algorithm. UP-BFD can avoid SLA violations and needless migrations by taking into consideration the current and predicted future resource requirements for allocation, consolidation, and placement of VMs. Finally, the seventh and the last contribution is a novel Self-Adaptive Resource Management System (SARMS) in data centers. To achieve scala- bility, SARMS uses a hierarchical architecture that is partially inspired from HiVM. Moreover, SARMS provides self-adaptive ability for resource mana- gement by dynamically adjusting the utilization thresholds for each server in data centers.  </div

Deep Learning Based Multi-Modal Fusion Architectures for Maritime Vessel Detection

Object detection is a fundamental computer vision task for many real-world applications. In the maritime environment, this task is challenging due to varying light, view distances, weather conditions, and sea waves. In addition, light reflection, camera motion and illumination changes may cause to false detections. To address this challenge, we present three fusion architectures to fuse two imaging modalities: visible and infrared. These architectures can provide complementary information from two modalities in different levels: pixel-level, feature-level, and decision-level. They employed deep learning for performing fusion and detection. We investigate the performance of the proposed architectures conducting a real marine image dataset, which is captured by color and infrared cameras on-board a vessel in the Finnish archipelago. The cameras are employed for developing autonomous ships, and collect data in a range of operation and climatic conditions. Experiments show that feature-level fusion architecture outperforms the state-of-the-art other fusion level architectures

Anomaly-based Intrusion Detection Using Deep Neural Networks

Identification of network attacks is a matter of great concern for network operators due to extensive the number of vulnerabilities in computer systems and creativity of the attackers. Anomaly-based Intrusion Detection Systems (IDSs) present a significant opportunity to identify possible incidents, logging information and reporting attempts. However, these systems generate a low detection accuracy rate with changing network environment or services. To overcome this problem, we present a deep neural network architecture based on a combination of a stacked denoising autoencoder and a softmax classifier. Our architecture can extract important features from data and learn a model for detecting abnormal behaviors. The model is trained locally to denoise corrupted versions of their inputs based on stacking layers of denoising autoencoders in order to achieve reliable intrusion detection. Experimental results on real KDD-CUP'99 dataset show that our architecture outperformed shallow learning architectures and other deep neural network architectures. </p

Deep Learning Based Multi-Modal Fusion Architectures for Maritime Vessel Detection

Object detection is a fundamental computer vision task for many real-world applications. In the maritime environment, this task is challenging due to varying light, view distances, weather conditions, and sea waves. In addition, light reflection, camera motion and illumination changes may cause to false detections. To address this challenge, we present three fusion architectures to fuse two imaging modalities: visible and infrared. These architectures can provide complementary information from two modalities in different levels: pixel-level, feature-level, and decision-level. They employed deep learning for performing fusion and detection. We investigate the performance of the proposed architectures conducting a real marine image dataset, which is captured by color and infrared cameras on-board a vessel in the Finnish archipelago. The cameras are employed for developing autonomous ships, and collect data in a range of operation and climatic conditions. Experiments show that feature-level fusion architecture outperforms the state-of-the-art other fusion level architectures

2016 IEEE 9th International Conference on Cloud Computing (CLOUD)

Resource management in cloud infrastructures is one of the most challenging problems due to the heterogeneity of resources, variability of the workload and scale of data centers. Efficient management of physical and virtual resources can be achieved considering performance requirements of hosted applications and infrastructure costs. In this paper, we present a self-adaptive resource management system based on a hierarchical multi-agent based architecture. The system uses novel adaptive utilization threshold mechanism and benefits from reinforcement learning technique to dynamically adjust CPU and memory thresholds for each Physical Machine (PM). It periodically runs a Virtual Machine (VM) placement optimization algorithm to keep the total resource utilization of each PM within given thresholds for improving Service Level Agreement (SLA) compliance. Moreover, the algorithm consolidates VMs into the minimum number of active PMs in order to reduce the energy consumption. Experimental results on real workload traces show that our recourse management system provides substantial improvement over other approaches in terms of performance requirements, energy consumption and the number of VM migrations.</p

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

Abstract— Designing accurate and automatic multi-target detection is a challenging problem for autonomous vehicles. To address this problem, we propose a late multi-modal fusion framework in this paper. The framework provides complimentary information from RGB and thermal infrared cameras in order to improve the detection performance. For this purpose, it first employs RetinaNet as a dense simple deep model for each input image separately to extract possible candidate proposals which likely contain the targets of interest. Then, all proposals are generated by concatenating the obtained proposals from two modalities. Finally, redundant proposals are removed by Non-Maximum Suppression (NMS). We evaluate the proposed framework on a real marine dataset which is collected by a sensor system onboard a vessel in the Finnish archipelago. This system is used for developing autonomous vessels, and records data in a range of operation and climatic conditions. The experimental results show that our late fusion framework can get more detection accuracy compared with middle fusion and uni-modal frameworks. </p