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

Power aware resource allocation and virtualization algorithms for 5G core networks

Most of the algorithms that solved the resource allocation problem, used to apply greedy algorithms to select the physical nodes and shortest paths to select the physical edges, without sufficient coordination between selecting the physical nodes and edges. This lack of coordination may degrade the overall acceptance ratios and network performance as whole, in addition, that may include non-necessary physical resources, which will consume more power and computational processing capacities, as well as cause more delays. Therefore, the main objective of this PhD thesis is to develop power aware resource allocation and virtualization algorithms for 5G core networks, which will be achieved through developing a virtualization resource allocation technique to perform virtual nodes and edges allocations in full coordination, and on the least physical resources. The algorithms will be general and solve the resource allocation problem for virtual network embedding and network function virtualization frameworks, while minimizing the total consumed power in the physical network, and consider end-to-end delay and migration as new optional features. This thesis suggested to solve the power aware resource allocation problem through brand new algorithms adopting a new technique called segmentation, which fully coordinates allocating the virtual nodes and edges together, and guarantees to use the very least physical resources to minimize the total power consumption, through consolidating the virtual machines into least number of nodes as much as possible. The proposed algorithms, solves virtual network embedding problem for off-line and on-line scenarios, and solves resource allocations for network function virtualization environment for off-line, on-line, and migration scenarios. The evaluations of the proposed off-line virtual network embedding algorithm, PaCoVNE, showed that it managed to save physical network power consumption by 57% in average, and the on-line algorithm, oPaCoVNE, managed to minimize the average power consumption in the physical network by 24% in average. Regarding allocation times of PaCoVNE and oPaCoVNE, they were in the ranges of 20-40 ms. For network function virtualization environment, the evaluations of the proposed offline NFV power aware algorithm, PaNFV, showed that on average it had lower total costs and lower migration cost by 32% and 65:5% respectively, compared to the state-of-art algorithms, while the on-line algorithm, oPaNFV, managed to allocate the Network Services in average times of 60 ms, and it had very negligible migrations. Nevertheless, this thesis suggests that future enhancements for the proposed algorithms need to be focused around modifying the proposed segmentation technique to solve the resource allocation problem for multiple paths, in addition to consider power aware network slicing, especially for mobile edge computing, and modify the algorithms for application aware resource allocations for very large scale networks. Moreover, future work can modify the segmentation technique and the proposed algorithms, by integrating machine learning techniques for smart traffic and optimal paths prediction, as well as applying machine learning for better energy efficiency, faster load balancing, much accurate resource allocations based on verity of quality of service metrics.La mayoría de los algoritmos que resolvieron el problema de asignación de recursos, se utilizaron para aplicar algoritmos codiciosos para seleccionar los nodos físicos y las rutas más cortas para seleccionar los bordes físicos, sin una coordinación suficiente entre la selección de los nodos físicos y los bordes. Esta falta de coordinación puede degradar los índices de aceptación generales y el rendimiento de la red en su totalidad, además, que puede incluir recursos físicos no necesarios, que consumirán más potencia y capacidades de procesamiento computacional, además de causar más retrasos. Por lo tanto, el objetivo principal de esta tesis doctoral es desarrollar algoritmos de virtualización y asignación de recursos para las redes centrales 5G, que se lograrán mediante el desarrollo de una técnica de asignación de recursos de virtualización para realizar nodos virtuales y asignaciones de bordes en total coordinación, y al menos recursos físicos. Los algoritmos serán generales y resolverán el problema de asignación de recursos para la integración de redes virtuales y los marcos de virtualización de funciones de red, al tiempo que minimizan la potencia total consumida en la red física y consideran el retraso y la migración de extremo a extremo como nuevas características opcionales. Esta tesis sugirió resolver el problema de la asignación de recursos conscientes de la potencia a través de nuevos algoritmos que adoptan una nueva técnica llamada segmentación, que coordina completamente la asignación de los nodos virtuales y los bordes, y garantiza el uso de los recursos físicos mínimos para minimizar el consumo total de energía, a través de consolidar las máquinas virtuales en el menor número de nodos tanto como sea posible. Los algoritmos propuestos solucionan el problema de integración de la red virtual para los escenarios sin conexión y en línea, y resuelve las asignaciones de recursos para el entorno de virtualización de la función de red para los escenarios sin conexión, en línea y de migración. Las evaluaciones del algoritmo de integración de red virtual sin conexión propuesto, PaCoVNE, mostraron que logró ahorrar el consumo de energía de la red física en un 57% en promedio, y el algoritmo en línea, oPaCoVNE, logró minimizar el consumo de energía promedio en la red física en un 24% en promedio. Con respecto a los tiempos de asignación de PaCoVNE y oPaCoVNE, estuvieron en los rangos de 20-40 ms. Para el entorno de virtualización de la función de red, las evaluaciones del algoritmo consciente de la potencia NFV sin conexión propuesto, PaNFV, mostraron que, en promedio, tenía menores costos totales y menores costos de migración en un 32% y 65: 5% respectivamente, en comparación con el estado de la técnica. Los algoritmos, mientras que el algoritmo en línea, oPaNFV, logró asignar los Servicios de Red en tiempos promedio de 60 ms, y tuvo migraciones muy insignificantes. Sin embargo, esta tesis sugiere que las futuras mejoras para los algoritmos propuestos deben centrarse en modificar la técnica de segmentación propuesta para resolver el problema de asignación de recursos para múltiples rutas, además de considerar el corte de la red que requiere energía, especialmente para la computación de borde móvil, y modificar el Algoritmos para asignaciones de recursos conscientes de la aplicación para redes de gran escala. Además, el trabajo futuro puede modificar la técnica de segmentación y los algoritmos propuestos, mediante la integración de técnicas de aprendizaje automático para el tráfico inteligente y la predicción de rutas óptimas, así como la aplicación del aprendizaje automático para una mejor eficiencia energética, un equilibrio de carga más rápido, asignaciones de recursos mucho más precisas basadas en la veracidad de Métricas de calidad de servicio

Load Balancing in Distributed Cloud Computing: A Reinforcement Learning Algorithms in Heterogeneous Environment

Balancing load in cloud based is an important aspect that plays a vital role in order to achieve sharing of load between different types of resources such as virtual machines that lay on servers, storage in the form of hard drives and servers. Reinforcement learning approaches can be adopted with cloud computing to achieve quality of service factors such as minimized cost and response time, increased throughput, fault tolerance and utilization of all available resources in the network, thus increasing system performance. Reinforcement Learning based approaches result in making effective resource utilization by selecting the best suitable processor for task execution with minimum makespan. Since in the earlier related work done on sharing of load, there are limited reinforcement learning based approaches. However this paper, focuses on the importance of RL based approaches for achieving balanced load in the area of distributed cloud computing. A Reinforcement Learning framework is proposed and implemented for execution of tasks in heterogeneous environments, particularly, Least Load Balancing (LLB) and Booster Reinforcement Controller (BRC) Load Balancing. With the help of reinforcement learning approaches an optimal result is achieved for load sharing and task allocation. In this RL based framework processor workload is taken as an input. In this paper, the results of proposed RL based approaches have been evaluated for cost and makespan and are compared with existing load balancing techniques for task execution and resource utilization.

An artificial intelligence-based collaboration approach in industrial IoT manufacturing : key concepts, architectural extensions and potential applications

The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented

Scalable and Distributed Resource Management Protocols for Cloud and Big Data Clusters

Cloud data centers require an operating system to manage resources and satisfy operational requirements and management objectives. The growth of popularity in cloud services causes the appearance of a new spectrum of services with sophisticated workload and resource management requirements. Also, data centers are growing by addition of various type of hardware to accommodate the ever-increasing requests of users. Nowadays a large percentage of cloud resources are executing data-intensive applications which need continuously changing workload fluctuations and specific resource management. To this end, cluster computing frameworks are shifting towards distributed resource management for better scalability and faster decision making. Such systems benefit from the parallelization of control and are resilient to failures. Throughout this thesis we investigate algorithms, protocols and techniques to address these challenges in large-scale data centers. We introduce a distributed resource management framework which consolidates virtual machine to as few servers as possible to reduce the energy consumption of data center and hence decrease the cost of cloud providers. This framework can characterize the workload of virtual machines and hence handle trade-off energy consumption and Service Level Agreement (SLA) of customers efficiently. The algorithm is highly scalable and requires low maintenance cost with dynamic workloads and it tries to minimize virtual machines migration costs. We also introduce a scalable and distributed probe-based scheduling algorithm for Big data analytics frameworks. This algorithm can efficiently address the problem job heterogeneity in workloads that has appeared after increasing the level of parallelism in jobs. The algorithm is massively scalable and can reduce significantly average job completion times in comparison with the-state of-the-art. Finally, we propose a probabilistic fault-tolerance technique as part of the scheduling algorithm

Non-determinism in the narrative structure of video games

PhD ThesisAt the present time, computer games represent a finite interactive system. Even in their more experimental forms, the number of possible interactions between player and NPCs (non-player characters) and among NPCs and the game world has a finite number and is led by a deterministic system in which events can therefore be predicted. This implies that the story itself, seen as the series of events that will unfold during gameplay, is a closed system that can be predicted a priori. This study looks beyond this limitation, and identifies the elements needed for the emergence of a non-finite, emergent narrative structure. Two major contributions are offered through this research. The first contribution comes in the form of a clear categorization of the narrative structures embracing all video game production since the inception of the medium. In order to look for ways to generate a non-deterministic narrative in games, it is necessary to first gain a clear understanding of the current narrative structures implemented and how their impact on users’ experiencing of the story. While many studies have observed the storytelling aspect, no attempt has been made to systematically distinguish among the different ways designers decide how stories are told in games. The second contribution is guided by the following research question: Is it possible to incorporate non-determinism into the narrative structure of computer games? The hypothesis offered is that non-determinism can be incorporated by means of nonlinear dynamical systems in general and Cellular Automata in particular