Towards a novel biologically-inspired cloud elasticity framework

With the widespread use of the Internet, the popularity of web applications has significantly increased. Such applications are subject to unpredictable workload conditions that vary from time to time. For example, an e-commerce website may face higher workloads than normal during festivals or promotional schemes. Such applications are critical and performance related issues, or service disruption can result in financial losses. Cloud computing with its attractive feature of dynamic resource provisioning (elasticity) is a perfect match to host such applications. The rapid growth in the usage of cloud computing model, as well as the rise in complexity of the web applications poses new challenges regarding the effective monitoring and management of the underlying cloud computational resources. This thesis investigates the state-of-the-art elastic methods including the models and techniques for the dynamic management and provisioning of cloud resources from a service provider perspective. An elastic controller is responsible to determine the optimal number of cloud resources, required at a particular time to achieve the desired performance demands. Researchers and practitioners have proposed many elastic controllers using versatile techniques ranging from simple if-then-else based rules to sophisticated optimisation, control theory and machine learning based methods. However, despite an extensive range of existing elasticity research, the aim of implementing an efficient scaling technique that satisfies the actual demands is still a challenge to achieve. There exist many issues that have not received much attention from a holistic point of view. Some of these issues include: 1) the lack of adaptability and static scaling behaviour whilst considering completely fixed approaches; 2) the burden of additional computational overhead, the inability to cope with the sudden changes in the workload behaviour and the preference of adaptability over reliability at runtime whilst considering the fully dynamic approaches; and 3) the lack of considering uncertainty aspects while designing auto-scaling solutions. This thesis seeks solutions to address these issues altogether using an integrated approach. Moreover, this thesis aims at the provision of qualitative elasticity rules. This thesis proposes a novel biologically-inspired switched feedback control methodology to address the horizontal elasticity problem. The switched methodology utilises multiple controllers simultaneously, whereas the selection of a suitable controller is realised using an intelligent switching mechanism. Each controller itself depicts a different elasticity policy that can be designed using the principles of fixed gain feedback controller approach. The switching mechanism is implemented using a fuzzy system that determines a suitable controller/- policy at runtime based on the current behaviour of the system. Furthermore, to improve the possibility of bumpless transitions and to avoid the oscillatory behaviour, which is a problem commonly associated with switching based control methodologies, this thesis proposes an alternative soft switching approach. This soft switching approach incorporates a biologically-inspired Basal Ganglia based computational model of action selection. In addition, this thesis formulates the problem of designing the membership functions of the switching mechanism as a multi-objective optimisation problem. The key purpose behind this formulation is to obtain the near optimal (or to fine tune) parameter settings for the membership functions of the fuzzy control system in the absence of domain experts’ knowledge. This problem is addressed by using two different techniques including the commonly used Genetic Algorithm and an alternative less known economic approach called the Taguchi method. Lastly, we identify seven different kinds of real workload patterns, each of which reflects a different set of applications. Six real and one synthetic HTTP traces, one for each pattern, are further identified and utilised to evaluate the performance of the proposed methods against the state-of-the-art approaches

Towards a novel biologically-inspired cloud elasticity framework

With the widespread use of the Internet, the popularity of web applications has significantly increased. Such applications are subject to unpredictable workload conditions that vary from time to time. For example, an e-commerce website may face higher workloads than normal during festivals or promotional schemes. Such applications are critical and performance related issues, or service disruption can result in financial losses. Cloud computing with its attractive feature of dynamic resource provisioning (elasticity) is a perfect match to host such applications. The rapid growth in the usage of cloud computing model, as well as the rise in complexity of the web applications poses new challenges regarding the effective monitoring and management of the underlying cloud computational resources. This thesis investigates the state-of-the-art elastic methods including the models and techniques for the dynamic management and provisioning of cloud resources from a service provider perspective. An elastic controller is responsible to determine the optimal number of cloud resources, required at a particular time to achieve the desired performance demands. Researchers and practitioners have proposed many elastic controllers using versatile techniques ranging from simple if-then-else based rules to sophisticated optimisation, control theory and machine learning based methods. However, despite an extensive range of existing elasticity research, the aim of implementing an efficient scaling technique that satisfies the actual demands is still a challenge to achieve. There exist many issues that have not received much attention from a holistic point of view. Some of these issues include: 1) the lack of adaptability and static scaling behaviour whilst considering completely fixed approaches; 2) the burden of additional computational overhead, the inability to cope with the sudden changes in the workload behaviour and the preference of adaptability over reliability at runtime whilst considering the fully dynamic approaches; and 3) the lack of considering uncertainty aspects while designing auto-scaling solutions. This thesis seeks solutions to address these issues altogether using an integrated approach. Moreover, this thesis aims at the provision of qualitative elasticity rules. This thesis proposes a novel biologically-inspired switched feedback control methodology to address the horizontal elasticity problem. The switched methodology utilises multiple controllers simultaneously, whereas the selection of a suitable controller is realised using an intelligent switching mechanism. Each controller itself depicts a different elasticity policy that can be designed using the principles of fixed gain feedback controller approach. The switching mechanism is implemented using a fuzzy system that determines a suitable controller/- policy at runtime based on the current behaviour of the system. Furthermore, to improve the possibility of bumpless transitions and to avoid the oscillatory behaviour, which is a problem commonly associated with switching based control methodologies, this thesis proposes an alternative soft switching approach. This soft switching approach incorporates a biologically-inspired Basal Ganglia based computational model of action selection. In addition, this thesis formulates the problem of designing the membership functions of the switching mechanism as a multi-objective optimisation problem. The key purpose behind this formulation is to obtain the near optimal (or to fine tune) parameter settings for the membership functions of the fuzzy control system in the absence of domain experts’ knowledge. This problem is addressed by using two different techniques including the commonly used Genetic Algorithm and an alternative less known economic approach called the Taguchi method. Lastly, we identify seven different kinds of real workload patterns, each of which reflects a different set of applications. Six real and one synthetic HTTP traces, one for each pattern, are further identified and utilised to evaluate the performance of the proposed methods against the state-of-the-art approaches

Auto-scaling techniques for cloud-based Complex Event Processing

One key topic in cloud computing is elasticity, which is the ability of the cloud environment to timely adapt the resource assignment along with the workload demand. According to cloud on-demand model, the infrastructure should be able to scale up and down to unpredictable workloads, in order to achieve both a guaranteed service level and cost efficiency. This work addresses the cloud elasticity problem, with particular reference to the Complex Event Processing (CEP) systems. CEP systems are designed to process large volumes of event-driven data streams and continuously provide results with a low latency and in real-time. CEP systems need to adapt to changing query and events loads. Because of the high computational requirements and varying loads, CEP are distributed system and running on cloud infrastructures. In this work we review the cloud computing auto-scaling solutions, and study their suit- ability in the CEP model. We implement some solutions in a CEP prototype and evaluate the experimental results

Adaptable Service Oriented Infrastructure Provisioning with Lightweight Containers Virtualization Technology

Modern computing infrastructures should enable realization of converged provisioning and governance operations on virtualized computing, storage and network resources used on behalf of users' workloads. These workloads must have ensured sufficient access to the resources to satisfy required QoS. This requires flexible platforms providing functionality for construction, activation and governance of Runtime Infrastructure which can be realized according to Service Oriented Infrastructure (SOI) paradigm. Implementation of the SOI management framework requires definition of flexible architecture and utilization of advanced software engineering and policy-based techniques. The paper presents an Adaptable SOI Provisioning Platform which supports adaptable SOI provisioning with lightweight virtualization, compliant with the structured process model suitable for construction, activation and governance of IT environments. The requirements, architecture and implementation of the platform are all discussed. Practical usage of the platform is presented on the basis of a complex case study for provisioning JEE middleware on top of the Solaris 10 lightweight virtualization platform

A survey on elasticity management in PaaS systems

[EN] Elasticity is a goal of cloud computing. An elastic system should manage in an autonomic way its resources, being adaptive to dynamic workloads, allocating additional resources when workload is increased and deallocating resources when workload decreases. PaaS providers should manage resources of customer applications with the aim of converting those applications into elastic services. This survey identifies the requirements that such management imposes on a PaaS provider: autonomy, scalability, adaptivity, SLA awareness, composability and upgradeability. This document delves into the variety of mechanisms that have been proposed to deal with all those requirements. Although there are multiple approaches to address those concerns, providers main goal is maximisation of profits. This compels providers to look for balancing two opposed goals: maximising quality of service and minimising costs. 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Wide-Area Situation Awareness based on a Secure Interconnection between Cyber-Physical Control Systems

Posteriormente, examinamos e identificamos los requisitos especiales que limitan el diseño y la operación de una arquitectura de interoperabilidad segura para los SSC (particularmente los SCCF) del smart grid. Nos enfocamos en modelar requisitos no funcionales que dan forma a esta infraestructura, siguiendo la metodología NFR para extraer requisitos esenciales, técnicas para la satisfacción de los requisitos y métricas para nuestro modelo arquitectural. Estudiamos los servicios necesarios para la interoperabilidad segura de los SSC del SG revisando en profundidad los mecanismos de seguridad, desde los servicios básicos hasta los procedimientos avanzados capaces de hacer frente a las amenazas sofisticadas contra los sistemas de control, como son los sistemas de detección, protección y respuesta ante intrusiones. Nuestro análisis se divide en diferentes áreas: prevención, consciencia y reacción, y restauración; las cuales general un modelo de seguridad robusto para la protección de los sistemas críticos. Proporcionamos el diseño para un modelo arquitectural para la interoperabilidad segura y la interconexión de los SCCF del smart grid. Este escenario contempla la interconectividad de una federación de proveedores de energía del SG, que interactúan a través de la plataforma de interoperabilidad segura para gestionar y controlar sus infraestructuras de forma cooperativa. La plataforma tiene en cuenta las características inherentes y los nuevos servicios y tecnologías que acompañan al movimiento de la Industria 4.0. Por último, presentamos una prueba de concepto de nuestro modelo arquitectural, el cual ayuda a validar el diseño propuesto a través de experimentaciones. Creamos un conjunto de casos de validación que prueban algunas de las funcionalidades principales ofrecidas por la arquitectura diseñada para la interoperabilidad segura, proporcionando información sobre su rendimiento y capacidades.Las infraestructuras críticas (IICC) modernas son vastos sistemas altamente complejos, que precisan del uso de las tecnologías de la información para gestionar, controlar y monitorizar el funcionamiento de estas infraestructuras. Debido a sus funciones esenciales, la protección y seguridad de las infraestructuras críticas y, por tanto, de sus sistemas de control, se ha convertido en una tarea prioritaria para las diversas instituciones gubernamentales y académicas a nivel mundial. La interoperabilidad de las IICC, en especial de sus sistemas de control (SSC), se convierte en una característica clave para que estos sistemas sean capaces de coordinarse y realizar tareas de control y seguridad de forma cooperativa. El objetivo de esta tesis se centra, por tanto, en proporcionar herramientas para la interoperabilidad segura de los diferentes SSC, especialmente los sistemas de control ciber-físicos (SCCF), de forma que se potencie la intercomunicación y coordinación entre ellos para crear un entorno en el que las diversas infraestructuras puedan realizar tareas de control y seguridad cooperativas, creando una plataforma de interoperabilidad segura capaz de dar servicio a diversas IICC, en un entorno de consciencia situacional (del inglés situational awareness) de alto espectro o área (wide-area). Para ello, en primer lugar, revisamos las amenazas de carácter más sofisticado que amenazan la operación de los sistemas críticos, particularmente enfocándonos en los ciberataques camuflados (del inglés stealth) que amenazan los sistemas de control de infraestructuras críticas como el smart grid. Enfocamos nuestra investigación al análisis y comprensión de este nuevo tipo de ataques que aparece contra los sistemas críticos, y a las posibles contramedidas y herramientas para mitigar los efectos de estos ataques

Multi-access edge computing: A survey

Multi-access Edge Computing (MEC) is a key solution that enables operators to open their networks to new services and IT ecosystems to leverage edge-cloud benefits in their networks and systems. Located in close proximity from the end users and connected devices, MEC provides extremely low latency and high bandwidth while always enabling applications to leverage cloud capabilities as necessary. In this article, we illustrate the integration of MEC into a current mobile networks' architecture as well as the transition mechanisms to migrate into a standard 5G network architecture.We also discuss SDN, NFV, SFC and network slicing as MEC enablers. Then, we provide a state-of-the-art study on the different approaches that optimize the MEC resources and its QoS parameters. In this regard, we classify these approaches based on the optimized resources and QoS parameters (i.e., processing, storage, memory, bandwidth, energy and latency). Finally, we propose an architectural framework for a MEC-NFV environment based on the standard SDN architecture