New simulation techniques for energy aware cloud computing environments

In this thesis we propose a new simulation platform specifically designed for modelling cloud computing environments, its underlying architectures, and the energy consumed by hardware devices. The models that consists on servers are divided into the five basic subsystems: processing system, memory system, network system, storage system, and the power supply unit. Each one of these subsystems has been built including new strategies to simulate energy aware. On the top of these models, there have been deployed the virtualization models to simulate the hypervisor and its scheduling policies. In addition, the cloud manager, the core of the simulation platform, is responsible for the provisioning resources management policies. It design offers to researchers APIs, allowing to perform studies on scheduling policies of cloud computing systems. This simulation platform is aimed to model existent and new designs of cloud computing architectures, with a customizable environment to configure the energy consumption of different components. The main characteristics of this platform are flexibility, allowing a wide possibility of designs; scalability to study large environments; and to provide a good compromise between accuracy and performance. A validation process of the simulation platform has been reached by comparing results from real experiments, with results from simulation executions obtained by modelling the real experiments. Therefore, to evaluate the possibility to foresee the energy consumption of a real cloud environment, an experiment of deploying a model of a real application has been studied. Finally, scalability experiments has been performed to study the behaviour of the simulation platform with large scale environments experiments. The main aim of scalability tests, is to calculate both, the amount of time and memory needed to execute large simulations, depending on the size of the environment simulated, and the availability of hardware resources to execute them.En esta tesis se propone una nueva plataforma de simulación específicamente diseñada para modelar entornos de computación en la nube, sus arquitecturas subyacentes, y la energía consumida por los dispositivos hardware. Los modelos que constituyen los servidores se encuentran divididos en los cinco subsistemas básicos: sistema de procesamiento, sistema de memoria, sistema de almacenamiento, sistema de red, y fuente de alimentación. Cada uno de estos subsistemas ha sido modelado incluyendo nuevas estrategias para simular su consumo energético. Sobre estos modelos se despliegan los modelos de virtualización con la finalidad de simular el hipervisor y sus políticas de planificación. Además, se ha realizado el modelo del gestor de la nube, la pieza central de la plataforma de simulación y responsable de la gestión de las políticas de aprovisionamiento de recursos. Su diseño ofrece interfaces a los investigadores, permitiendo realizar sus estudios sobre políticas de planificación en entornos de computación en la nube. Los objetivos de esta plataforma de simulación son permitir el modelado de entornos existentes y nuevos diseños arquitectónicos de computación en la nube, con un entorno configurable que permita modificar valores de consumo energético de los distintos componentes. Las principales características de esta plataforma son su flexibilidad, permitiendo una amplia posibilidad de diseños; escalabilidad, para estudiar entornos con gran número de elementos; y proveer un buen compromiso entre la precisión de los resultados y su rendimiento. Se ha realizado el proceso de validación de la plataforma de simulación mediante la comparación de resultados de experimentos realizados en entornos reales, con los resultados de simulación obtenidos de modelar dichos entornos reales. Tras ello, se ha realizado una evaluación mostrando la capacidad de prever el consumo energético de un entorno de computación en la nube que modela una aplicación real. Finalmente, se han realizado experimentos para analizar la escalabilidad, con el fin de estudiar el comportamiento de la plataforma ante la simulación de entornos de gran escala. El principal objetivo de los test de escalabilidad consiste en calcular la cantidad de tiempo y de memoria necesarios para ejecutar simulaciones grandes, dependiendo del tamaño del entorno simulado, y de la disponibilidad de recursos físicos para ejecutarlas.This work has been partially funded under the grant TIN2013-41350-P of the Spanish Ministry of Economics and Competitiveness, the COST Action IC1305,”Network on Sustainable Ultrascale Computing (NESUS)”, ESTuDIo (TIN2012-36812-C02-01), SICOMORo-CM (S2013/ICE-3006), the SEPE (Servicio Público de Empleo Estatal) commonly known as INEM, my entire savings, and part from my parents.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Félix García Carballeira.- Secretario: Jorge Enrique Pérez Martínez.- Vocal: Manuel Núñez Garcí

Self-organising, self-managing frameworks and strategies

A novel, general framework that can be used for constructing a self-organising and self-managing system is introduced. This framework is independent of the application domain. It embodies directed evolution, can be parameterised with different strategies, and supports both local and global goals. This framework is then used to apply the principles of self-organisation and self-management to resource management within the CloudLightning architecture

Cloud architectures and management approaches

An overview of the traditional three-layer cloud architecture is presented as background for motivating the transition to clouds containing heterogeneous resources. Whereas this transition adds many important features to the cloud, including improved service delivery and reduced energy consumption, it also results in a number of challenges associated with the efficient management of these new and diverse resources. The CloudLightning architecture is proposed as a candidate for addressing this emerging complexity, and a description of its components and their relationships is given

iCanCloud: a flexible and scalable cloud infrastructure simulator

Simulation techniques have become a powerful tool for deciding the best starting conditions on pay-as-you-go scenarios. This is the case of public cloud infrastructures, where a given number and type of virtual machines (in short VMs) are instantiated during a specified time, being this reflected in the final budget. With this in mind, this paper introduces and validates iCanCloud, a novel simulator of cloud infrastructures with remarkable features such as flexibility, scalability, performance and usability. Furthermore, the iCanCloud simulator has been built on the following design principles: (1) it's targeted to conduct large experiments, as opposed to others simulators from literature; (2) it provides a flexible and fully customizable global hypervisor for integrating any cloud brokering policy; (3) it reproduces the instance types provided by a given cloud infrastructure; and finally, (4) it contains a user-friendly GUI for configuring and launching simulations, that goes from a single VM to large cloud computing systems composed of thousands of machines.This research was partially supported by the following projects: Spanish MEC project TESIS (TIN2009-14312-C02-01), and Spanish Ministry of Science and Innovation under the grant TIN2010-16497.Publicad

Energy-efficient servers and cloud

As the sizes of cloud infrastructures continue to grow, the complexity of the cloud is becoming more and more difficult to manage. Currently, centralised management schemes dominate and there are already signs that these are no longer fit for purpose. The CloudLightning project takes a novel route, making use of self-organisation techniques to address the problems emerging from the confluence of issues in the emerging cloud: rising complexity and energy costs, problems of management and efficiency of use, the need to efficiently deploy services to a growing community of non-specialist users and the need to facilitate solutions based on heterogeneous components. CloudLightning efficiently addresses three main challenges in the domain of heterogeneous cloud computing: energy efficiency, improved accessibility to cloud and support for heterogeneity. The chapter provides an overview of the CloudLightning system

A decentralized cloud management architecture based on Application Autonomous Systems

Driven by the successful business model, cloud computing is evolving rapidly from a moderate size data center consisting of homogeneous resources to a hyper-scale heterogeneous computing environment. The evolution has made the computing environment ever-increasingly complex, thus, raises challenges for the traditional approaches for managing a cloud environment in an efficient and effective manner. In response, a decentralized system architecture for cloud management is introduced. In this architecture, the management responsibility and resource organization in a conventional cloud environment are re-considered. The re-consideration results in composing a cloud environment into three entities including the Infrastructure, the Cloud Utility and Information Base, and Application Autonomous Systems. In this configuration, service providers focus on providing connected physical resources and introducing featured resources. Information related to the Infrastructure is stored and periodically updated in the Information Base. A consumer employs an Application Autonomous System for managing the life-cycle of a cloud application. An Application Autonomous System in the context of this paper is defined as a self-contained entity that encapsulates a cloud application, the associated resources and the management functions. An Application Autonomous System uses the Information Base and Cloud Utilities to locate and acquire desired resources, subsequently resources are deployed on the Infrastructure by invoking Cloud Utilities. Thereafter, the Application Autonomous System manages the life-cycle of both the application and the associated resources. Consumers are offered opportunities to employ preferred algorithms and strategies for this management. Thus, the responsibility of cloud application management and partially the resource management has shifted from service providers to the consumers in this decentralized system architecture

Cloud architectures and management approaches

An overview of the traditional three-layer cloud architecture is presented as background for motivating the transition to clouds containing heterogeneous resources. Whereas this transition adds many important features to the cloud, including improved service delivery and reduced energy consumption, it also results in a number of challenges associated with the efficient management of these new and diverse resources. The CloudLightning architecture is proposed as a candidate for addressing this emerging complexity, and a description of its components and their relationships is given

Final results from optimisation models validation and experimentation: project deliverable D6.5

Since the arrival of cloud computing, a significant amount of research has been and continues to be carried out towards the creation of efficient optimisation strategies for meeting certain optimisation goals such as energy efficiency, resource consolidation or performance improvement within virtualised data centres. However, investigating whether specific optimisation algorithms can achieve the desired function in a production environment, and investigating how well they operate are quite complex tasks. Untested optimisation rules typically cannot be directly deployed in the production system, instead requiring manual test-bed experiments. This technique can be prohibitively costly, time consuming and cannot always account for scale and other constraints. This work presents a design-time optimisation evaluation solution based on discrete event simulation for cloud computing. By using a simulation toolkit (CactoSim) coupled with a runtime optimisation toolkit (CactoOpt), a cloud architect is able to create a direct replica model of the data centre production environment and then run simulations which take into account optimisation strategies. Results produced by such simulations can be used to estimate the optimisation algorithm performance under various conditions. With CACTOS addressing the efficient management of IaaS data centres running Scientific Computing, Business Analytics and White-Box applications, the CACTOS Prediction Toolkit supports design time decision-making via simulation for each of these areas. Typical scenarios for each of the three use cases of scientific computing, business analytics and white box applications have been modelled, run and analysed using simulation, taking the optimisation algorithms into account, and these are presented in this document. This deliverable represents the final part of two iterative pieces of work

Preliminary results from optimisation models validation and experimentation: project deliverable D6.2

Since the arrival of cloud computing, a significant amount of research has been and continues to be carried out towards the creation of efficient optimisation strategies for meeting certain optimisation goals such as energy efficiency, resource consolidation or performance improvement within virtualised data centres. However, investigating whether specific optimisation algorithms can achieve the desired function in a production environment, and investigating how well they operate are quite complex tasks. Untested optimisation rules typically cannot be directly deployed in the production system, instead requiring manual test-bed experiments. This technique can be prohibitively costly, time consuming and cannot always account for scale and other constraints. This work presents a design-time optimisation evaluation solution based on discrete event simulation for cloud computing. By using a simulation toolkit (CactoSim) coupled with a runtime optimisation toolkit (CactoOpt), a cloud architect is able to create a direct replica model of the data centre production environment and then run simulations which take into account optimisation strategies. Results produced by such simulations can be used to estimate the optimisation algorithm performance under various conditions. In order to test the CactoSim and CactoOpt integration concept, a validation process has been performed on two different scenarios. The first scenario investigates the VM placement algorithm performance within a simulated testbed when admitting new VMs into the system. The second scenario analyses consolidation optimisation strategy impact on resource utilisation, with the objective being to free up nodes towards the goal of energy saving. This deliverable represents the initial part of two iterative pieces of work

Parallel trace analysis: project deliverable D4.3

CactoScale provides monitoring and data analysis functionality to CACTOS. This deliverable presents the framework and algorithms used by CactoScale for parallel trace analysis. We describe different CactoScale framework extensions which enable the implementation of parallel correlation analysis of system utilisation metric traces and cloud data logs. We also present the implementation of Lambda Architecture into CactoScale which parallelises several aspects of monitoring and exchanging information in CACTOS. CactoScale trace analysis tackles parallelism on various dimensions. We describe a hierarchical log analysis and anomaly detection framework. The anomaly detection utilises parallel data analysis frameworks such as Spark and mapreduce framework for parallel analysis of workload traces and system logs, coupled with HDFS for in-memory processing of the data. The trace analysis also involves the pre-processing of raw data logs for storage in HDFS. It allows executing anomaly detection algorithms hierarchically, both utilising the compute nodes in situ and the parallel HDFS monitoring facility. This is feasible by pairing the CactoScale agents with in situ analytics modules to cover the cases such as workload spike detection, but also to filter the data that flows to the database for post-processing. An in situ analytic module is a process designed to run locally in a node. This tactic provides the advantage of data locality. The data are pre-processed by the local node before being collected by a remote distributed service for further processing. In this way, the hierarchical design of data analysis allows for an additional level of real-time processing which is much closer to the data source. CactoScale has different features and capabilities for parallel trace analysis which are demonstrated in this deliverable by using different algorithms for anomaly detection. Anomaly detection involves the use of trace analysis algorithms that detects outliers (numerical, textual, or correlation based) in data traces. Detecting outliers can trigger actions in resource management and for this reason we focus in anomaly detection as a use case. We demonstrate a Lightweight Anomaly Detection Tool based on correlation analysis. This tool utilises a monitoring cluster to perform parallel trace analysis using Spark and mapreduce. The online data analysis modules that we demonstrate include a log analysis module and several spike detection methods. Workload spikes are one of the main causes of QoS degradation in cloud applications. The log analysis demonstrates how information on cloud platform can contribute in reducing any false positive alerts