Containerization in Cloud Computing: performance analysis of virtualization architectures

La crescente adozione del cloud è fortemente influenzata dall’emergere di tecnologie che mirano a migliorare i processi di sviluppo e deployment di applicazioni di livello enterprise. L’obiettivo di questa tesi è analizzare una di queste soluzioni, chiamata “containerization” e di valutare nel dettaglio come questa tecnologia possa essere adottata in infrastrutture cloud in alternativa a soluzioni complementari come le macchine virtuali. Fino ad oggi, il modello tradizionale “virtual machine” è stata la soluzione predominante nel mercato. L’importante differenza architetturale che i container offrono ha portato questa tecnologia ad una rapida adozione poichè migliora di molto la gestione delle risorse, la loro condivisione e garantisce significativi miglioramenti in termini di provisioning delle singole istanze. Nella tesi, verrà esaminata la “containerization” sia dal punto di vista infrastrutturale che applicativo. Per quanto riguarda il primo aspetto, verranno analizzate le performances confrontando LXD, Docker e KVM, come hypervisor dell’infrastruttura cloud OpenStack, mentre il secondo punto concerne lo sviluppo di applicazioni di livello enterprise che devono essere installate su un insieme di server distribuiti. In tal caso, abbiamo bisogno di servizi di alto livello, come l’orchestrazione. Pertanto, verranno confrontate le performances delle seguenti soluzioni: Kubernetes, Docker Swarm, Apache Mesos e Cattle

Introducing the new paradigm of Social Dispersed Computing: Applications, Technologies and Challenges

[EN] If last decade viewed computational services as a utility then surely this decade has transformed computation into a commodity. Computation is now progressively integrated into the physical networks in a seamless way that enables cyber-physical systems (CPS) and the Internet of Things (IoT) meet their latency requirements. Similar to the concept of ¿platform as a service¿ or ¿software as a service¿, both cloudlets and fog computing have found their own use cases. Edge devices (that we call end or user devices for disambiguation) play the role of personal computers, dedicated to a user and to a set of correlated applications. In this new scenario, the boundaries between the network node, the sensor, and the actuator are blurring, driven primarily by the computation power of IoT nodes like single board computers and the smartphones. The bigger data generated in this type of networks needs clever, scalable, and possibly decentralized computing solutions that can scale independently as required. Any node can be seen as part of a graph, with the capacity to serve as a computing or network router node, or both. Complex applications can possibly be distributed over this graph or network of nodes to improve the overall performance like the amount of data processed over time. In this paper, we identify this new computing paradigm that we call Social Dispersed Computing, analyzing key themes in it that includes a new outlook on its relation to agent based applications. We architect this new paradigm by providing supportive application examples that include next generation electrical energy distribution networks, next generation mobility services for transportation, and applications for distributed analysis and identification of non-recurring traffic congestion in cities. The paper analyzes the existing computing paradigms (e.g., cloud, fog, edge, mobile edge, social, etc.), solving the ambiguity of their definitions; and analyzes and discusses the relevant foundational software technologies, the remaining challenges, and research opportunities.Garcia Valls, MS.; Dubey, A.; Botti, V. (2018). Introducing the new paradigm of Social Dispersed Computing: Applications, Technologies and Challenges. Journal of Systems Architecture. 91:83-102. https://doi.org/10.1016/j.sysarc.2018.05.007S831029

Microservices-based IoT Applications Scheduling in Edge and Fog Computing: A Taxonomy and Future Directions

Edge and Fog computing paradigms utilise distributed, heterogeneous and resource-constrained devices at the edge of the network for efficient deployment of latency-critical and bandwidth-hungry IoT application services. Moreover, MicroService Architecture (MSA) is increasingly adopted to keep up with the rapid development and deployment needs of the fast-evolving IoT applications. Due to the fine-grained modularity of the microservices along with their independently deployable and scalable nature, MSA exhibits great potential in harnessing both Fog and Cloud resources to meet diverse QoS requirements of the IoT application services, thus giving rise to novel paradigms like Osmotic computing. However, efficient and scalable scheduling algorithms are required to utilise the said characteristics of the MSA while overcoming novel challenges introduced by the architecture. To this end, we present a comprehensive taxonomy of recent literature on microservices-based IoT applications scheduling in Edge and Fog computing environments. Furthermore, we organise multiple taxonomies to capture the main aspects of the scheduling problem, analyse and classify related works, identify research gaps within each category, and discuss future research directions.Comment: 35 pages, 10 figures, submitted to ACM Computing Survey

Opportunities of IoT in Fog Computing for High Fault Tolerance and Sustainable Energy Optimization

Today, the importance of enhanced quality of service and energy optimization has promoted research into sensor applications such as pervasive health monitoring, distributed computing, etc. In general, the resulting sensor data are stored on the cloud server for future processing. For this purpose, recently, the use of fog computing from a real-world perspective has emerged, utilizing end-user nodes and neighboring edge devices to perform computation and communication. This paper aims to develop a quality-of-service-based energy optimization (QoS-EO) scheme for the wireless sensor environments deployed in fog computing. The fog nodes deployed in specific geographical areas cover the sensor activity performed in those areas. The logical situation of the entire system is informed by the fog nodes, as portrayed. The implemented techniques enable services in a fog-collaborated WSN environment. Thus, the proposed scheme performs quality-of-service placement and optimizes the network energy. The results show a maximum turnaround time of 8 ms, a minimum turnaround time of 1 ms, and an average turnaround time of 3 ms. The costs that were calculated indicate that as the number of iterations increases, the path cost value decreases, demonstrating the efficacy of the proposed technique. The CPU execution delay was reduced to a minimum of 0.06 s. In comparison, the proposed QoS-EO scheme has a lower network usage of 611,643.3 and a lower execution cost of 83,142.2. Thus, the results show the best cost estimation, reliability, and performance of data transfer in a short time, showing a high level of network availability, throughput, and performance guarantee

Building an IoT platform based on service containerisation

IoT platforms have become quite complex from a technical viewpoint, becoming the cornerstone for information sharing, storing, and indexing given the unprecedented scale of smart services being available by massive deployments of a large set of data-enabled devices. These platforms rely on structured formats that exploit standard technologies to deal with the gathered data, thus creating the need for carefully designed customised systems that can handle thousands of heterogeneous data sensors/actuators, multiple processing frameworks, and storage solutions. We present the SCoT2.0 platform, a generic-purpose IoT Platform that can acquire, process, and visualise data using methods adequate for both real-time processing and long-term Machine Learning (ML)-based analysis. Our goal is to develop a large-scale system that can be applied to multiple real-world scenarios and is potentially deployable on private clouds for multiple verticals. Our approach relies on extensive service containerisation, and we present the different design choices, technical challenges, and solutions found while building our own IoT platform. We validate this platform supporting two very distinct IoT projects (750 physical devices), and we analyse scaling issues within the platform components.publishe

Processamento de eventos complexos como serviço em ambientes multi-nuvem

Orientadores: Luiz Fernando Bittencourt, Miriam Akemi Manabe CapretzTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O surgimento das tecnologias de dispositivos móveis e da Internet das Coisas, combinada com avanços das tecnologias Web, criou um novo mundo de Big Data em que o volume e a velocidade da geração de dados atingiu uma escala sem precedentes. Por ser uma tecnologia criada para processar fluxos contínuos de dados, o Processamento de Eventos Complexos (CEP, do inglês Complex Event Processing) tem sido frequentemente associado a Big Data e aplicado como uma ferramenta para obter informações em tempo real. Todavia, apesar desta onda de interesse, o mercado de CEP ainda é dominado por soluções proprietárias que requerem grandes investimentos para sua aquisição e não proveem a flexibilidade que os usuários necessitam. Como alternativa, algumas empresas adotam soluções de baixo nível que demandam intenso treinamento técnico e possuem alto custo operacional. A fim de solucionar esses problemas, esta pesquisa propõe a criação de um sistema de CEP que pode ser oferecido como serviço e usado através da Internet. Um sistema de CEP como Serviço (CEPaaS, do inglês CEP as a Service) oferece aos usuários as funcionalidades de CEP aliadas às vantagens do modelo de serviços, tais como redução do investimento inicial e baixo custo de manutenção. No entanto, a criação de tal serviço envolve inúmeros desafios que não são abordados no atual estado da arte de CEP. Em especial, esta pesquisa propõe soluções para três problemas em aberto que existem neste contexto. Em primeiro lugar, para o problema de entender e reusar a enorme variedade de procedimentos para gerência de sistemas CEP, esta pesquisa propõe o formalismo Reescrita de Grafos com Atributos para Gerência de Processamento de Eventos Complexos (AGeCEP, do inglês Attributed Graph Rewriting for Complex Event Processing Management). Este formalismo inclui modelos para consultas CEP e transformações de consultas que são independentes de tecnologia e linguagem. Em segundo lugar, para o problema de avaliar estratégias de gerência e processamento de consultas CEP, esta pesquisa apresenta CEPSim, um simulador de sistemas CEP baseado em nuvem. Por fim, esta pesquisa também descreve um sistema CEPaaS fundamentado em ambientes multi-nuvem, sistemas de gerência de contêineres e um design multiusuário baseado em AGeCEP. Para demonstrar sua viabilidade, o formalismo AGeCEP foi usado para projetar um gerente autônomo e um conjunto de políticas de auto-gerenciamento para sistemas CEP. Além disso, o simulador CEPSim foi minuciosamente avaliado através de experimentos que demonstram sua capacidade de simular sistemas CEP com acurácia e baixo custo adicional de processamento. Por fim, experimentos adicionais validaram o sistema CEPaaS e demonstraram que o objetivo de oferecer funcionalidades CEP como um serviço escalável e tolerante a falhas foi atingido. Em conjunto, esses resultados confirmam que esta pesquisa avança significantemente o estado da arte e também oferece novas ferramentas e metodologias que podem ser aplicadas à pesquisa em CEPAbstract: The rise of mobile technologies and the Internet of Things, combined with advances in Web technologies, have created a new Big Data world in which the volume and velocity of data generation have achieved an unprecedented scale. As a technology created to process continuous streams of data, Complex Event Processing (CEP) has been often related to Big Data and used as a tool to obtain real-time insights. However, despite this recent surge of interest, the CEP market is still dominated by solutions that are costly and inflexible or too low-level and hard to operate. To address these problems, this research proposes the creation of a CEP system that can be offered as a service and used over the Internet. Such a CEP as a Service (CEPaaS) system would give its users CEP functionalities associated with the advantages of the services model, such as no up-front investment and low maintenance cost. Nevertheless, creating such a service involves challenges that are not addressed by current CEP systems. This research proposes solutions for three open problems that exist in this context. First, to address the problem of understanding and reusing existing CEP management procedures, this research introduces the Attributed Graph Rewriting for Complex Event Processing Management (AGeCEP) formalism as a technology- and language-agnostic representation of queries and their reconfigurations. Second, to address the problem of evaluating CEP query management and processing strategies, this research introduces CEPSim, a simulator of cloud-based CEP systems. Finally, this research also introduces a CEPaaS system based on a multi-cloud architecture, container management systems, and an AGeCEP-based multi-tenant design. To demonstrate its feasibility, AGeCEP was used to design an autonomic manager and a selected set of self-management policies. Moreover, CEPSim was thoroughly evaluated by experiments that showed it can simulate existing systems with accuracy and low execution overhead. Finally, additional experiments validated the CEPaaS system and demonstrated it achieves the goal of offering CEP functionalities as a scalable and fault-tolerant service. In tandem, these results confirm this research significantly advances the CEP state of the art and provides novel tools and methodologies that can be applied to CEP researchDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação140920/2012-9CNP

A Scalable and Adaptive Network on Chip for Many-Core Architectures

In this work, a scalable network on chip (NoC) for future many-core architectures is proposed and investigated. It supports different QoS mechanisms to ensure predictable communication. Self-optimization is introduced to adapt the energy footprint and the performance of the network to the communication requirements. A fault tolerance concept allows to deal with permanent errors. Moreover, a template-based automated evaluation and design methodology and a synthesis flow for NoCs is introduced

Self-management for large-scale distributed systems

Autonomic computing aims at making computing systems self-managing by using autonomic managers in order to reduce obstacles caused by management complexity. This thesis presents results of research on self-management for large-scale distributed systems. This research was motivated by the increasing complexity of computing systems and their management. In the first part, we present our platform, called Niche, for programming self-managing component-based distributed applications. In our work on Niche, we have faced and addressed the following four challenges in achieving self-management in a dynamic environment characterized by volatile resources and high churn: resource discovery, robust and efficient sensing and actuation, management bottleneck, and scale. We present results of our research on addressing the above challenges. Niche implements the autonomic computing architecture, proposed by IBM, in a fully decentralized way. Niche supports a network-transparent view of the system architecture simplifying the design of distributed self-management. Niche provides a concise and expressive API for self-management. The implementation of the platform relies on the scalability and robustness of structured overlay networks. We proceed by presenting a methodology for designing the management part of a distributed self-managing application. We define design steps that include partitioning of management functions and orchestration of multiple autonomic managers. In the second part, we discuss robustness of management and data consistency, which are necessary in a distributed system. Dealing with the effect of churn on management increases the complexity of the management logic and thus makes its development time consuming and error prone. We propose the abstraction of Robust Management Elements, which are able to heal themselves under continuous churn. Our approach is based on replicating a management element using finite state machine replication with a reconfigurable replica set. Our algorithm automates the reconfiguration (migration) of the replica set in order to tolerate continuous churn. For data consistency, we propose a majority-based distributed key-value store supporting multiple consistency levels that is based on a peer-to-peer network. The store enables the tradeoff between high availability and data consistency. Using majority allows avoiding potential drawbacks of a master-based consistency control, namely, a single-point of failure and a potential performance bottleneck. In the third part, we investigate self-management for Cloud-based storage systems with the focus on elasticity control using elements of control theory and machine learning. We have conducted research on a number of different designs of an elasticity controller, including a State-Space feedback controller and a controller that combines feedback and feedforward control. We describe our experience in designing an elasticity controller for a Cloud-based key-value store using state-space model that enables to trade-off performance for cost. We describe the steps in designing an elasticity controller. We continue by presenting the design and evaluation of ElastMan, an elasticity controller for Cloud-based elastic key-value stores that combines feedforward and feedback control