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

A Ring to Rule Them All - Revising OpenStack Internals to Operate Massively Distributed Clouds: The Discovery Initiative - Where Do We Are ?

STACK_HCERES2020The deployment of micro/nano data-centers in network point of presence offers an opportunity to deliver a more sustainable and efficient infrastructure for Cloud Computing. Among the different challenges we need to address to favor the adoption of such a model, the development of a system in charge of turning such a complex and diverse network of resources into a collection of abstracted computing facilities that are convenient to administrate and use is critical.In this report, we introduce the premises of such a system. The novelty of our work is that instead of developing a system from scratch, we revised the OpenStack solution in order to operate such an infrastructure in a distributed manner leveraging P2P mechanisms. More precisely, we describe how we revised the Nova service by leveraging a distributed key/value store instead of the centralized SQL backend. We present experiments that validated the correct behavior of our prototype, while having promising performance using several clusters composed of servers of the Grid’5000 testbed. We believe that such a strategy is promising and paves the way to a first large-scale and WAN-wide IaaS manager.La tendance actuelle pour supporter la demande croissante d'informatique utilitaire consiste à construire des centres de données de plus en plus grands, dans un nombre limité de lieux stratégiques. Cette approche permet sans aucun doute de satisfaire la demande actuelle tout en conservant une approche centralisée de la gestion de ces ressources, mais elle reste loin de pouvoir fournir des infrastructures répondant aux contraintes actuelles et futures en termes d'efficacité, de juridiction ou encore de durabilité. L'objectif de l'initiative DISCOVERY est de concevoir le LUC OS, un système de gestion distribuée des ressources qui permettra de tirer parti de n'importe quel noeud réseau constituant la dorsale d'Internet afin de fournir une nouvelle génération d'informatique utilitaire, plus apte à prendre en compte la dispersion géographiquedes utilisateurs et leur demande toujours croissante.Après avoir rappelé les objectifs de l'initiative DISCOVERY et expliqué pourquoi les approches type fédération ne sont pas adaptées pour opérer une infrastructure d'informatique utilitaire intégrée au réseau, nous présentons les prémisses de notre système. Nous expliquerons notamment pourquoi et comment nous avons choisi de démarrer des travaux visant à revisiter la conception de la solution Openstack. De notre point de vue, choisir d'appuyer nos travaux sur cette solution est une stratégie judicieuse à la vue de la complexité des systèmes de gestion des plateformes IaaS et de la vélocité des solutions open-source

Big Data and Large-scale Data Analytics: Efficiency of Sustainable Scalability and Security of Centralized Clouds and Edge Deployment Architectures

One of the significant shifts of the next-generation computing technologies will certainly be in the development of Big Data (BD) deployment architectures. Apache Hadoop, the BD landmark, evolved as a widely deployed BD operating system. Its new features include federation structure and many associated frameworks, which provide Hadoop 3.x with the maturity to serve different markets. This dissertation addresses two leading issues involved in exploiting BD and large-scale data analytics realm using the Hadoop platform. Namely, (i)Scalability that directly affects the system performance and overall throughput using portable Docker containers. (ii) Security that spread the adoption of data protection practices among practitioners using access controls. An Enhanced Mapreduce Environment (EME), OPportunistic and Elastic Resource Allocation (OPERA) scheduler, BD Federation Access Broker (BDFAB), and a Secure Intelligent Transportation System (SITS) of multi-tiers architecture for data streaming to the cloud computing are the main contribution of this thesis study

RFaaS: RDMA-Enabled FaaS Platform for Serverless High-Performance Computing

The rigid MPI programming model and batch scheduling dominate high-performance computing. While clouds brought new levels of elasticity into the world of computing, supercomputers still suffer from low resource utilization rates. To enhance supercomputing clusters with the benefits of serverless computing, a modern cloud programming paradigm for pay-as-you-go execution of stateless functions, we present rFaaS, the first RDMA-aware Function-as-a-Service (FaaS) platform. With hot invocations and decentralized function placement, we overcome the major performance limitations of FaaS systems and provide low-latency remote invocations in multi-tenant environments. We evaluate the new serverless system through a series of microbenchmarks and show that remote functions execute with negligible performance overheads. We demonstrate how serverless computing can bring elastic resource management into MPI-based high-performance applications. Overall, our results show that MPI applications can benefit from modern cloud programming paradigms to guarantee high performance at lower resource costs

Transparent Orchestration of Task-based Parallel Applications in Containers Platforms

This paper presents a framework to easily build and execute parallel applications in container-based distributed computing platforms in a user-transparent way. The proposed framework is a combination of the COMP Superscalar (COMPSs) programming model and runtime, which provides a straightforward way to develop task-based parallel applications from sequential codes, and containers management platforms that ease the deployment of applications in computing environments (as Docker, Mesos or Singularity). This framework provides scientists and developers with an easy way to implement parallel distributed applications and deploy them in a one-click fashion. We have built a prototype which integrates COMPSs with different containers engines in different scenarios: i) a Docker cluster, ii) a Mesos cluster, and iii) Singularity in an HPC cluster. We have evaluated the overhead in the building phase, deployment and execution of two benchmark applications compared to a Cloud testbed based on KVM and OpenStack and to the usage of bare metal nodes. We have observed an important gain in comparison to cloud environments during the building and deployment phases. This enables better adaptation of resources with respect to the computational load. In contrast, we detected an extra overhead during the execution, which is mainly due to the multi-host Docker networking.This work is partly supported by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316 project, by the Generalitat de Catalunya under contracts 2014-SGR-1051 and 2014-SGR-1272, and by the European Union through the Horizon 2020 research and innovation program under grant 690116 (EUBra-BIGSEA Project). Results presented in this paper were obtained using the Chameleon testbed supported by the National Science Foundation.Peer ReviewedPostprint (author's final draft

NFV Platforms: Taxonomy, Design Choices and Future Challenges

Due to the intrinsically inefficient service provisioning in traditional networks, Network Function Virtualization (NFV) keeps gaining attention from both industry and academia. By replacing the purpose-built, expensive, proprietary network equipment with software network functions consolidated on commodity hardware, NFV envisions a shift towards a more agile and open service provisioning paradigm. During the last few years, a large number of NFV platforms have been implemented in production environments that typically face critical challenges, including the development, deployment, and management of Virtual Network Functions (VNFs). Nonetheless, just like any complex system, such platforms commonly consist of abounding software and hardware components and usually incorporate disparate design choices based on distinct motivations or use cases. This broad collection of convoluted alternatives makes it extremely arduous for network operators to make proper choices. Although numerous efforts have been devoted to investigating different aspects of NFV, none of them specifically focused on NFV platforms or attempted to explore their design space. In this paper, we present a comprehensive survey on the NFV platform design. Our study solely targets existing NFV platform implementations. We begin with a top-down architectural view of the standard reference NFV platform and present our taxonomy of existing NFV platforms based on what features they provide in terms of a typical network function life cycle. Then we thoroughly explore the design space and elaborate on the implementation choices each platform opts for. We also envision future challenges for NFV platform design in the incoming 5G era. We believe that our study gives a detailed guideline for network operators or service providers to choose the most appropriate NFV platform based on their respective requirements. Our work also provides guidelines for implementing new NFV platforms

Artificial intelligence driven anomaly detection for big data systems

The main goal of this thesis is to contribute to the research on automated performance anomaly detection and interference prediction by implementing Artificial Intelligence (AI) solutions for complex distributed systems, especially for Big Data platforms within cloud computing environments. The late detection and manual resolutions of performance anomalies and system interference in Big Data systems may lead to performance violations and financial penalties. Motivated by this issue, we propose AI-based methodologies for anomaly detection and interference prediction tailored to Big Data and containerized batch platforms to better analyze system performance and effectively utilize computing resources within cloud environments. Therefore, new precise and efficient performance management methods are the key to handling performance anomalies and interference impacts to improve the efficiency of data center resources. The first part of this thesis contributes to performance anomaly detection for in-memory Big Data platforms. We examine the performance of Big Data platforms and justify our choice of selecting the in-memory Apache Spark platform. An artificial neural network-driven methodology is proposed to detect and classify performance anomalies for batch workloads based on the RDD characteristics and operating system monitoring metrics. Our method is evaluated against other popular machine learning algorithms (ML), as well as against four different monitoring datasets. The results prove that our proposed method outperforms other ML methods, typically achieving 98–99% F-scores. Moreover, we prove that a random start instant, a random duration, and overlapped anomalies do not significantly impact the performance of our proposed methodology. The second contribution addresses the challenge of anomaly identification within an in-memory streaming Big Data platform by investigating agile hybrid learning techniques. We develop TRACK (neural neTwoRk Anomaly deteCtion in sparK) and TRACK-Plus, two methods to efficiently train a class of machine learning models for performance anomaly detection using a fixed number of experiments. Our model revolves around using artificial neural networks with Bayesian Optimization (BO) to find the optimal training dataset size and configuration parameters to efficiently train the anomaly detection model to achieve high accuracy. The objective is to accelerate the search process for finding the size of the training dataset, optimizing neural network configurations, and improving the performance of anomaly classification. A validation based on several datasets from a real Apache Spark Streaming system is performed, demonstrating that the proposed methodology can efficiently identify performance anomalies, near-optimal configuration parameters, and a near-optimal training dataset size while reducing the number of experiments up to 75% compared with naïve anomaly detection training. The last contribution overcomes the challenges of predicting completion time of containerized batch jobs and proactively avoiding performance interference by introducing an automated prediction solution to estimate interference among colocated batch jobs within the same computing environment. An AI-driven model is implemented to predict the interference among batch jobs before it occurs within system. Our interference detection model can alleviate and estimate the task slowdown affected by the interference. This model assists the system operators in making an accurate decision to optimize job placement. Our model is agnostic to the business logic internal to each job. Instead, it is learned from system performance data by applying artificial neural networks to establish the completion time prediction of batch jobs within the cloud environments. We compare our model with three other baseline models (queueing-theoretic model, operational analysis, and an empirical method) on historical measurements of job completion time and CPU run-queue size (i.e., the number of active threads in the system). The proposed model captures multithreading, operating system scheduling, sleeping time, and job priorities. A validation based on 4500 experiments based on the DaCapo benchmarking suite was carried out, confirming the predictive efficiency and capabilities of the proposed model by achieving up to 10% MAPE compared with the other models.Open Acces