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

The orchestration of Machine Learning frameworks with datastreams and GPU acceleration in Kafka-ML: A deep-learning performance comparative

Machine Learning (ML) applications need large volumes of data to train their modelsso that they can make high-quality predictions. Given digital revolution enablers suchas the Internet of Things (IoT) and the Industry 4.0, this information is generated inlarge quantities in terms of continuous data streams and not in terms of staticdatasets as it is the case with most AI (Artificial Intelligence) frameworks. Kafka-ML isa novel open-source framework that allows the complete management of ML/AIpipelines through data streams. In this article, we present new features for the Kafka-ML framework, such as the support for the well-known ML/AI framework PyTorch,as well as for GPU acceleration at different points along the pipeline. This pipelinewill be described by taking a real Industry 4.0 use case in the Petrochemical Industry.Finally, a comprehensive evaluation with state-of-the-art deep learning models willbe carried out to demonstrate the feasibility of the platform.Funding for open access charge: Universidad de Málaga / CBUA Consejería de Economía, Innovación, Ciencia y Empleo, Junta de Andalucía; European Commission; Ministerio de Ciencia, Innovación y Universidades. This work is funded by the Spanish projects RT2018-099777-B-100 (‘rFOG: Improving Latency and Reliability of Offloaded Computation to theFOG for Critical Services’), PY20_00788 (‘IntegraDos: Providing Real-Time Services for the Internet of Things through Cloud Sensor Integration’),TSI-063000-2021-116 (‘Digital vertical twins for B5G/6G networks’), TED2021-130167B-C33 (‘GEDIER: Application of Digital Twins to moresustainable irrigated farms’), and CPP2021-009032(‘ZeroVision: Enabling Zero impact wastewater treatment through Computer Vision and Fed-erated AI’); and the European project LIFEWATCH-2019-11-UMA-01-BD (‘EnBiC2-Lab - Environmental and Biodiversity Climate Change Lab’).This project has received funding from the European Union's Horizon Europe research and innovation proframme under the Marie Skłodowska-Curie grant agreement No 101086218. Funding for open access charge: Universidad de Málaga / CBUA

Objcache: An Elastic Filesystem over External Persistent Storage for Container Clusters

Container virtualization enables emerging AI workloads such as model serving, highly parallelized training, machine learning pipelines, and so on, to be easily scaled on demand on the elastic cloud infrastructure. Particularly, AI workloads require persistent storage to store data such as training inputs, models, and checkpoints. An external storage system like cloud object storage is a common choice because of its elasticity and scalability. To mitigate access latency to external storage, caching at a local filesystem is an essential technique. However, building local caches on scaling clusters must cope with explosive disk usage, redundant networking, and unexpected failures. We propose objcache, an elastic filesystem over external storage. Objcache introduces an internal transaction protocol over Raft logging to enable atomic updates of distributed persistent states with consistent hashing. The proposed transaction protocol can also manage inode dirtiness by maintaining the consistency between the local cache and external storage. Objcache supports scaling down to zero by automatically evicting dirty files to external storage. Our evaluation reports that objcache speeded up model serving startup by 98.9% compared to direct copies via S3 interfaces. Scaling up with dirty files completed from 2 to 14 seconds with 1024 dirty files.Comment: 13 page

Kafka-ML: Connecting the data stream with ML/AI frameworks

Machine Learning (ML) and Artificial Intelligence (AI) depend on data sources to train, improve, and make predictions through their algorithms. With the digital revolution and current paradigms like the Internet of Things, this information is turning from static data to continuous data streams. However, most of the ML/AI frameworks used nowadays are not fully prepared for this revolution. In this paper, we propose Kafka-ML, a novel and open-source framework that enables the management of ML/AI pipelines through data streams. Kafka-ML provides an accessible and user-friendly Web user interface where users can easily define ML models, to then train, evaluate, and deploy them for inferences. Kafka-ML itself and the components it deploys are fully managed through containerization technologies, which ensure their portability, easy distribution, and other features such as fault-tolerance and high availability. Finally, a novel approach has been introduced to manage and reuse data streams, which may eliminate the need for data storage or file systems.This work is funded by the Spanish projects RT2018-099777-B-100 (“rFOG: Improving Latency and Reliability of Offloaded Computation to the FOG for Critical Services”), PY20_00788 (“IntegraDos: Providing Real-Time Services for the Internet of Things through Cloud Sensor Integration”) and UMA18FEDERJA-215 (“Advanced Monitoring System Based on Deep Learning Services in Fog”). Cristian Martín was with a postdoc grant from the Spanish project TIC-1572 (“MIsTIca: Critical Infrastructures Monitoring based on Wireless Technologies”) and his research stay at IHP has been funded through a mobility grant from the University of Malaga and IHP funding. Funding for open access charge: Universidad de Malaga/CBUA . We are grateful for the work of all the reviewers who have greatly contributed to improving the quality of this article. We would like to express our gratitude to Kai Wähner for his inspiration and ideas through numerous articles and GitHub repositories on Kafka and its combination with TensorFlow

CrownLabs - A Collaborative Environment to Deliver Remote Computing Laboratories

The coronavirus pandemic hit the entire education sector hard. All students were sent home and lectures started to be delivered through video-conferencing systems. CrownLabs is an open-source project providing an answer to the problem of delivering remote computing laboratories. Simplicity is one of its main characteristics, requiring nothing but a simple web browser to interact with the system and being all heavyweight computations performed at the university premises. Cooperation and mentoring are also encouraged through parallel access to the same remote desktop. The entire system is built up using components from the Kubernetes ecosystem, to replicate a "cloud grade" infrastructure, coupled with custom software implementing the core business logic. To this end, most of the complexity has been delegated to the infrastructure, to speed up the development process and reduce the maintenance burden. An extensive evaluation has been performed in both real and simulated scenarios to validate the overall performance: the results are encouraging, as well as the feedback from the early adopters of the system