1,379 research outputs found
Advanced sequencing technologies applied to human cytomegalovirus
The betaherpesvirus human cytomegalovirus (HCMV) is a ubiquitous viral pathogen. It is the most common cause of congenital infection in infants and of opportunistic infections in immunocompromised patients worldwide. The large double-stranded DNA genome of HCMV (236 kb) contains several genes that exhibit a high degree of variation among strains within an otherwise highly conserved sequence. These hypervariable genes encode immune escape, tropism or regulatory factors that may affect virulence. Variation arising from these genes and from an evolutionary history of recombination between strains has been hypothesised to be linked to disease severity. To investigate this, the HCMV genome has been scrutinised in detail over the years using a variety of molecular techniques, most looking only at one or a few of these genes at a time. The advent of high-throughput sequencing (HTS) technology 20 years ago then started to enable more in-depth whole-genome analyses. My study extends this field by using both HTS and the more recently developed long-read nanopore technology to determine HCMV genome sequences directly from clinical samples. Firstly, I used an Illumina HTS pipeline to sequence HCMV strains directly from formalin-fixed, paraffin-embedded (FFPE) tissues. FFPE samples are a valuable repository for the study of relatively rare diseases, such as congenital HCMV (cCMV). However, formalin fixation induces DNA fragmentation and cross-linking, making this a challenging sample type for DNA sequencing. I successfully sequenced five whole HCMV genomes from FFPE tissues. Next, I developed a pipeline utilising the single-molecule, long-read sequencer from Oxford Nanopore Technologies (ONT) to sequence HCMV initially from high-titre cellcultured laboratory strains and then from clinical samples with high HCMV loads. Finally, I utilised a direct RNA sequencing protocol with the ONT sequencer to characterise novel HCMV transcripts produced during infection in cell culture, demonstrating the existence of transcript isoforms with multiple splice sites. Overall, my findings demonstrate how advanced sequencing technologies can be used to characterise the genome and transcriptome of a large DNA virus, and will facilitate future studies on HCMV prognostic factors, novel antiviral targets and vaccine development
Serverless Strategies and Tools in the Cloud Computing Continuum
Tesis por compendio[ES] En los últimos años, la popularidad de la computación en nube ha permitido a los usuarios acceder a recursos de cómputo, red y almacenamiento sin precedentes bajo un modelo de pago por uso. Esta popularidad ha propiciado la aparición de nuevos servicios para resolver determinados problemas informáticos a gran escala y simplificar el desarrollo y el despliegue de aplicaciones. Entre los servicios más destacados en los últimos años se encuentran las plataformas FaaS (Función como Servicio), cuyo principal atractivo es la facilidad de despliegue de pequeños fragmentos de código en determinados lenguajes de programación para realizar tareas específicas en respuesta a eventos. Estas funciones son ejecutadas en los servidores del proveedor Cloud sin que los usuarios se preocupen de su mantenimiento ni de la gestión de su elasticidad, manteniendo siempre un modelo de pago por uso de grano fino.
Las plataformas FaaS pertenecen al paradigma informático conocido como Serverless, cuyo propósito es abstraer la gestión de servidores por parte de los usuarios, permitiéndoles centrar sus esfuerzos únicamente en el desarrollo de aplicaciones. El problema del modelo FaaS es que está enfocado principalmente en microservicios y tiende a tener limitaciones en el tiempo de ejecución y en las capacidades de computación (por ejemplo, carece de soporte para hardware de aceleración como GPUs). Sin embargo, se ha demostrado que la capacidad de autoaprovisionamiento y el alto grado de paralelismo de estos servicios pueden ser muy adecuados para una mayor variedad de aplicaciones. Además, su inherente ejecución dirigida por eventos hace que las funciones sean perfectamente adecuadas para ser definidas como pasos en flujos de trabajo de procesamiento de archivos (por ejemplo, flujos de trabajo de computación científica).
Por otra parte, el auge de los dispositivos inteligentes e integrados (IoT), las innovaciones en las redes de comunicación y la necesidad de reducir la latencia en casos de uso complejos han dado lugar al concepto de Edge computing, o computación en el borde. El Edge computing consiste en el procesamiento en dispositivos cercanos a las fuentes de datos para mejorar los tiempos de respuesta. La combinación de este paradigma con la computación en nube, formando arquitecturas con dispositivos a distintos niveles en función de su proximidad a la fuente y su capacidad de cómputo, se ha acuñado como continuo de la computación en la nube (o continuo computacional).
Esta tesis doctoral pretende, por lo tanto, aplicar diferentes estrategias Serverless para permitir el despliegue de aplicaciones generalistas, empaquetadas en contenedores de software, a través de los diferentes niveles del continuo computacional. Para ello, se han desarrollado múltiples herramientas con el fin de: i) adaptar servicios FaaS de proveedores Cloud públicos; ii) integrar diferentes componentes software para definir una plataforma Serverless en infraestructuras privadas y en el borde; iii) aprovechar dispositivos de aceleración en plataformas Serverless; y iv) facilitar el despliegue de aplicaciones y flujos de trabajo a través de interfaces de usuario. Además, se han creado y adaptado varios casos de uso para evaluar los desarrollos conseguidos.[CA] En els últims anys, la popularitat de la computació al núvol ha permès als usuaris accedir a recursos de còmput, xarxa i emmagatzematge sense precedents sota un model de pagament per ús. Aquesta popularitat ha propiciat l'aparició de nous serveis per resoldre determinats problemes informàtics a gran escala i simplificar el desenvolupament i desplegament d'aplicacions. Entre els serveis més destacats en els darrers anys hi ha les plataformes FaaS (Funcions com a Servei), el principal atractiu de les quals és la facilitat de desplegament de petits fragments de codi en determinats llenguatges de programació per realitzar tasques específiques en resposta a esdeveniments. Aquestes funcions són executades als servidors del proveïdor Cloud sense que els usuaris es preocupen del seu manteniment ni de la gestió de la seva elasticitat, mantenint sempre un model de pagament per ús de gra fi.
Les plataformes FaaS pertanyen al paradigma informàtic conegut com a Serverless, el propòsit del qual és abstraure la gestió de servidors per part dels usuaris, permetent centrar els seus esforços únicament en el desenvolupament d'aplicacions. El problema del model FaaS és que està enfocat principalment a microserveis i tendeix a tenir limitacions en el temps d'execució i en les capacitats de computació (per exemple, no té suport per a maquinari d'acceleració com GPU). Tot i això, s'ha demostrat que la capacitat d'autoaprovisionament i l'alt grau de paral·lelisme d'aquests serveis poden ser molt adequats per a més aplicacions. A més, la seva inherent execució dirigida per esdeveniments fa que les funcions siguen perfectament adequades per ser definides com a passos en fluxos de treball de processament d'arxius (per exemple, fluxos de treball de computació científica).
D'altra banda, l'auge dels dispositius intel·ligents i integrats (IoT), les innovacions a les xarxes de comunicació i la necessitat de reduir la latència en casos d'ús complexos han donat lloc al concepte d'Edge computing, o computació a la vora. L'Edge computing consisteix en el processament en dispositius propers a les fonts de dades per millorar els temps de resposta. La combinació d'aquest paradigma amb la computació en núvol, formant arquitectures amb dispositius a diferents nivells en funció de la proximitat a la font i la capacitat de còmput, s'ha encunyat com a continu de la computació al núvol (o continu computacional).
Aquesta tesi doctoral pretén, doncs, aplicar diferents estratègies Serverless per permetre el desplegament d'aplicacions generalistes, empaquetades en contenidors de programari, a través dels diferents nivells del continu computacional. Per això, s'han desenvolupat múltiples eines per tal de: i) adaptar serveis FaaS de proveïdors Cloud públics; ii) integrar diferents components de programari per definir una plataforma Serverless en infraestructures privades i a la vora; iii) aprofitar dispositius d'acceleració a plataformes Serverless; i iv) facilitar el desplegament d'aplicacions i fluxos de treball mitjançant interfícies d'usuari. A més, s'han creat i s'han adaptat diversos casos d'ús per avaluar els desenvolupaments aconseguits.[EN] In recent years, the popularity of Cloud computing has allowed users to access unprecedented compute, network, and storage resources under a pay-per-use model. This popularity led to new services to solve specific large-scale computing challenges and simplify the development and deployment of applications. Among the most prominent services in recent years are FaaS (Function as a Service) platforms, whose primary appeal is the ease of deploying small pieces of code in certain programming languages to perform specific tasks on an event-driven basis. These functions are executed on the Cloud provider's servers without users worrying about their maintenance or elasticity management, always keeping a fine-grained pay-per-use model.
FaaS platforms belong to the computing paradigm known as Serverless, which aims to abstract the management of servers from the users, allowing them to focus their efforts solely on the development of applications. The problem with FaaS is that it focuses on microservices and tends to have limitations regarding the execution time and the computing capabilities (e.g. lack of support for acceleration hardware such as GPUs). However, it has been demonstrated that the self-provisioning capability and high degree of parallelism of these services can be well suited to broader applications. In addition, their inherent event-driven triggering makes functions perfectly suitable to be defined as steps in file processing workflows (e.g. scientific computing workflows).
Furthermore, the rise of smart and embedded devices (IoT), innovations in communication networks and the need to reduce latency in challenging use cases have led to the concept of Edge computing. Edge computing consists of conducting the processing on devices close to the data sources to improve response times. The coupling of this paradigm together with Cloud computing, involving architectures with devices at different levels depending on their proximity to the source and their compute capability, has been coined as Cloud Computing Continuum (or Computing Continuum).
Therefore, this PhD thesis aims to apply different Serverless strategies to enable the deployment of generalist applications, packaged in software containers, across the different tiers of the Cloud Computing Continuum. To this end, multiple tools have been developed in order to: i) adapt FaaS services from public Cloud providers; ii) integrate different software components to define a Serverless platform on on-premises and Edge infrastructures; iii) leverage acceleration devices on Serverless platforms; and iv) facilitate the deployment of applications and workflows through user interfaces. Additionally, several use cases have been created and adapted to assess the developments achieved.Risco Gallardo, S. (2023). Serverless Strategies and Tools in the Cloud Computing Continuum [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/202013Compendi
Pathway: a fast and flexible unified stream data processing framework for analytical and Machine Learning applications
We present Pathway, a new unified data processing framework that can run
workloads on both bounded and unbounded data streams. The framework was created
with the original motivation of resolving challenges faced when analyzing and
processing data from the physical economy, including streams of data generated
by IoT and enterprise systems. These required rapid reaction while calling for
the application of advanced computation paradigms (machinelearning-powered
analytics, contextual analysis, and other elements of complex event
processing). Pathway is equipped with a Table API tailored for Python and
Python/SQL workflows, and is powered by a distributed incremental dataflow in
Rust. We describe the system and present benchmarking results which demonstrate
its capabilities in both batch and streaming contexts, where it is able to
surpass state-of-the-art industry frameworks in both scenarios. We also discuss
streaming use cases handled by Pathway which cannot be easily resolved with
state-of-the-art industry frameworks, such as streaming iterative graph
algorithms (PageRank, etc.)
Empirical Investigation of Factors influencing Function as a Service Performance in Different Cloud/Edge System Setups
Experimental data can aid in gaining insights about a system operation, as
well as determining critical aspects of a modelling or simulation process. In
this paper, we analyze the data acquired from an extensive experimentation
process in a serverless Function as a Service system (based on the open source
Apache Openwhisk) that has been deployed across 3 available cloud/edge
locations with different system setups. Thus, they can be used to model
distribution of functions through multi-location aware scheduling mechanisms.
The experiments include different traffic arrival rates, different setups for
the FaaS system, as well as different configurations for the hardware and
platform used. We analyse the acquired data for the three FaaS system setups
and discuss their differences presenting interesting conclusions with relation
to transient effects of the system, such as the effect on wait and execution
time. We also demonstrate interesting trade-offs with relation to system setup
and indicate a number of factors that can affect system performance and should
be taken under consideration in modelling attempts of such systems.Comment: 24 pages, 14 Figures, Journal pape
Architectural Vision for Quantum Computing in the Edge-Cloud Continuum
Quantum processing units (QPUs) are currently exclusively available from
cloud vendors. However, with recent advancements, hosting QPUs is soon possible
everywhere. Existing work has yet to draw from research in edge computing to
explore systems exploiting mobile QPUs, or how hybrid applications can benefit
from distributed heterogeneous resources. Hence, this work presents an
architecture for Quantum Computing in the edge-cloud continuum. We discuss the
necessity, challenges, and solution approaches for extending existing work on
classical edge computing to integrate QPUs. We describe how warm-starting
allows defining workflows that exploit the hierarchical resources spread across
the continuum. Then, we introduce a distributed inference engine with hybrid
classical-quantum neural networks (QNNs) to aid system designers in
accommodating applications with complex requirements that incur the highest
degree of heterogeneity. We propose solutions focusing on classical layer
partitioning and quantum circuit cutting to demonstrate the potential of
utilizing classical and quantum computation across the continuum. To evaluate
the importance and feasibility of our vision, we provide a proof of concept
that exemplifies how extending a classical partition method to integrate
quantum circuits can improve the solution quality. Specifically, we implement a
split neural network with optional hybrid QNN predictors. Our results show that
extending classical methods with QNNs is viable and promising for future work.Comment: 16 pages, 5 figures, Vision Pape
Saturn: An Optimized Data System for Large Model Deep Learning Workloads
Large language models such as GPT-3 & ChatGPT have transformed deep learning
(DL), powering applications that have captured the public's imagination. These
models are rapidly being adopted across domains for analytics on various
modalities, often by finetuning pre-trained base models. Such models need
multiple GPUs due to both their size and computational load, driving the
development of a bevy of "model parallelism" techniques & tools. Navigating
such parallelism choices, however, is a new burden for end users of DL such as
data scientists, domain scientists, etc. who may lack the necessary systems
knowhow. The need for model selection, which leads to many models to train due
to hyper-parameter tuning or layer-wise finetuning, compounds the situation
with two more burdens: resource apportioning and scheduling. In this work, we
tackle these three burdens for DL users in a unified manner by formalizing them
as a joint problem that we call SPASE: Select a Parallelism, Allocate
resources, and SchedulE. We propose a new information system architecture to
tackle the SPASE problem holistically, representing a key step toward enabling
wider adoption of large DL models. We devise an extensible template for
existing parallelism schemes and combine it with an automated empirical
profiler for runtime estimation. We then formulate SPASE as an MILP.
We find that direct use of an MILP-solver is significantly more effective
than several baseline heuristics. We optimize the system runtime further with
an introspective scheduling approach. We implement all these techniques into a
new data system we call Saturn. Experiments with benchmark DL workloads show
that Saturn achieves 39-49% lower model selection runtimes than typical current
DL practice.Comment: Under submission at VLDB. Code available:
https://github.com/knagrecha/saturn. 12 pages + 3 pages references + 2 pages
appendi
Analytical validation of innovative magneto-inertial outcomes: a controlled environment study.
peer reviewe
Serverless Cloud Computing: A Comparative Analysis of Performance, Cost, and Developer Experiences in Container-Level Services
Serverless cloud computing is a subset of cloud computing considerably adopted to build modern web applications, while the underlying server and infrastructure management duties are abstracted from customers to the cloud vendors. In serverless computing, customers must pay for the runtime consumed by their services, but they are exempt from paying for the idle time. Prior to serverless containers, customers needed to provision, scale, and manage servers, which was a bottleneck for rapidly growing customer-facing applications where latency and scaling were a concern.
The viability of adopting a serverless platform for a web application regarding performance, cost, and developer experiences is studied in this thesis. Three serverless container-level services are employed in this study from AWS and GCP. The services include GCP Cloud Run, GKE AutoPilot, and AWS EKS with AWS Fargate. Platform as a Service (PaaS) underpins the former, and Container as a Service (CaaS) the remainder. A single-page web application was created to perform incremental and spike load tests on those services to assess the performance differences. Furthermore, the cost differences are compared and analyzed. Lastly, the final element considered while evaluating the developer experiences is the complexity of using the services during the project implementation.
Based on the results of this research, it was determined that PaaS-based solutions are a high-performing, affordable alternative for CaaS-based solutions in circumstances where high levels of traffic are periodically anticipated, but sporadic latency is never a concern. Given that this study has limitations, the author recommends additional research to strengthen it
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