118 research outputs found
Serverless Automated Assessment of Programming Assignments
This thesis explores the use of serverless technology for automating the assessment
of programming assignments (AAPA). The aim of this study is to investigate the
effectiveness of serverless solutions for AAPA, and to explore the potential benefits
and challenges of using serverless technology in learning management systems. The
research questions addressed in this study are: (1) How serverless solutions for AAPA
affect the response time of grading? (2) How much does the serverless solution impact
infrastructure cost? (3) What are the environmental aspects of moving from locally
hosted VM-based solutions to cloud-based serverless solutions?
To answer these research questions, a design science research methodology was
used to design and implement a prototype solution for serverless AAPA. The solution
was implemented using AWS API Gateway, SQS, DynamoDB and Lambda, and
it was evaluated through experiments with real-world programming exercises. The
results of the experiments showed that the serverless solution was able to significantly
improve the response time of grading programming assignments in bursts, compared
to a locally hosted VM-based solution. Additionally, the serverless solution was
potentially able to reduce infrastructure costs, as it only used resources when needed,
and it was able to scale automatically to handle varying levels of traffic.
The environmental impact of serverless technology for programming exercise
assessment was also explored. The findings suggest that serverless technology can
have a positive impact on the environment by reducing energy consumption, carbon
emissions and hardware lifecycle management. However, there are also limitations
and challenges to using serverless technology for programming exercise assessment,
such as data privacy and security.
Overall, this thesis provides a framework for future research and development in
the field of serverless AAPA and highlights the potential benefits and challenges of
using serverless technology in learning management systems
Design, implementation and experimental evaluation of a network-slicing aware mobile protocol stack
Mención Internacional en el título de doctorWith the arrival of new generation mobile networks, we currently observe a paradigm
shift, where monolithic network functions running on dedicated hardware are now
implemented as software pieces that can be virtualized on general purpose hardware
platforms. This paradigm shift stands on the softwarization of network functions and
the adoption of virtualization techniques. Network Function Virtualization (NFV)
comprises softwarization of network elements and virtualization of these components.
It brings multiple advantages: (i) Flexibility, allowing an easy management of the virtual
network functions (VNFs) (deploy, start, stop or update); (ii) efficiency, resources can be
adequately consumed due to the increased flexibility of the network infrastructure; and
(iii) reduced costs, due to the ability of sharing hardware resources. To this end, multiple
challenges must be addressed to effectively leverage of all these benefits.
Network Function Virtualization envisioned the concept of virtual network, resulting in
a key enabler of 5G networks flexibility, Network Slicing. This new paradigm represents
a new way to operate mobile networks where the underlying infrastructure is "sliced"
into logically separated networks that can be customized to the specific needs of the
tenant. This approach also enables the ability of instantiate VNFs at different locations
of the infrastructure, choosing their optimal placement based on parameters such as the
requirements of the service traversing the slice or the available resources. This decision
process is called orchestration and involves all the VNFs withing the same network slice.
The orchestrator is the entity in charge of managing network slices. Hands-on experiments
on network slicing are essential to understand its benefits and limits, and to validate the
design and deployment choices. While some network slicing prototypes have been built
for Radio Access Networks (RANs), leveraging on the wide availability of radio hardware
and open-source software, there is no currently open-source suite for end-to-end network
slicing available to the research community. Similarly, orchestration mechanisms must
be evaluated as well to properly validate theoretical solutions addressing diverse aspects
such as resource assignment or service composition.
This thesis contributes on the study of the mobile networks evolution regarding its
softwarization and cloudification. We identify software patterns for network function
virtualization, including the definition of a novel mobile architecture that squeezes the virtualization architecture by splitting functionality in atomic functions.
Then, we effectively design, implement and evaluate of an open-source network
slicing implementation. Our results show a per-slice customization without paying the
price in terms of performance, also providing a slicing implementation to the research
community. Moreover, we propose a framework to flexibly re-orchestrate a virtualized
network, allowing on-the-fly re-orchestration without disrupting ongoing services. This
framework can greatly improve performance under changing conditions. We evaluate
the resulting performance in a realistic network slicing setup, showing the feasibility and
advantages of flexible re-orchestration.
Lastly and following the required re-design of network functions envisioned during
the study of the evolution of mobile networks, we present a novel pipeline architecture
specifically engineered for 4G/5G Physical Layers virtualized over clouds. The proposed
design follows two objectives, resiliency upon unpredictable computing and parallelization
to increase efficiency in multi-core clouds. To this end, we employ techniques such as tight
deadline control, jitter-absorbing buffers, predictive Hybrid Automatic Repeat Request,
and congestion control. Our experimental results show that our cloud-native approach
attains > 95% of the theoretical spectrum efficiency in hostile environments where stateof-
the-art architectures collapse.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Francisco Valera Pintor.- Secretario: Vincenzo Sciancalepore.- Vocal: Xenofon Fouka
Microservice chatbot architecture for chronic patient support
Chatbots are able to provide support to patients suffering from very different conditions. Patients with chronic diseases or comorbidities could benefit the most from chatbots which can keep track of their condition, provide specific information, encourage adherence to medication, etc. To perform these functions, chatbots need a suitable underlying software architecture. In this paper, we introduce a chatbot architecture for chronic patient support grounded on three pillars: scalability by means of microservices, standard data sharing models through HL7 FHIR and standard conversation modeling using AIML. We also propose an innovative automation mechanism to convert FHIR resources into AIML files, thus facilitating the interaction and data gathering of medical and personal information that ends up in patient health records. To align the way people interact with each other using messaging platforms with the chatbot architecture, we propose these very same channels for the chatbot-patient interaction, paying special attention to security and privacy issues. Finally, we present a monitored-data study performed in different chronic diseases, and we present a prototype implementation tailored for one specific chronic disease, psoriasis, showing how this new architecture allows the change, the addition or the improvement of different parts of the chatbot in a dynamic and flexible way, providing a substantial improvement in the development of chatbots used as virtual assistants for chronic patients
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Making Data Storage Efficient in the Era of Cloud Computing
We enter the era of cloud computing in the last decade, as many paradigm shifts are happening on how people write and deploy applications. Despite the advancement of cloud computing, data storage abstractions have not evolved much, causing inefficiencies in performance, cost, and security.
This dissertation proposes a novel approach to make data storage efficient in the era of cloud computing by building new storage abstractions and systems that bridge the gap between cloud computing and data storage and simplify development. We build four systems to address four data inefficiencies in cloud computing.
The first system, Grandet, solves the data storage inefficiency caused by the paradigm shift from upfront provisioning to a variety of pay-as-you-go cloud services. Grandet is an extensible storage system that significantly reduces storage costs for web applications deployed in the cloud. Under the hood, it supports multiple heterogeneous stores and unifies them by placing each data object at the store deemed most economical. Our results show that Grandet reduces their costs by an average of 42.4%, and it is fast, scalable, and easy to use.
The second system, Unic, solves the data inefficiency caused by the paradigm shift from single-tenancy to multi-tenancy. Unic securely deduplicates general computations. It exports a cache service that allows cloud applications running on behalf of mutually distrusting users to memoize and reuse computation results, thereby improving performance. Unic achieves both integrity and secrecy through a novel use of code attestation, and it provides a simple yet expressive API that enables applications to deduplicate their own rich computations. Our results show that Unic is easy to use, speeds up applications by an average of 7.58x, and with little storage overhead.
The third system, Lambdata, solves the data inefficiency caused by the paradigm shift to serverless computing, where developers only write core business logic, and cloud service providers maintain all the infrastructure. Lambdata is a novel serverless computing system that enables developers to declare a cloud function's data intents, including both data read and data written. Once data intents are made explicit, Lambdata performs a variety of optimizations to improve speed, including caching data locally and scheduling functions based on code and data locality. Our results show that Lambdata achieves an average speedup of 1.51x on the turnaround time of practical workloads and reduces monetary cost by 16.5%.
The fourth system, CleanOS, solves the data inefficiency caused by the paradigm shift from desktop computers to smartphones always connected to the cloud. CleanOS is a new Android-based operating system that manages sensitive data rigorously and maintains a clean environment at all times. It identifies and tracks sensitive data, encrypts it with a key, and evicts that key to the cloud when the data is not in active use on the device. Our results show that CleanOS limits sensitive-data exposure drastically while incurring acceptable overheads on mobile networks
Modern computing: Vision and challenges
Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress
Technologies and Applications for Big Data Value
This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems
Supporting remote therapeutic interventions with voice assistant technology
Nowadays, digital personal assistants are incorporated in many devices. Smart TVs, smartphones and stand-alone voice assistants like Amazon Alexa allow owners to control their smart home systems, play music on command or lookup information on the internet via voice queries. Using custom skills from various third-party vendors, almost any company can have a skill supporting the needs of their customers or control their devices. Furthermore, therapeutic interventions represent a vital part of most therapies, but there are some underlying struggles during therapies for which therapists can utilize the support of smart mobile devices. As an extension of an already existing system called Albatros, its features have been converted into an custom Alexa skill called remote interventions. But voice assistants can do more than improving everyday life, like helping people during medical therapies. A vital part of such therapies are therapeutic interventions, but therapists often face struggles when monitoring a patients progress and results. To overcome this problem, an existing system called Albatros allows a therapist to review the patients status. As an extension to the existing Albatros system, its features have been incorporated into a custom Alexa skill called remote interventions. Aiming to contribute to the proof of concept, the objective of this thesis is to demonstrate the development process of a custom Alexa skill which implements the features of retrieving exercises, allowing patients to record feedback via a smart speaker which can then be accessed by the therapist. With the addition of a notification feature the system also supports patients in remembering how and when to do their exercises properly. Due to the proof of concept nature of the project, apart from the actual development process, an analysis of whether or not the ideas and features translate well into a voice driven platform is performed
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