244 research outputs found
Classification and Performance Study of Task Scheduling Algorithms in Cloud Computing Environment
Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc. With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers. To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment. It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance. To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations. This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance. A comparison of the performance of these algorithms with existing ones is also given. Additionally, the future research work in the reviewed articles (if available) is also pointed out. This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study. Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms. Keywords: Cloud computing, Task scheduling, Load balancing, Makespan, Energy-aware, Turnaround time, Response time, Cost of task, QoS, Multi-objective. DOI: 10.7176/IKM/12-5-03 Publication date:September 30th 2022
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
New techniques to integrate blockchain in Internet of Things scenarios for massive data management
Mención Internacional en el título de doctorNowadays, regardless of the use case, most IoT data is processed using
workflows that are executed on different infrastructures (edge-fog-cloud),
which produces dataflows from the IoT through the edge to the fog/cloud.
In many cases, they also involve several actors (organizations and users),
which poses a challenge for organizations to establish verification of the
transactions performed by the participants in the dataflows built by the
workflow engines and pipeline frameworks. It is essential for organizations,
not only to verify that the execution of applications is performed in the
strict sequence previously established in a DAG by authenticated participants,
but also to verify that the incoming and outgoing IoT data of each
stage of a workflow/pipeline have not been altered by third parties or by the
users associated to the organizations participating in a workflow/pipeline.
Blockchain technology and its mechanism for recording immutable transactions
in a distributed and decentralized manner, characterize it as an
ideal technology to support the aforementioned challenges and challenges since it allows the verification of the records generated in a secure manner.
However, the integration of blockchain technology with workflows for IoT
data processing is not trivial considering that it is a challenge not to lose
the generalization of workflows and/or pipeline engines, which must be
modified to include the embedded blockchain module. The main objective
of this doctoral research was to create new techniques to use blockchain
in the Internet of Things (IoT). Thus, we defined the main goal of this thesis
is to develop new techniques to integrate blockchain in Internet of
Things scenarios for massive data management in edge-fog-cloud environments.
To fulfill this general objective, we have designed a content
delivery model for processing big IoT data in Edge-Fog-Cloud computing
by using micro/nanoservice composition, a continuous verification model
based on blockchain to register significant events from the continuous delivery
model, selecting techniques to integrate blockchain in quasi-real systems
that allow ensuring traceability and non-repudiation of data obtained
from devices and sensors. The evaluation proposed has been thoroughly
evaluated, showing its feasibility and good performance.Hoy en día, independientemente del caso de uso, la mayoría de los datos
de IoT se procesan utilizando flujos de trabajo que se ejecutan en diferentes
infraestructuras (edge-fog-cloud) desde IoT a través del edge hasta la
fog/cloud. En muchos casos, también involucran a varios actores (organizaciones
y usuarios), lo que plantea un desafío para las organizaciones a la
hora de verificar las transacciones realizadas por los participantes en los
flujos de datos. Es fundamental para las organizaciones, no solo para verificar
que la ejecución de aplicaciones se realiza en la secuencia previamente
establecida en un DAG y por participantes autenticados, sino también para
verificar que los datos IoT entrantes y salientes de cada etapa de un flujo
de trabajo no han sido alterados por terceros o por usuarios asociados a
las organizaciones que participan en el mismo. La tecnología Blockchain,
gracias a su mecanismo para registrar transacciones de manera distribuida
y descentralizada, es un tecnología ideal para soportar los retos y desafíos
antes mencionados ya que permite la verificación de los registros generados de manera segura. Sin embargo, la integración de la tecnología blockchain
con flujos de trabajo para IoT no es baladí considerando que es un desafío
proporcionar el rendimiento necesario sin perder la generalización de los
motores de flujos de trabajo, que deben ser modificados para incluir el
módulo blockchain integrado. El objetivo principal de esta investigación
doctoral es desarrollar nuevas técnicas para integrar blockchain en Internet
de las Cosas (IoT) para la gestión masiva de datos en un entorno
edge-fog-cloud. Para cumplir con este objetivo general, se ha diseñado
un modelo de flujos para procesar grandes datos de IoT en computación
Edge-Fog-Cloud mediante el uso de la composición de micro/nanoservicio,
un modelo de verificación continua basado en blockchain para registrar
eventos significativos de la modelo de entrega continua de datos, seleccionando
técnicas para integrar blockchain en sistemas cuasi-reales que
permiten asegurar la trazabilidad y el no repudio de datos obtenidos de
dispositivos y sensores, La evaluación propuesta ha sido minuciosamente
evaluada, mostrando su factibilidad y buen rendimiento.This work has been partially supported by the project "CABAHLA-CM: Convergencia
Big data-Hpc: de los sensores a las Aplicaciones" S2018/TCS-4423
from Madrid Regional Government.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Paolo Trunfio.- Secretario: David Exposito Singh.- Vocal: Rafael Mayo Garcí
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