Scheduling in cloud and fog architecture: identification of limitations and suggestion of improvement perspectives

Application execution required in cloud and fog architectures are generally heterogeneous in terms of device and application contexts. Scaling these requirements on these architectures is an optimization problem with multiple restrictions. Despite countless efforts, task scheduling in these architectures continue to present some enticing challenges that can lead us to the question how tasks are routed between different physical devices, fog nodes and cloud. In fog, due to its density and heterogeneity of devices, the scheduling is very complex and in the literature, there are still few studies that have been conducted. However, scheduling in the cloud has been widely studied. Nonetheless, many surveys address this issue from the perspective of service providers or optimize application quality of service (QoS) levels. Also, they ignore contextual information at the level of the device and end users and their user experiences. In this paper, we conducted a systematic review of the literature on the main task by: scheduling algorithms in the existing cloud and fog architecture; studying and discussing their limitations, and we explored and suggested some perspectives for improvement.Calouste Gulbenkian Foundation, PhD scholarship No.234242, 2019.info:eu-repo/semantics/publishedVersio

Cost-Effective Scheduling in Fog Computing: An Environment Based on Modified PROMETHEE Technique

With the rising use of Internet of Things (IoT)-enabled devices, there is a significant increase in the use of smart applications that provide their response in real time. This rising demand imposes many issues such as scheduling, cost, overloading of servers, etc. To overcome these, a cost-effective scheduling technique has been proposed for the allocation of smart applications. The aim of this paper is to provide better profit by the Fog environment and minimize the cost of smart applications from the user end. The proposed framework has been evaluated with the help of a test bed containing four analysis phases and is compared on the basis of five metrics- average allocation time, average profit by the Fog environment, average cost of smart applications, resource utilization and number of applications run within given latency. The proposed framework performs better under all the provided metrics.&nbsp

Task scheduling in the Fog Computing paradigm: proposal of a context-aware model and evaluation of its performance

Os pedidos de execução de aplicações na arquitetura cloud e no paradigma fog são geralmente heterogéneos e o escalonamento nessas arquiteturas é um problema de otimização com múltiplas restrições. Neste artigo, fizemos um levantamento sobre os trabalhos relacionados com o escalonamento na arquitetura cloud e no paradigma fog, identificamos as suas limitações, explorarmos perspetivas de melhorias e propomos um modelo de escalonamento sensíveis ao contexto para o paradigma fog. A solução proposta utiliza a normalização Min-Max, para resolver a heterogeneidade e normalizar os diferentes parâmetros de contexto. A prioridade dos pedidos é definida através da aplicação da técnica de análise de Regressão Linear Múltipla e o escalonamento é feito utilizando a técnica de Otimização de Programação Não Linear Multiobjetivo. Os resultados obtidos a partir de simulações no kit de ferramentas iFogSim, demonstram que a nossa proposta apresenta um melhor desempenho em comparação com as propostas não sensíveis ao contexto.Application execution requests in cloud architecture and fog paradigm are generally heterogeneous and scheduling in these architectures is an optimization problem with multiple constraints. In this paper, we conducted a survey on the related works on scheduling in cloud architecture and fog paradigm, we identify their limitations, we explore some prospects for improvements and we propose a context-aware scheduling model for fog paradigm. The proposed solution uses Min-Max normalization, to solve heterogeneity and normalize the different. context parameters. The priority of requests is set by applying the Multiple Linear Regression analysis technique and the scheduling is done using the Multiobjective Nonlinear Programming Optimization technique. The results obtained from simulations on iFogSim toolkit, show that our proposal performs better compared to the non-context-aware proposals.Os autores agradecem à Fundação Calouste Gulbenkian pelo financiamento desta investigação através da bolsa de doutoramento sob a referência n.º 234242, 2019-Bolsas de Pós-Graduação para estudantes dos PALOP e de Timor-Leste.info:eu-repo/semantics/publishedVersio

A proposal of context-aware scheduling of mobile applications for the fog computing paradigm

Escalonamento na arquitetura cloud e no paradigma fog continuam a apresentar alguns desafios aliciantes. Na cloud, segundo o conhecimento dos autores, ela é amplamente estudada e em muitas pesquisas é abordada na perspetiva de provedores de serviço. Na fog, é muito complexo e, existem poucos estudos. Procurando trazer contributos inovadores nas áreas de escalonamento de tarefas, neste artigo, propomos uma solução para o problema de escalonamento de aplicações móveis sensíveis ao contexto para o paradigma fog computing onde diferentes parâmetros de contexto são normalizados através da normalização Min-Max, as prioridades são definidas através da aplicação da técnica da Regressão Linear Múltipla (RLM) e o escalonamento é feito recorrendo a técnica de Otimização de Programação Não Linear Multi-objetivo (MONLP).Scheduling in cloud architecture and in the fog paradigm continue to present some exciting challenges. In the cloud, according to the authors' knowledge, it is widely studied and in many researches, it is addressed from the perspective of service providers. In fog, it is very complex and there are few studies. Trying to bring innovative contributions in the areas of task scheduling, in this paper we propose a solution to the problem of context-aware scheduling of mobile applications for the fog computing paradigm, where different context parameters are normalized through Min-Max normalization, priorities are defined by applying the Multiple Linear Regression (MLR) technique and scheduling is performed using Multi-Objective Nonlinear Programming Optimization (MONLP) technique.info:eu-repo/semantics/publishedVersio

Enhancement of Metaheuristic Algorithm for Scheduling Workflows in Multi-fog Environments

Whether in computer science, engineering, or economics, optimization lies at the heart of any challenge involving decision-making. Choosing between several options is part of the decision- making process. Our desire to make the "better" decision drives our decision. An objective function or performance index describes the assessment of the alternative's goodness. The theory and methods of optimization are concerned with picking the best option. There are two types of optimization methods: deterministic and stochastic. The first is a traditional approach, which works well for small and linear problems. However, they struggle to address most of the real-world problems, which have a highly dimensional, nonlinear, and complex nature. As an alternative, stochastic optimization algorithms are specifically designed to tackle these types of challenges and are more common nowadays. This study proposed two stochastic, robust swarm-based metaheuristic optimization methods. They are both hybrid algorithms, which are formulated by combining Particle Swarm Optimization and Salp Swarm Optimization algorithms. Further, these algorithms are then applied to an important and thought-provoking problem. The problem is scientific workflow scheduling in multiple fog environments. Many computer environments, such as fog computing, are plagued by security attacks that must be handled. DDoS attacks are effectively harmful to fog computing environments as they occupy the fog's resources and make them busy. Thus, the fog environments would generally have fewer resources available during these types of attacks, and then the scheduling of submitted Internet of Things (IoT) workflows would be affected. Nevertheless, the current systems disregard the impact of DDoS attacks occurring in their scheduling process, causing the amount of workflows that miss deadlines as well as increasing the amount of tasks that are offloaded to the cloud. Hence, this study proposed a hybrid optimization algorithm as a solution for dealing with the workflow scheduling issue in various fog computing locations. The proposed algorithm comprises Salp Swarm Algorithm (SSA) and Particle Swarm Optimization (PSO). In dealing with the effects of DDoS attacks on fog computing locations, two Markov-chain schemes of discrete time types were used, whereby one calculates the average network bandwidth existing in each fog while the other determines the number of virtual machines existing in every fog on average. DDoS attacks are addressed at various levels. The approach predicts the DDoS attack’s influences on fog environments. Based on the simulation results, the proposed method can significantly lessen the amount of offloaded tasks that are transferred to the cloud data centers. It could also decrease the amount of workflows with missed deadlines. Moreover, the significance of green fog computing is growing in fog computing environments, in which the consumption of energy plays an essential role in determining maintenance expenses and carbon dioxide emissions. The implementation of efficient scheduling methods has the potential to mitigate the usage of energy by allocating tasks to the most appropriate resources, considering the energy efficiency of each individual resource. In order to mitigate these challenges, the proposed algorithm integrates the Dynamic Voltage and Frequency Scaling (DVFS) technique, which is commonly employed to enhance the energy efficiency of processors. The experimental findings demonstrate that the utilization of the proposed method, combined with the Dynamic Voltage and Frequency Scaling (DVFS) technique, yields improved outcomes. These benefits encompass a minimization in energy consumption. Consequently, this approach emerges as a more environmentally friendly and sustainable solution for fog computing environments