Fuzzy Logic-based Robust Failure Handling Mechanism for Fog Computing

Fog computing is an emerging computing paradigm which is mainly suitable for time-sensitive and real-time Internet of Things (IoT) applications. Academia and industries are focusing on the exploration of various aspects of Fog computing for market adoption. The key idea of the Fog computing paradigm is to use idle computation resources of various handheld, mobile, stationery and network devices around us, to serve the application requests in the Fog-IoT environment. The devices in the Fog environment are autonomous and not exclusively dedicated to Fog application processing. Due to that, the probability of device failure in the Fog environment is high compared with other distributed computing paradigms. Solving failure issues in Fog is crucial because successful application execution can only be ensured if failure can be handled carefully. To handle failure, there are several techniques available in the literature, such as checkpointing and task migration, each of which works well in cloud based enterprise applications that mostly deals with static or transactional data. These failure handling methods are not applicable to highly dynamic Fog environment. In contrast, this work focuses on solving the problem of managing application failure in the Fog environment by proposing a composite solution (combining fuzzy logic-based task checkpointing and task migration techniques with task replication) for failure handling and generating a robust schedule. We evaluated the proposed methods using real failure traces in terms of application execution time, delay and cost. Average delay and total processing time improved by 56% and 48% respectively, on an average for the proposed solution, compared with the existing failure handling approaches.Comment: 12 Pages,12 Figure

Uma arquitetura resiliente baseada em agentes para instâncias transientes na computação em nuvem

Tese (doutorado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2019.Os provedores de nuvem computacional estão explorando seus recursos ociosos e oferecendo- os em forma de serviços não confiáveis. Conhecidos como instâncias transientes, estes serviços são sublocados a preços consideravelmente inferiores aos on-demand e podem ser revogados sem a intervenção do usuário. A Amazon AWS oferece este serviço, chamando de Instâncias Spot, por meio de um modelo de leilão, tendo o seu preço definido de acordo com a lei de oferta e procura. O usuário adquire uma Instância Spot enquanto o seu lance for superior ou igual ao valor da instância no mercado, sendo revogado caso contrário. Este serviço vem sendo utilizado para a execução de aplicações que necessitam de um consumo excessivo de CPU e memória. No entanto, para explorar de maneira eficiente estas instâncias, as aplicações precisam implementar técnicas de tolerância a falhas para evitar perda de dados. Neste cenário, esta pesquisa aborda o problema que envolve a utilização de instâncias transientes em um ambiente confiável, que garanta a execução da aplicação no menor tempo possível. Para uso adequado das instâncias tran- sientes, em uma abordagem multi-estratégica, esta pesquisa apresenta uma arquitetura baseada em agentes inteligentes para prover um ambiente resiliente e tolerante a falhas, denominada BRA2Cloud (A Brand new Agent Based Architecture for Cloud Comput- ing). BRA2Cloud combina a utilização de raciocínio baseado em casos com um modelo estatístico na predição do tempo de sobrevivência em instâncias transientes, refinando parâmetros de tolerância a falhas para reduzir o tempo total de execução. Experimentos demonstram que o modelo proposto é capaz de apresentar uma predição na garantia de tempo de revogação com níveis de acurácia de até 91%. A avaliação da proposta utilizou aproximadamente 21 milhões de registros de mudanças de preços reais, coletados a par- tir das Instâncias Spot, possibilitando a criação de mais de 357 milhões de registros na base de casos. Como resultado, a abordagem utilizada obteve uma redução no custo e uma diminuição no tempo total de execução de até 70,12% quando comparada a outras abordagens na literatura.Unused resources are being exploited by cloud computing providers, which are offering them as unreliable services. Known as transient instances, these services are sublet with low costs compared to on-demand, but without availability guarantee. Spot instances are transient instances offered by Amazon AWS, with rules that define prices according to supply and demand in a market model. These instances will run for as long as the current price is lower than the maximum bid price given by users. Spot instances have been in- creasingly used for executing computation and memory-intensive applications. By using dynamic fault-tolerant mechanisms and appropriate strategies, users can effectively use spot instances to run applications at a cheaper price and avoid losing processed data. In this scenario, this research addresses the problem involving the use of transient instances in a trusted environment that ensures application execution in the shortest possible time. This research presents a resilient agent-based architecture for transient instances in cloud computing, namely BRA2Cloud (A Brand new Agent Based Architecture for Cloud Com- puting). For appropriate usage of transient instances, using an multi-strategic approach, the architecture uses intelligent agents that combines machine learning and a statistical model to predict instance survival time, refine fault tolerance parameters and reduce total execution time. Experiments show that the proposed model is capable of presenting a prediction on revocation time guarantee with levels of accuracy up to 91%. The proposal evaluation used approximately 21 million records of actual price changes collected from Spot Instances, enabling the creation of over 357 million records in the case base. As a result, the approach used achieved a reduction in cost and a reduction in total execution time of up to 70.12% compared to other approaches in the literature

Reliable scheduling and resource allocation for IoT applications in fog computing

The massive development of ubiquitous computing leads the modern world to latency sensitive Internet of Things (IoT) applications such as smart transportation systems, healthcare services and emergency response services, in order to enable a better quality of life. These services generate a huge amount of data which need to be processed near to the edge. These kinds of applications can be handled effectively and efficiently using Cloud infrastructure because of the on-demand services and scalability feature of the Cloud. However, managing IoT applications in the Cloud exclusively is not a good solution for some applications, especially for those which are latency-sensitive. Thus, Fog computing has emerged which resides between the Cloud and the end devices. Generally, IoT devices and sensors are connected to the Fog devices. These Fog devices are located in close proximity and are responsible for intermediate computation and storage. Allocating resources for user applications to the highly dynamic, heterogeneous and complex Fog environment is challenging. In addition, the user might change their requirements dynamically and also require better reliability from the providers. Hence, reliable resource allocation and task scheduling techniques are required which consider the dynamic behaviour of the users’ requirements. In Fog devices, available resources are changing over time since they are not dedicated to running Fog applications. More-over, resource failure and link failure could occur frequently in the Fog environment because of a lack of central management, the autonomous characteristics of the devices and wireless connectivity. Furthermore, most of the Fog devices are battery-powered; therefore, resource allocation and scheduling techniques need to be energy-efficient. To address these problems, this thesis proposes several resource allocation and failure handling techniques to make the Fog environment reliable. The influence of strict execution time and data transfer time is also considered during resource allocation and scheduling. In the Fog, some users may request cost-effectiveness, rather than fast execution. Hence, cost-effectiveness is also investigated. An evaluation of the proposed methods was tested in a simulated environment. This thesis adds to the body of the knowledge by making the following contributions: 1. An extensive survey on architecture, resource allocation and scheduling and failure handling in the Fog computing environment. 2. A comprehensive study on Fog computing architecture to develop a simulation environment for Fog computing. 3. A deadline-based dynamic resource allocation and provisioning in an hierarchical and hybrid fashion with dynamic user behaviour. 4. A multi-criteria-based dynamic user behaviour aware resource allocation in which resources are dynamic. 5. A fuzzy logic-based failure handling mechanism to handle predicted and unpredicted failures in the Fog environment, in order to ensure robust scheduling. 6. A multiple linear regression-based energy-aware resource allocation mechanism to ensure reliable execution when most of the devices have limited available energy for operation