A Fog Computing Framework for Quality of Service Optimisation in the Internet of Things (IoT) Ecosystem

W. T. Vambe, K. Sibanda
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引用次数: 2

Abstract

Fog computing plays a pivotal role in the Internet of Things (IoT) ecosystem because of its ability to support delay-sensitive tasks, bringing resources from cloud servers closer to the “ground” to support IoT devices that are resource-constrained. Although fog computing offers a lot of benefits such as quick response to requests, geo-distributed data processing and data processing in the proximity of the IoT devices, the exponential increase of IoT devices and large volumes of data being generated has led to a new set of challenges. One such challenge is the allocation of resources to IoT tasks to match their computational needs and QoS requirements whilst meeting task deadlines. Most proposed solutions in existing works suggest task offloading mechanisms where IoT devices would offload their tasks randomly to the fog layer. Of course, this helps in minimizing the communication delay, however, most tasks would end up missing their deadlines as many delays are experienced when fog node is deciding to process part of the task or offloading it to the next fog node. In this paper, we propose and introduce a Resource Allocation Scheduler (RAS) at the IoT-Fog gateway whose goal is to decide where and when a task is to be offloaded either to the fog layer or the cloud layer based on their priority needs, computational needs and QoS requirements and minimize round-trip time. The study followed the four phases of the top-down methodology. To test the efficiency and effectiveness of the RAS, a model was evaluated in a simulated smart home setup. The important metrics that were used are the queuing time, offloading time and throughput. The results showed that RAS helps in minimizing the round-trip time, increase throughput and improve QoS. Furthermore, the approach addressed the starvation problem, which was affecting low priority tasks. Most importantly, the results provide evidence that if resource allocation and assignment are done properly, round-trip time (queuing time and offloading time) can be reduced and QoS can be improved in fog computing.
物联网生态系统中服务质量优化的雾计算框架
雾计算在物联网(IoT)生态系统中发挥着关键作用,因为它能够支持延迟敏感任务,使云服务器的资源更接近“地面”,以支持资源受限的物联网设备。尽管雾计算提供了许多好处,例如对请求的快速响应、地理分布式数据处理和物联网设备附近的数据处理,但物联网设备的指数级增长和生成的大量数据带来了一系列新的挑战。其中一个挑战是为物联网任务分配资源,以匹配其计算需求和QoS要求,同时满足任务期限。现有工作中提出的大多数解决方案都建议任务卸载机制,其中物联网设备将随机地将其任务卸载到雾层。当然,这有助于最小化通信延迟,但是,当雾节点决定处理部分任务或将其卸载到下一个雾节点时,大多数任务最终会错过截止日期。在本文中,我们在物联网雾网关中提出并引入了一个资源分配调度器(RAS),其目标是根据雾层或云层的优先级需求、计算需求和QoS要求,决定任务在何时何地卸载到雾层或云层,并最大限度地减少往返时间。该研究遵循了自上而下方法的四个阶段。为了测试RAS的效率和有效性,在模拟智能家居设置中评估了一个模型。使用的重要指标是排队时间、卸载时间和吞吐量。结果表明,RAS有助于减少往返时间,提高吞吐量和改善QoS。此外,这种方法还解决了饥饿问题,这影响到低优先级的任务。最重要的是,结果提供了证据,如果资源分配和分配得当,可以减少往返时间(排队时间和卸载时间),并可以提高雾计算中的QoS。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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