CaPTS scheduler: A context-aware priority tuple scheduling for Fog computing paradigm,Transactions on Emerging Telecommunications Technologies

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CaPTS scheduler: A context-aware priority tuple scheduling for Fog computing paradigm
Transactions on Emerging Telecommunications Technologies ( IF 2.5 ) Pub Date : 2022-09-15 , DOI: 10.1002/ett.4647
Mir Salim Ul Islam ₁ , Ashok Kumar _{1,

2}

Affiliation

With the increase in real-time latency-sensitive Internet of Things (IoT) applications, a huge amount of data is generated in the Fog-IoT paradigm. There is a need to schedule and execute this huge workload over Fog devices efficiently to support the increasing demand of these applications. But, Fog devices are resource-constrained in terms of processing/computing power, bandwidth as well as storage capacity which makes tuple scheduling a challenging problem. Moreover, due to the rise in IoT devices per application, a sharp increase in service response time, network congestion, and inefficiency in terms of energy consumption, and execution cost has been observed. Consequently, an efficient tuple scheduling algorithm is desirable that can reduce latency and network usage and optimize energy consumption and cost. Therefore, in this work, CaPTS scheduler: A Context-aware Priority Tuple Scheduling for Fog computing paradigm is designed and proposed. It takes into consideration various context-aware parameters such as task load of application, networking requirement, and data flow rate to set the priority of tuples and schedule them across Fog computing nodes while ensuring quick service response time and satisfying quality of service requirements of end-users. The CaPTS scheduler is implemented and evaluated using iFogSim toolkit on various performance metrics such as latency, network usage, energy consumption, and cost. Its performance is validated through a case study on the smart mining industry system. The results show that on an average the latency and network usage are minimized by 35.93% and 44.20%, while energy consumption and cost are optimized by 4.55% and 30.92%, respectively, in comparison with baseline techniques.

中文翻译：

CaPTS 调度器：用于雾计算范式的上下文感知优先级元组调度

随着对实时延迟敏感的物联网 (IoT) 应用程序的增加，雾物联网范例中会生成大量数据。需要在 Fog 设备上高效地安排和执行这种巨大的工作负载，以支持这些应用程序不断增长的需求。但是，雾设备在处理/计算能力、带宽以及存储容量方面受到资源限制，这使得元组调度成为一个具有挑战性的问题。此外，由于每个应用程序中物联网设备的增加，服务响应时间急剧增加、网络拥塞以及能源消耗和执行成本方面的低效率已经被观察到。因此，需要一种有效的元组调度算法来减少延迟和网络使用并优化能源消耗和成本。因此，在这项工作中，上下文优先级元组_ _ _ _设计并提出了雾计算范式的调度。综合考虑应用的任务负载、网络需求、数据流量等各种上下文感知参数，设置元组的优先级，并跨雾计算节点进行调度，同时保证快速的服务响应时间和满足端服务质量要求-用户。CaPTS 调度器是使用 iFogSim 工具包根据各种性能指标（如延迟、网络使用、能耗和成本）实施和评估的。通过对智能采矿业系统的案例研究验证了其性能。结果表明，与基线技术相比，延迟和网络使用平均减少了 35.93% 和 44.20%，而能耗和成本分别优化了 4.55% 和 30.92%。

更新日期：2022-09-15

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