SPORTS: A Semi-partitioned Real-Time Scheduler for Heterogeneous Multicore Platforms

Sharma, Yanshul; Das, Zinea; Moulik, Sanjay

doi:10.1007/978-981-16-0010-4_35

Cited by 2 publications

(1 citation statement)

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“…Scheduling in the fog landscape involves complexity because of heterogeneous devices and the resource-constraint nature of end devices. This heterogeneity makes resource scheduling an NP-hard optimization problem [143]. It can also be described as the optimal placement of different IoT-based tasks on fog nodes in order to meet realtime QoS requirements by minimizing task execution time and abiding by user SLAs [144].…”

Section: E Resource Schedulingmentioning

confidence: 99%

AI-Empowered Fog/Edge Resource Management for IoT Applications: A Comprehensive Review, Research Challenges, and Future Perspectives

Walia,

Kumar,

Gill

2024

IEEE Commun. Surv. Tutorials

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The proliferation of ubiquitous Internet of Things (IoT) sensors and smart devices in several domains embracing healthcare, Industry 4.0, transportation and agriculture are giving rise to a prodigious amount of data requiring everincreasing computations and services from cloud to the edge of the network. Fog/Edge computing is a promising and distributed computing paradigm that has drawn extensive attention from both industry and academia. The infrastructural efficiency of these computing paradigms necessitates adaptive resource management mechanisms for offloading decisions and efficient scheduling. Resource Management (RM) is a non-trivial issue whose complexity is the result of heterogeneous resources, incoming transactional workload, edge node discovery, and Quality of Service (QoS) parameters at the same time, which makes the efficacy of resources even more challenging. Hence, the researchers have adopted Artificial Intelligence (AI)-based techniques to resolve the abovementioned issues. This paper offers a comprehensive review of resource management issues and challenges in Fog/Edge paradigm by categorizing them into provisioning of computing resources, task offloading, resource scheduling, service placement, and load balancing. In addition, existing AI and non-AI based state-of-the-art solutions have been discussed, along with their QoS metrics, datasets analysed, limitations and challenges. The survey provides mathematical formulation corresponding to each categorized resource management issue. Our work sheds light on promising research directions on cutting-edge technologies such as Serverless computing, 5G, Industrial IoT (IIoT), blockchain, digital twins, quantum computing, and Software-Defined Networking (SDN), which can be integrated with the existing frameworks of fog/edge-of-things paradigms to improve business intelligence and analytics amongst IoT-based applications.

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Section: E Resource Schedulingmentioning

confidence: 99%