Fog Computing in a Developing World Context: Jumping on the Bandwagon

Bvuma, Stella; Bwalya, Kelvin Joseph

doi:10.1007/978-3-319-94890-4_4

Cited by 5 publications

(1 citation statement)

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“…However, Cloud and Fog computing paradigms are no longer suitable for smart real-time applications, since they do not offer sufficiently low single-digit latency to support cognitive assistance, virtual reality, and augmented reality applications, to name a few examples [8]- [11]. Furthermore, many underdeveloped regions and countries lack both the appropriate network infrastructure to support stable connections to the cloud, and the resources for setting up city-level fog servers [12], [13].…”

Section: Introductionmentioning

confidence: 99%

MAESTRO: Orchestrating Computational Offloading to Multiple FemtoClouds in Various Communication Environments

2022

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Many novel IoT-based applications demand low latency, large compute resources and high privacy. These requirements have motivated the emergence of fog and edge computing to complement the low-privacy and high-latency cloud. The idea of Fog computing is to place computational servers closer to the user, typically within the city's vicinity, to reduce latency. However, because of the high deployment cost of these servers at scale, and unreliable network infrastructures in many countries and areas, edge computing was proposed. Edge computing advocates leveraging compute resources, typically 0-hops away, on distributed ensembles of colocated devices called FemtoClouds. In this paper, we propose Maestro, a system that enables users to offload computational jobs to multiple FemtoClouds in their immediate vicinity. For Maestro, we propose an integrated architecture that includes two new scheduling algorithms for assigning computing workloads to FemtoClouds. Each of our scheduling algorithms is designed to allow the system to operate more efficiently given poor or strong network infrastructure. We implement a full prototype of our system to assess its performance on our experimental testbed. The results indicate that in communication-challenged environments, our specialized scheduler outperforms state-of-the-art schedulers by up to 55%, while in communication-friendly environments our other specialized scheduler outperforms state-of-the-art schedulers by up to 67%.

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Section: Introductionmentioning

confidence: 99%