Mobile applications have become increasingly sophisticated.
Emerging cognitive assistance applications can involve multiple
computationally intensive modules working continuously
and concurrently, further straining the already limited
resources on these mobile devices. While computation offloading
to the edge or the cloud is still the de facto solution, existing
approaches are limited by intra-application operations
only or edge-/cloud-centric scheduling. Instead, we argue that
operating system level coordination is needed on the mobile
side to adequately support the prospects of multi-application
offloading. Specifically, both the local mobile system resource
and the network bandwidth to reach the cloud need to be allocated
intelligently among concurrent offloading jobs.
In this paper, we build a system-level scheduler service,
LinkShare, that wraps over the operating system scheduler
to coordinate among multiple offloading requests. We further
study the scheduling requirements and suitable metrics, and
find that the most intuitive approaches of minimizing the end-to-
end processing time or earliest-deadline first scheduling
do not work well. Instead, LinkShare adopts earliest-deadline
first with limited sharing (EDF-LS), that balances real-time
requirements and fairness. Extensive evaluation of an Android
implementation of LinkShare shows that adding this
additional scheduler is essential, and that EDF-LS reduces the
deadline miss events by up to 30% compared to the baseline.
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Edge Cloud Offloading Algorithms: Issues, Methods, and Perspectives
Mobile devices supporting the "Internet of Things" (IoT), often have limited capabilities in computation, battery energy, and storage space, especially to support resource-intensive applications involving virtual reality (VR), augmented reality (AR), multimedia delivery and artificial intelligence (AI), which could require broad bandwidth, low response latency and large computational power. Edge cloud or edge computing is an emerging topic and technology that can tackle the deficiency of the currently centralized-only cloud computing
model and move the computation and storage resource closer to the devices in support of the above-mentioned applications. To make this happen, efficient coordination mechanisms and “offloading” algorithms are needed to allow the mobile devices and the edge cloud to work together smoothly. In this survey paper, we investigate the key issues, methods, and various state-of-the-art efforts related to the offloading problem. We adopt a new characterizing model to study the whole process of offloading from mobile devices to the edge cloud. Through comprehensive discussions, we aim to draw an overall “big picture” on the existing efforts and research directions. Our study also indicates that the offloading algorithms in edge cloud have demonstrated profound potentials for future technology and application development.
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- Award ID(s):
- 1647084
- NSF-PAR ID:
- 10082132
- Date Published:
- Journal Name:
- ACM computing surveys
- ISSN:
- 0360-0300
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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