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Task offloading in edge computing infrastructure remains a challenge for dynamic and complex environments, such as Industrial Internet-of-Things. The hardware resource constraints of edge servers must be explicitly considered to ensure that system resources are not overloaded. Many works have studied task offloading while focusing primarily on ensuring system resilience. However, in the face of deep learning-based services, model performance with respect to loss/accuracy must also be considered. Deep learning services with different implementations may provide varying amounts of loss/accuracy while also being more complex to run inference on. That said, communication latency can be reduced to improve overall Quality-of-Service by employing compression techniques. However, such techniques can also have the side-effect of reducing the loss/accuracy provided by deep learning-based service. As such, this work studies a joint optimization problem for task offloading decisions in 3-tier edge computing platforms where decisions regarding task offloading are made in tandem with compression decisions. The objective is to optimally offload requests with compression such that the trade-off between latency-accuracy is not greatly jeopardized. We cast this problem as a mixed integer nonlinear program. Due to its nonlinear nature, we then decompose it into separate subproblems for offloading and compression. An efficient algorithm is proposed to solve the problem. Empirically, we show that our algorithm attains roughly a 0.958-approximation of the optimal solution provided by a block coordinate descent method for solving the two sub-problems back-to-back.
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Gremlin: scheduling interactions in vehicular computing
Vehicular applications must not demand too much of a driver's attention. They often run in the background and initiate interactions with the driver to deliver important information. We argue that the vehicular computing system must schedule interactions by considering their priority, the attention they will demand, and how much attention the driver currently has to spare. Based on these considerations, it should either allow a given interaction or defer it. We describe a prototype called Gremlin that leverages edge computing infrastructure to help schedule interactions initiated by vehicular applications. It continuously performs four tasks: (1) monitoring driving conditions to estimate the driver's available attention, (2) recording interactions for analysis, (3) generating a user-specific quantitative model of the attention required for each distinct interaction, and (4) scheduling new interactions based on the above data. Gremlin performs the third task on edge computing infrastructure. Offload is attractive because the analysis is too computationally demanding to run on vehicular platforms. Since recording size for each interaction can be large, it is preferable to perform the offloaded computation at the edge of the network rather than in the cloud, and thereby conserve wide-area network bandwidth. We evaluate Gremlin by comparing its decisions to those recommended by a vehicular UI expert. Gremlin's decisions agree with the expert's over 90% of the time, much more frequently than the coarse-grained scheduling policies used by current vehicle systems. Further, we find that offloading of analysis to edge platforms reduces use of wide-area networks by an average of 15MB per analyzed interaction.
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- Award ID(s):
- 1717064
- PAR ID:
- 10073742
- Date Published:
- Journal Name:
- ACM/IEEE Symposium on Edge Computing
- Page Range / eLocation ID:
- 1 to 13
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3132211.3134450
