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Abstract: Task offloading, which refers to processing (computation-intensive) data at facilitating servers, is an exemplary service that greatly benefits from the fog computing paradigm, which brings computation resources to the edge network for reduced application latency. However, the resource-consuming nature of task execution, as well as the sheer scale of IoT systems, raises an open and challenging question: whether fog is a remedy or a resource drain, considering frequent and massive offloading operations? This question is nontrivial, because participants of offloading processes, i.e., fog nodes, may have diversified technical specifications, while task generators, i.e., task nodes, may employ a variety of criteria to select offloading targets, resulting in an unmanageable space for performance evaluation. To overcome these challenges of heterogeneity, we propose a gravity model that characterizes offloading criteria with various gravity functions, in which individual/system resource consumption can be examined by the device/network effort metrics, respectively. Simulation results show that the proposed gravity model can flexibly describe different offloading schemes in terms of application and node-level behavior. We find that the expected lifetime and device effort of individual tasks decrease as O(1/N) over the network size N , while the network effort decreases much slower, even remain O(1) when load balancing measures are employed, indicating a possible resource drain in the edge network.
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Hyperprofile-Based Computation Offloading for Mobile Edge Networks
In recent studies, researchers have developed various computation offloading frameworks for bringing cloud services closer to the user via edge networks. Specifically, an edge device needs to offload computationally intensive tasks because of energy and processing constraints. These constraints present the challenge of identifying which edge nodes should receive tasks to reduce overall resource consumption. We propose a unique solution to this problem which incorporates elements from Knowledge-Defined Networking (KDN) to make intelligent predictions about offloading costs based on historical data. Each server instance can be represented in a multidimensional feature space where each dimension corresponds to a predicted metric. We compute features for a "hyperprofile" and position nodes based on the predicted costs of offloading a particular task. We then perform a k-Nearest Neighbor (kNN) query within the hyperprofile to select nodes for offloading computation. This paper formalizes our hyperprofile-based solution and explores the viability of using machine learning (ML) techniques to predict metrics useful for computation offloading. We also investigate the effects of using different distance metrics for the queries. Our results show various network metrics can be modeled accurately with regression, and there are circumstances where kNN queries using Euclidean distance as opposed to rectilinear distance is more favorable.
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- PAR ID:
- 10085560
- Date Published:
- Journal Name:
- 2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)
- Page Range / eLocation ID:
- 525 to 529
- 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/10.1109/MASS.2017.91
