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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Towards Elasticity in Heterogeneous Edge-dense Environments
Edge computing has enabled a large set of emerging
edge applications by exploiting data proximity and offloading
computation-intensive workloads to nearby edge servers. However,
supporting edge application users at scale poses challenges
due to limited point-of-presence edge sites and constrained
elasticity. In this paper, we introduce a densely-distributed edge
resource model that leverages capacity-constrained volunteer
edge nodes to support elastic computation offloading. Our model
also enables the use of geo-distributed edge nodes to further
support elasticity. Collectively, these features raise the issue of
edge selection. We present a distributed edge selection approach
that relies on client-centric views of available edge nodes to
optimize average end-to-end latency, with considerations of
system heterogeneity, resource contention and node churn. Elasticity
is achieved by fine-grained performance probing, dynamic
load balancing, and proactive multi-edge node connections per
client. Evaluations are conducted in both real-world volunteer
environments and emulated platforms to show how a common
edge application, namely AR-based cognitive assistance, can
benefit from our approach and deliver low-latency responses to
distributed users at scale.
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- Award ID(s):
- 1908566
- NSF-PAR ID:
- 10354987
- Date Published:
- Journal Name:
- ICDCS
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
