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Title: Elastic Multi-resource Network Slicing: Can Protection Lead to Improved Performance?
In order to meet the performance/privacy require- ments of future data-intensive mobile applications, e.g., self- driving cars, mobile data analytics, and AR/VR, service providers are expected to draw on shared storage/computation/connectivity resources at the network “edge”. To be cost-effective, a key functional requirement for such infrastructure is enabling the shar- ing of heterogeneous resources amongst tenants/service providers supporting spatially varying and dynamic user demands. This paper proposes a resource allocation criterion, namely, Share Constrained Slicing (SCS), for slices allocated predefined shares of the network’s resources, which extends traditional α−fairness criterion, by striking a balance among inter- and intra-slice fairness vs. overall efficiency. We show that SCS has several desirable properties including slice-level protection, envyfreeness, and load- driven elasticity. In practice, mobile users’ dynamics could make the cost of implementing SCS high, so we discuss the feasibility of using a simpler (dynamically) weighted max-min as a surrogate resource allocation scheme. For a setting with stochastic loads and elastic user requirements, we establish a sufficient condition for the stability of the associated coupled network system. Finally, and perhaps surprisingly, we show via extensive simulations that while SCS (and/or the surrogate weighted max-min allocation) provides inter-slice protection, they can achieve improved job delay and/or perceived throughput, as compared to other weighted max- min based allocation schemes whose intra-slice weight allocation is not share-constrained, e.g., traditional max-min or discriminatory processor sharing.  more » « less
Award ID(s):
1731658
NSF-PAR ID:
10097243
Author(s) / Creator(s):
;
Date Published:
Journal Name:
International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks WiOpt 2019
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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