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.
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Optimizing Network Slicing via Virtual Resource Pool Partitioning
This paper focuses on optimizing resource allocation amongst a set of tenants, network slices, supporting dynamic customer loads over a set of distributed resources, e.g., base stations. The aim is to reap the benefits of statistical multiplexing resulting from flexible sharing of ‘pooled’ resources, while enabling tenants to differentiate and protect their performance from one another’s load fluctuations. To that end we consider a setting where resources are grouped into Virtual Resource Pools (VRPs) wherein resource allocation is jointly and dynam- ically managed. Specifically for each VRP we adopt a Share- Constrained Proportionally Fair (SCPF) allocation scheme where each tenant is allocated a fixed share (budget). This budget is to be distributed equally amongst its active customers which in turn are granted fractions of their associated VRP resources in proportion to customer shares. For a VRP with a single resource, this translates to the well known Generalized Processor Sharing (GPS) policy. For VRPs with multiple resources SCPF provides a flexible means to achieve load elastic allocations across tenants sharing the pool. Given tenants’ per resource shares and expected loads, this paper formulates the problem of determining optimal VRP partitions which maximize the overall expected shared weighted utility while ensuring protection guarantees. For a high load/capacity setting we exhibit this network utility function explicitly, quantifying the benefits and penalties of any VRP partition, in terms of network slices’ ability to achieve performance differentiation, load balancing, and statistical multiplexing. Although the problem is shown to be NP-Hard, a simple greedy heuristic is shown to be effective. Analysis and simulations confirm that the selection of optimal VRP partitions provide a practical avenue towards improving network utility in network slicing scenarios with dynamic loads.
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- Award ID(s):
- 1731658
- NSF-PAR ID:
- 10097244
- Date Published:
- Journal Name:
- WIOPT : Workshop on Resource Allocation, Cooperation and Competition in Wireless Networks
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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