Data Uncertainty in Virtual Network Embedding: Robust Optimization and Protection Levels

Coniglio, Stefano; Koster, Arie M. C. A.; Tieves, Martin

doi:10.1007/s10922-016-9376-x

Cited by 17 publications

(7 citation statements)

References 115 publications

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“…Moreover, [15] measures the risk of dynamical resource reservation by CVaR. Some studies on NSE also consider some uncertain factors for better robust [16][17][18]. To the best of our knowledge, NSE-RRS is the first time to model resource-shared NSE to improve utilization from the aspect of risk measure.…”

Section: B Risk Measurementioning

confidence: 99%

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NSE-RRS: Network Slicing Embedding With Risk-Controlled Resource Sharing

Liu

Huang

et al. 2021

IEEE Access

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Network slicing (NS) is a promising technology of 5G that provides end-to-end customized network service to multi-tenant. The network slicing embedding (NSE) problem is one of the essential issues in NS technology. In most NSE solutions, the resource conservation is dedicated and utilizationinefficient. Applying the resource sharing into NSE is the most direct way to improve resource utilization, but introduce the collision risk. This paper puts forward a novel NSE solution with risk-controlled resource sharing, namely NSE-RRS. We abstract the embedded slices' loads as the uncertainties and formulate the risk as chance constraints. Hence NSE-RRS is formulated as a chance-constrained program. We apply the distributionally robust approach in terms of conditional value at risk (CVaR) to give a deterministic and tractable form of NSE-RRS. Then, we use a two-steps heuristic algorithm to solve NSE-RRS. Finally, numerical evaluation verifies that NSE-RRS can effectively improve utilization with controlled risk.

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Section: B Risk Measurementioning

confidence: 99%

“…So far, replacing the chance constraints ( 13) and ( 14) by the corresponding conservative approximation ( 18) and ( 19) respectively, we can transform the chance constrained optimization problem (16) into the deterministic optimization problem as follows.…”

Section: Conservative Approximation Of Chance Constraintsmentioning

confidence: 99%

NSE-RRS: Network Slicing Embedding With Risk-Controlled Resource Sharing

Liu

Huang

et al. 2021

IEEE Access

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“…In order to cope with uncertain input parameters, RO [10] is applied to solve optimization problems. Indeed, RO has been successfully employed in different contexts, such as in resource allocation in OFDM networks [26] with uncertainty in channel state information, in cloud resource provisioning without perfect knowledge of demands and price [21], and even in the virtual network embedding problem on a physical network substrate in recent works [27][28][29][30] that usually deal with uncertain service demands.…”

Section: Related Workmentioning

confidence: 99%

A Robust Optimization Based Energy-Aware Virtual Network Function Placement Proposal for Small Cell 5G Networks with Mobile Edge Computing Capabilities

Blanco

Taboada

Fajardo

et al. 2017

Mobile Information Systems

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In the context of cloud-enabled 5G radio access networks with network function virtualization capabilities, we focus on the virtual network function placement problem for a multitenant cluster of small cells that provide mobile edge computing services. Under an emerging distributed network architecture and hardware infrastructure, we employ cloud-enabled small cells that integrate microservers for virtualization execution, equipped with additional hardware appliances. We develop an energy-aware placement solution using a robust optimization approach based on service demand uncertainty in order to minimize the power consumption in the system constrained by network service latency requirements and infrastructure terms. Then, we discuss the results of the proposed placement mechanism in 5G scenarios that combine several service flavours and robust protection values. Once the impact of the service flavour and robust protection on the global power consumption of the system is analyzed, numerical results indicate that our proposal succeeds in efficiently placing the virtual network functions that compose the network services in the available hardware infrastructure while fulfilling service constraints.

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“…Reddy, Baumgartner and Bauschert propose a similar approach in [7] using Γ-robustness optimization to handle unpredictable short-term mobile data traffic increase. Coniglio et al [8] developed a chance-constraint formalization of the general VNE problem without latency constraints. The approaches above share the assumption that the probability distributions of the uncertain parameters are unknown.…”

Section: Introduction and Related Workmentioning

confidence: 99%

An Efficient Model for Mobile Network Slice Embedding under Resource Uncertainty

Fendt

Mannweiler

Schmelz

et al. 2019

2019 16th International Symposium on Wireless Communication Systems (ISWCS)

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The fifth generation (5G) of mobile networks will support several new use cases, like the Internet of Things (IoT), massive Machine Type Communication (mMTC) and Ultra-Reliable and Low Latency Communication (URLLC) as well as significant improvements of the conventional Mobile Broadband (MBB) use case. End-to-end network slicing is a key-feature of 5G since it allows to share and at the same time isolate resources between several different use cases as well as between tenants by providing logical network. The virtual separation of the network slices on a common end-to-end mobile network infrastructure enables an efficient usage of the underlying network resources and provides means for security and safety related isolation of the defined logical networks. A much-discussed challenge is the reuse or overbooking of resources guaranteed by contract. However, there is a consensus that over-provisioning of mobile communication bands is economically infeasible and a certain risk of network overload is acceptable for the majority of the 5G use cases. In this paper, an efficient model for mobile network slice embedding is presented which enables an informed decision on network slice admission. This is based on the guaranteed endto-end mobile network resources that have to be provided on the one hand and the capacities and capabilities of the underlying network infrastructure on the other hand. The network slice embedding problem is solved in form of a Mixed Integer Linear Program with an uncertainty-aware objective function. Subsequently, the confidence in the availability of each resource is analyzed.

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Data Uncertainty in Virtual Network Embedding: Robust Optimization and Protection Levels

Cited by 17 publications

References 115 publications

NSE-RRS: Network Slicing Embedding With Risk-Controlled Resource Sharing

NSE-RRS: Network Slicing Embedding With Risk-Controlled Resource Sharing

A Robust Optimization Based Energy-Aware Virtual Network Function Placement Proposal for Small Cell 5G Networks with Mobile Edge Computing Capabilities

An Efficient Model for Mobile Network Slice Embedding under Resource Uncertainty

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