Deploying Chains of Virtual Network Functions: On the Relation Between Link and Server Usage

Kuo, Tung-Wei; Liou, Bang-Heng; Lin, Kate Ching-Ju; Tsai, Ming‐Jer

doi:10.1109/tnet.2018.2842798

Cited by 172 publications

(69 citation statements)

References 40 publications

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“…Furthermore, the constraints considered in [18] force the DAG tasks to be only mapped into the leaves of the tree-shaped graph of the underlying data center. The contributions in [19] and [20] focus on the (quite recent) problem of the embedding of the service chains. Triggered by the emerging trend of Network Function Virtualization (NFV), the common goal of these contributions is to map a linear application DAG (i.e., a DAG with chain topology) onto the physical path joining fixed source and sink computing nodes, so that a sequential chain of operations may be performed on the data packets moving from the source node to the destination one.…”

Section: Related Workmentioning

confidence: 99%

EcoMobiFog–Design and Dynamic Optimization of a 5G Mobile-Fog-Cloud Multi-Tier Ecosystem for the Real-Time Distributed Execution of Stream Applications

2019

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The emerging 5G paradigm will enable multi-radio smartphones to run high-rate stream applications. However, since current smartphones remain resource and battery-limited, the 5G era opens new challenges on how to actually support these applications. In principle, the service orchestration capability of the Fog and Cloud Computing paradigms could be an effective means of dynamically providing resource-augmentation to smartphones. Motivated by these considerations, the peculiar focus of this paper is on the joint and adaptive optimization of the resource and task allocations of mobile stream applications in 5G-supported multi-tier Mobile-Fog-Cloud virtualized ecosystems. The objective is the minimization of the computing-plus-network energy of the overall ecosystem under hard constraints on the minimum streaming rate and the maximum computing-plus-networking resources. To this end, (i) we model the target ecosystem energy by explicitly accounting for the virtualized and multi-core nature of the Fog/Cloud servers; (ii) since the resulting problem is nonconvex and involves both continuous and discrete variables, we develop an optimality-preserving decomposition into the cascade of a (continuous) resource allocation sub-problem and a (discrete) task-allocation sub-problem; (iii) we numerically solve the first sub-problem through a suitably designed set of gradient-based adaptive iterations, while we approach the solution of the second sub-problem by resorting to an ad-hoc-developed elitary Genetic algorithm. Finally, we design the main blocks of EcoMobiFog, a technological virtualized platform for supporting the developed solver. Extensive numerical tests confirm that the energy-delay performance of the proposed solving framework is typically within a few per-cent the benchmark one of the exhaustive searchbased solution.

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Section: Related Workmentioning

confidence: 99%

EcoMobiFog–Design and Dynamic Optimization of a 5G Mobile-Fog-Cloud Multi-Tier Ecosystem for the Real-Time Distributed Execution of Stream Applications

2019

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“…Many works, including [12]- [16], tackle the problems of VNF placement and routing from a network-centric viewpoint, i.e., they aim at minimizing the load of network resources. In particular, [12] seeks to balance the load on links and servers, while [13] studies how to optimize routing to minimize network utilization. The above approaches formulate mixed-integer linear programming (MILP) problems and propose heuristic strategies to solve them.…”

Section: Related Workmentioning

confidence: 99%

“…(13) The intuition behind constraints (11)- (13) is that Φ(h, l, q, r) mimics the behavior of the productÃ(h, q)Ã(l, r): if ei-therÃ(h, q) orÃ(l, r) are close to 0, then (11) and (12) guarantee that Φ(h, l, q, r) will also be close to zero; if both values are close to one, then (13) allows also Φ(h, l, q, r) to be close to one.…”

Section: Steps 1-2: Convex Formulationmentioning

confidence: 99%

VNF Placement and Resource Allocation for the Support of Vertical Services in 5G Networks

Agarwal

Malandrino

Chiasserini

et al. 2019

IEEE/ACM Trans. Networking

112

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One of the main goals of 5G networks is to support the technological and business needs of various industries (the so-called verticals), which wish to offer to their customers a wide range of services characterized by diverse performance requirements. In this context, a critical challenge lies in mapping in an automated manner the requirements of verticals into decisions concerning the network infrastructure, including VNF placement, resource assignment, and traffic routing. In this paper, we seek to make such decisions jointly, accounting for their mutual interaction, and efficiently. To this end, we formulate a queuingbased model and use it at the network orchestrator to optimally match the vertical's requirements to the available system resources. We then propose a fast and efficient solution strategy, called MaxZ, which allows us to reduce the solution complexity. Our performance evaluation, carried out accounting for multiple scenarios representative of real-world services, shows that MaxZ performs substantially better than state-ofthe-art alternatives and consistently close to the optimum. INTRODUCTION5G networks are envisioned to provide the computational, memory, and storage resources needed to run multiple third parties (referred to as vertical industries or verticals) with diverse communication and computation needs. Verticals provide network operators with the specification of the services they want to provide, e.g., the virtual network functions (VNFs) they want to use to process their data and the associated quality of service.Mobile network operators are in charge of mapping the requirements of the verticals into infrastructure management decisions. This task is part of the network orchestration, and includes making decisions concerning (i) the placement of the VNFs needed by the verticals across the infrastructure; (ii) the assignment of CPU, memory and storage resources to the VNFs; (iii) the routing of data across network nodes.These decisions interact with each other in ways that are complex and often counterintuitive. In this paper, we focus on the allocation of computational and network resources, and make such decisions jointly, accounting for (i) the requirements of each VNF and vertical; (ii) the capabilities of the network operator's infrastructure; (iii) the capacity • S. Agarwal is with IIT Guwahati, India. F. Malandrino and C.-F. Chiasserini are with Politecnico di Torino, Italy and CNR-IEIIT, Italy. S. De is with IIT Delhi, India. • A preliminary version [1] of this work was presented at the IEEE INFOCOM 2018 conference.

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“…5G will significantly exploit the MEC paradigm, and a substantial body of research is devoted to the problem of placing VNFs across the network providers' servers. As an example, [18], [19], [20], [21], [22] tackle the problems of VNF placement and routing from a network-centric viewpoint, i.e., they aim at minimizing the load of network resources. In particular, [18] seeks to balance the load on links and servers, while [19] studies how to optimize routing to minimize network utilization.…”

Section: Related Workmentioning

confidence: 99%

From Megabits to CPU Ticks: Enriching a Demand Trace in the Age of MEC

Malandrino

Chiasserini

Avino

et al. 2020

IEEE Trans. Big Data

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All the content consumed by mobile users, be it a web page or a live stream, undergoes some processing along the way; as an example, web pages and videos are transcoded to fit each device's screen. The recent multi-access edge computing (MEC) paradigm envisions performing such processing within the cellular network, as opposed to resorting to a cloud server on the Internet. Designing a MEC network, i.e., placing and dimensioning the computational facilities therein, requires information on how much computational power is required to produce the contents needed by the users. However, real-world demand traces only contain information on how much data is downloaded. In this paper, we demonstrate how to enrich demand traces with information about the computational power needed to process the different types of content, and we show the substantial benefit that can be obtained from using such enriched traces for the design of MEC-based networks.

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Deploying Chains of Virtual Network Functions: On the Relation Between Link and Server Usage

Cited by 172 publications

References 40 publications

EcoMobiFog–Design and Dynamic Optimization of a 5G Mobile-Fog-Cloud Multi-Tier Ecosystem for the Real-Time Distributed Execution of Stream Applications

EcoMobiFog–Design and Dynamic Optimization of a 5G Mobile-Fog-Cloud Multi-Tier Ecosystem for the Real-Time Distributed Execution of Stream Applications

VNF Placement and Resource Allocation for the Support of Vertical Services in 5G Networks

From Megabits to CPU Ticks: Enriching a Demand Trace in the Age of MEC

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