VNF placement algorithms to address the mono-and multi-tenant issues in edge and core networks

Morin, Christine; Texier, Géraldine; Caillouet, Christelle; Desmangles, Gilles; Phan, Cao-Thanh

doi:10.1109/cloudnet47604.2019.9064108

Cited by 8 publications

(8 citation statements)

References 11 publications

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“…Following our Platform components dimension, we distinguish the cloud-edge and the multi-edge scenarios depending on the placement options. The authors of [58], [61], [62], [65]- [67], [73], [76], [125], [126], [128], [162]- [164], [167], [175], [179] consider the cloud-edge scenario where the service components can be run either in the available edge domains or in the central cloud. Other research papers [83], [88], [89], [91], [92], [96], [97], [134], [138] investigate the multi-edge option where the central cloud cannot be used as a runtime environment.…”

Section: Multiple Components With Connectionmentioning

confidence: 99%

“…However, envisioned large scale, e.g., global scale, service deployments require compute and network resources from multiple providers and only a federation of operators can provision these services and applications. For example, in [58], [167], novel mechanisms for multiprovider scenarios are proposed and investigated.…”

Section: Multiple Components With Connectionmentioning

confidence: 99%

“…In some papers, these entities are VMs, containers, or for example, pods in the realm of Kubernetes [6]. Nevertheless, the models are the abstract translations of the actual entities into, most often, combinatorial problems, in which the placement decision is to be made on possibly multiple layers of our architecture Convex optimization [80] Interior-point method [142] Alternating direction method of multipliers Assignment method [126], [128] Hungarian method ILP Solver [58], [63], [120], [175] Linear programming [88], [176], [178] LP with fractal solution rounding…”

Section: A Problem Formulationmentioning

confidence: 99%

“…Graph theory [118] Shortest path [51], [136] Bipartite graph matching Game theory [84] Best response search [97] Matching game Search algorithms [73], [106] Tabu search Evolutionary algorithms [182] Particle swarm optimization Other heuristic solution [54], [58], [74], [83], [90], [103], [105], [107], [111], [121], [163], [177], [180] n/a [49] MILP Solver [57] Linear programming [59] LP with fractal solution rounding [52], [53] Column generation Convex optimization [117] Semidefinite relaxation Stochastic control [127] Cache replacement policies Assignment method [131], [140] Hungarian method Search algorithms [79] Branch and bound Other heuristic solution [91], [146], [169], [171], [172], [174] Nonlinear [56], [60] Hungarian method…”

Section: A Problem Formulationmentioning

confidence: 99%

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Survey on Placement Methods in the Edge and Beyond

Sonkoly

Czentye

Szalay

et al. 2021

IEEE Commun. Surv. Tutorials

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Edge computing is a (r)evolutionary extension of traditional cloud computing. It expands central cloud infrastructure with execution environments close to the users in terms of latency in order to enable a new generation of cloud applications. This paradigm shift has opened the door for telecommunications operators, mobile and fixed network vendors: they have joined the cloud ecosystem as essential stakeholders considerably influencing the future success of the technology. A key problem in edge computing is the optimal placement of computational units (virtual machines, containers, tasks or functions) of novel distributed applications. These components are deployed to a geographically distributed virtualized infrastructure and heterogeneous networking technologies are invoked to connect them while respecting quality requirements. The optimal hosting environment should be selected based on multiple criteria by novel scheduler algorithms which can cope with the new challenges of distributed cloud architecture where networking aspects cannot be ignored. The research community has dedicated significant efforts to this topic during recent years and a vast number of theoretical results have been published addressing different variants of the related mathematical problems. However, a comprehensive survey focusing on the technical and analytical aspects of the placement problem in various edge architectures is still missing. This survey provides a comprehensive summary and a structured taxonomy of the vast research on placement of computational entities in emerging edge infrastructures. Following the given taxonomy, the research papers are analyzed and categorized according to several dimensions, such as the capabilities of the underlying platforms, the structure of the supported services, the problem formulation, the applied mathematical methods, the objectives and constraints incorporated in the optimization problems, and the complexity of the proposed methods. We summarize the gained insights and important lessons learned, and finally, we reveal some important research gaps in the current literature.

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Section: Multiple Components With Connectionmentioning

confidence: 99%

Section: Multiple Components With Connectionmentioning

confidence: 99%

Section: A Problem Formulationmentioning

confidence: 99%

Section: A Problem Formulationmentioning

confidence: 99%

See 2 more Smart Citations

Survey on Placement Methods in the Edge and Beyond

Sonkoly

Czentye

Szalay

et al. 2021

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

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“…In this work, we have selected Germany50 as the network topology from SNDlib [40] [41]. Germany50 consists of 50 nodes and they are interconnected by 88 links.…”

Section: A Simulation Setupmentioning

confidence: 99%

Resilient and Latency-aware Orchestration of Network Slices Using Multi-connectivity in MEC-enabled 5G Networks

Thiruvasagam,

Chakraborty,

Murthy

2021

Preprint

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Network slicing and multi-access edge computing (MEC) are new paradigms which play key roles in 5G and beyond networks. In particular, network slicing allows network operators (NOs) to divide the available network resources into multiple logical network slices (NSs) for providing dedicated virtual networks tailored to the specific service/business requirements. MEC enables NOs to provide diverse ultra-low latency services for supporting the needs of different industry verticals by moving computing facilities to the network edge. An NS can be constructed/deployed by instantiating a set of virtual network functions (VNFs) on top of MEC cloud servers for provisioning diverse latency-sensitive/time-critical communication services (e.g., autonomous driving and augmented reality) on demand at a lesser cost and time. However, VNFs, MEC cloud servers, and communication links are subject to failures due to software bugs, misconfiguration, overloading, hardware faults, cyber attacks, power outage, and natural/man-made disaster. Failure of a critical network component disrupts services abruptly and leads to users' dissatisfaction, which may result in revenue loss for the NOs. In this paper, we present a novel approach based on multi-connectivity in 5G networks to tackle this problem and our proposed approach is resilient against i) failure of VNFs, ii) failure of local servers within MEC, iii) failure of communication links, and iv) failure of an entire MEC cloud facility in regional level. To this end, we formulate the problem as a binary integer programming (BIP) model in order to optimally deploy NSs with the minimum cost, and prove it is NP-hard. Since the exact optimal solution for the NP-hard problem cannot be efficiently computed in polynomial time, we propose an efficient genetic algorithm based heuristic to obtain near-optimal solution in polynomial time. By extensive simulations, we show that our proposed approach not only reduces resource wastage, but also improves throughput while providing high resiliency against failures.

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MAP-MIND: An Offline Algorithm for Optimizing Game Engine Module Placement in Cloud Gaming

Lotfimahyari,

De Giovanni,

Gadia

et al. 2024

IEEE Access

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Online gaming has seen a significant surge in popularity, becoming a dominant form of entertainment worldwide. This growth has necessitated the evolution of game servers from centralized to distributed models, leading to the emergence of distributed game engines. These engines allow for the distribution of game engine modules (GEMs) across multiple servers, improving scalability and performance. However, this distribution presents a new challenge: the game engine module placement problem. This problem involves strategically placing GEMs to maximize the number of accepted placement requests while minimizing the delay experienced by players, a critical factor in enhancing the gaming experience. The problem can be formulated as an Integer Linear Programming (ILP) model, which provides an optimal solution but suffers from high computational complexity, making it impractical for real-world applications. To address this challenge, this paper introduces two novel heuristic algorithms, MAP-MIND and MAP-MIND*. The MAP-MIND algorithm demonstrates superior performance, achieving near-optimal delay and more than 92% GEM request acceptance in the worst heterogeneous scenarios. The MAP-MIND* algorithm, while slightly under-performing MAP-MIND in terms of delay, proves to be significantly faster, making it a viable alternative for real-world applications with equal GEM request acceptance. The tradeoff between the two algorithms offers a flexible approach to GEM placement, balancing performance and computational efficiency.INDEX TERMS Cloud gaming, distributed game engines, placement algorithm

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VNF placement algorithms to address the mono-and multi-tenant issues in edge and core networks

Cited by 8 publications

References 11 publications

Survey on Placement Methods in the Edge and Beyond

Survey on Placement Methods in the Edge and Beyond

Resilient and Latency-aware Orchestration of Network Slices Using Multi-connectivity in MEC-enabled 5G Networks

MAP-MIND: An Offline Algorithm for Optimizing Game Engine Module Placement in Cloud Gaming

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