Journal Article10.1109/TMC.2019.2927214
Reliability-Aware Virtualized Network Function Services Provisioning in Mobile Edge Computing
77
TL;DR: This paper formulate a novel reliability-aware VNF instance placement problem by provisioning primary and secondary VNF instances at different cloudlets in MEC for each user while meeting the specified reliability requirement of the user request, and formulates an Integer Linear Programming (ILP) solution.
read more
Abstract: Along with Network Function Virtualization (NFV), Mobile Edge Computing (MEC) is becoming a new computing paradigm that enables accommodating innovative applications and services with stringent response delay and resource requirements, including autonomous vehicles and augmented reality. Provisioning reliable network services for users is the top priority of most network service providers, as unreliable services or severe service failures can result in tremendous losses of users, particularly for their mission-critical applications. In this paper, we study reliability-aware VNF instances provisioning in an MEC, where different users request different network services with different reliability requirements through paying their requested services with the aim to maximize the network throughput. To this end, we first formulate a novel reliability-aware VNF instance placement problem by provisioning primary and secondary VNF instances at different cloudlets in MEC for each user while meeting the specified reliability requirement of the user request. We then show that the problem is NP-hard and formulate an Integer Linear Programming (ILP) solution. Due to the NP-hardness of the problem, we instead devise an approximation algorithm with a logarithmic approximation ratio for the problem. Moreover, we also consider two special cases of the problem. For one special case where each request only requests one primary and one secondary VNF instances, the problem is still NP-hard, and we devise a constant approximation algorithm for it. For another special case where different VNFs have the same amounts of computing resource demands, we show that it is polynomial-time solvable by developing a dynamic programming solution for it. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising, and the empirical results of the algorithms outperform their analytical counterparts as theoretical estimations usually are very conservative.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A Survey of Recent Advances in Edge-Computing-Powered Artificial Intelligence of Things
TL;DR: An extensive survey of an end-edge-cloud orchestrated architecture for flexible AIoT systems and the emerging technologies for AI models regarding inference and training at the edge of the network are reviewed.
245
Reverse Auction-Based Computation Offloading and Resource Allocation in Mobile Cloud-Edge Computing
TL;DR: Simulations show that RACORAM is very close to the optimal method with significantly reduced computational complexity, and greatly outperforms the other baseline methods in terms of the CSC’s cost under different scenarios.
90
Service Provisioning for UAV-Enabled Mobile Edge Computing
TL;DR: This paper jointly optimize the service placement, UAV movement trajectory, task scheduling, and computation resource allocation, to minimize the overall energy consumption of all terrestrial user equipments (UEs) and proposes two alternating optimization-based suboptimal solutions with different time complexities.
81
Reliability-Aware Network Service Provisioning in Mobile Edge-Cloud Networks
TL;DR: This article focuses on reliable VNF service provisioning in MECs, by placing primary and backup VNF instances to cloudlets in an MEC network to meet the service reliability requirements of users and develops two efficient online algorithms for the problem.
80
Holu: Power-Aware and Delay-Constrained VNF Placement and Chaining
TL;DR: This work proposes Holu, a fast heuristic framework that efficiently solves the PD-VPR problem in an online manner and outperforms the state-of-the-art algorithms in terms of total power consumption and acceptance rate.
78
References
Emergence of Scaling in Random Networks
TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
39.1K
Fog computing and its role in the internet of things
Flavio Bonomi,Rodolfo A. Milito,Jiang Zhu,Sateesh Addepalli +3 more
- 17 Aug 2012
TL;DR: This paper argues that the above characteristics make the Fog the appropriate platform for a number of critical Internet of Things services and applications, namely, Connected Vehicle, Smart Grid, Smart Cities, and, in general, Wireless Sensors and Actuators Networks (WSANs).
The Case for VM-Based Cloudlets in Mobile Computing
TL;DR: The results from a proof-of-concept prototype suggest that VM technology can indeed help meet the need for rapid customization of infrastructure for diverse applications, and this article discusses the technical obstacles to these transformations and proposes a new architecture for overcoming them.
Fog Computing and Its Role in the Internet of Things
C. V. Nisha Angeline,Raja Lavanya +1 more
- 01 Jan 2019
TL;DR: This chapter argues that the above characteristics make the Fog the appropriate platform for a number of critical internet of things services and applications, namely connected vehicle, smart grid, smart cities, and in general, wireless sensors and actuators networks (WSANs).
2.5K
Network Function Virtualization: State-of-the-Art and Research Challenges
TL;DR: In this article, the authors survey the state-of-the-art in NFV and identify promising research directions in this area, and also overview key NFV projects, standardization efforts, early implementations, use cases, and commercial products.