Availability-aware Service Function Chain Placement in Mobile Edge Computing

Yin, Xiaohan; Cheng, Bo; Wang, Meng; Chen, Junliang

doi:10.1109/services48979.2020.00028

Cited by 17 publications

(2 citation statements)

References 9 publications

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“…A greedy algorithm is a type of algorithm that aims to solve a problem by iteratively selecting the locally optimal choice at each step. Several studies [13,[30][31][32][33][34] have utilized the widely recognized greedy method as a comparative approach for evaluating the SFC deployment problem. While a greedy algorithm is often characterized by its simplicity and efficiency, this does not guarantee the attainment of an optimal solution in all cases.…”

Section: Greedy Algorithmmentioning

confidence: 99%

Service Function Chaining to Support Ultra-Low Latency Communication in NFV

Erbati,

Tajiki,

Schiele

2023

Electronics

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Network function virtualization (NFV) has the potential to fundamentally transform conventional network architecture through the decoupling of software from dedicated hardware. The convergence of virtualization and cloud computing technologies has revolutionized the networking landscape, offering a wide range of advantages, including improved flexibility, manageability, and scalability. The importance of network capability in enabling ultra-low latency applications has been greatly amplified in the current era due to the increased demand for emerging services such as autonomous driving, teleoperated driving, virtual reality, and remote surgery. This paper presents a novel and efficient methodology for service function chaining (SFC) in an NFV-enabled network that aims to minimize latency and optimize the utilization of physical network resources, with a specific focus on ultra-low latency applications. In our proposed methodology, we offer flow prioritization and an adjustable priority coefficient factor (µ) to reserve a portion of physical network resources exclusively for ultra-low latency applications in order to optimize the deployment paths of these applications further. We formulate the SFC deployment problem as an integer linear programming (ILP) optimization model. Furthermore, we propose a set of heuristic algorithms that yield near-optimal solutions with minimal optimality gaps and execution times, making them practical for large-scale network topologies. Performance evaluations demonstrate the effectiveness of our proposed methodology in enabling ultra-low latency applications in an NFV-enabled network. Compared to existing algorithms, our proposed methodology achieves notable enhancements in terms of the end-to-end delay (up to 22 percent), bandwidth utilization (up to 28 percent), and SFC acceptance rate (up to 13 percent).

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Section: Greedy Algorithmmentioning

confidence: 99%

Service Function Chaining to Support Ultra-Low Latency Communication in NFV

Erbati,

Tajiki,

Schiele

2023

Electronics

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“…With the preliminaries of superior testbeds and architectural systems, the decision-making and management entities of intelligent modules can be deployed within specified interfaces and network service demands which interact with operations support system (OSS)/business support system (BSS). Therefore, the confluence of network functions virtualization (NFV), software-defined networking (SDN), and edge computing allows an adaptive configuration of intelligent model policies on resource pooling adjustment, networking abstractions, and efficient slicing capabilities in multi-type Quality-of-Service (QoS) indicators with different criticality requirements [4][5][6][7].…”

Section: Introduction 1motivation and Problem Statementmentioning

confidence: 99%

Adaptive Partial Task Offloading and Virtual Resource Placement in SDN/NFV-Based Network Softwarization

Tam¹,

Math²,

Kim³

2023

Computer Systems Science and Engineering

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Edge intelligence brings the deployment of applied deep learning (DL) models in edge computing systems to alleviate the core backbone network congestions. The setup of programmable software-defined networking (SDN) control and elastic virtual computing resources within network functions virtualization (NFV) are cooperative for enhancing the applicability of intelligent edge softwarization. To offer advancement for multi-dimensional model task offloading in edge networks with SDN/NFV-based control softwarization, this study proposes a DL mechanism to recommend the optimal edge node selection with primary features of congestion windows, link delays, and allocatable bandwidth capacities. Adaptive partial task offloading policy considered the DL-based recommendation to modify efficient virtual resource placement for minimizing the completion time and termination drop ratio. The optimization problem of resource placement is tackled by a deep reinforcement learning (DRL)-based policy following the Markov decision process (MDP). The agent observes the state spaces and applies value-maximized action of available computation resources and adjustable resource allocation steps. The reward formulation primarily considers taskrequired computing resources and action-applied allocation properties. With defined policies of resource determination, the orchestration procedure is configured within each virtual network function (VNF) descriptor using topology and orchestration specification for cloud applications (TOSCA) by specifying the allocated properties. The simulation for the control rule installation is conducted using Mininet and Ryu SDN controller. Average delay and task delivery/drop ratios are used as the key performance metrics.

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