A Heuristically Assisted Deep Reinforcement Learning Approach for Network Slice Placement

Esteves, Jose Jurandir Alves; Boubendir, Amina; Guillemin, Fabrice; Sens, Pierre

doi:10.1109/tnsm.2021.3132103

Cited by 28 publications

(16 citation statements)

References 27 publications

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“…Integer linear programming (ILP) [22][23][24][25][26][27] Mixed integer linear programming (MILP) [28][29][30][31][32][33] Heuristic method 23,24,26,27,29,[31][32][33] Machine learning (ML) [34][35][36][37][38] Instead, heuristics (e.g., greedy algorithms) and meta-heuristics produce comparatively faster but suboptimal results that usually achieve less objectives (e.g., low response time or reduced communication delay or load balancing or limited energy consumption). On the other hand, ML-based approaches (e.g., genetic algorithm and ant colony) are known to be more accurate solutions 34 thanks to their interactive learning and decision making abilities.…”

Section: Methods Referencementioning

confidence: 99%

“…Instead, heuristics (e.g., greedy algorithms) and meta-heuristics produce comparatively faster but sub-optimal results that usually achieve less objectives (e.g., low response time or reduced communication delay or load balancing or limited energy consumption). On the other hand, MLbased approaches (e.g., genetic algorithm, ant colony) are known to be more accurate solutions [33] thanks to their interactive learning and decision making abilities. [21][22][23][24][25][26] Mixed Interger Linear Programming (MILP) [27][28][29][30][31][32] Heuristic Method [22,23,25,26,28,[30][31][32]] Machine Learning (ML) [33][34][35][36][37] As detailed in Table 2, the above container placement strategies can be further categorized based on target architecture (cloud, fog, edge), type of placement (static versus dynamic), key objectives, algorithm to solve and evaluation method.…”

Section: Container Placement Strategiesmentioning

confidence: 99%

“…On the other hand, MLbased approaches (e.g., genetic algorithm, ant colony) are known to be more accurate solutions [33] thanks to their interactive learning and decision making abilities. [21][22][23][24][25][26] Mixed Interger Linear Programming (MILP) [27][28][29][30][31][32] Heuristic Method [22,23,25,26,28,[30][31][32]] Machine Learning (ML) [33][34][35][36][37] As detailed in Table 2, the above container placement strategies can be further categorized based on target architecture (cloud, fog, edge), type of placement (static versus dynamic), key objectives, algorithm to solve and evaluation method. In the case of static placement, an initial placement is typically proposed only once (at start).…”

Section: Container Placement Strategiesmentioning

confidence: 99%

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Container placement and migration strategies for cloud, fog, and edge data centers: A survey

2022

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The last decade has witnessed important development of network softwarization that has revolutionized the practice of networks. Virtualized networks bring novel and specific requirements for the control and orchestration of containerized network functions that are scattered across the network. In this regard, the migration of virtualized network functions plays a pivotal role to best meet the requirements of optimal resource utilization, load balancing and fault tolerance. The purpose of this survey is to offer a detailed overview of the progress on container migration so as to provide a better understanding of the trade-off between the benefits associated with the migration and the practical challenges. The paper includes a classification of the placement algorithms that map the containerized network functions on the virtualized infrastructure. Following, a taxonomy of the migration techniques that perform the transfer of the containerized microservices is proposed.

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Section: Methods Referencementioning

confidence: 99%

Section: Container Placement Strategiesmentioning

confidence: 99%

Section: Container Placement Strategiesmentioning

confidence: 99%

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Container placement and migration strategies for cloud, fog, and edge data centers: A survey

2022

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“…Machine learning techniques have already been used for solving the problem of the SFC service deployment in [9], [29]- [37]. For instance, in [32], RL is adopted for VNF-SC deployment in Elastic optical networks with the objective of load balancing and minimising service delay.…”

Section: Related Workmentioning

confidence: 99%

“…The work in [36] adopts DQN for adaptive resource allocation with the objective of minimising cost. The work in [37] seeks to jointly maximise load balance and acceptance ratio while minimising resource consumption. In [9] the focus is set on solving the fault-tolerant placement problem of stateful VNFs with the goal of reducing the state update overhead.…”

Section: Related Workmentioning

confidence: 99%

A Reinforcement Learning Approach for Virtual Network Function Chaining and Sharing in Softwarized Networks

Kibalya

Serrat-Fernández

Gorricho

et al. 2022

IEEE Trans. Netw. Serv. Manage.

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Cognizant of the ease with which softwarized functions can be dynamically scaled according to real time resource requirements, and the fact that multiple services can have common VNFs in their chaining, this paper tackles the problem of cost effective deployment of online services from the perspective of sharing their VNF instances. First, we formally formulate the deployment problem under VNFs sharing. Secondly, given the NP-hard nature of the above problem, we propose a reinforcement learning (RL) algorithm capable of making intelligent placement decisions while considering multiple conflicting costs. Costs of transmission, VNF instantiation or energy consumption, among others. Thanks to the intelligence of the RL algorithm, simulation results show that the performance of the proposed algorithm is within a 14% margin and similar to an optimal solution in terms of request provisioning cost and acceptance ratio, respectively. Moreover, the algorithm results in more than a 20% and a 70% improvement in terms of request deployment cost and time compared to a state-of-the-art algorithm, and up to more than a 40% improvement in terms of cost compared to an algorithm that greedily minimizes the transmission or VNF activation costs.

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An adaptive heuristic algorithm to solve the network slicing resource management problem

Chen

2023

Int J Communication

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Artificial intelligence-based (AI-based) network slicing bandwidth allocation enables the 5G/6G service providers to create multiple virtual networks atop a shared physical infrastructure while fulfilling varying end-user demands. Some researchers argue that AI-enable network may run the danger of having private information compromised. We still need a backup rule-based methodology to allocate bandwidth resource to each slice, if the AI-based method suddenly encounters security issues. To design such a rule-based methodology, this study attempts to answer two questions: (1) Is the network slicing bandwidth allocation problem the nondeterministic polynomial-time completeness (NP-completeness)? ( 2) Is there a heuristic methodology without any training process, which has equivalent performance compared to the AI-based methodology? This study first proves the classical network slicing bandwidth allocation problems is NP-completeness. This shows that the designed heuristic method is inescapably suboptimal to the network slicing bandwidth allocation problem. Secondly, this study proposes the Adaptive Hungarian Algorithm (AHA), which outperforms previous AI-empowered method and does not need any training process. The experiments demonstrate that AHA reached 93%-97% of the maximal system throughput by brute-and-force algorithm, compared to other methodologies only having at most 93% of the maximal system throughput. This also indicates that AHA is capable to solve the network slicing bandwidth allocation problem, if the telecommunication operators do not have sufficient sample complexity to train an AI model.

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A Heuristically Assisted Deep Reinforcement Learning Approach for Network Slice Placement

Cited by 28 publications

References 27 publications

Container placement and migration strategies for cloud, fog, and edge data centers: A survey

Container placement and migration strategies for cloud, fog, and edge data centers: A survey

A Reinforcement Learning Approach for Virtual Network Function Chaining and Sharing in Softwarized Networks

An adaptive heuristic algorithm to solve the network slicing resource management problem

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