Ensuring Profit and QoS When Dynamically Embedding Delay-Constrained ICN and IP Slices for Content Delivery

Rayani, Marsa; Ebrahimzadeh, Amin; Glitho, Roch; Elbiaze, Halima

doi:10.1109/tnse.2021.3132556

Cited by 7 publications

(2 citation statements)

References 46 publications

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“…SDN deployment, which affects capital expenditures (CAPEX), and VNF placement, which affects operational expenditures (OPEX), were optimized together. Rayani et al 16 examined infrastructure cost and quality of service when deploying network slices. The use of heuristic‐based solutions improved QoS and profits for infrastructure providers.…”

Section: Related Workmentioning

confidence: 99%

DRL‐based customised resource allocation for sub‐slices in 6G network slicing

2024

Trans Emerging Tel Tech

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Abstract6G network services demand significant computer resources. Network slicing offers a potential solution by enabling customized services on shared infrastructure. However, dynamic service needs in heterogeneous environments pose challenges to resource provisioning. 6G applications like extended reality and connected vehicles require service differentiation for optimal quality of experience (QoE). Granular resource allocation within slices is a complex issue. To address the complexity of QoE services in dynamic slicing, a deep reinforcement learning (DRL) approach called customized sub‐slicing is proposed. This approach involves splitting access, transport, and core slices into sub‐slices to handle service differentiation among 6G applications. The focus is on creating sub‐slices and dynamically scaling slices for intelligent resource allocation and reallocation based on QoS requirements for each sub‐slice. The problem is formulated as an integer linear programming (ILP) optimization problem with real‐world constraints. To effectively allocate sub‐slices and dynamically scale resources, the Advantage Actor‐Critic (A2C)‐based Network Sub‐slice Allocation and Optimization (NS‐AO) algorithm is proposed. Experimental results demonstrate that the proposed algorithm outperforms the state of the art in terms of training stability, learning time, sub‐slice acceptance rate, and resilience to topology changes.

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Section: Related Workmentioning

confidence: 99%

DRL‐based customised resource allocation for sub‐slices in 6G network slicing

2024

Trans Emerging Tel Tech

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“…ICN lacks differentiated deployment for nodes in different network regions, including cache size and cache duration. Although extensive researches are currently concentrated on this [80][81][82][83][84][85][86][87][88][89], the fundamental pain-points of ICN have not been resolved. With the high concurrency and fast handover of the terminal services, the ICN is faced with the problems that the content sender and the requester cannot dynamically match, and the terminal has switched to another place when the content is returned.…”

Section: Combination Structure For Icnmentioning

confidence: 99%

Survey on the scheme evaluation, opportunities and challenges of software defined‐information centric network

Ai,

Zhang,

Zhang

et al. 2023

IET Communications

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As a promising architecture of next‐generation network, software defined‐information centric network (SD‐ICN) inherits the advantages of software defined network (SDN) and information‐centric network (ICN) to enable flexible and fast content retrieval, especially in the current era of artificial intelligence. However, the existing researches mainly focus on a single respective in this field, which motivates in comprehensively providing a forward‐looking guidance and development direction for scholars and engineers. To this end, the latest developments of SD‐ICN is presented. First, the widely‐accepted concepts and impacts on traditional networks are introduced. Second, the shortcomings of SDN and ICN over conventional networks are respectively analyzed to illustrate the necessity of SD‐ICN. Third, based on extensive analysis and deep deliberation, a methodical taxonomy for existing combination studies is proposed. They are divided into SDN over ICN, ICN over SDN, and mutual immersive pattern. Fourth, the performances of three integration categories are compared and the limitations of related works are highlighted. Fifth, the maturity index from six development indicators are evaluated. Further, the maturity and practicality of these schemes are generalized. Based on the above studies and comparisons, the lessons learned by SDN and ICN developments are concluded. Finally, future research directions and opportunities are discussed for the readers.

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