Multiservice-Based Network Slicing Orchestration With Impatient Tenants

Algorithmics and Modeling Aspects of Network Slicing in 5G and Beyonds Network: Survey

“…Content may change prior to final publication. [49] focused on the AC issue by means of a heterogeneous tenant request multi-queuing system. They derive the model of predictive behavior and define the logical technique of tenants in queue planted slice AC system.…”

Admission Control for 5G Network Slicing based on (Deep) Reinforcement Learning

“…Several investigations have addressed the AC problem in 5G. Han et al [6] propose a utility-driven and multi-service based solution for network slicing based on Queuing Theory to maximize network utility. This solution considers different queues for two types of requests and accounts for impatient customers.…”

“…The mechanisms in the aforecited papers [3], [6]- [13] make admission decisions for individual requests as they arrive, preventing the selection of a set of NSLRs that can potentially optimize a given objective in a specific time window. Furthermore, these papers do not differentiate requests according to standardized 5G use cases, neglecting the diversity of QoS requirements of 5G service types.…”

“…This approach does not follow the ITU use cases for 5G. Sun et al [24] propose an RA algorithm for vehicular networks based on DRL to provide QoS inefficiently in which the DRL-agent adjusts the [3] RL (Q-Learning) Provider profit [6] Queuing theory Utility rate, admission rate, request waiting time [7] Big Data Analytics Provider profit [8] Heuristic algorithm QoE, resource utilization [9] Heuristic algorithm System resource utilization [10] Heuristic algorithm Provider revenue [11] Knapsack problem Monetization ratio [12] RL (Q-Learning) Provider revenue [13] DRL (DQN) Provider revenue [15] Heuristic algorithm Acceptance ratio and Execution time [16] Heuristic algorithm Acceptance ratio, provider revenue [17] Queuing Theory Running time [18] Complex Network Theory Resource efficiency, acceptance ratio, execution time [19] Integer Programming CPU Utilization [20] Alternating Direction Method of Multipliers Latency [21] Artificial Neural Networks Latency [22] DRL (DQN) Spectrum efficiency and QoE [23] DRL (DQN) Resource Utilization [24] DRL (DQN) Resource Utilization and Satisfaction ratio [25] DRL (DDPG) Provider revenue SARA/DSARA Q-learning and DQN Provider profit, resource utilization, acceptance ratio allocated resources for a slice as a function of the demand. Zhang et al [25] perform slicing in data center networks by using a DRL-agent.…”

“…Several papers have addressed AC by employing different theoretical frameworks such as Queuing Theory [6], Big Data [7], Heuristics [8]- [11], Reinforcement Learning (RL) [3], [12], and Deep RL (DRL) [13]. These papers propose making admission decisions considering individual NSLRs, which can lead to sub-optimal decisions since more profitable requests arriving in the short term can be rejected due to the unavailability of recently allocated resources [14] Moreover, most of these proposals neither consider the QoS requirements of different service types (use cases) nor the allocation of resources in the 5G core network.…”