Conference Paper

Slice Scheduling with QoS-Guarantee Towards 5G

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... Another work, by Schmidt et al. [16], follows the approach of NVS but with the introduction of on-demand slices. The scheduler serves the eMBB-type slices according to the same process as NVS. ...
... The works that are most similar to the objectives of this work are the works by Guo and Suárez [14] and Schmidt et al. [16]. Of these two, the work by Guo and Suárez allows for more aggressive exploration of the flexibility of eMBB traffic. ...
... These works allow for a scenario where competing tenants share one infrastructure using a contractual agreement. In this sense, the possible parameterisations of each slice are: (i) the number of resources associated with a deadline [14], or (ii) the delay target associated with reliability and maximum resource share per scheduling opportunity [16]. The scheduler provides a fair treatment regarding the slice parameters object of contract. ...
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Enforcing network slice isolation in 5G Radio Access Networks (RANs) can degrade a network slicing solution’s ability to efficiently support different types of traffic. In addition, competing virtual network operators may share one network infrastructure that forces network slicing solutions to provide fair treatment between them. This work presents Flexible Priority Scheduling (FPS). This RAN network slicing solution provides isolated treatment to slices with different traffic types, further exploring their needs and flexibility to achieve a more efficient solution. It does so by defining a contract interface that allows a flexible representation of different types of traffic, from heavy throughput services to low latency and bursty traffic. Furthermore, the presented contract representation allows a fair treatment of the tenant’s traffic in the Medium Access Control (MAC) scheduler that enforces this slicing solution: Priority Adaptation Slice Scheduler (PASS).
... • The agent updates the Q-value function in a TD manner as follows: Q-learning may also be thought of as an asynchronous dynamic programming technique (DP). It allows agents to learn how to act optimally in Markovian domains by experiencing the consequences of their actions rather of having to develop maps of the domains [103]. It makes no assumptions about the agent's knowledge of the state-transition and reward models. ...
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The 5th Generation (5G) and beyond networks are expected to offer huge throughputs, connect large number of devices, support low latency and large numbers of business services. To realize this vision, there is a need for a paradigm shift in the way cellular networks are designed, built, and maintained. Network slicing divides the physical network infrastructure into multiple virtual networks to support diverse business services, enterprise applications and use cases. Multiple services and use cases with varying architectures and quality of service requirements on such shared infrastructure complicates the network environment. Moreover, the dynamic and heterogeneous nature of 5G and beyond networks will exacerbate network management and operations complexity. Inspired by the successful application of machine learning tools in solving complex mobile network decision making problems, deep reinforcement learning (Deep RL) methods provide potential solutions to address slice lifecycle management and operation challenges in 5G and beyond networks. This paper aims to bridge the gap between Deep RL and the 5G network slicing research, by presenting a comprehensive survey of their existing research association. First, the basic concepts of Deep RL framework are presented. 5G network slicing and virtualization principles are then discussed. Thirdly, we review challenges in 5G network slicing and the current research efforts to incorporate Deep RL in addressing them. Lastly, we present open research problems and directions for future research.
... [15] presents an RL method for resource optimization in 5G network slicing, and the results show an improvement in network utility and scalability. A QoS-aware slicing algorithm is proposed in [16] where the bandwidth is distributed based on utility function and priority rules. In [17], a network slicing and multi-tenancy based dynamic resource allocation scheme is presented, in which hierarchical decomposition is adopted to reduce complexity in optimization. ...
Preprint
5G is regarded as a revolutionary mobile network, which is expected to satisfy a vast number of novel services, ranging from remote health care to smart cities. However, heterogeneous Quality of Service (QoS) requirements of different services and limited spectrum make the radio resource allocation a challenging problem in 5G. In this paper, we propose a multi-agent reinforcement learning (MARL) method for radio resource slicing in 5G. We model each slice as an intelligent agent that competes for limited radio resources, and the correlated Q-learning is applied for inter-slice resource block (RB) allocation. The proposed correlated Q-learning based interslice RB allocation (COQRA) scheme is compared with Nash Q-learning (NQL), Latency-Reliability-Throughput Q-learning (LRTQ) methods, and the priority proportional fairness (PPF) algorithm. Our simulation results show that the proposed COQRA achieves 32.4% lower latency and 6.3% higher throughput when compared with LRTQ, and 5.8% lower latency and 5.9% higher throughput than NQL. Significantly higher throughput and lower packet drop rate (PDR) is observed in comparison to PPF.
... In Reference [19] three types of slices are defined, namely the Fixed, the On-demand and the Dynamic slices. Firstly, dedicated communication resources are allocated to the Fixed slices considering the Service Layer Agreement (SLA) of each user. ...
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Fifth generation Vehicular Cloud Computing (5G-VCC) systems support various services with strict Quality of Service (QoS) constraints. Network access technologies such as Long-Term Evolution Advanced Pro with Full Dimensional Multiple-Input Multiple-Output (LTE-A Pro FD-MIMO) and LTE Vehicle to Everything (LTE-V2X) undertake the service of an increasing number of vehicular users, since each vehicle could serve multiple passenger with multiple services. Therefore, the design of efficient resource allocation schemes for 5G-VCC infrastructures is needed. This paper describes a network slicing scheme for 5G-VCC systems that aims to improve the performance of modern vehicular services. The QoS that each user perceives for his services as well as the energy consumption that each access network causes to user equipment are considered. Subsequently, the satisfactory grade of the user services is estimated by taking into consideration both the perceived QoS and the energy consumption. If the estimated satisfactory grade is above a predefined service threshold, then the necessary Resource Blocks (RBs) from the current Point of Access (PoA) are allocated to support the user’s services. On the contrary, if the estimated satisfactory grade is lower than the aforementioned threshold, additional RBs from a Virtual Resource Pool (VRP) located at the Software Defined Network (SDN) controller are committed by the PoA in order to satisfy the required services. The proposed scheme uses a Management and Orchestration (MANO) entity implemented by a SDN controller, orchestrating the entire procedure avoiding situations of interference from RBs of neighboring PoAs. Performance evaluation shows that the suggested method improves the resource allocation and enhances the performance of the offered services in terms of packet transfer delay, jitter, throughput and packet loss ratio.
... For our use case, we consider three different RAN slice types: constant bit rate (CBR), minimum bit rate (MBR) and best effort (BE). These types are similar to the ones explored in [41], [42]. Applications that need constant bit rates use the CBR slice, e.g., video conference and voice-over-IP. ...
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Chapter
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