To effectively express and satisfy network application requirements, intent-based network management has emerged as a promising solution. In intent-based methods, users and applications express their intent in a high-level abstract language to the network. Although this abstraction simplifies network operation, it induces many challenges to efficiently express applications' intents and map them to different network capabilities. Therefore, in this work, we propose an AI-based framework for intent profiling and translation. We consider a scenario where applications interacting with the network express their needs for network services in their domain language. The machine-to-machine communication (i.e., between applications and the network) is complex since it requires networks to learn how to understand the domain languages of each application, which is neither practical nor scalable. Instead, a framework based on emergent communication is proposed for intent profiling, in which applications express their abstract quality-of-experience (QoE) intents to the network through emergent communication messages. Subsequently, the network learns how to interpret these communication messages and map them to network capabilities (i.e., slices) to guarantee the requested Quality-of-Service (QoS). Simulation results show that the proposed method outperforms self-learning slicing and other baselines, and achieves a performance close to the perfect knowledge baseline.

In this paper, we leverage a multi-agent reinforcement learning (MARL) framework to jointly learn a computation of-floading decision and multichannel access policy with corresponding signaling. Specifically, the base station and industrial Internet of Things mobile devices are reinforcement learning agents that need to cooperate to execute their computation tasks within a deadline constraint. We adopt an emergent communication protocol learning framework to solve this problem. The numerical results illustrate the effectiveness of emergent communication in improving the channel access success rate and the number of successfully computed tasks compared to contention-based, contention-free, and no-communication approaches. Moreover, the proposed task offloading policy outperforms remote and local computation baselines.

Edge computing is the next-generation computing paradigm that brings the processing capability closer to the location where it is needed. 5G and beyond 5G aim to achieve substantial improvement for the performance of edge computing in terms of e.g. higher throughput and lower latency. Smart base stations are often attached with edge datacenters consisting of many edge servers equipped with computing and storage capabilities. These servers are used to execute offloaded tasks from edge equipment such as Internet of Things. It is important to have an efficient offloading algorithm that can guarantee specific service-level objectives (SLOs) by assigning tasks to appropriate edge servers. Traditional offloading schemes such as static and learning-based algorithms either have limited performance or result in high overhead for task assignment to servers. In this paper, we propose an efficient game-theoretical scheduling algorithm for offloaded tasks at edge datacenters. The core contribution of the algorithm is to design a public goods investment model for edge servers. Based on the model, we design a lightweight scheduling algorithm to reduce the average load of edge servers and enhance the stability of edge datacenter systems. Experimental results demonstrate the significant benefits of the proposed algorithm in reducing the response latency of tasks and balancing the workload of edge servers.

Distributed data services usually rely on consensus protocols like Paxos and Raft to provide fault-tolerance and data consistency across global and local-distributed data centers. Erasure coding replication has appealing storage and network cost saving compared with full copy replication, which helps consensus protocols achieve low latency, high fault tolerance, and high throughput for data access. Applying erasure coding in consensus protocols directly will degrade the liveness level when the number of failure servers reaches a certain level. To address the challenge, CRaft just stores full copy replication instead of erasure coding replication when the number of failed servers reaches a certain threshold. In such situation, CRaft will be downgraded sharply to the same storage and network costs as Raft. To overcome the shortcoming of CRaft, we propose a protocol, called HRaft, which can adapt the placement of data blocks in order to always have enough blocks to recover the stored value when servers fail. By replenishing some coded blocks in healthy servers instead of full copy replication, it can avoid switching to the full replication when a certain threshold on the number of failures is reached. We designed and implemented a key-value (KV) storage prototype to validate the proposed protocol and evaluate its performance. The experimental results show HRaft can significantly reduce storage and network costs and improve write performance while keeping the liveness level compared to CRaft.

This paper studies cached-aided multicast transmissions in fronthaul fog radio access networks (F-RANs). While index coding and cached-aided non-orthogonal multiple access (NOMA) are techniques commonly used for utilizing cache contents to save transmit energy, there is a lack of general framework to integrate them. This work proposes index-coded NOMA and dynamic coded-NOMA to investigate energy performance of the integration of index coding and NOMA under whole-file and subfile caching, respectively. Besides, dynamic cache space allocation is applied to both caching schemes, which allocates cache sizes to the fog access points (F-APs) according to their large-scale channel conditions. For index-coded NOMA, the general grouping problem is proved to be NP-hard and optimal solutions for some special cases are given. Furthermore, efficient heuristic grouping algorithms are proposed. For dynamic coded-NOMA, we obtain the closed-form minimum transmit energy. The numerical results validate the good performance of our proposed algorithms. Index-coded NOMA and dynamic coded-NOMA have comparable performance and both of them save much energy than the existing schemes. When there are 12 F-APs under small-cache scenarios, index-coded NOMA saves energy by 70.3% compared to traditional NOMA.

Both index coding and non-orthogonal multiple access (NOMA) are useful techniques for a transmitter to send information to multiple cache-enabled receivers. In former works, either index coding or cache-aided NOMA is applied in the system, while the combination of index coding and cache-aided NOMA has not been fully investigated. This work is the first attempt to integrate these two techniques. A two-phase transmission algorithm is proposed to first partition receivers into index coding groups and next pair these groups up for superposition coding. Cache-aided interference cancellation (CIC) is employed at the receiver. This new method is applied to a cache-enabled fog radio access network (F-RAN). Besides, a distinct-file caching scheme with imbalanced cache size at fog access points (F-APs) is proposed. For this particular caching scheme, the two-phase algorithm can be fine-tuned in a way so that its time complexity becomes linear in the number of F-APs, which is very fast and particularly desirable from a practical viewpoint. Furthermore, simulation results show that our proposed method can significantly reduce the power consumption for transmissions over the fronthaul link of the F-RAN.

Distributed data services usually rely on consensus protocols, such as Paxos and Raft, to provide fault-tolerance and data consistency across distributed data centers and even edge networks. In consensus protocols, erasure coded replication has appealing storage and network cost savings compared with full copy replication, which help achieve low latency, high fault-tolerance and high throughput. However, the liveness level will inevitably decrease when erasure codes are naively applied in consensus protocols. To keep the original liveness level, an existing protocol, called CRaft, switches from erasure coded replication to full copy replication when the number of failures exceeds a certain threshold. Such a solution, however, degrades system performance sharply. To tackle this problem, this work proposes a novel protocol called HRaft to enable graceful degradation on storage and network efficiency when failures happen. Without using full copy replication, it replenishes some coded blocks in healthy servers to reduce storage and network costs and to keep data consistency. The performance of the proposed protocol will be evaluated by deploving it into practical networks.

This work considers cache placement for ultra-dense fog radio access networks (F-RANs). In an F-RAN, the fog access points (F-APs) form overlapping clusters based on their geographical locations. A cluster of F-APs then acts as a distributed cache to cooperatively serve user requests. The fronthaul traffic minimization problem is formulated in information-theoretic terms. For the k-association networks, cache placement can be optimized by concatenating an MDS code with a repetition code. By repeating the same packet in some F-APs, multicasting over the fronthaul link can be done in cache placement, which saves energy and bandwidth. Such an idea is applied to both uncoded and coded caching schemes based on hypergraph coloring. Their associated optimization problems are shown to be NP-complete. For the uncoded case, a suboptimal algorithm is proposed, which carefully repeats the subfiles. For the coded case, a heuristic algorithm that minimizes the field size requirement of the MDS repetition scheme is proposed. Simulation results demonstrate the outstanding performance of the proposed uncoded and coded caching schemes during both the cache placement phase and content delivery phase in reducing fronthaul traffic load and energy consumption.

In fog radio access networks, the limited capacity of the fronthaul link is the bottleneck, which renders a high quality of service for video streaming difficult. To circumvent the problem, popular files can be cached in fog access points during off-peak hours. This work points out that beamforming in the access network can be exploited to reduce fronthaul traffic load by a joint design of cache placement scheme at the fog access points and index-coded transmission scheme over the fronthaul. Simulation results show that a percentage reduction of fronthaul traffic by more than 30% can be achieved.

Cooperative caching using maximum distance separable (MDS) codes and repetition codes in ultra-dense networks is studied, with the objective of maximizing the code rate while ensuring that end users can restore the file from the associating small base stations (SBSs) without the use of the backhaul link. It is proved that MDS-coded caching is optimal in general. In contrast, repetition caching is optimal only for some special cases. Repetition caching is, in general, suboptimal, and the associated code rate maximization problem is shown to be NP-hard and a heuristic algorithm is designed to evaluate the potential coding gain in arbitrary 2-dimensional (2D) network. Simulation results show that MDS-coded caching can save about 40% storage space when compared with repetition caching, and this coding gain increases when the amount of overlapping between clusters increases.