Mobile edge caching plays a crucial role in traffic offloading for access networks. By storing frequently requested content items close to subscribers, it significantly reduces data retrieval latency, mitigates backhaul congestion, and alleviates the load on remote servers. Among various caching strategies, the two-tier Least Recently Used (2-LRU) policy has been widely adopted due to its efficient popularity-aware filtering capability while maintaining$\mathcal {O}(1)$computational complexity. However, conventional 2-LRU often allocates an equal number of entries to both LRU tiers, overlooking the potential cache hit ratio gains achievable through asymmetric tier-size configurations. Therefore, in this paper, we present a comprehensive analysis of 2-LRU under asymmetric tier sizing, and leverage the obtained insights to further guide performance optimization. In particular, we first construct a discrete-time Markov chain model to characterize the state transitions of 2-LRU and derive a closed-form expression for its cache hit probability, i.e., the hit ratio of its second-tier LRU ($C_{2}$). We then perform extensive simulations to validate accuracy of the proposed model and investigate the optimal size settings for the first-tier LRU ($C_{1}$). Building on the key implications of the associated results, we further propose 2LRU-$\Delta C_{1}$, an enhanced 2-LRU scheme that dynamically adjusts the size of$C_{1}$to accelerate the population of$C_{2}$with popular content data, thereby improving the cache hit ratio of$C_{2}$. Finally, we implement 2LRU-$\Delta C_{1}$in NS-3 and evaluate its performance against several baseline strategies, including LRU, 2-LRU ($C_{1}=C_{2}$), LFU, and a DRL-based policy. Corresponding results have confirmed the efficiency of our proposed scheme.

Recent advances in Cloud Computing have shown great power in enhancing intelligent devices to support various applications. Nevertheless, conventional Cloud Computing fails to keep up with the ever-advancing requirements of efficient task execution, mainly resulting from its drawbacks in communication delay. To this end, multi-layer cloud computing with local, edge, and remote data centers has gained high interest yet remains challenging because of the inherent complexity of cross-layer orchestration. In particular, with more participants involved, it is nontrivial to achieve customized service provision while guaranteeing system stability. Hence, we address the workload scheduling issue in the multi-layer cloud paradigm in this paper, with task offloading and service reconfiguration considered jointly. We first formulate it as a stochastic optimization problem, where statistical service requirements are imposed on queue lengths. Then, we divide the original optimization into three individual low-complex sub-problems with optimal solutions provided. To improve system performance, we introduce a request-rejecting mechanism that augments our approach with delay-optimality. Theoretical analysis confirms that our approaches can guarantee system stability and are asymptotically optimal within a small gap from the optimum. Finally, we validate the efficiency of our approaches through extensive simulation results in performance guarantees and customized workload scheduling.

Edge caching has been regarded as a promising technique for low-latency, high-rate data delivery in future networks, and there is an increasing interest to leverage Machine Learning (ML) for better content placement instead of traditional optimization-based methods due to its self-adaptive ability under complex environments. Despite many efforts on ML-based cooperative caching, there are still several key issues that need to be addressed, especially to reduce computation complexity and communication costs under the optimization of cache efficiency. To this end, in this paper, we propose an efficient cooperative caching (FDDL) framework to address the issues in mobile edge networks. Particularly, we propose a DRL-CA algorithm for cache admission, which extracts a boarder set of attributes from massive requests to improve the cache efficiency. Then, we present an lightweight eviction algorithm for fine-grained replacements of unpopular contents. Moreover, we present a Federated Learning-based parameter sharing mechanism to reduce the signaling overheads in collaborations. We implement an emulation system and evaluate the caching performance of the proposed FDDL. Emulation results show that the proposed FDDL can achieve a higher cache hit ratio and traffic offloading rate than several conventional caching policies and DRL-based caching algorithms, and effectively reduce communication costs and training time.

Recently, there is a growing interest in Double-Layered Satellite Networks (DLSN) which integrate Medium-Earth-Orbit (MEO) and Low-Earth-Orbit (LEO) satellites for provision of mobile and personal services. However, it is still in the early stage with several challenges unaddressed, and one of the key problems is the inter-layer link allocations between MEO and LEO satellites, as DLSN topology is dynamically changed over the time and satellites are with the limited number of connections onboard. To this end, we propose a corresponding Inter-layer Link Allocation (ILA) scheme in this paper, taking the visible duration between satellites, transmitting power consumed onboard and geographical distributions of user load into account, aiming to maximize the utilization efficiency of DLSN inter-layer links. Then, we formulate it as a constrained multi-objective linear programming problem and evaluate its performance with other three benchmarks. Numerical results have demonstrated that the proposed ILA scheme can decrease the number of ILL handovers and average inter-satellite distance, with load balanced between LEO and MEO satellites.

Since Internet of Things (IoT) communications can enjoy many advantages brought by content-centric networking (CCN) in nature, there is an increasing interest on their integration for better information retrieval and distribution. Nevertheless, different from the conventional multimedia traffic of which contents are hardly changed, IoT data are always transient and updated by their producers according to the actual situation. As a result, if without any effective countermeasures, outdated copies are inevitably stored by CCN routers and then distributed to the associated consumers, degrading both caching efficiency and user experience. In fact, most of related policies take little account of information freshness for cached contents, and how to tackle transient IoT data in CCN is still an ignored but crucial issue required for further explorations. Therefore, in this article, we propose an efficient popularity-based cache consistency management scheme, which aims to guarantee freshness of IoT data returned by on-path routers and avoid heavy signalling costs introduced at the same time. Extensive simulations were performed under both real-world scare-free and binary-tree topologies, and corresponding results have proved the efficiency of the proposed scheme in timely evictions of outdated IoT data stored by CCN in-network caching.

Recent advances in the Internet of Things (IoT) technologies have enabled ubiquitous smart devices to sense and process various kinds of data. However, these innovations also raise the concern of efficient data transmission. Tackling the above issue is nontrivial since the resource constraints and environmental randomness in IoT require a lightweight transmission scheme while guaranteeing system stability. In this paper, we formulate the transmission scheduling problem of multi‐interface IoT devices as a concave optimization, aimed at accommodating the randomness of the IoT environment within the network capacity. By applying the Lyapunov optimization technique, we divide the stochastic problem into a series of low‐complex subproblems, which can be individually solved per time slot, and develop a dynamical control algorithm that does not require a priori knowledge such as link states. Theoretical analysis shows that our algorithms nicely bound the average queue length and are asymptotically optimal. Finally, extensive simulation results verify the theoretical conclusions and validate the effectiveness of the proposed algorithm.