The integration of edge computing and satellite networks has emerged as a promising solution to support remote terrestrial computation with wide coverage and low latency. However, single-satellite computing leads to uneven resource utilization and degraded service quality. To address this, peer offloading is required to improve both service quality and resource efficiency. Asides from peer offloading, diverse service requests also call for an appropriate service deployment strategy, which should be jointly optimize with offloading decision. In this paper, taking into processing cost and service update cost, we formulate a long-term optimization for service deployment and peer offloading. To pursue long-term performance, the problem is first reformulated into a sequence of time-invariant problems. Since frequent service deployment adjustments incur overhead and may cause service interruption, we decompose the time-invariant problem into a service deployment subproblem and a peer offloading subproblem, optimized at different timescales. A hierarchical method iteratively solve the two subproblems. In particular, we propose an online distributed algorithm for small-timescale peer offloading. Each local peer offloading problem is transformed into a capacity-constrained minimum cost maximum flow problem, enabling a low-complexity solution via the successive shortest path algorithm. We provide theoretical analysis showing that the proposed algorithm asymptotically approaches the offline optimum at the expense of system congestion. Moreover, we show that the performance bound grows with the large-timescale interval. Simulations results validate the theoretical analysis and demonstrate the effectiveness of the propose algorithm in terms of processing cost and service update cost.

Unmanned aerial vehicle (UAV) is expected to bring transformative improvements to the integrated sensing and communication (ISAC) systems, due to its high flexibility, high autonomy, large coverage and strong adaptability to various terrains. Sensory data is gathered by sensing UAVs (SUs) from the coverage area and then relayed to the corresponding fusion center UAVs (FCUs). Afterwards, terrestrial base stations receive the sensory data from FCUs in such air-ground networks. However, due to complex task execution environment and transmission environment, it is challenging to capture the network-layer performance of the sensory data transmission and evaluate the trade-off relationship between sensing and communication. In this work, we model and analyze the network-layer delay violation for an ISAC UAV network to address this challenge. Specifically, the UAV formation is distributed according to a Poisson cluster process (PCP). Then, the successful sensing probability is derived, with which the sensory data traffic can be captured. Under the sensory data flow, the delay violation probability is calculated for the two-stage sensory data transmission queue by exploiting stochastic network calculus (SNC). Furthermore, a delay minimization problem is proposed to reveal the trade-off relationship between sensing and communication under the power allocation strategy. Based on the long-term network-layer queue backlog evaluated, we are devoted to analyze the delay violation probability under an emergency that results in the antenna misalignment for one typical sensing UAV during a certain period. The steady-state and transient analysis for the ISAC UAV network not only illustrate the trade-off relationship between sensing and communication for the network, but also provide insights for on-demand power allocation, network deployment, control module provisioning and sensory data flow control under certain performance requirements.

Distributed edge clouds (ECs) have been recently shown with remarkable advantages in enhancing customized service provisioning by leveraging user proximity and edge resources. However, operating a massive EC network would inevitably incur a huge amount of energy cost to EC providers, which would offset their operating revenue without proper energy cost management. In this paper, we focus on conserving energy cost of ECs by taking advantage of both electricity price-aware geographical task dispatching and dynamic central processing unit (CPU) provisioning according to the spatiotemporal diversities of electricity prices and user task demands. Due to the significant switching cost of turning CPUs and services on/off, we formulate a multi-timescale energy cost minimization problem that integrates both large-timescale CPU provisioning and service placement, and small-timescale geographical task dispatching and CPU resource allocation. The Lagrange dual decomposition theory is exploited to deal with the spatio-temporal variable couplings. A distributed and online mini-batch learning (MBL) algorithm that relies on parameter approximation for large-timescale decision makings is proposed to learn the optimal Lagrange multipliers. Simulation results show the outstanding performance of the MBL algorithm.

Cooperative task offloading has emerged as a compelling computing paradigm for balancing spatially uneven task workloads and computational resources in distributed mobile edge computing (MEC) systems. However, enabling cooperation among multiple MEC nodes inevitably requires extra communication and computational energy overheads which might counteract the cooperation gain without energy-efficient offloading mechanisms. This paper presents an adaptive cooperative task offloading algorithm aiming at maximizing the time-averaged energy efficiency for small cell MEC networks enabled by millimeter-wave backhauls. With the considered network dynamics, the proposed algorithm makes a good tradeoff between the harvested cooperation utility and the total energy consumption in the long term. In addition, our algorithm ensures the network stability and fulfills the task admission rate requirement of each individual user equipment, by making slot-based decisions over time without requiring a-priori knowledge of the network dynamics. Simulation results verify the outstanding performance of the proposed algorithm by comparing with the static cooperative and adaptive non-cooperative schemes.

The development of the 5G network is envisioned to offer various types of services like virtual reality/augmented reality and autonomous vehicles applications with low-latency requirements in Internet-of-Things (IoT) networks. Mobile-edge computing (MEC) has become a promising solution for enhancing the computation capacity of mobile devices at the edge of the network in a 5G wireless network. Additionally, multiple radio access technologies (multi-RATs) have been verified with the potential in lowering the transmission latency and energy consumption, while improving the Quality of Services (QoS). Benefiting from the cooperation of multi-RATs, large latency-sensitive computing service tasks (L2SC) can be offloaded by different RATs simultaneously, which has great practical significance for data partitioned oriented applications with large task sizes. In this article, to enhance the L2SC offloading services for satisfying low-latency requirements with low energy consumption, we investigate the energy-latency tradeoff problem for partial task offloading in the MEC-enhanced multi-RAT network, considering the limitation of energy and computing in capability-constrained end devices in IoT networks. Specifically, we formulated the L2SC task computation offloading problem to minimize the weighted sum of the latency cost and the energy consumption by jointly optimizing the local computing frequency, task splitting, and transmit power, while guaranteeing the stringent latency requirement and the residual energy constraint. Due to the nonsmoothness and nonconvexity of the formulated problem with high complexity, we convert the tradeoff problem into a smooth biconvex problem and propose an alternate convex search-based algorithm, which can greatly reduce the computational complexity. Numerical simulation results show the effectiveness of the proposed algorithm with various performance parameters.

To capture the ubiquitous randomness such as time-varying wireless channel and unpredictable task arrivals in the non-orthogonal multiple access aided multi-access edge computing networks, we formulate a stochastic optimization problem aiming to minimize the time-average cost for Internet of Things devices in this paper. Due to the absence of distribution of random network information, we develop a stochastic gradient descent (SGD) based method to learn the randomness online and minimize the cost asymptotically. The proposed SGD method makes decisions only depending on the observed network information in each time-slot and achieves an [O(ε),O(1/ε)]-tradeoff between the cost-optimality and task queue backlog. To polish this tradeoff, we further propose a momentum-based SGD method by amending SGD iterations with momentum terms, which can efficiently accelerate algorithm convergence while reducing the task queue backlog without loss of cost-optimality. Finally, simulation results confirm the outstanding performance of the proposed methods.

Recently, millimetre‐wave (mmWave) wireless fronthauls have been regarded as an effective solution to deploy remote radio heads with higher flexibility and efficiency in cloud radio access networks (C‐RANs). Different from the traditional fibre fronthauls, in order to maximise the utilisation of the time‐frequency resource, the mmWave wireless fronthauls are more expected to operate in a dynamic allocation manner. In this study, the energy efficient mmWave fronthaul and OFDMA resource optimisation in C‐RANs is investigated. The TDMA‐based fronthaul allocation mechanism is first presented and then the joint resource optimisation is formulated as an energy efficiency (EE) maximisation problem which is in the form of a mixed‐integer non‐linear fractional programming (MINLFP) problem. By taking advantage of the Dinkelbach method, the MINLFP problem is transformed into a subtractive optimisation problem and solved by using the Lagrange dual decomposition theory. Moreover, a maximal weighted bipartite graph matching approach is proposed to determine the optimal resource block allocation. Finally, extensive simulation results are provided to evaluate the EE performance of the proposed algorithm by comparing with several benchmark schemes, and it shows that the proposed algorithm can achieve great EE performance gain over the benchmark schemes.