The integration of reconfigurable intelligent surfaces (RIS) into unmanned aerial vehicle (UAV) networks presents a transformative solution for achieving energy-efficient and reliable communication, particularly within the rapidly expanding low-altitude economy (LAE). As UAVs facilitate diverse aerial services—spanning logistics to smart surveillance—their limited energy reserves create significant challenges. RIS effectively addresses this issue by dynamically shaping the wireless environment to enhance signal quality, blackuce power consumption, and extend UAV operation time, thus enabling sustainable and scalable deployment across various LAE applications. This survey provides a comprehensive review of RIS-assisted UAV networks, focusing on energy-efficient design within LAE applications. We begin by introducing the fundamentals of RIS, covering its operational modes, deployment architectures, and roles in both terrestrial and aerial environments. Next, advanced energy efficiency (EE)-driven strategies for integrating RIS and UAVs. Techniques such as trajectory optimization, power control, beamforming, and dynamic resource management are examined. Emphasis is placed on collaborative solutions that incorporate UAV-mounted RIS, wireless energy harvesting (EH), and intelligent scheduling frameworks. We further categorize RIS-enabled schemes based on key performance objectives relevant to LAE scenarios. These objectives include sum rate maximization, coverage extension, quality of service (QoS) guarantees, secrecy rate improvement, latency blackuction, and age of information (AoI) minimization. The survey also delves into RIS-UAV synergy with emerging technologies like multi-access edge computing (MEC), non-orthogonal multiple access (NOMA), vehicle-to-everything (V2X) communication, and wireless power transfer (WPT). Finally, we outline open research challenges and future directions, emphasizing the critical role of energy-aware, RIS-enhanced UAV networks in shaping scalable, sustainable, and intelligent infrastructures within the LAE.

Current data centers rely on failure protection mechanisms to ensure data reliability. However, recent research indicates that failures within the same node or rack are common in data centers that use flash-based solid-state drives (SSDs) as the primary storage medium. Such correlated failures bring challenges for traditional protection mechanisms to achieve high reliability and repair performance. To this end, we propose a product erasure code (PECode) that encodes data blocks in multiple stripes cooperatively to generate intrastripe and interstripe parity blocks. Then, we design a multistripe cooperative repair algorithm (MSCRepair). MSCRepair first creates the failure distribution matrix (FDM) to represent the distribution of failure blocks in nodes and racks, and then conducts FDM-guided repair to minimize cross-rack traffic upon correlated failures. We prove that MSCRepair achieves the least cross-rack repair traffic at the cost of a longer repair time. We further propose a correlated failure repair scheduling algorithm for MSCRepair, which reduces the repair time by balancing the load and delivering data from links with higher bandwidths. We evaluate MSCRepair through both large-scale simulations and real experiments. In the mise-en-scene of its state-of-the-art alternatives, MSCRepair stands out by reducing up to 19.6%–49.9% of cross-rack traffic, while simultaneously reducing 16.2%–51.4% of recovery time of correlated failures.

Erasure coding is a storage-efficient redundancy scheme for modern key–value (KV) stores, storing stripes of data and parity chunks in multiple nodes. To accommodate the highly skewed and time-varying nature of the workload, KV stores require erasure code that dynamically optimizes its parameters, known as redundancy converting. Stretched Reed–Solomon (SRS) and elastic Reed–Solomon (ERS) codes represent promising candidates for meeting such requirements. However, both SRS and ERS are limited to RS$(d,r)\to $RS$(d^{\prime },r^{\prime })$converting, where$d^{\prime }>d,r^{\prime }=r$, failing to fully meet actual needs. This work presents an advanced ERS code (AERS code), which builds upon flexible encoding matrices and placement strategies, serving different types of redundancy converting, and minimizing converting traffic. We further prove that the AERS code is an optimal redundancy converting solution that achieves the theoretical lower bound on data traffic during redundancy converting while guaranteeing node-level fault tolerance. We evaluate the AERS code through both mathematical analysis and experiments. In the mise-en-scène of its state-of-the-art alternatives, AERS stands out by reducing network traffic up to 50%–85.7% while accelerating redundancy converting.

Hierarchical federated learning (HFL) has recently emerged as a more practical machine learning (ML) paradigm, which enables edge servers (ESs) in close proximity to conduct partial model aggregation. Despite its utility, local training and model aggregation incur considerable computation and communication time. client selection (CS) has proven effective for minimizing latency. However, CS faces the following challenges in hierarchical federated learning (HFL). First, the accessible clients, computation resources and network bandwidth are time-varying and unpredictable. Second, certain dynamics can only be observed after the decisions are made. Third, multiple ESs face different unknown clients, increasing the difficulty of selecting clients in an online manner. Finally, resource usage may be excessively violated during the training process. Existing HFL researches are insufficient to tackle these challenges. This work proposes a multi- ESs CS framework (MCS), which is based on multiarmed bandit (MAB) technique. MCS aims to reduce the cumulative computation and communication time, using two algorithms: 1) an online learning-based CS algorithm (OCA) makes the CS decisions for each ES, based on empirical learning results; and 2) a randomized rounding algorithm (RRA) converts fractional decisions obtained by OCA into binary solutions. Theoretically, MCS can enjoy the sublinear regret and violation compared to the optimal strategy. Practically, extensive experiments on real-world data sets demonstrate the empirical superiority of MCS over multiple state-of-the-art algorithms in minimizing cumulative latency.

Large-scale distributed machine learning (ML) systems involve extensive and costly computational resources. Pricing and scheduling, as two promising techniques for resource management, have garnered significant attention. However, existing job pricing and scheduling algorithms in cloud computing either charge fixed resource fees based on known job runtime or implement dynamic price setting with job preemption, unsuitable for distributed ML systems with high uncertainties and switching cost. First, whether the resources of a distributed ML job are placed together or not results in different job runtime. Second, various time-varying factors, including job arrival rates and competitors’ pricing, affect resource prices. Third, frequent price changes for the same resource can easily lead to system instability, ultimately jeopardizing user satisfaction. Addressing these uncertainties is challenging. This article introducesAPOS, an adaptive pricing and online scheduling framework, aiming at maximizing the operator’s overall revenue.APOSincorporates two innovations: 1) Intelligent Pricing: We represent each price using a feature vector that encapsulates relevant factors. Subsequently, based on the linear upper confidence bound (UCB) techniques, we establish relationships between price features and two revenue-associated elements: a) job arrival rates and b) resource consumption rates. To ensure system stability, we introduce batch pricing to reduce the frequency of resource price updates and 2) Online Scheduling: We strive to compute a nonpreemptive schedule that balances job utility with corresponding resource cost. We rigorously prove thatAPOSachieves truthfulness, individual rationality, system stability, and sublinear regret in polynomial time. Finally, extensive trace-driven simulations confirm thatAPOSoutperforms four state-of-the-art baselines, yielding a minimum of 23.3% improvement in total operator revenue.

Federated learning (FL) has emerged as a new paradigm that enables distributed mobile devices to learn a global model collaboratively. Since mobile devices (a.k.a, clients) exhibit diversity in model training quality, client selection (CS) becomes critical for efficient FL. CS faces the following challenges: First, the client’s availability, the training data volumes, and the network connection status are time-varying and cannot be easily predicted. Second, clients for training and the number of local iterations would seriously affect the model accuracy. Thus, selecting a subset of available clients and controlling local iterations should guarantee model quality. Third, renting clients for model training needs cost. It is necessary to dynamically administrate the use of the long-term budget without knowledge of future inputs. To this end, we propose a federated edge learning (FedL) framework, which can select appropriate clients and control the number of training iterations in real-time. FedL aims to reduce the completion time while reaching the desired model convergence and satisfying the long-term budget for renting clients. FedL consists of two algorithms: i) the online learning algorithm makes CS and iteration decisions according to historic learning results; ii) the online rounding algorithm translates fractional decisions derived by the online learning algorithm into integers to satisfy feasibility constraints. Rigorous mathematical proof reveals that dynamic regret and dynamic fit have sub-linear upper-bounds with time for a given budget. Extensive experiments based on realistic datasets suggest that FedL outperforms multiple state-of-the-art algorithms. In particular, FedL reduces at least 38% completion time compared with others.

As a new paradigm, edge caching is deemed an effective alternative by fetching contents at the network edge. However, designing an efficient caching mechanism is challenging. First, the content library is a dynamic set rather than a static set. Second, the content may be prevalent in different small base stations (SBSs), resulting in different rewards. Thus, the above reasons require each SBS could learn its caching decisions in a multi-SBSs network. Existing reinforcement learning algorithms either fail to consider the non-stationary environment or do not provide any performance guarantee. Thus, previous algorithms work well no longer. This work proposes a multi-agent multi-armed bandit caching framework, MAMAB-C, which navigates SBSs to cache contents in a distributed manner. Specifically, we formulate the multi-SBSs caching optimization problem as an online integer linear program (ILP) and convert it into a multi-agent multi-armed bandit (MAMAB) problem with resource constraints. MAMAB-C can realize the sub-linear metric property and significantly outperform multiple state-of-the-art algorithms.

Clustered Federated Learning (FL) addresses heterogeneous objectives from different client groups, by capturing the intrinsic relationship between data distributions of clients. This work aims to minimize the completion time of clustered FL training while guaranteeing convergence, given the following challenges. First, clients’ data distributions are not static since their interests are usually time-varying. Obsolete data may incur training failures, requiring detection of distribution changes at runtime. Second, even with the same distribution, client datasets may have different contributions to model accuracy. Besides, the training data typically arrive at clients dynamically, which brings uncertainties to assessing the quality of client data. Third, the execution environments of clients and networks are often unstable and stochastic, leading to uncertainties in calculating computation and communication time. Given the above challenges, we propose Acct with two innovations: i) change detection: we first model the time-varying interests of clients as piecewise stationary based on practical observations, then apply generalized likelihood ratio detectors to FL for detecting changes in client distributions; ii) client selection: we adopt the multi-armed bandit (MAB) technique to account for the uncertainties in measuring data quality, computation and communication time. Based on the upper confidence bound (UCB) method, we construct a novel “double UCB” policy to adaptively select clients with high data quality and low computation and communication overhead. We rigorously prove the convergence of Acct and sub-linear regret regarding the proposed client selection policy. Finally, we implement Acct using PyTorch and conduct experiments showing that Acct reduces the completion time by almost 18.2% compared with three state-of-the-art FL frameworks.