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Federated learning (FL) offers many benefits, such as better privacy preservation and less communication overhead for scenarios with frequent data generation. In FL, local models are trained on end-devices and then migrated to the network edge or cloud for global aggregation. This aggregated model is shared back with end-devices to further improve their local models. This iterative process continues until convergence is achieved. Although FL has many merits, it has many challenges. The prominent one is computing resource constraints. End-devices typically have fewer computing resources and are unable to learn well the local models. Therefore, split FL (SFL) was introduced to address this problem. However, enabling SFL is also challenging due to wireless resource constraints and uncertainties. We formulate a joint end-devices computing resources optimization, task-offloading, and resource allocation problem for SFL at the network edge. Our problem formulation has a mixed-integer non-linear programming problem nature and hard to solve due to the presence of both binary and continuous variables. We propose a double deep Q-network (DDDQN) and optimization-based solution. Finally, we validate the proposed method using extensive simulation results. 

The rapid advancement of metaverse applications in wireless environments necessitates efficient resource management to enhance Quality of Experience (QoE). This paper presents a novel framework for optimizing wireless resource allocation within the metaverse to optimize QoE using convex optimization and matching theory. We formulate a QoE optimization problem considering packet error rate (PER) and immersive experience. Our problem also enables us to trade off between immersive experience and PER while computing QoE. The formulated problem is a mixed-integer non-linear programming (MINLP) problem, which is addressed through decomposition, convex optimization, matching theory, and block successive upper-bound minimization (BSUM). Specifically, for a solution, our proposed model integrates matching theory, BSUM, and convex optimization to optimize the association, transmit power allocation, and resource allocation. Finally, numerical results are provided. 

Federated learning (FL) is a promising technique for decentralized privacy-preserving Machine Learning (ML) with a diverse pool of participating devices with varying device capabilities. However, existing approaches to handle such heterogeneous environments do not consider “fairness” in model aggregation, resulting in significant performance variation among devices. Meanwhile, prior works on FL fairness remain hardware-oblivious and cannot be applied directly without severe performance penalties. To address this issue, we propose a novel hardware-sensitive FL method called\(\mathsf {FairHetero}\)that promotes fairness among heterogeneous federated clients. Our approach offers tunable fairness within a group of devices with the same ML architecture as well as across different groups with heterogeneous models. Our evaluation underMNIST,FEMNIST,CIFAR10, andSHAKESPEAREdatasets demonstrates that\(\mathsf {FairHetero}\)can reduce variance among participating clients’ test loss compared to the existing state-of-the-art techniques, resulting in increased overall performance. 

The growing necessity for enhanced processing capabilities in edge devices with limited resources has led us to develop effective methods for improving high-performance computing (HPC) applications. In this paper, we introduce LASP (Lightweight Autotuning of Scientific Application Parameters), a novel strategy designed to address the parameter search space challenge in edge devices. Our strategy employs a multi-armed bandit (MAB) technique focused on online exploration and exploitation. Notably, LASP takes a dynamic approach, adapting seamlessly to changing environments. We tested LASP with four HPC applications: Lulesh, Kripke, Clomp, and Hypre. Its lightweight nature makes it particularly well-suited for resource-constrained edge devices. By employing the MAB framework to efficiently navigate the search space, we achieved significant performance improvements while adhering to the stringent computational limits of edge devices. Our experimental results demonstrate the effectiveness of LASP in optimizing parameter search on edge devices. 

Freshwater scarcity is a global problem that requires collective efforts across all industry sectors. Nevertheless, a lack of access to operational water footprint data bars many applications from exploring optimization opportunities hidden within the temporal and spatial variations. To break this barrier into research in water sustainability, we build a dataset for operation direct water usage in the cooling systems and indirect water embedded in electricity generation. Our dataset consists of the hourly water efficiency of major U.S. cities and states from 2019 to 2023. We also offer cooling system models that capture the impact of weather on water efficiency. We present a preliminary analysis of our dataset and discuss three potential applications that can benefit from it. Our dataset is publicly available at Open Science Framework (OSF). 

The growing adoption of residential distributed energy resources (DERs) introduces more uncertain variability in power grid operation. More importantly, the residential DERs operate behind customers’ energy meters, and therefore, the utility cannot “directly” monitor them. Prior approaches to enable visibility into behind-the-meter (BTM) DERs either depend on estimations or require intrusive instrumentation on the customer side. To address the critical need for direct real-time monitoring of BTM DERs, in this paper, we propose a novel approach for utility-side direct real-time monitoring of residential BTM DERs. We utilize high-frequency (> 10kHz) conducted electromagnetic interference (EMI) from residential DERs’ grid-tied inverters to monitor their power generation. We discuss the working principle of our approach and present supporting results using three of-the-shelf grid-tied inverters. 

Server-level power monitoring in data centers can significantly contribute to its efficient management. Nevertheless, due to the cost of a dedicated power meter for each server, most data center power management only focuses on UPS or cluster-level power monitoring. In this paper, we propose a low-cost novel power monitoring approach that uses only one sensor to extract power consumption information of all servers. We utilize the conducted electromagnetic interference (EMI) of server power supplies to measure their power consumption from non-intrusive single-point voltage measurements. We present a theoretical characterization of conducted EMI generation in server power supply and its propagation through the data center power network. Using a set of ten commercial-grade servers (six Dell PowerEdge and four Lenovo ThinkSystem), we demonstrate that our approach can estimate each server's power consumption with less than ~7% mean absolute error. 

Significant power consumption is one of the major challenges for current and future high-performance computing (HPC) systems. All the while, HPC systems generally remain power underutilized, making them a great candidate for applying power oversubscription to reclaim unused capacity. However, an oversubscribed HPC system may occasionally get overloaded. In this paper, we propose MPR (Market-based Power Reduction), a scalable market-based approach where users actively participate in reducing the HPC system’s power consumption to mitigate overloads. In MPR, HPC users bid to supply, in exchange for incentives, the resource reduction required for handling the overloads. Using several real-world trace-based simulations, we extensively evaluate MPR and show that, by participating in MPR, users always receive more rewards than the cost of performance loss. At the same time, the HPC manager enjoys orders of magnitude more resource gain than her incentive payoff to the users. We also demonstrate the real-world effectiveness of MPR on a prototype system. 

Server-level power monitoring in data centers can significantly contribute to its efficient management. Nevertheless, due to the cost of a dedicated power meter for each server, most data center power management only focuses on UPS or cluster-level power monitoring. In this paper, we propose a low-cost novel power monitoring approach that uses only one sensor to extract power consumption information of all servers. We utilize the conducted electromagnetic interference of server power supplies to measure its power consumption from non-intrusive single-point voltage measurement. Using a pair of commercial grade Dell PowerEdge servers, we demonstrate that our approach can estimate each server's power consumption with ~3% mean absolute percentage error.