Efficiently managing electricity supply and demand, especially during peak times to minimize waste, remains a key challenge for the electric grid. An effective solution involves incentivizing users to shift their shiftable loads, such as dishwashers and washing machines, to off-peak periods. Non-Intrusive Load Monitoring (NILM) provides a cost-effective and pragmatic approach for detailed appliance energy consumption insights. Among Deep Learning models, TreeCNN has shown superior performance compared to RNN and traditional CNN models in energy disaggregation. However, its evaluation has been limited to the Dataport dataset. To fully assess TreeCNN’s capabilities, comprehensive testing with diverse datasets like REDD, UK-DALE, DRED and others is essential. Additionally, integrating TreeCNN into NILMTK, a dataset standardization tool, enables thorough comparisons with 16 formatted datasets and other disaggregation algorithms. In this work, we integrated TreeCNN into NILMTK toolkit and benchmarked, providing valuable insights into its effectiveness and real-world usability.

Federated Learning (FL) is a machine learning approach that addresses privacy and data transfer costs by computing data at the source. It’s particularly popular for Edge and IoT applications where the aggregator server of FL is in resource-capped edge data centers for reducing communication costs. Existing cloud-based aggregator solutions are resource-inefficient and expensive at the Edge, leading to low scalability and high latency. To address these challenges, this study compares prior and new aggregation methodologies under the changing demands of IoT and Edge applications. This work is the first to propose an adaptive FL aggregator at the Edge, enabling users to manage the cost and efficiency trade-off. An extensive comparative analysis demonstrates that the design improves scalability by up to 4$\times$, time efficiency by 8$\times$, and reduces costs by more than 2$\times$ compared to extant cloud-based static methodologies.

Federated learning (FL) is becoming an important distributed machine learning approach that considers privacy and security concerns while training a shared model across various clients with localized data. One of the key challenges in FL is heterogeneity in both hardware resources and local datasets due to the nature of incorporating diverse clients. Given the resource heterogeneity, the availability of participating clients is not stable over time and their resource usage patterns become dynamic. This leads to resource wastage and straggler issues. Additional challenges are introduced due to data heterogeneity, causing model biasness and poor model performance. However, most existing FL systems are not well suited to heterogeneous environments because those approaches are not adaptive to various and dynamically changing resource usage patterns and accuracy trends during training process. To this end, we propose a heterogeneity-aware scheduling which is adaptive to the accuracy trends and various resource usage patterns. Our proposed scheduling provides different scheduling knobs for achieving different goals such as resource-efficient fast training, resource fairness, accuracy fairness, and high model performance. To the best of our knowledge, this is the first effort to mitigate effects of resource and data heterogeneity while providing adaptive scheduling based on dynamically changing resource usage patterns and accuracy trends.

High Performance Computing (HPC) big data problems require efficient distributed storage systems. However, at scale, such storage systems often experience load imbalance and resource contention due to two factors: the bursty nature of scientific application I/O; and the complex I/O path that is without centralized arbitration and control. For example, the extant Lustre parallel file system-that supports many HPC centers-comprises numerous components connected via custom network topologies, and serves varying demands of a large number of users and applications. Consequently, some storage servers can be more loaded than others, which creates bottlenecks and reduces overall application I/O performance. Existing solutions typically focus on per application load balancing, and thus are not as effective given their lack of a global view of the system. In this paper, we propose a data-driven approach to load balance the I/O servers at scale, targeted at Lustre deployments. To this end, we design a global mapper on Lustre Metadata Server, which gathers runtime statistics from key storage components on the I/O path, and applies Markov chain modeling and a minimum-cost maximum-flow algorithm to decide where data should be placed. Evaluation using a realistic system simulator and a real setup shows that our approach yields better load balancing, which in turn can improve end-to-end performance.

Linear algebraic operations such as matrix manipulations form the kernel of many machine learning and other crucial algorithms. Scaling up as well as scaling out such algorithms are key to supporting large scale data analysis that require efficient processing over millions of data samples. To this end, we present, ARION, a hardware acceleration based approach for scaling-up individual tasks of Spark, a popular data-parallel analytics platform. We support both linear algebraic operations of between two dense matrices, and between sparse and dense matrices in distributed environments. ARION provides a flexible control of acceleration according to matrix density, along with efficient scheduling based on runtime resource utilization. We demonstrate the benefit of our approach for general matrix multiplication operations over large matrices with up to four billion elements by using Gramian matrix computation that is commonly used in machine learning. Experiments show that our approach achieves more than 2× and 1.5× end-to-end performance speedups for dense and sparse matrices, respectively, and up to 57.04× faster computation compared to MLlib, a state of the art Spark-based implementation. This work is sponsored in part by the NSF under the grants: CNS-1565314, CNS-1405697, and CNS-1615411. The manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

A major obstacle in sustaining high performance and scalability in the Hadoop data processing framework is managing the growing data and the need for very high I/O rates. Solid State Disks (SSDs) are promising and are being employed alongside the slower hard disk drives (HDDs) in emerging storage architectures. However, we observed that SSDs are not always a cost-effective option for all Hadoop workloads, and there is a critical need to identify usecases where SSDs can help. To this end, we present VENU, a dynamic data management system for Hadoop. VENU aims to improve overall I/O throughput via effective use of SSDs as a cache for the slower HDDs, not for all data, but for only the workloads that are expected to benefit from SSDs. In addition, we design placement and retrieval schemes to efficiently use the SSD cache. We evaluate our implementation of VENU on a medium-sized cluster and show that it achieves 11% improvement in application completion times when 10% of the available storage is provided by SSDs.

Shared-nothing environments, exemplified by systems such as MapReduce and Hadoop, employ node-local storage to achieve high scalability. The exponential growth in application datasets, however, demands ever higher I/O throughput and disk capacity. Simply equipping individual nodes in a Hadoop cluster with more disks is not scalable as it: increases the per-node cost, increases the probability of storage failure at the node, and worsens node failure recovery times. To this end, we propose dividing a Hadoop rack into several (small) sub-racks, and consolidating disks of a sub-rack's compute nodes into a separate shared Localized Storage Node (LSN) within the subrack. Such a shared LSN is easier to manage and provision, and can offer an economically better solution by employing overall fewer disks at the LSN than the total of the sub-rack's individual nodes, while still achieving high I/O performance. In this paper, we provide a quantitative study on the impact of shared storage in Hadoop clusters. We utilize several typical Hadoop applications and test them on a medium-sized cluster and via simulations. Our evaluation shows that: (i) the staggered workload allows our design to support the same number of compute nodes at a comparable or better throughput using fewer total disks than in the node-local case, thus providing more efficient resource utilization; (ii) the impact of lost locality can be mitigated by better provisioning the LSN-node network interconnect and the number of disks in an LSN; and (iii) the consolidation of disks into an LSN is a viable and efficient alternative to the extant node-local storage design. Finally, we show that LSN-based design can deliver up to 39% performance improvement over standard Hadoop.