Systems

Non-intrusive load monitoring (NILM), as a key load monitoring technology, can much reduce the deployment cost of traditional power sensors. Previous research has largely focused on developing cloud-exclusive NILM algorithms, which often result in high computation costs and significant service delays. To address these issues, we propose a three-tier framework to enhance the real-world applicability of NILM systems through edgecloud collaboration. Considering the computational resources available at both the edge and cloud, we implement a lightweight NILM model at the edge and a deep learning based model at the cloud, respectively. In addition to the differential model implementations, we also design a NILM-specific deployment scheme that integrates Gunicorn and NGINX to bridge the gap between theoretical algorithms and practical applications. To verify the effectiveness of the proposed framework, we apply real-world NILM scenario settings and implement the entire process of data acquisition, model training, and system deployment. The results demonstrate that our framework can achieve high decomposition accuracy while significantly reducing the cloud workload and communication overhead under practical considerations.

The geographically distributed edge servers can naturally draw power from nearby renewable energy generators. Complemented by the dynamic scheduling of energy storage batteries, edge service providers (ESPs) can thus build low- or even zero-carbon edge computing systems. Nevertheless, the distributed and heterogeneous nature of edge computing systems, as well as the limited information sharing among ESPs, leads to a more complex battery planning problem than that in cloud computing. The unpredictability of RE resources further complicates the problem, making conventional model-based approaches ineffective.
We propose and design a multi-agent deep reinforcement learning (MADRL) approach for the independent decision making of individual ESPs. Particularly, MADRL takes privacy into account by ensuring that no sensitive information is disclosed among ESPs. For better model training, we further customize the invalid action masking and develop action transformation techniques based on segmented linear optimization. Extensive experiments demonstrate that, with our proposed approach, the overall carbon emission of edge computing systems can be significantly reduced (by over 60%) while maintaining acceptable operation costs in battery scheduling.

Energy disaggregation, which aims to break down the total energy consumption of household into that of individual appliances, plays an important role in energy conservation and has caught more and more attention. Realizing that current energy disaggregation approaches are hard to perform for the ordinary consumers and free and open applications/services are not generally available, we provide a consumer-oriented web service, Smart Saver, which is not only open and free to the consumers but also user-friendly and easy-to-use for energy disaggregation. Based on a simple power model, a sparse switching event recovery model is established as the core of Smart Saver. By feeding the basic power information of appliances into Smart Saver, the users will be provided with 1) online energy disaggregation and appliance monitoring if they have smart meters communicating with our service, or 2) offline energy disaggregation if they upload their aggregated power data.

Fine-grained power monitoring, which refers to power monitoring at the server level, is critical to the efficient operation and energy saving of datacenters. Fined-grained power monitoring, however, is extremely challenging in legacy datacenters that host server systems not equipped with power monitoring sensors. Installing power monitoring hardware at the server level not only incurs high costs but also complicates the maintenance of high-density server clusters and enclosures.
We propose and design a zero-cost, purely software-based solution to this challenging problem. We use a novel technique of non-intrusive power disaggregation (NIPD) that establishes power mapping functions (PMFs) between the states of servers and their power consumption and infer the power consumption of each server with the aggregated power of the entire datacenter. We implement and evaluate NIPD over a real-world datacenter with 326 nodes. The results show that our solution can provide high precision power estimation at the rack level, with mean relative error of 2.6%, and the server level, with mean relative error of 10.3% and 8.2% for the estimation of idle power and peak power, respectively.