Edge computing enables time-sensitive applications by bringing computation closer to data sources. However, existing orchestration frameworks such as Kubernetes often overlook performance variability, leading to inefficient resource management. While recent advances have introduced performance-aware scheduling, load balancing remains largely reactive. In particular, performance-aware load balancers are still missing from mainstream systems, limiting their ability to meet subsecond latency requirements. This paper presents a unified extension to orchestration frameworks that integrates both performance-aware scheduling and load balancing. We design lightweight performance predictors, trained on historical monitoring data, to estimate performance fluctuations. Variability predictors inform the scheduler, while Round-Trip Time (RTT) predictors guide load balancing decisions. Our approach is validated using a Single Particle Analysis (SPA) application deployed on a Kubernetes-based edge computing testbed. Results demonstrate that the predictors achieve approximately 85% accuracy in capturing RTT variability. Simulation results show reductions in application completion times ranging from 50% to 78% for scheduling and 68% for load balancing, highlighting the effectiveness of predictive orchestration in dynamic edge/cloud environments.

Edge computing is a promising technology for deploying time-sensitive and privacy-sensitive applications closer to the premises of users. However, it is crucial to identify the sources of performance variability caused by application co-location to meet user requirements effectively. Monitoring systems typically expose hundreds of metrics, making comprehensive analysis challenging. As a result, researchers often rely on a small, arbitrarily selected subset of metrics for tasks such as building performance predictors. In this paper, we examine how the available monitoring metrics are correlated with Round Trip Time (RTT) fluctuations and suggest directions for building performance models. Our experiments focus on a Single Particle Analysis (SPA) applications for an electron microscopy use case, deployed in a Kubernetes environment and monitored by Prometheus. We demonstrate that while a subset of monitoring metrics consistently correlates with performance, the specific metrics in this subset can vary due to dynamic application co-locations and observation windows. Consequently, the optimal number of metrics and the choice of machine learning model needed to accurately capture performance variability vary between different scenarios (co-location and cluster nodes). These differences directly impact the effectiveness of scheduling decisions in resource clusters, which depend on performance predictors. Our work presents a method to systematically identify the most relevant monitoring metrics to changes in RTT and determining the most representative observation window, ensuring a more generalizable understanding of the performance of the application throughout its lifecycle.

Switching the traffic in the optical domain has been considerably investigated as a future-proof solution to overcome the intrinsic bandwidth bottleneck of electrical switches in data center networks (DCNs). However, due to the lack of fast and scalable optical switch control mechanism, the lack of optical buffers for contention resolution, and the complicated implementation of fast clock and data recovery (CDR), the practical deployment of fast optical switches in data centers (DCs) remains a big challenge. In this work, we develop and experimentally demonstrate for the first time a flow-controlled and clock-distributed optical switch and control system, implementing 43.4 ns optical switch configuration time, less than 3.0E-10 packet loss rate resulting from the packet contention, and 3.1 ns fast CDR time. Experimental results confirm that zero buffer overflow caused packet loss and lower than$3~ \boldsymbol {\mu }\text{s}$server-to-server latency are achieved for network deploying a smaller electrical buffer of 8192 bytes at a traffic load of 0.5. Real servers running the Transmission Control Protocol (TCP) traffic generating and monitoring tools are exploited in this switch and control system as well, validating its capability of running practical DCNs services and applications with full TCP bandwidth.

In elastic optical inter-datacenter networks, multiple software-defined networking (SDN) controllers have been used to improve scalability and reliability. The static controller deployment faces the problem of controller load imbalance under dynamic changes in network traffic. In response to this problem, existing research works proposed to use dynamic switch migration to achieve the controller load balancing. However, the efficiency of the existing switch migration is low, because the load balancing performance of controllers does not improve significantly after the switch migration and the migration activities can also cause additional migration costs. Thus the efficiency of switch migration has not yet been well resolved. In this work, we propose a highly-efficient switch migration (HESM) for controller load balancing. The proposed HESM method defines multiple load metrics to measure the load of controllers, and selects the optimal target controller with the largest remaining resource, which improves the load balancing performance. HESM selects switches based on minimizing migration costs, reducing additional migration costs. In addition, HESM can handle the load of multiple controllers in parallel during a switch migration activity, which improves the efficiency of migration. The simulation results show that HESM significantly improves load balancing performance of controllers and reduces the migration cost compared to existing solutions.