As a dominant paradigm in modern web applications, cloud computing has seen a surge in adoption. The deployment of vast and various workloads encapsulated within containers has become ubiquitous across cloud platforms, imposing substantial demands on the supporting infrastructure. However, Kubernetes (k8s), the de facto standard for container orchestration, struggles with low scheduling throughput and high latency in large-scale clusters. The primary challenges are identified as excessive load from read requests and resource contention between co-located components. In this paper, we present MerKury, a general and lightweight framework designed to enhance the Kubernetes performance for large-scale clusters. MerKury employs a dual strategy: first, it preprocesses specific requests to alleviate excessive load; second, it introduces an adaptive resource allocation algorithm to mitigate resource contention. Evaluations across various cluster scales demonstrate that MerKury notably augments node capacity by up to 4.5×, increases scheduling throughput by up to 7.3×, and reduces request latency by 5.6%-57.7%, outperforming vanilla Kubernetes and baseline resource allocation methods.

In this paper, we explore the utilisation of the complex coherence between HH and VV channels as a tool for crop monitoring. The analysis includes the examination of time series of polarimetric data collected from both airborne and space-borne platforms at L and C band. Results suggest that the L band exhibits a higher sensitivity to crop changes than the C-band in both amplitude and phase of the coherence between the co-polar channels. Moreover, incidence angle is identified as a factor influencing the values of the complex coherence, and hence its sensitivity to crop features.

The growth of service ecosystems, which include diversified services such as cloud and edge services, has led to a thriving service transaction market. However, service pricing remains a major obstacle to further progress. Without proper pricing guidance, service providers tend to formulate pricing strategies solely based on their own interests, which frequently hinders the maximization of overall market benefits. This problem is even more challenging in edge computing scenarios as different Edge Service Providers (ESPs) are located in distributed regions and influenced by multiple factors, making it difficult to formulate a single pricing model. This paper proposes a multi-participant stochastic game model to formalize the multi-edge service pricing problem. An incentive mechanism based on Pareto improvement is then proposed to drive the game to the Pareto optimal direction with optimal profits. Finally, an improved PSO algorithm is proposed to solve the game model and analyze the equilibrium states under different evolutionary mechanisms. Experimental results indicate that the proposed pricing incentive mechanism can promote a more effective and reasonable pricing allocation, avoiding 21.6% anarchism loss of the overall profits, while showcasing the effectiveness of our algorithm in solving the game.