With the emergence and development of new software architectures such as microservices, how to effectively handle the service load and ensure the service capability of the system has become an urgent problem to be solved. Load balancing technology needs to achieve high availability of microservices without affecting the delayed response of requests. According to different principles of adoption, mainstream load balancing technologies have emerged, such as polling methods, hash algorithms, and artificial intelligence technologies. This article categorizes and summarizes load balancing technologies for microservice architecture, and elaborates the methods and characteristics of current mainstream load balancing technologies. Based on the comparative analysis of existing technologies, this paper summarizes and points out the future development direction of load balancing technology.

In this paper, we consider the problem of reconstructing the temporal and spatial profile of some physical phenomena monitored by large-scale Wireless Sensor Networks (WSNs) in an energy efficient manner. Compressive sensing is one of the popular choices to reduce the energy consumption of the data collection in WSNs. The existing solutions only consider sparsity of sensors’ data from either temporal or spatial dimensions. In this paper, we propose a novel data collection strategy, CS²-collector, for WSNs based on the theory of Two Dimensional Compressive Sensing (2DCS). It exploits both temporal and spatial sparsity, i.e., 2D-sparsity of WSNs and achieves significant gain on the tradeoff between the compression ratio and reconstruction accuracy as the numerical simulations and evaluations on different types of sensors’ data. More intuitively, with the same given energy budget, CS²-collector provides significantly more accurate reconstruction of the profile of the physical phenomena that are temporal-spatially sparse.