Nowadays, wireless sensor networks have played an important role in many applications. In these applications, a large number of wireless sensors are deployed in an environment to collect information and form network to transmit collected information to the base station or sink. Because wireless sensors have to work for a long period of time without maintenance, the energy consumption of wireless sensors has a great impact on the lifetime of wireless sensor network. To reduce and balance the energy consumption of wireless sensors, many information transmission routing approaches have been proposed. However, most of them do not consider all energy consumption factors of wireless sensors. To this end, an innovative information transmission routing approach based on Q-learning is proposed in this paper, which enables wireless sensors to adaptively select suitable neighboring sensors to achieve energy efficient information transmission in a decentralized manner. Based on the proposed approach, a wireless sensor first collects state and action information of its neighboring sensors, which includes information transmission direction and distance, the remaining energy, the energy consumption for information transmission and information transmission action. Then, all this information is used to update the Q-values of neighboring sensors, so as to enable the wireless sensor to select suitable neighboring sensor to transmit information according to Q-values. From simulation experiments, it can be seen that the proposed approach enables wireless sensors to reduce and balance the energy consumption of wireless sensors and extend the lifetime of the entire wireless sensor network.

The workload co-location, such as deploying offline analysis workloads with online service workloads on the same node, has become common for modern data centers. Workload co-location deployment improves data center resource utilization significantly. Still, it also introduces resource contention, resulting in the online service’s quality of service fluctuation, which we call performance interference. As the online service is a tail-latency-sensitive workload, the tail-latency metric can reflect the performance interference degree of the co-location workloads at the application level. However, to guide system design and evaluation, quantitatively evaluating the resource contention of the co-located workloads at the system level is also essential. This article proposes a novel metric called System-Level Entropy (SLE). As a system-level metric, SLE can measure quantitatively resource contention of the co-location systems and perform the apples-to-apples comparison between systems. The experimental results show that SLE can accurately reflect the performance interference of workloads and then evaluate the system resource contention. We also demonstrate two case studies of the SLE. We quantify the affinity of different co-location combinations, including three online services and five offline workloads. Furthermore, we evaluate the effects of state-of-the-art isolated mechanisms (the container and the CPU–affinity binding) with these co-location combinations.