Abstract: The present disclosure relates to an intelligent prevention passage control system for an electronic device, which comprises a detection device for an electronic device, a traffic control device integrated with the detection device for the electronic device, an industrial control host, a server, an access controller and a management terminal inside the traffic control device. The present disclosure can more accurately judge violations by scoring violations, and can make the system more intelligent by accurately judging violations, thus realizing no manual attendance, solving the problems of personnel recruitment, shift attendance and personnel management, and saving a lot of labor cost. At the same time, the system can avoid the hidden dangers caused by human feelings, sense of responsibility and inertia brought by manual prevention, and ensure the consistent, accurate and reliable standards of detection work.

Abstract: The present disclosure relates to an intelligent prevention passage control system for an electronic device, which comprises a detection device for an electronic device, a traffic control device integrated with the detection device for the electronic device, an industrial control host, a server, an access controller and a management terminal inside the traffic control device, The present disclosure can more accurately judge violations by scoring violations, and can make the system more intelligent by accurately judging violations, thus realizing no manual attendance, solving the problems of personnel recruitment, shift attendance and personnel management, and saving a lot of labor cost. At the same time, the system can avoid the hidden dangers caused by human feelings. sense of responsibility and inertia brought b manual prevention, and ensure the consistent, accurate and reliable standards of detection work.

Abstract: The disclosed embodiments provide a system that detects anomalous events in a virtual machine. During operation, the system obtains time-series garbage-collection (GC) data collected during execution of a virtual machine in a computer system. Next, the system generates one or more seasonal features from the time-series GC data. The system then uses a sequential-analysis technique to analyze the time-series GC data and the one or more seasonal features for an anomaly in the GC activity of the virtual machine. Finally, the system stores an indication of a potential out-of-memory (OOM) event for the virtual machine based at least in part on identifying the anomaly in the GC activity of the virtual machine.

Abstract: The disclosed embodiments provide a system that detects anomalous events in a virtual machine. During operation, the system obtains time-series garbage-collection (GC) data collected during execution of a virtual machine in a computer system. Next, the system generates one or more seasonal features from the time-series GC data. The system then uses a sequential-analysis technique to analyze the time-series GC data and the one or more seasonal features for an anomaly in the GC activity of the virtual machine. Finally, the system stores an indication of a potential out-of-memory (OOM) event for the virtual machine based at least in part on identifying the anomaly in the GC activity of the virtual machine.

Abstract: A method for managing system resources including creating a container, wherein creating the container comprises allocating a first portion of a first resource to the container, associating the container with a resource pool, wherein the resource pool is associated with the first resource, determining whether the first portion of the first resource is valid, and activating the container if the first portion of the first resource is valid.