Abstract: In a distributed cloud environment, a collecting agent deployed external to a kernel of a compute host collects network data packets describing various raw events communicated between compute instances of the environment and metadata associated with the events from various sources. The collecting agent communicates the metadata to a cloud application. The collecting agent also communicates the packets to a stream processor that is decoupled from the collecting agent. The stream processor processes the packets in a stateful manner to generate a set of time series data. The time series data is communicated to the cloud application, where a set of enhanced time series data is generated by merging the time series data with the metadata in a distributed manner. A topology map describing the compute instances of the distributed cloud environment is then generated based on the set of enhanced time series data. The topology map is then displayed.

Abstract: In a distributed cloud environment, a collecting agent deployed external to a kernel of a compute host collects network data packets describing various raw events communicated between compute instances of the distributed cloud environment and metadata associated with the events from various sources. The collecting agent communicates the metadata to a cloud service where it may be stored. The collecting agent communicates the packets to a stream processor that is decoupled from the collecting agent. The stream processor processes the packets in a stateful manner to generate a set of time series data. The time series data is communicated to the cloud application, where a set of enhanced time series data is generated by merging the time series data with the metadata in a distributed manner. A topology map describing the compute instances of the distributed cloud environment is then generated based on the set of enhanced time series data.

Abstract: In a distributed cloud environment, a collecting agent deployed external to a kernel of a compute host collects network data packets describing various raw events communicated between compute instances of the distributed cloud environment and metadata associated with the events from various sources. The collecting agent communicates the metadata to a cloud service where it may be stored. The collecting agent communicates the packets to a stream processor that is decoupled from the collecting agent. The stream processor processes the packets in a stateful manner to generate a set of time series data. The time series data is communicated to the cloud application, where a set of enhanced time series data is generated by merging the time series data with the metadata in a distributed manner. A topology map describing the compute instances of the distributed cloud environment is then generated based on the set of enhanced time series data.

Abstract: In a distributed cloud environment, a collecting agent deployed external to a kernel of a compute host collects network data packets describing various raw events communicated between compute instances of the environment and metadata associated with the events from various sources. The collecting agent communicates the metadata to a cloud application. The collecting agent also communicates the packets to a stream processor that is decoupled from the collecting agent. The stream processor processes the packets in a stateful manner to generate a set of time series data. The time series data is communicated to the cloud application, where a set of enhanced time series data is generated by merging the time series data with the metadata in a distributed manner. A topology map describing the compute instances of the distributed cloud environment is then generated based on the set of enhanced time series data. The topology map is then displayed.

Abstract: A method provides an on-chip repair technique to fix defective row or I/O memory lines in an ASIC memory array with redundancy row or I/O memory lines. The method employs progressive urgency and dynamic repair schemes to optimize the allotted time for repairing defective row and I/O memory lines. Progressive urgency scheme increases the need to repair relative to the available redundancy row or I/O memory lines over the entire repairing time. Dynamic repair executes a mandatory-row or a mandatory-I/O repair as defective row or I/O memory lines are detected. In addition, a recurrence error reroutes the address location of a redundancy memory line to another address location of another redundancy memory line in the event that such redundancy memory line itself is defective, and thus requires further repair.

Abstract: A built-in self-repair system includes an on-chip clock generator for triggering the repairing process to repair defective memory lines or blocks in a memory array of an ASIC chip. The on-chip clock generator enables the self-repair process to start at the power up of a computer system without a need for an external test-triggering signal. The system includes a built-in self-test circuit that tests for a defective row memory line or a defective I/O memory block. The system further includes a fault-latching-and repair-execution circuit that repairs a row memory line or an I/O memory block. Repairing an IO memory block effectively repairs faults that occur between any two adjacent column shorts within an IO memory block. The preferred repairing scheme adopts a 15N diagnosis to achieve high fault correction so that a large percentage of defective memory cells can be replaced by redundant row memory lines or redundant I/O memory blocks.