公开(公告)号：US20220121507A1

公开(公告)日：2022-04-21

申请号：US17143203

申请日：2021-01-07

Applicant: VMWARE, INC.

Inventor： RITESH JHA ,  JOBIN RAJU GEORGE ,  NIKHIL JAISWAL ,  PUSHIKAR PATIL ,  VAIDIC JOSHI

IPC: G06F9/54

Abstract: The current document is directed to methods and systems that sample log/event messages for downstream processing by log/event-message systems incorporated within distributed computer facilities. The data-collection, data-storage, and data-querying functionalities of log/event-message systems provide a basis for distributed log-analytics systems which, in turn, provide a basis for automated and semi-automated system-administration-and-management systems. By sampling log/event-messages, rather than processing and storing every log/event-message generated within a distributed computer system, a log/event-message system significantly decreases data-storage-capacity, computational-bandwidth, and networking-bandwidth overheads involved in processing and retaining large numbers of log/event messages that do not provide sufficient useful information to justify these costs. Increase in efficiencies of log/event-message systems obtained by sampling translate directly into increases in bandwidths of distributed computer systems, in general, and to increases in time periods during which useful log/event messages can be stored.

公开(公告)号：US20220113938A1

公开(公告)日：2022-04-14

申请号：US17133479

申请日：2020-12-23

Applicant: VMWARE, INC.

Inventor： RITESH JHA ,  NIKHIL JAISWAL ,  JOBIN RAJU GEORGE ,  VAIDIC JOSHI ,  SHIVAM SATIJA

IPC: G06F7/08 ,  G06F11/07 ,  G06F16/23

Abstract: Methods and systems that automatically rank log/event messages and log/event-message transactions to facilitate analysis of log/event-messages generated within distributed-computer systems are disclosed. A base-window dataset and current-window dataset are selected for diagnosis of a particular error or failure and processed to generate a transaction sequence for each dataset corresponding to log/event-message traces identified in the datasets. Then, frequencies of occurrence of log/event-message types relative to transaction types are generated for each dataset. From these two sets of relative frequencies of occurrence, changes in the relative frequency of occurrence for each log/event-message-type/transaction-type pair are generated. Normalized scores for log/event-message-type/transaction-type pairs and scores for transaction types are then generated from the changes in the relative frequency of occurrence. The generated scores reflect the relevance of log/event-messages in traces corresponding to particular transaction as well as the relevance of transaction types to the error or failure.