State machine replication has been widely used in modern cluster-based database systems.Most commonly deployed configurations adopt the Raft-like consensus protocol,which has a single strong leader which replicates th...State machine replication has been widely used in modern cluster-based database systems.Most commonly deployed configurations adopt the Raft-like consensus protocol,which has a single strong leader which replicates the log to other followers.Since the followers can handle read requests and many real workloads are usually read-intensive,the recovery speed of a crashed follower may significantly impact on the throughput.Different from traditional database recovery,the recovering follower needs to repair its local log first.Original Raft protocol takes many network round trips to do log comparison between leader and the crashed follower.To reduce network round trips,an optimization method is to truncate the follower’s uncertain log entries behind the latest local commit point,and then to directly fetch all committed log entries from the leader in one round trip.However,if the commit point is not persisted,the recovering follower has to get the whole log from the leader.In this paper,we propose an accurate and efficient log repair(AELR)algorithm for follower recovery.AELR is more robust and resilient to follower failure,and it only needs one network round trip to fetch the least number of log entries for follower recovery.This approach is implemented in the open source database system OceanBase.We experimentally show that the system adopting AELR has a good performance in terms of recovery time.展开更多
Business processes described by formal or semi-formal models are realized via information systems.Event logs generated from these systems are probably not consistent with the existing models due to insufficient design...Business processes described by formal or semi-formal models are realized via information systems.Event logs generated from these systems are probably not consistent with the existing models due to insufficient design of the information system or the system upgrade.By comparing an existing process model with event logs,we can detect inconsistencies called deviations,verify and extend the business process model,and accordingly improve the business process.In this paper,some abnormal activities in business processes are formally defined based on Petri nets.An efficient approach to detect deviations between the process model and event logs is proposed.Then,business process models are revised when abnormal activities exist.A clinical process in a healthcare information system is used as a case study to illustrate our work.Experimental results show the effectiveness and efficiency of the proposed approach.展开更多
基金This research was supported in part by National Key R&D Program of China(2018YFB1003303)the National Natural Science Foundation of China(Grant Nos.61432006,61732014 and 61972149).
文摘State machine replication has been widely used in modern cluster-based database systems.Most commonly deployed configurations adopt the Raft-like consensus protocol,which has a single strong leader which replicates the log to other followers.Since the followers can handle read requests and many real workloads are usually read-intensive,the recovery speed of a crashed follower may significantly impact on the throughput.Different from traditional database recovery,the recovering follower needs to repair its local log first.Original Raft protocol takes many network round trips to do log comparison between leader and the crashed follower.To reduce network round trips,an optimization method is to truncate the follower’s uncertain log entries behind the latest local commit point,and then to directly fetch all committed log entries from the leader in one round trip.However,if the commit point is not persisted,the recovering follower has to get the whole log from the leader.In this paper,we propose an accurate and efficient log repair(AELR)algorithm for follower recovery.AELR is more robust and resilient to follower failure,and it only needs one network round trip to fetch the least number of log entries for follower recovery.This approach is implemented in the open source database system OceanBase.We experimentally show that the system adopting AELR has a good performance in terms of recovery time.
基金supported by the National Natural Science Foundation of China(61170078,61472228,61903229,61902222)the “Taishan Scholar” Construction Project of Shandong Province,China,the Natural Science Foundation of Shandong Province(ZR2018MF001)+1 种基金the Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents(2017RCJJ044)the Key Research and Development Program of Shandong Province(2018GGX101011)
文摘Business processes described by formal or semi-formal models are realized via information systems.Event logs generated from these systems are probably not consistent with the existing models due to insufficient design of the information system or the system upgrade.By comparing an existing process model with event logs,we can detect inconsistencies called deviations,verify and extend the business process model,and accordingly improve the business process.In this paper,some abnormal activities in business processes are formally defined based on Petri nets.An efficient approach to detect deviations between the process model and event logs is proposed.Then,business process models are revised when abnormal activities exist.A clinical process in a healthcare information system is used as a case study to illustrate our work.Experimental results show the effectiveness and efficiency of the proposed approach.
