Block synchronization is an essential component of blockchain systems.Traditionally,blockchain systems tend to send all the transactions from one node to another for synchronization.However,such a method may lead to a...Block synchronization is an essential component of blockchain systems.Traditionally,blockchain systems tend to send all the transactions from one node to another for synchronization.However,such a method may lead to an extremely high network bandwidth overhead and significant transmission latency.It is crucial to speed up such a block synchronization process and save bandwidth consumption.A feasible solution is to reduce the amount of data transmission in the block synchronization process between any pair of peers.However,existing methods based on the Bloom filter or its variants still suffer from multiple roundtrips of communications and significant synchronization delay.In this paper,we propose a novel protocol named Gauze for fast block synchronization.It utilizes the Cuckoo filter(CF)to discern the transactions in the receiver’s mempool and the block to verify,providing an efficient solution to the problem of set reconciliation in the P2P(Peer-to-Peer Network)network.By up to two rounds of exchanging and querying the CFs,the sending node can acknowledge whether the transactions in a block are contained by the receiver’s mempool or not.Based on this message,the sender only needs to transfer the missed transactions to the receiver,which speeds up the block synchronization and saves precious bandwidth resources.The evaluation results show that Gauze outperforms existing methods in terms of the average processing latency(about lower than Graphene)and the total synchronization space cost(about lower than Compact Blocks)in different scenarios.展开更多
Counting the cardinality of flows for massive high-speed traffic over sliding windows is still a challenging work under time and space constrains, but plays a key role in many network applications, such as traffic man...Counting the cardinality of flows for massive high-speed traffic over sliding windows is still a challenging work under time and space constrains, but plays a key role in many network applications, such as traffic management and routing optimization in software defined network. In this pa- per, we propose a novel data structure （called LRU-Sketch） to address the problem. The significant contributions are as follows. 1） The proposed data structure adapts a well-known probabilistic sketch to sliding window model; 2） By using the least-recently used （LRU） replacement policy, we design a highly time-efficient algorithm for timely forgetting stale information, which takes constant （O（1）） time per time slot; 3） Moreover, a further memory-reducing schema is given at a cost of very little loss of accuracy; 4） Comprehensive ex- periments, performed on two real IP trace files, confirm that the proposed schema attains high accuracy and high time efficiency.ferences including IEEE TPDS, ACM ToS, JCST, MIDDLEWARE, CLUSTER, NAS, etc. Currently, his research interests include big data management, cloud storage, and distributed file systems.展开更多
基金This work was supported in part by the National Natural Science Foundation of China(Grant No.62032017).
文摘Block synchronization is an essential component of blockchain systems.Traditionally,blockchain systems tend to send all the transactions from one node to another for synchronization.However,such a method may lead to an extremely high network bandwidth overhead and significant transmission latency.It is crucial to speed up such a block synchronization process and save bandwidth consumption.A feasible solution is to reduce the amount of data transmission in the block synchronization process between any pair of peers.However,existing methods based on the Bloom filter or its variants still suffer from multiple roundtrips of communications and significant synchronization delay.In this paper,we propose a novel protocol named Gauze for fast block synchronization.It utilizes the Cuckoo filter(CF)to discern the transactions in the receiver’s mempool and the block to verify,providing an efficient solution to the problem of set reconciliation in the P2P(Peer-to-Peer Network)network.By up to two rounds of exchanging and querying the CFs,the sending node can acknowledge whether the transactions in a block are contained by the receiver’s mempool or not.Based on this message,the sender only needs to transfer the missed transactions to the receiver,which speeds up the block synchronization and saves precious bandwidth resources.The evaluation results show that Gauze outperforms existing methods in terms of the average processing latency(about lower than Graphene)and the total synchronization space cost(about lower than Compact Blocks)in different scenarios.
基金This work was supported by the National High Tech- nology Research and Development Program of China （2012AA01A510 and 2012AA01AS09）, and partially supported by the National Natural Science Foundation of China （NSFC） （Grant Nos. 61402518, 61403060）, and the Jiangsu Province Science Foundation for Youths （BK20150722）.
文摘Counting the cardinality of flows for massive high-speed traffic over sliding windows is still a challenging work under time and space constrains, but plays a key role in many network applications, such as traffic management and routing optimization in software defined network. In this pa- per, we propose a novel data structure （called LRU-Sketch） to address the problem. The significant contributions are as follows. 1） The proposed data structure adapts a well-known probabilistic sketch to sliding window model; 2） By using the least-recently used （LRU） replacement policy, we design a highly time-efficient algorithm for timely forgetting stale information, which takes constant （O（1）） time per time slot; 3） Moreover, a further memory-reducing schema is given at a cost of very little loss of accuracy; 4） Comprehensive ex- periments, performed on two real IP trace files, confirm that the proposed schema attains high accuracy and high time efficiency.ferences including IEEE TPDS, ACM ToS, JCST, MIDDLEWARE, CLUSTER, NAS, etc. Currently, his research interests include big data management, cloud storage, and distributed file systems.
