In cooperative cache research domain, most of previous work engage in peer-to-peer systems and distributed systems, but do not involve applying cooperative semantic cache in mobile computing environments, which wirele...In cooperative cache research domain, most of previous work engage in peer-to-peer systems and distributed systems, but do not involve applying cooperative semantic cache in mobile computing environments, which wireless communications disconnect at times and clients move frequently. In this paper, we extend the general semantic cache mechanism by enabling mobile clients to share their local semantic caches in a cooperative matter, and the process way and flow chart of the algorithm are described in detail. In addition, we discuss the methods used in cache consistence maintenance, which focus on confirm receiver of the periodic cache invalidation report and the process of validate client＇s local cache. The experiment results indicate cooperative semantic cache mechanism could reduce query response time and increase cache hit ratio effectively.展开更多
Log-structured merge tree(LSM-tree)is adopted by many distributed storage systems.It contains a Memtable and a number of SSTables.The Memtable is an in-memory structure and the SSTable is a disk-based structure.Data r...Log-structured merge tree(LSM-tree)is adopted by many distributed storage systems.It contains a Memtable and a number of SSTables.The Memtable is an in-memory structure and the SSTable is a disk-based structure.Data records are horizontally partitioned over the primary key and stored in different SSTables.Data writes on records are first served by the Memtable and then compacted to SSTables periodically.Although this design optimizes data writes by avoiding random disk writes,it is unfriendly to read request since the results should be retrieved and merged from both Memtable and SSTables.In particular,when the Memtable and SSTables are distributed on different nodes,it incurs expensive costs to serve range queries.A range query on nonprimary key columns has to scan all partitions,which generates many network and I/O expenses.In this paper,we propose a partition pruning strategy to save cost for range queries.A statistics cache is designed to determine whether a partition contains the desired data or not,which enables read requests to avoid scanning useless partitions.As records can be updated in Memtable freely,to prevent incorrect filtering,a version-based cache synchronization strategy is proposed to ensure the queries to obtain the latest data state.We implement the proposed method in an open source distributed database and conduct comprehensive experiments.Experimental results reveal that the performance of range queries increased 30%~40%with our partition pruning technique.展开更多
基金supported by the National Basic Research and Development Program of China (2007CB07100, 2007CB07106)the Ministry of Education in China Project of Humanities and Social Sciences (11YJCZH195)
文摘In cooperative cache research domain, most of previous work engage in peer-to-peer systems and distributed systems, but do not involve applying cooperative semantic cache in mobile computing environments, which wireless communications disconnect at times and clients move frequently. In this paper, we extend the general semantic cache mechanism by enabling mobile clients to share their local semantic caches in a cooperative matter, and the process way and flow chart of the algorithm are described in detail. In addition, we discuss the methods used in cache consistence maintenance, which focus on confirm receiver of the periodic cache invalidation report and the process of validate client＇s local cache. The experiment results indicate cooperative semantic cache mechanism could reduce query response time and increase cache hit ratio effectively.
基金supported by the Youth Science and Technology-“Yang Fan”Program of Shanghai(17YF1427800)Youth Foundation of Natural Science Foundation(61702189)+1 种基金National Hightech R&D Program(863 Program)(2015AA015307)the National Natural Science Foundation of China(Grant Nos.61432006 and 61672232).
文摘Log-structured merge tree(LSM-tree)is adopted by many distributed storage systems.It contains a Memtable and a number of SSTables.The Memtable is an in-memory structure and the SSTable is a disk-based structure.Data records are horizontally partitioned over the primary key and stored in different SSTables.Data writes on records are first served by the Memtable and then compacted to SSTables periodically.Although this design optimizes data writes by avoiding random disk writes,it is unfriendly to read request since the results should be retrieved and merged from both Memtable and SSTables.In particular,when the Memtable and SSTables are distributed on different nodes,it incurs expensive costs to serve range queries.A range query on nonprimary key columns has to scan all partitions,which generates many network and I/O expenses.In this paper,we propose a partition pruning strategy to save cost for range queries.A statistics cache is designed to determine whether a partition contains the desired data or not,which enables read requests to avoid scanning useless partitions.As records can be updated in Memtable freely,to prevent incorrect filtering,a version-based cache synchronization strategy is proposed to ensure the queries to obtain the latest data state.We implement the proposed method in an open source distributed database and conduct comprehensive experiments.Experimental results reveal that the performance of range queries increased 30%~40%with our partition pruning technique.
