With the rapid growth of the amount of information, cloud computing servers need to process and analyze large amounts of high-dimensional and unstructured data timely and accurately, which usually requires many query operations. Due to simplicity and ease of use, cuckoo hashing schemes have been widely used in real-world cloud-related applications. However, due to the potential hash collisions, the cuckoo hashing suffers from endless loops and high insertion latency, even high risks of re-construction of entire hash table. In order to address this problem, we propose a cost-efficient cuckoo hashing scheme, called MinCounter. The idea behind MinCounter is to alleviate the occurrence of endless loops in the data insertion. MinCounter selects the “cold” (infrequently accessed) buckets to handle hash collisions rather than random buckets. MinCounter has the salient features of offering efficient insertion and query services and obtaining performance improvements in cloud servers, as well as enhancing the experiences for cloud users. We have implemented MinCounter in a large-scale cloud testbed and examined the performance by using two real-world traces. Extensive experimental results demonstrate the efficacy and efficiency of MinCounter.
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