Abstract
Characteristics of long-running applications in cloud and big data environment are various and significantly influence the performance of cache systems. The gap between existing cache systems and the increasing performance requirements motivates us to propose the Application-oriented cache allocation and prefetching method (ACAP) to improve data access performance. An application-oriented cache allocation approach is designed based on hit count growth rates for a higher overall hit rate. Two application-oriented sequential prefetching approaches are proposed to improve the hit rate and prefetching accuracy by learning average read sizes of long-running applications. Based on correlation of data accesses, a parallelized correlated-directed prefetching approach is proposed to further increase the hit rate. Above approaches are intergrated to obtain the maximized hit rate and prefetching accuracy. Experimental results on 12 public real system traces show that ACAP achieves 14.03% (up to 33.82%) higher prefetching accuracy and 2.01% (up to 7.54%) higher hit rate compared with the best combination of baselines.
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