Prefetching is a widely used technique in modern data storage systems. We study the most widely used class of prefetching algorithms known as sequential prefetching. There are two problems that plague the state-of-the-art sequential prefetching algorithms: (i) cache pollution, which occurs when prefetched data replaces more useful prefetched or demand-paged data, and (ii) prefetch wastage, which happens when prefetched data is evicted from the cache before it can be used.A sequential prefetching algorithm can have a fixed or adaptive degree of prefetch and can be either synchronous (when it can prefetch only on a miss) or asynchronous (when it can also prefetch on a hit). To capture these distinctions we define four classes of prefetching algorithms: fixed synchronous (FS), fixed asynchronous (FA), adaptive synchronous (AS), and adaptive asynchronous (AA). We find that the relatively unexplored class of AA algorithms is in fact the most promising for sequential prefetching. We provide a first formal analysis of the criteria necessary for optimal throughput when using an AA algorithm in a cache shared by multiple steady sequential streams. We then provide a simple implementation called AMP (adaptive multistream prefetching) which adapts accordingly, leading to near-optimal performance for any kind of sequential workload and cache size.Our experimental setup consisted of an IBM xSeries 345 dual processor server running Linux using five SCSI disks. We observe that AMP convincingly outperforms all the contending members of the FA, FS, and AS classes for any number of streams and over all cache sizes. As anecdotal evidence, in an experiment with 100 concurrent sequential streams and varying cache sizes, AMP surpasses the FA, FS, and AS algorithms by 29-172%, 12-24%, and 21-210%, respectively, while outperforming OBL by a factor of 8. Even for complex workloads like SPC1-Read, AMP is consistently the best-performing algorithm. For the SPC2 video-on-demand workload, AMP can sustain at least 25% more streams than the next best algorithm. Furthermore, for a workload consisting of short sequences, where optimality is more elusive, AMP is able to outperform all the other contenders in overall performance.Finally, we implemented AMP in the state-of-the-art enterprise storage system, the IBM system storage DS8000 series. We demonstrated that AMP dramatically improves performance for common sequential and batch processing workloads and delivers up to a twofold increase in the sequential read capacity.

The unprecedented volume of data generated by contemporary business users and consumers has created enormous data storage and management challenges. In order to control data storage cost, many users are moving their data to online storage clouds, and applying capacity usage reducing data transformation techniques like de-duplication, compression, and transcoding. These give rise to several challenges, such as which cloud to choose, and what data transformation techniques to apply for optimizing cost.This paper presents an integrated storage service called iCostale that reduces the overall cost of data storage through automatic selection and placement of users data into one of many storage clouds. Further, it intelligently transforms data based on its type, access frequency, transformation overhead, and the cost model of the storage cloud providers. We demonstrate the efficacy of iCostale through a series of micro-and applicationlevel benchmarks. Our experimental results show that, through intelligent data placement and transformation, iCostale can reduce overall cost of data storage by more than 50%.

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