Due to the imperative need to reduce the management costs of large data centers, operators multiplex several concurrent database applications on a server farm connected to shared network attached storage. Determining and enforcing perapplication resource quotas in the resulting cache hierarchy, on the fly, poses a complex resource allocation problem spanning the database server and the storage server tiers. This problem is further complicated by the need to provide strict Quality of Service (QoS) guarantees to hosted applications. In this paper, we design and implement a novel coordinated partitioning technique of the database buffer pool and storage cache between applications for any given cache replacement policy and per-application access pattern. We use statistical regression to dynamically determine the mapping between cache quota settings and the resulting perapplication QoS. A resource controller embedded within the database engine actuates the partitioning of the two-level cache, converging towards the configuration with maximum application utility, expressed as the service provider revenue in that configuration, based on a set of latency sample points. Our experimental evaluation, using the MySQL database engine, a server farm with consolidated storage, and two ecommerce benchmarks, shows the effectiveness of our technique in enforcing application QoS, as well as maximizing the revenue of the service provider in shared server farms. 1.

The multi-level storage architecture has been widely adopted in servers and data centers. However, while prefetching has been shown as a crucial technique to exploit the sequentiality in accesses common for such systems and hide the increasing relative cost of disk I/O, existing multi-level storage studies have focused mostly on cache replacement strategies. In this paper, we show that prefetching algorithms designed for single-level systems may have their limitations magnified when applied to multi-level systems. Overly conservative prefetching will not be able to effectively use the lower-level cache space, while overly aggressive prefetching will be compounded across levels and generate large amounts of wasted prefetch. We take an innovative approach to this problem: rather than designing

TaP is a storage cache sequential prefetching and caching technique to improve the read-ahead cache hit rate and system response time. A unique feature of TaP is the use of a table to detect sequential access patterns in the I/O workload and to dynamically determine the optimum prefetch cache size. When compared to some popular prefetching techniques, TaP gives a better hit rate and response time while using a read cache that is often an order of magnitude smaller than that needed by other techniques. TaP is especially efficient when the I/O workload consists of interleaved requests from various applications, where only some of the applications are accessing their data sequentially. For example, TaP achieves the same hit rate as the other techniques with a cache length that is 100 times smaller than the cache needed by other techniques when the interleaved workload consists of 10 % sequential application data and 90% random application data. Index Terms: RAID, prefetch cache, sequential stream detection, read caches, read-ahead hit rate, I/O performance

In many of today’s applications, access to storage constitutes the major cost of processing a user request. Data prefetching has been used to alleviate the storage access latency. Under current prefetching techniques, the storage system prefetches a batch of blocks upon detecting an access pattern. However, the high level of concurrency in today’s applications typically leads to interleaved block accesses, which makes detecting an access pattern a very challenging problem. Towards this, we propose and evaluate QuickMine, a novel, lightweight and minimally intrusive method for contextaware prefetching. Under QuickMine, we capture application contexts, such as a transaction or query, and leverage them for context-aware prediction and improved prefetching effectiveness in the storage cache. We implement a prototype of our context-aware prefetching algorithm in a storage-area network (SAN) built using Network Block Device (NBD). Our prototype shows that context-aware prefetching clearly outperforms existing context-oblivious prefetching algorithms, resulting in factors of up to 2 improvements in application latency for two e-commerce workloads with repeatable access patterns, TPC-W and RUBiS. 1

As an important technique to hide disk I/O latency, prefetching has been widely studied, and dynamic adaptive prefetching techniques have been deployed in diverse storage environments. However, two issues are not well addressed by previous research: (1) how to handle the prefetching resource allocation between concurrent sequential access streams with different request rates, and (2) how to coordinate prefetching at multiple levels in the data access path. Interestingly, we found that these problems bear a strong resemblance to situations long studied in the field of supply chain management (SCM), used by retailers such as Wal-Mart. In this paper, we demonstrate how to perform the problem mapping and then apply SCM principles in practice, particularly from the branch of inventory theory, to improve data prefetching performance in storage systems. More specifically, we applied (1) two SCM policies to dynamically configure the sequential prefetching parameters, and (2) an SCM solution to correct the access pattern information distortion in multi-level prefetching. We implemented these SCM-based strategies in the Linux kernel prefetching algorithm and a multi-level storage simulator, and evaluated the performance with three types of work-Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.