This paper reports our research results that improve second level buer cache performance. Several previous studies have shown that a good single level cache replacement algorithm such as LRU does not work well with second level buer caches. Second level buer caches have dierent access pattern from rst level buer caches because Accesses to second level buer caches are actually misses from rst level buer caches.

systems. These correlations can be exploited for improving the effectiveness of storage caching, prefetching, data layout and disk scheduling. Unfortunately, information about block correlations is not available at the storage system level. Previous approaches for discovering file correlations in file systems do not scale well enough to be used for discovering block correlations in storage systems.

As improvements in processor performance continue to far outpace improvements in storage performance, I /O is increasingly the bottleneck in computer systems, especially in large database systems that manage huge amounts of data. The key to achieving good I /O performance is to thoroughly understand its characteristics. In this article we present a comprehensive analysis of the logical I/O reference behavior of the peak production database workloads from ten of the world’s largest corporations. In particular, we focus on how these workloads respond to different techniques for caching, prefetching, and write buffering. Our findings include several broadly applicable rules of thumb that describe how effective the various I /O optimization techniques are for the production workloads. For instance, our results indicate that the buffer pool miss ratio tends to be related to the ratio of buffer pool size to data size by an inverse square root rule. A similar fourth root rule relates the write miss ratio and the ratio of buffer pool size to data size. In addition, we characterize the reference characteristics of workloads similar to the Transaction Processing Performance Council (TPC) benchmarks C (TPC-C) and D (TPC-D), which are de facto standard performance measures for online transaction processing (OLTP) systems and decision support systems (DSS), respectively. Since benchmarks such as TPC-C and TPC-D can only be

Abstract. Work ow management is a ubiquitous task faced by many organizations, and entails the coordination of various activities. This coordination is increasingly carried out by software systems called workow management systems (WFMS). An important component ofmany WFMSs is a DBMS for keeping track ofwork ow activity. This DBMS maintains an audit trail, or event history, that records the results of each activity. Like other data, the event history can be indexed and queried, and views can be de ned on top of it. In addition, a WFMS must accommodate frequent work ow changes, which result from a rapidly evolving business environment. Since the database schema depends on the workow, the DBMS must also support dynamic schema evolution. These requirements are especially challenging in high-throughput WFMSs|i:e:, systems for managing high-volume, mission-critical work ows. Unfortunately, existing database benchmarks do not capture the combination of exibility and performance required by these systems. To address this issue,

Energy consumption is an important concern at data centers, where storage systems consume a significant fraction of the total energy. A recent study proposed power-aware storage cache management to provide more opportunities for the underlying disk power management scheme to save energy. However, the on-line algorithm proposed in that study requires cumbersome parameter tuning for each workload and is therefore difficult to apply to real systems.

Characterizing shared-memory applications provides insight to design efficient systems, and provides awareness to identify and correct application performance bottlenecks. Configuration dependent analysis is often used to simulate detailed application traces on a particular hardware model. The communication traffic and computation workload generated by the application trace is used as a characterization of this application. This paper demonstrates that configuration independent analysis is a useful tool to characterize shared-memory applications. Configuration independent analysis characterizes inherent application characteristics that do not change from one configuration to another. While configuration dependent analysis is repeated for each target configuration, configuration independent analysis is only performed once. Moreover, configuration independent analysis does not require developing models for the target configurations and is faster than detailed simulation. However, configuration dependent analysis directly provides more information about specific configurations. A combination of the two analysis types constitutes a comprehensive and efficient methodology for characterizing shared-memory applications. In this paper, we show how configuration independent

Abstract—Buffer caches are commonly used in servers to reduce the number of slow disk accesses or network messages. These buffer caches form a multilevel buffer cache hierarchy. In such a hierarchy, second-level buffer caches have different access patterns from first-level buffer caches because accesses to a second-level are actually misses from a first-level. Therefore, commonly used cache management algorithms such as the Least Recently Used (LRU) replacement algorithm that work well for single-level buffer caches may not work well for second-level. This paper investigates multiple approaches to effectively manage second-level buffer caches. In particular, it reports our research results in 1) second-level buffer cache access pattern characterization, 2) a new local algorithm called Multi-Queue (MQ) that performs better than nine tested alternative algorithms for second-level buffer caches, 3) a set of global algorithms that manage a multilevel buffer cache hierarchy globally and significantly improve second-level buffer cache hit ratios over corresponding local algorithms, and 4) implementation and evaluation of these algorithms in a real storage system connected with commercial database servers (Microsoft SQL Server and Oracle) running industrial-strength online transaction processing benchmarks. Index Terms—Cache memories, storage hierarchy, storage management. 1

Administration tasks increasingly dominate the total cost of ownership of database management systems. A key task, and a very difficult one for an administrator, is to justify upgrades of CPU, memory and storage resources with quantitative predictions of the expected improvement in workload performance. Current database systems are not designed with such prediction in mind and hence offer only limited help to the administrator. This paper proposes changes to database system design that enable a Resource Advisor to answer “whatif” questions about resource upgrades. A prototype Resource Advisor built to work with a commercial DBMS shows the efficacy of our approach in predicting the effect of upgrading a key resource — buffer pool size — on OLTP workloads in a highly concurrent system. 1

Reducing energy consumption is an important issue for data centers. Among the various components of a data center, storage is one of the biggest energy consumers. Previous studies have shown that the average idle period for a server disk in a data center is very small compared to the time taken to spin down and spin up. This significantly limits the effectiveness of disk power management schemes. This article proposes several power-aware storage cache management algorithms that provide more opportunities for the underlying disk power management schemes to save energy. More specifically, we present an off-line energy-optimal cache replacement algorithm using dynamic programming which minimizes the disk energy consumption. We also present an off-line power-aware greedy algorithm that is more energy-efficient than Belady’s off-line algorithm (which minimizes cache misses only). We also propose two online power-aware algorithms, PA-LRU and PB-LRU. Simulation results with both real system and synthetic workloads show that, compared to LRU, our online algorithms can save up to 22% more disk energy and provide up to 64 % better average response time. We have also investigated the effects of four storage cache write policies on disk energy consumption.

The performance of electronic commerce systems will have a major impact on their acceptability to users. Different users will also demand different levels of performance from the system, that is, they will have different Quality of Service (QoS) requirements. Electronic commerce systems are the integration of several different types of servers and each server must contribute to meeting the QoS demands of the users. In this paper we focus on the role, and the performance, of a database server within an electronic commerce system. We examine the characteristics of the workload placed on a database server by an electronic commerce system and suggest a range of QoS requirements for the database server based on the examination of the workload. We argue that a database server must be able to dynamically reallocate its resources in order to meet the QoS requirements of different transactions as the workload changes. We describe Quartermaster, which is a system to support dynamic goal-oriented resource management in database management systems, and discuss how it can be used to help meet the QoS requirements of the electronic commerce database server. We provide an example of the use of Quartermaster that illustrates how the dynamic reallocation of memory resources can improve the performance of the database server under an example electronic commerce workload. We briefly describe the memory reallocation algorithms used by Quartermaster and present experiments to show the impact of the algorithms on the performance of the database server.