As critical records are increasingly stored in electronic form, which tends to make for easy destruction and clandestine modification, it is imperative that they be properly managed to preserve their trustworthiness, i.e., their ability to provide irrefutable proof and accurate details of events that have occurred. The need for proper record keeping is further underscored by the recent corporate misconduct and ensuing attempts to destroy incriminating records. Currently, the industry practice and regulatory requirements (e.g., SEC Rule 17a-4) rely on storing records in WORM storage to immutably preserve the records. In this paper, we contend that simply storing records in WORM storage is increasingly inadequate to ensure that they are trustworthy. Specifically, with the large volume of records that are typical today, meeting the ever more stringent query response time requires the use of direct access mechanisms such as indexes. Relying on indexes for accessing records could, however, provide a means for effectively altering or deleting records, even those stored in WORM storage.In this paper, we establish the key requirements for a fossilized index that protects the records from such logical modification. We also analyze current indexing methods to determine how they fall short of these requirements. Based on our insights, we propose the Generalized Hash Tree (GHT). Using both theoretical analysis and simulations with real system data, we demonstrate that the GHT can satisfy the requirements of a fossilized index with performance and cost that are comparable to regular indexing techniques such as the B-tree. We further note that as records are indexed on multiple fields to facilitate search and retrieval, the records can be reconstructed from the corresponding index entries even after the records expire and are disposed of, Therefore, we also present a novel method to eliminate this disclosure risk by allowing an index entry to be effectively disposed of when its record expires.

Energy consumption has become an important issue in high-end data centers, and disk arrays are one of the largest energy consumers within them. Although several attempts have been made to improve disk array energy management, the existing solutions either provide little energy savings or significantly degrade performance for data center workloads.Our solution, Hibernator, is a disk array energy management system that provides improved energy savings while meeting performance goals. Hibernator combines a number of techniques to achieve this: the use of disks that can spin at different speeds, a coarse-grained approach for dynamically deciding which disks should spin at which speeds, efficient ways to migrate the right data to an appropriate-speed disk automatically, and automatic performance boosts if there is a risk that performance goals might not be met due to disk energy management.In this paper, we describe the Hibernator design, and present evaluations of it using both trace-driven simulations and a hybrid system comprised of a real database server (IBM DB2) and an emulated storage server with multi-speed disks. Our file-system and on-line transaction processing (OLTP) simulation results show that Hibernator can provide up to 65% energy savings while continuing to satisfy performance goals (6.5--26 times better than previous solutions). Our OLTP emulated system results show that Hibernator can save more energy (29%) than previous solutions, while still providing an OLTP transaction rate comparable to a RAID5 array with no energy management.

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 offline energy-optimal cache replacement algorithm using dynamic programming, which minimizes the disk energy consumption. We also present an offline power-aware greedy algorithm that is more energy-efficient than Belady's offline algorithm (which minimizes cache misses only). We also propose two online power-aware algorithms, PA-LRU and PB-LRU. Simulation results with both a real system and synthetic workloads show that, compared to LRU, our online algorithms can save up to 22 percent more disk energy and provide up to 64 percent better average response time. We have also investigated the effects of four storage cache write policies on disk energy consumption.

Reducing energy consumption is an important issue for data centers. Among the various components of a data center, storage is one of the biggest consumers of energy. 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 paper 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 power-aware greedy algorithm that is more energy-efficient than Belady’s off-line algorithm (which minimizes cache misses only). We also propose an online power-aware cache replacement algorithm. Our trace-driven simulations show that, compared to LRU, our algorithm saves 16% more disk energy and provides 50% better average response time for OLTP I/O workloads. We have also investigated the effects of four storage cache write policies on disk energy consumption.

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.This paper presents a new power-aware on-line algorithm called PB-LRU (Partition-Based LRU) that requires little parameter tuning. Our results with both real system and synthetic workloads show that PB-LRU without any parameter tuning provides similar or even better performance and energy savings than the previous power-aware algorithm with the best parameter setting for each workload.