The Architecture and Performance Evaluation of iSCSI-Based United Storage Network Merging NAS and SAN

With the ever increasing volume of data in networks, the traditional storage architecture is greatly challenged; more and more people pay attention to network storage. Currently, the main technology of network storage is represented by NAS (Network Attached Storage) and SAN (Storage Area Network). They are different, but mutually complementary and used under different circumstances; however, both NAS and SAN may be needed in the same company. To reduce the TOC (total of cost), for easier implementation, etc., people hope to merge the two technologies. Additionally, the main internetworking technology of SAN is the Fibre Channel; however, the major obstacles are in its poor interoperability, lack of trained staff, high implementation costs, etc. To solve the above-mentioned issues, this paper creatively introduces a novel storage architecture called USN (United Storage Networks), which uses the iSCSI to build the storage network, and merges the NAS and SAN techniques supplying the virtues and overcoming the drawbacks of both, and provides both file I/O and block I/O service simultaneously

Space-Efficient Predictive Block Management

With growing disk and storage capacities, the amount of required metadata for tracking all blocks in a system becomes a daunting task by itself. In previous work, we have demonstrated a system software effort in the area of predictive data grouping for reducing power and latency on hard disks. The structures used, very similar to prior efforts in prefetching and prefetch caching, track access successor information at the block level, keeping a fixed number of immediate successors per block. While providing powerful predictive expansion capabilities and being more space efficient in the amount of required metadata than many previous strategies, there remains a growing concern of how much data is actually required. In this paper, we present a novel method of storing equivalent information, SESH, a Space Efficient Storage of Heredity. This method utilizes the high amount of block-level predictability observed in a number of workload trace sets to reduce the overall metadata storage by up to 99% without any loss of information. As a result, we are able to provide a predictive tool that is adaptive, accurate, and robust in the face of workload noise, for a tiny fraction of the metadata cost previously anticipated; in some cases, reducing the required size from 12 gigabytes to less than 150 megabytes

Cut-and-paste file-systems: integrating simulators and file-systems

We have implemented an integrated and configurable file system called the PFS and a trace-driven file-system simulator called Patsy. Patsy is used for off-line analysis of file-system algorithms, PFS is used for on-line file-system data storage. Algorithms are first analyzed in Patsy and when we are satisfied\ud with the performance results, migrated into PFS for on-line usage. Since Patsy and PFS are derived from a common cut-and-paste file-system framework, this migration proceeds smoothly.\ud We have found this integration quite useful: algorithm bottlenecks have been found through Patsy that could have led to performance degradations in PFS. Off-line simulators are simpler to analyze compared to on-line file-systems because a work load can repeatedly be replayed on the same off-line simulator. This is almost impossible in on-line file-systems since it is hard to provide similar conditions for each experiment run. Since simulator and file-system are integrated (hence, use the same code), experiment results from the simulator have relevance in the real system. \ud This paper describes the cut-and-paste framework, the instantiation of the framework to PFS and Patsy and finally, some of the experiments we conducted in Patsy

Adaptive Disk Spindown via Optimal Rent-to-Buy in Probabilistic Environments

The original publication is available at www.springerlink.comIn the single rent-to-buy decision problem, without a priori knowledge of the amount of time a resource will be used we need to decide when to buy the resource, given that we can rent the resource for $1 per unit time or buy it once and for all for$c. In this paper we study algorithms that make a sequence of single rent-to-buy decisions, using the assumption that the resource use times are independently drawn from an unknown probability distribution. Our study of this rent- to-buy problem is motivated by important systems applications, speci cally, problems arising from deciding when to spindown disks to conserve energy in mobile computers [DKM, LKH, MDK], thread blocking decisions during lock acquisition in multiprocessor applications [KLM], and virtual circuit holding times in IP-over-ATM networks [KLP, SaK]. We develop a provably optimal and computationally e cient algorithm for the rent-to-buy problem. Our algorithm uses O(pt) time and space, and its expected cost for the tth resource use converges to optimal as O(plog t=t), for any bounded probability distribution on the resource use times. Alternatively, using O(1) time and space, the algorithm almost converges to optimal. We describe the experimental results for the application of our algorithm to one of the motivating systems problems: the question of when to spindown a disk to save power in a mobile computer. Simulations using disk access traces obtained from an HP workstation environment suggest that our algorithm yields signi cantly improved power/response time performance over the non-adaptive 2-competitive algorithm which is optimal in the worst-case competitive analysis model

Divided disk cache and SSD FTL for improving performance in storage

Although there are many efficient techniques to minimize the speed gap between processor and the memory, it remains a bottleneck for various commercial implementations. Since secondary memory technologies are much slower than main memory, it is challenging to match memory speed to the processor. Usually, hard disk drives include semiconductor caches to improve their performance. A hit in the disk cache eliminates the mechanical seek time and rotational latency. To further improve performance a divided disk cache, subdivided between metadata and data, has been proposed previously. We propose a new algorithm to apply the SSD that is flash memory-based solid state drive by applying FTL. First, this paper evaluates the performance of such a disk cache via simulations using DiskSim. Then, we perform an experiment to evaluate the performance of the proposed algorithm.clos

Bridging the Gap between Application and Solid-State-Drives

Data storage is one of the important and often critical parts of the computing system in terms of performance, cost, reliability, and energy. Numerous new memory technologies, such as NAND flash, phase change memory (PCM), magnetic RAM (STT-RAM) and Memristor, have emerged recently. Many of them have already entered the production system. Traditional storage optimization and caching algorithms are far from optimal because storage I/Os do not show simple locality. To provide optimal storage we need accurate predictions of I/O behavior. However, the workloads are increasingly dynamic and diverse, making the long and short time I/O prediction challenge. Because of the evolution of the storage technologies and the increasing diversity of workloads, the storage software is becoming more and more complex. For example, Flash Translation Layer (FTL) is added for NAND-flash based Solid State Disks (NAND-SSDs). However, it introduces overhead such as address translation delay and garbage collection costs. There are many recent studies aim to address the overhead. Unfortunately, there is no one-size-fits-all solution due to the variety of workloads. Despite rapidly evolving in storage technologies, the increasing heterogeneity and diversity in machines and workloads coupled with the continued data explosion exacerbate the gap between computing and storage speeds. In this dissertation, we improve the data storage performance from both top-down and bottom-up approach. First, we will investigate exposing the storage level parallelism so that applications can avoid I/O contentions and workloads skew when scheduling the jobs. Second, we will study how architecture aware task scheduling can improve the performance of the application when PCM based NVRAM are equipped. Third, we will develop an I/O correlation aware flash translation layer for NAND-flash based Solid State Disks. Fourth, we will build a DRAM-based correlation aware FTL emulator and study the performance in various filesystems