We propose and evaluate the concept of a semantically-smart disk system (SDS). As opposed to a traditional "smart" disk, an SDS has detailed knowledge of how the file system above is using the disk system, including information about the on-disk data structures of the file system. An SDS exploits this knowledge to transparently improve performance or enhance functionality beneath a standard block read/write interface. To automatically acquire this knowledge, we introduce a tool (EOF) that can discover file-system structure for certain types of file systems, and then show how an SDS can exploit this knowledge on-line to understand file-system behavior. We quantify the space and time overheads that are common in an SDS, showing that they are not excessive. We then study the issues surrounding SDS construction by designing and implementing a number of prototypes as case studies; each case study exploits knowledge of some aspect of the file system to implement powerful functionality beneath the standard SCSI interface. Overall, we find that a surprising amount of functionality can be embedded within an SDS, hinting at a future where disk manufacturers can compete on enhanced functionality and not simply cost-per-byte and performance.

Flash Memory based Solid State Drive (SSD) has been called a “pivotal technology ” that could revolutionize data storage systems. Since SSD shares a common interface with the traditional hard disk drive (HDD), both physically and logically, an effective integration of SSD into the storage hierarchy is very important. However, details of SSD hardware implementations tend to be hidden behind such narrow interfaces. In fact, since sophisticated algorithms are usually, of necessity, adopted in SSD controller firmware, more complex performance dynamics are to be expected in SSD than in HDD systems. Most existing literature or product specifications on SSD just provide high-level descriptions and standard performance data, such as bandwidth and latency. In order to gain insight into the unique performance characteristics

This paper presents the design, implementation, and evaluation of BORG, a self-optimizing storage system that performs automatic block reorganization based on the observed I/O workload. BORG is motivated by three characteristics of I/O workloads: non-uniform access frequency distribution, temporal locality, and partial determinism in non-sequential accesses. To achieve its objective, BORG manages a small, dedicated partition on the disk drive, with the goal of servicing a majority of the I/O requests from within this partition with significantly reduced seek and rotational delays. BORG is transparent to the rest of the storage stack, including applications, file system(s), and I/O schedulers, thereby requiring no or minimal modification to storage stack implementations. We evaluated a Linux implementation of BORG using several real-world workloads, including individual user desktop environments, a web-server, a virtual machine monitor, and an SVN server. These experiments comprehensively demonstrate BORG’s effectiveness in improving I/O performance and its incurred resource overhead. 1

State-of-the-art networked storage servers are equipped with increasingly powerful computing capability and large DRAM memory as storage caches. However, their contribution to the performance improvement of networked storage system has become increasingly limited. This is because the client-side memory sizes are also increasing, which reduces capacity misses in the client buffer caches as well as access locality in the storage servers, thus weakening the caching effectiveness of server storage caches. Proactive caching in storage servers is highly desirable to reduce cold misses in clients. We propose an effective way to improve the utilization of storage server resources through prefetching in storage servers for clients. In particular, our design well utilizes two unique strengths of networked storage servers which are not leveraged in existing storage server prefetching schemes. First, powerful storage servers have idle CPU cycles, under-utilized disk bandwidth, and abundant memory space, providing many opportunities for aggressive disk data prefetching. Second, the servers have the knowledge about high-latency operations in storage devices, such as disk head positioning, which enables efficient disk data prefetching based on an accurate cost-benefit analysis of prefetch operations. We present STEP – a Sequentiality and Thrashing dEtection based Prefetching scheme, and its implementation with Linux Kernel 2.6.16. Our performance evaluation by replaying Storage Performance Council (SPC)’s OLTP traces shows that server performance improvements are up to 94% with an average of 25%. Improvements with frequently used Unix applications are up to 53 % with an average of 12%. Our experiments also show that STEP has little effect on workloads with random access patterns, such as SPC ’ Web-Search traces. 1

In this thesis, I show how network storage servers can infer useful information about the requests they are likely to see in the future by analyzing the history of requests they have observed in the past. I also show that this information can be used to improve future decisions about disk block allocation and read-ahead and thereby increase network storage server performance without any change to its clients or the applications running on its clients.

A pattern is a model or a template used to summarize and describe the behavior (or the trend) of a data having generally some recurrent events. Patterns have received a considerable attention in recent years and were widely studied in the data mining field. Various pattern mining approaches have been proposed and used for different applications such as network monitoring, moving object tracking, financial or medical data analysis, scientific data processing, etc. In these different contexts, discovered patterns were useful to detect anomalies, to predict data behavior (or trend), or more generally, to simplify data processing or to improve system performance. However, to the best of our knowledge, patterns have never been used in the context of web archiving. Web archiving is the process of continuously collecting and preserving portions of the World Wide Web for future generations. In this paper, we show how patterns of page changes can be useful tools to efficiently archive web sites. We first define our pattern model that describes the changes of pages. Then, we present the strategy used to (i) extract the temporal evolution of page changes, to (ii) discover patterns and to (iii) exploit them to improve web archives. We choose the archive of French public TV channels France Télévisions as a case study 1 in order to validate our approach. Our experimental evaluation based on real web pages shows the utility of patterns to improve archive quality and to optimize indexing or storing.

We introduce EnvyFS, an N-version local file system designed to improve reliability in the face of file-system bugs. EnvyFS, implemented as a thin VFS-like layer near the top of the storage stack, replicates file-system metadata and data across existing and diverse commodity file systems (e.g., ext3, ReiserFS, JFS). It uses majority-consensus to operate correctly despite the sometimes faulty behavior of an underlying commodity child file system. Through experimentation, we show EnvyFS is robust to a wide range of failure scenarios, thus delivering on its promise of increased fault tolerance; however, performance and capacity overheads can be significant. To remedy this issue, we introduce SubSIST, a novel single-instance store designed to operate in an N-version environment. In the common case where all child file systems are working properly, SubSIST coalesces most blocks and thus greatly reduces time and space overheads. In the rare case where a child makes a mistake, SubSIST does not propagate the error to other children, and thus preserves the ability of EnvyFS to detect and recover from bugs that affect data reliability. Overall, EnvyFS and SubSIST combine to significantly improve reliability with only modest space and time overheads. 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

User I/O intensity can significantly impact the performance of on-line RAID reconstruction due to contention for the shared disk bandwidth. Based on this observation, this paper proposes a novel scheme, called WorkOut (I/O Workload Outsourcing), to significantly boost RAID reconstruction performance. WorkOut effectively outsources all write requests and popular read requests originally targeted at the degraded RAID set to a surrogate RAID set during reconstruction. Our lightweight prototype implementation of WorkOut and extensive tracedriven and benchmark-driven experiments demonstrate that, compared with existing reconstruction approaches, WorkOut significantly speeds up both the total reconstruction time and the average user response time. Importantly, WorkOut is orthogonal to and can be easily incorporated into any existing reconstruction algorithms. Furthermore, it can be extended to improving the performance of other background support RAID tasks, such as re-synchronization and disk scrubbing. 1

Abstract. With the rapid adoption of the Service Oriented Architecture (SOA), sophisticated software systems are increasingly built by composing coarse-grained service components offered by different organizations through standard web service interfaces. The ability to quantify end-to-end security risks of composite software services is extremely valuable to businesses that increasingly rely on web applications to interact with their customers and partners. In this position paper, we propose a framework that predicts the probability of end-to-end security breaches of a software service by using a combination of three models: (1) a software security model that describes the probability distribution of security bugs in individual components, (2) a service composition model that describes the interactions of components and the contribution of security bugs in individual components to the overall security of the service, and (3) a hacking exposure model that estimates hackers ’ knowledge of individual components and hence the probability that a security hole, if exists, may be exploited. 1