Spurred by financial scandals and privacy concerns, governments worldwide have moved to ensure confidence in digital records by regulating their retention and deletion. These requirements have led to a huge market for compliance storage servers, which ensure that data are not shredded or altered before the end of their mandatory retention period. These servers preserve unstructured and semi-structured data at a file-level granularity: email, spreadsheets, reports, instant messages. In this paper, we extend this level of protection to structured data residing in relational databases. We propose a compliant DBMS architecture and two refinements that illustrate the additional security that one can gain with only a slight performance penalty, with almost no modifications to the DBMS kernel. We evaluate our proposed architecture through experiments with TPC-C on a high-performance DBMS, and show that the runtime overhead for transaction processing is approximately 10% in typical configurations.

In response to regulatory focus on secure retention of electronic records, businesses are using magnetic disks configured as write-once read-many (WORM) compliance storage devices to store business documents such as electronic mail for their mandated retention periods. A document committed to a compliance storage device cannot be altered or deleted even by a superuser until its retention period is over, and hence is secure from attacks originating from company insiders. Secure retention, however, is only a part of a document's lifecycle: it is often crucial to properly delete documents once their retention period ends. It is relatively simple to delete a document, but much harder to remove its index entries from WORM. Yet if these entries are not obliterated, the contents of the deleted document can often be reconstructed.

To carry out work assignments, small groups distributed within a larger enterprise often need to share documents among themselves while shielding those documents from others’ eyes. In this situation, users need an indexing facility that can quickly locate relevant documents that they are allowed to access, without (1) leaking information about the remaining documents, (2) imposing a large management burden as users, groups, and documents evolve, or (3) requiring users to agree on a central completely trusted authority. To address this problem, we propose the concept of r-confidentiality, which captures the degree of information leakage from an index about the terms contained in inaccessible documents. Then we propose the r-confidential Zerber indexing facility for sensitive documents, which uses secret splitting and term merging to provide tunable limits on information leakage, even under statistical attacks; requires only limited trust in a central indexing authority; and is extremely easy to use and administer. Experiments with real-world data show that Zerber offers excellent performance for index insertions and lookups while requiring only a modest amount of storage space and network bandwidth

Regulations require businesses to archive many electronic documents for extended periods of time. Given the sheer volume of documents and the response time requirements, documents that are unlikely to ever be accessed should be stored on an inexpensive device (such as tape), while documents that are likely to be accessed should be placed on a more expensive, higher-performance device. Unfortunately, traditional data partitioning techniques either require substantial manual involvement, or are not suitable for read-rarely workloads. In this paper, we present a novel technique to address this problem. We estimate the future access likelihood for a document based on past workloads of keyword queries and the click-through behavior for top-K query answers, then use this information to drive partitioning decisions. Our overall best scheme, the document-split inverted index, does not require any parameter tuning and yet performs close to the optimal partitioning strategy. Experiments show that document-split partitioning improves performance on a large intranet query workload by a factor of 4 when we add a fast storage server that holds 20% of the data.

The data used by today's scientific applications are often very high in dimensionality and staggering in size. These characteristics necessitate the use of a good multidimensional indexing strategy to provide efficient access to the data. Researchers have previously proposed the use of bitmap indexes for high-dimension scientific data as a way of overcoming the drawbacks of traditional multidimensional indexes such as R-trees and KD-trees, which are bulky and whose performance does not scale well as the number of dimensions increases. However, the techniques proposed in previous work on bitmap indexes are not sufficient to address all problems that arise in practice. In experiments with real datasets, we experienced problems with index size and query performance. To overcome these shortcomings, we propose the use of adaptive, multilevel, multi-resolution bitmap indexes, and evaluate their performance in two scientific domains. Our preliminary experiments with a parallel query processor and index creator also show that it is very easy to parallelize a bitmap index

Scientific simulations are often very I/O intensive, requiring high I/O bandwidth to store the data generated by the simulation. Traditional supercomputers have specialized I/O systems with multiple I/O nodes and specialized interconnects to handle such high I/O loads. However, with the increased availability of inexpensive clusters of workstations, more and more simulations are now run on clusters. Unfortunately, cluster supercomputers are usually not very well equipped for I/O, making I/O a serious bottleneck for such applications. To address this problem, we propose log-based I/O (LBIO), an approach that can substantially increase the I/O performance of simulations on clusters by utilizing free space on the cluster's local disks to stage data on its way to remote storage. LBIO uses local disks to create a log of all I/O calls, and uses a background thread to replay the log at the rate that best utilizes the server and network resources. LBIO is implemented as an easy-to-use, non-intrusive library - a user can turn on LBIO by adding a single initialization call to the simulation code. LBIO also works with existing scientific I/O libraries like HDF, as well as collective libraries like ROMIO. Our performance studies on microbenchmarks and a real-world scientific simulation code show that LBIO can provide upto 35% improvement in I/O performance for raw I/O and over 50% for I/O through libraries like ROMIO or HDF

Streaming media applications represent an important class of applications for embedded systems. Recent advances in design-space exploration of architectures for such applications have pointed towards the suitability of Multiprocessor System on Chip (SoC) solutions. Multiprocessor SoCs not only offer higher performance, but can also lead to solutions which are cheaper cost wise. A typical synthesis methodology for such architectures would require a validation stage at the end of final system integration. The wide availability of cheap and large FPGA devices, advances in automatic synthesis from VHDL/Verilog and abundance of high performance computing platforms enables the design of a generic validation system for such Multiprocessor SoCs.In this paper we present the design and implementation of Srijan Multiprocessor Prototyping System (SMPS). SMPS is a system for rapid prototyping and validation of single chip application specific multiprocessor systems. The individual computing elements are RISC processors, coprocessors which lie in the processor pipeline, and ASICs which connect directly to system bus. The system is a tightly coupled multiprocessor with shared memory and shared address space. A Real-time Operating System (RTOS) provides task scheduling and access to shared resources. The system is presented as a parameterized VHDL based on the open source Sparc~V8 compliant LEON processor and a homegrown light-weight RTOS, RtKer-MP. The entire VHDL is configurable using a GUI, has support for cache coherency, choice of arbitration policy and easy integration of custom processing engines. RtKer-MP allows for a pluggable scheduler, dynamic and static scheduling policies, static and dynamic task migrations domains and variable interruption frequencies for separate processors. The pluggable scheduler interface allows for quick exploration of various scheduling policies for a feedback to the estimation systems.