- Paris, France
- +1 (831) 459-2817
Darrell D E Long
University of California, Santa Cruz, Computer Science, Faculty Member
- Dr. Darrell D. E. Long is Distinguished Professor of Engineering at the University of California, Santa Cruz. He hold... moreDr. Darrell D. E. Long is Distinguished Professor of Engineering at the University of California, Santa Cruz. He holds the Kumar Malavalli Endowed Chair of Storage Systems Research and is Director of the Storage Systems Research Center. He has authored highly cited research papers on web caching, distributed file systems, power-aware hard disk management in mobile computing, and low-bandwidth multicast techniques for video on demand, among other topics.
He received his B.S. degree in Computer Science from San Diego State University, and his M.S. and Ph.D. from the University of California, San Diego. While in graduate school and before joining the University of California, Santa Cruz, he was a lecturer in Mathematics at San Diego State University and taught at the University of California, San Diego.
In 2006 he was elevated to Fellow of the Institute of Electrical and Electronics Engineers (IEEE) "for contributions to storage systems architecture and performance". In 2008 he was inducted a Fellow of the American Association for the Advancement of Science (AAAS). He is a member of the IEEE Computer Society, the Association for Computing Machinery, the American Society for Engineering Education, the Usenix Association, Upsilon Pi Epsilon and Sigma Xi.
He has held visiting faculty positions at the Université Paris—Dauphine (Paris IX), the Conservatoire National des Arts et Métiers, the Université Paris—Descartes (Paris V), the University of Technology, Sydney, the Center for Communications Research, the United States Naval Postgraduate School and is Professor ad Honorem de la Universidad Católica del Uruguay. He is an Associate Member of the European Organization for Nuclear Research (CERN).
He has broad research interests in many areas of mathematics and science, and in the area of computer science include data storage systems, operating systems, distributed computing, reliability & fault tolerance, and computer security. His research has been supported by the National Science Foundation, the Department of Energy (Office of Science and National Nuclear Security Administration), Lawrence Livermore, Los Alamos and Sandia National Laboratories, NASA, the Office of Naval Research, and a number of industrial sponsors that include IBM, Microsoft, NetApp, Symantec, LSI Logic, Samsung, Hewlett-Packard, Avago, Exablox, Huawei, Intel, Sandisk, Seagate, SK Hynix, Veritas and Data Domain.
He served as the Vice Chair and then Chair of the University of California Committee on Research Policy. He has served on the University of California President's Council on the National Laboratories, and on the Science & Technology, National Security and Intelligence committees for those laboratories. He currently serves on the University of California Academic Council Special Committee on Laboratory Issues (ACSCOLI). He served for a number of years on the National Research Council's Standing Committee on Technology Insight-Gauge, Evaluate and Review (TIGER), on the Committee on Defense Intelligence Agency Technology Forecasts and Reviews and on the National Research Council's Committee on Science and Technology for Defense Warning. He currently serves on the Intelligence Science and Technology Experts Group (ISTEG) for the National Academies of Sciences, Engineering and Medicine.edit - Jehan-François Pârisedit
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The ability to automatically hoard data on a computer's local store would go a long way towards freeing the mobile user from dependence on the network and potentially un- bounded latencies. An important step in developing a fully... more
The ability to automatically hoard data on a computer's local store would go a long way towards freeing the mobile user from dependence on the network and potentially un- bounded latencies. An important step in developing a fully automated file hoarding algorithm is the ability to automat- ically identify strong relationships between files. We present a mechanism for visualizing the
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The subject of this article is differential compression , the algorithmic task of finding common strings between versions of data and using them to encode one version compactly by describing it as a set of changes from its companion. A... more
The subject of this article is differential compression , the algorithmic task of finding common strings between versions of data and using them to encode one version compactly by describing it as a set of changes from its companion. A main goal of this work is to present new differencing algorithms that (i) operate at a fine granularity (the atomic unit of change), (ii) make no assumptions about the format or alignment of input data, and (iii) in practice use linear time, use constant space, and give good compression. We present new algorithms, which do not always compress optimally but use considerably less time or space than existing algorithms. One new algorithm runs in O ( n ) time and O (1) space in the worst case (where each unit of space contains [log n ] bits), as compared to algorithms that run in O ( n ) time and O ( n ) space or in O ( n 2 ) time and O (1) space. We introduce two new techniques for differential compression and apply these to give additional algorithms th...
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SeaOS is a simple operating system developed for fast prototyping of research concepts. SeaOS implements a POSIX-like interface in less than 30,000 lines of C kernel code. Its userspace is complete enough to be self-hosting and includes... more
SeaOS is a simple operating system developed for fast prototyping of research concepts. SeaOS implements a POSIX-like interface in less than 30,000 lines of C kernel code. Its userspace is complete enough to be self-hosting and includes ports of gcc, bash, grub, binutils, and coreutils. SeaOS runs on modern x86 and x86_64 architectures and is designed to support our lab's operating system research in scheduling, virtualization and security architectures.
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The field of consistency control protocols for replicated data objects has existed for about ten years. Its birth coincides with the advent of distributed data bases and the communications technology required to support them. When data... more
The field of consistency control protocols for replicated data objects has existed for about ten years. Its birth coincides with the advent of distributed data bases and the communications technology required to support them. When data objects are replicated around a computer network, a protocol must be chosen to ensure a consistent view to an accessing process. The replicas of the data object are then said to be mutually consistent. The protocols used to insure mutual consistency are known as replica control or consistency control protocols. There are several advantages to a distributed system over a single processor system. Among these are increased computing power and the ability to tolerate partial failures due to the malfunction of individual components. The redundancy present in a distributed system has been the focus of much research in the area of distributed data base systems. Another benefit of this natural redundancy, along with the relatively independent failure modes of the processors, is that it allows the system to continue operation even after some of the processors have failed. This can be used to construct data objects that are robust in the face of partial system failures. The focus of this dissertation is the exploitation of the redundancy present in distribution systems in order to attain an increased level of fault tolerance for data objects. The use of replication as a method of increasing fault tolerance is a well-known technique. Replication introduces the additional complexity of maintaining mutual consistency among the replicas of the data object. The protocols that manage the replicated data and provide the user with a single consistent view of that data are studied, and a comprehensive analysis of the fault tolerance provided by several of the most promising protocols are presented. Several techniques are employed, including Markov analysis and discrete event simulation. Simulation is used to confirm and extend the results obtained using analytic techniques.
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Micro-Electro-Mechanical Systems (MEMS)-based storage is an emerging non-volatile secondary storage technology. Combining a movable non-rotating media sled and a stationary array with thousands of read/write probe tips into a single chip... more
Micro-Electro-Mechanical Systems (MEMS)-based storage is an emerging non-volatile secondary storage technology. Combining a movable non-rotating media sled and a stationary array with thousands of read/write probe tips into a single chip as small as a dime, MEMS-based storage provides up to ten gigabytes of storage with low access latency, high streaming bandwidth, low power consumption, and low entry cost. It is equally important that MEMS-based storage exhibits distinct low-level device-specific features, such as two-dimensional independent media positioning and fast power state transitions. The theme of this thesis research is to exploit the distinct properties of MEMS-based storage to deliver better performance, higher cost/performance ratios, lower power consumption, and higher reliability of storage systems. We developed an analytical model of seek times for MEMS-based storage and identified seek time equivalence regions. This finding has great impacts on request scheduling and data layout on MEMS storage devices. We have developed MEMS-aware request scheduling algorithms that achieve superior service performance without sacrificing fairness. Data layout that leverages the knowledge of MEMS seek time equivalence regions also results in better performance under randomly-accessed workloads. Our MEMS-aware power conservation techniques reduces half of the power consumed by storage devices with negligible I/O performance penalties. With internal redundancy, sufficient online spares as well as optional maintenance, MEMS storage drives, which are built as disk replacement, are highly reliable in the economic lifetime. MEMS-based storage can also be used as fast front-end for disks to build high-performing, cost-effective hybrid MEMS/disk storage devices.
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It is increasingly important and a big challenge to detect intrusion behavior variants in today's world. Previous host-based intrusion detection methods typically explore the sequence of system calls or unix shell commands to detect... more
It is increasingly important and a big challenge to detect intrusion behavior variants in today's world. Previous host-based intrusion detection methods typically explore the sequence of system calls or unix shell commands to detect the intrusion behavior. This article abstracts the detection of intrusion behavior variants as the comparison between different sequences when the sequence order or length transforms. To overcome the impact of sequence transformation on the detection accuracy, we propose P-Gaussian, a provenance-based Gaussian distribution detection scheme which comprises two key design features: (1) it utilizes provenance to describe and identify intrusion behavior variants, and eliminates the impact of sequence order transformation on the detection accuracy. (2) it adopts Gaussian distribution principle to accurately compute the similarity between intrusion behavior and its variant, and eliminates the impact of intrusion behavior sequence length increase on the detection accuracy. To improve the detection performance, P-Gaussian employs a Redis memory database with multiple Redis instances and multiple threads to enable the parallelism of provenance processing in multi-core environments. It also classifies hot and cold provenance to provide high-efficient long-term forensic analysis. Experimental results on widely-used real world applications demonstrate the performance and efficiency of our system.
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Removing hard drives from a data center may expose sensitive data, such as encryption keys or passwords. To prevent exposure, data centers have security policies in place to physically secure drives in the system, and securely delete data... more
Removing hard drives from a data center may expose sensitive data, such as encryption keys or passwords. To prevent exposure, data centers have security policies in place to physically secure drives in the system, and securely delete data from drives that are removed. Despite advances in security technology and best practices, implementation of these security measures is often done incorrectly. We anticipate that physical security will fail, and fixing the issue after the failure is costly and ineffective. We propose Inkpack, a protocol that prevents an attacker from reading data from a drive removed from the data center even if the attacker has the user key linked to the data. An implementation of this protocol encrypts data, and secret splits the key over a number of drives. Recovering the key requires communicating with other drives, thereby denying access to the data if a few drives have been removed. Inkpack also requires the system to verify the validity of individual drives before normal operation. A prototype created within the Ceph storage system executed individual key split, key rebuild, and drive validation operations in 100--150 μs. We also show that our protocol is sensitive to small data write overheads, demonstrating potential performance gains if implemented on smart solid state storage devices, and propose a solution to increase performance.