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A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenTue, 16 Apr 2024 00:26:49 +0000On the capacity of bounded rank modulation for flash memories
https://resolver.caltech.edu/CaltechAUTHORS:20100816-142932373
Authors: {'items': [{'id': 'Wang-Zhiying', 'name': {'family': 'Wang', 'given': 'Zhiying'}}, {'id': 'Jiang-Anxiao-Andrew', 'name': {'family': 'Jiang', 'given': 'Anxiao (Andrew)'}}, {'id': 'Bruck-J', 'name': {'family': 'Bruck', 'given': 'Jehoshua'}, 'orcid': '0000-0001-8474-0812'}]}
Year: 2009
DOI: 10.1109/ISIT.2009.5205972
Rank modulation has been introduced as a new information representation scheme for flash memories. Given the charge levels of a group of flash cells, sorting is used to induce a permutation, which in turn represents data. Motivated by the lower sorting complexity of smaller cell groups, we consider bounded rank modulation, where a sequence of permutations of given sizes are used to represent data. We study the capacity of bounded rank modulation under the condition that permutations can overlap for higher capacity.https://authors.library.caltech.edu/records/7f210-bjx91Partial Rank Modulation for Flash Memories
https://resolver.caltech.edu/CaltechAUTHORS:20110331-130545474
Authors: {'items': [{'id': 'Wang-Zhiying', 'name': {'family': 'Wang', 'given': 'Zhiying'}}, {'id': 'Bruck-J', 'name': {'family': 'Bruck', 'given': 'Jehoshua'}, 'orcid': '0000-0001-8474-0812'}]}
Year: 2010
DOI: 10.1109/ISIT.2010.5513597
Rank modulation was recently proposed as an information representation for multilevel flash memories, using permutations or ranks of n flash cells. The current decoding process finds the cell with the i-th highest charge level at iteration i, for i = 1, 2,...,n - 1. Motivated by the need to reduce the number of such iterations, we consider k-partial permutations, where only the highest k cell levels are considered for information representation. We propose a generalization of Gray codes for k-partial permutations such that information is updated efficiently.https://authors.library.caltech.edu/records/dzswn-2ar83Rebuilding for Array Codes in Distributed Storage Systems
https://resolver.caltech.edu/CaltechAUTHORS:20110707-082718436
Authors: {'items': [{'id': 'Wang-Zhiying', 'name': {'family': 'Wang', 'given': 'Zhiying'}}, {'id': 'Dimakis-A-G', 'name': {'family': 'Dimakis', 'given': 'Alexandros G.'}}, {'id': 'Bruck-J', 'name': {'family': 'Bruck', 'given': 'Jehoshua'}, 'orcid': '0000-0001-8474-0812'}]}
Year: 2010
DOI: 10.1109/GLOCOMW.2010.5700274
In distributed storage systems that use coding, the issue of minimizing the communication required to rebuild a storage node after a failure arises. We consider the problem of repairing an erased node in a distributed storage system that uses an EVENODD code. EVENODD codes are maximum distance separable (MDS) array codes that are used to protect against erasures, and only require XOR operations for encoding and decoding. We show that when there are two redundancy nodes, to rebuild one erased systematic node, only 3/4 of the information needs to be transmitted. Interestingly, in many cases, the required disk I/O is also minimized.https://authors.library.caltech.edu/records/fd7qm-72d07Patterned cells for phase change memories
https://resolver.caltech.edu/CaltechAUTHORS:20170213-160905267
Authors: {'items': [{'id': 'Jiang-Anxiao-Andrew', 'name': {'family': 'Jiang', 'given': 'Anxiao (Andrew)'}}, {'id': 'Zhou-Hongchao', 'name': {'family': 'Zhou', 'given': 'Hongchao'}}, {'id': 'Wang-Zhiying', 'name': {'family': 'Wang', 'given': 'Zhiying'}}, {'id': 'Bruck-J', 'name': {'family': 'Bruck', 'given': 'Jehoshua'}, 'orcid': '0000-0001-8474-0812'}]}
Year: 2011
DOI: 10.1109/ISIT.2011.6033979
Phase-change memory (PCM) is an emerging nonvolatile memory technology that promises very high performance. It currently uses discrete cell levels to represent data, controlled by a single amorphous/crystalline domain in a cell. To improve data density, more levels per cell are needed. There exist a number of challenges, including cell programming noise, drifting of cell levels, and the high power requirement for cell programming. In this paper, we present a new cell structure called patterned cell, and explore its data representation schemes. Multiple domains per cell are used, and their connectivity is used to store data. We analyze its storage capacity, and study its error-correction capability and the construction of error-control codes.https://authors.library.caltech.edu/records/dpzzx-7bf24MDS Array Codes with Optimal Rebuilding
https://resolver.caltech.edu/CaltechAUTHORS:20120406-093959188
Authors: {'items': [{'id': 'Tamo-I', 'name': {'family': 'Tamo', 'given': 'Itzhak'}}, {'id': 'Wang-Zhiying', 'name': {'family': 'Wang', 'given': 'Zhiying'}}, {'id': 'Bruck-J', 'name': {'family': 'Bruck', 'given': 'Jehoshua'}, 'orcid': '0000-0001-8474-0812'}]}
Year: 2011
DOI: 10.1109/ISIT.2011.6033733
MDS array codes are widely used in storage systems to protect data against erasures. We address the rebuilding ratio problem, namely, in the case of erasures, what is the the fraction of the remaining information that needs to be accessed in order to rebuild exactly the lost information? It is clear that when the number of erasures equals the maximum number of erasures that an MDS code can correct then the rebuilding ratio is 1 (access all the remaining information). However, the interesting (and more practical) case is when the number of erasures is smaller than the erasure correcting capability of the code. For example, consider an MDS code that can correct two erasures: What is the smallest amount of information that one needs to access in order to correct a single erasure? Previous work showed that the rebuilding ratio is bounded between 1/2 and 3/4, however, the exact value was left as an open problem. In this paper, we solve this open problem and prove that for the case of a single erasure with a 2-erasure correcting code, the rebuilding ratio is 1/2. In general, we construct a new family of r-erasure correcting MDS array codes that has optimal rebuilding ratio of 1/r in the case of a single erasure. Our array codes have efficient encoding and decoding algorithms (for the case r = 2 they use a finite field of size 3) and an optimal update property.https://authors.library.caltech.edu/records/2rrs6-0d438Long MDS Codes for Optimal Repair Bandwidth
https://resolver.caltech.edu/CaltechAUTHORS:20130204-132322886
Authors: {'items': [{'id': 'Wang-Zhiying', 'name': {'family': 'Wang', 'given': 'Zhiying'}}, {'id': 'Tamo-I', 'name': {'family': 'Tamo', 'given': 'Itzhak'}}, {'id': 'Bruck-J', 'name': {'family': 'Bruck', 'given': 'Jehoshua'}, 'orcid': '0000-0001-8474-0812'}]}
Year: 2012
DOI: 10.1109/ISIT.2012.6283041
MDS codes are erasure-correcting codes that can correct the maximum number of erasures given the number of redundancy or parity symbols. If an MDS code has r parities and no more than r erasures occur, then by transmitting all the remaining data in the code one can recover the original information. However, it was shown that in order to recover a single symbol erasure, only a fraction of 1/r of the information needs to be transmitted. This fraction is called the repair bandwidth (fraction). Explicit code constructions were given in previous works. If we view each symbol in the code as a vector or a column, then the code forms a 2D array and such codes are especially widely used in storage systems. In this paper, we ask the following question: given the length of the column l, can we construct high-rate MDS array codes with optimal repair bandwidth of 1/r, whose code length is as long as possible? In this paper, we give code constructions such that the code length is (r + l)log_r l.https://authors.library.caltech.edu/records/gktdd-nq646Long MDS Codes for Optimal Repair Bandwidth
https://resolver.caltech.edu/CaltechAUTHORS:20120829-103740126
Authors: {'items': [{'id': 'Wang-Zhiying', 'name': {'family': 'Wang', 'given': 'Zhiying'}}, {'id': 'Tamo-I', 'name': {'family': 'Tamo', 'given': 'Itzhak'}}, {'id': 'Bruck-J', 'name': {'family': 'Bruck', 'given': 'Jehoshua'}, 'orcid': '0000-0001-8474-0812'}]}
Year: 2012
DOI: 10.1109/ISIT.2012.6283041
MDS codes are erasure-correcting codes that can correct the maximum number of erasures given the number of redundancy or parity symbols. If an MDS code has r parities and no more than r erasures occur, then by transmitting all the remaining data in the code one can recover the original information. However, it was shown that in order to recover a single symbol erasure, only a fraction of 1/r of the information needs to be transmitted. This fraction is called the repair bandwidth (fraction). Explicit code constructions were given in previous works. If we view each symbol in the code as a vector or a column, then the code forms a 2D array and such codes are especially widely used in storage systems. In this paper, we ask the following question: given the length of the column l, can we construct high-rate MDS array codes with optimal repair bandwidth of 1/r, whose code length is as long as possible? In this paper, we give code constructions such that the code length is (r + l)logr l.https://authors.library.caltech.edu/records/hgwxz-m6n08Access vs. Bandwidth in Codes for Storage
https://resolver.caltech.edu/CaltechAUTHORS:20120829-092120549
Authors: {'items': [{'id': 'Tamo-I', 'name': {'family': 'Tamo', 'given': 'Itzhak'}}, {'id': 'Wang-Zhiying', 'name': {'family': 'Wang', 'given': 'Zhiying'}}, {'id': 'Bruck-J', 'name': {'family': 'Bruck', 'given': 'Jehoshua'}, 'orcid': '0000-0001-8474-0812'}]}
Year: 2012
DOI: 10.1109/ISIT.2012.6283042
Maximum distance separable (MDS) codes are widely used in storage systems to protect against disks (nodes) failures. An (n, k, l) MDS code uses n nodes of capacity l to store k information nodes. The MDS property guarantees the resiliency to any n − k node failures. An optimal bandwidth (resp. optimal access) MDS code communicates (resp. accesses) the minimum amount of data during the recovery process of a single failed node. It was shown that this amount equals a fraction of 1/(n − k) of data stored in each node. In previous optimal bandwidth constructions, l scaled polynomially with k in codes with asymptotic rate < 1. Moreover, in constructions with constant number of parities, i.e. rate approaches 1, l scaled exponentially w.r.t. k. In this paper we focus on the practical case of n − k = 2, and ask the following question: Given the capacity of a node l what is the largest (w.r.t. k) optimal bandwidth (resp. access) (k + 2, k, l) MDS code. We give an upper bound for the general case, and two tight bounds in the special cases of two important families of codes.https://authors.library.caltech.edu/records/8ne9e-4q567