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A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenTue, 16 Apr 2024 13:50:31 +0000Optimal content delivery with network coding
https://resolver.caltech.edu/CaltechAUTHORS:20100510-112135867
Authors: {'items': [{'id': 'Leong-Derek', 'name': {'family': 'Leong', 'given': 'Derek'}}, {'id': 'Ho-Tracey', 'name': {'family': 'Ho', 'given': 'Tracey'}}, {'id': 'Cathey-R', 'name': {'family': 'Cathey', 'given': 'Rebecca'}}]}
Year: 2009
DOI: 10.1109/CISS.2009.5054756
We present a unified linear program formulation for optimal content delivery in content delivery networks (CDNs), taking into account various costs and constraints associated with content dissemination from the origin server to storage nodes, data storage, and the eventual fetching of content from storage nodes by end users. Our formulation can be used to achieve a variety of performance goals and system behavior, including the bounding of fetch delay, load balancing, and robustness against node and arc failures. Simulation results suggest that our formulation performs significantly better than the traditional minimum k-median formulation for the delivery of multiple content, even under modest circumstances (small network, few objects, low storage budget, low dissemination costs).https://authors.library.caltech.edu/records/wtwkh-es150Distributed Storage Allocation Problems
https://resolver.caltech.edu/CaltechAUTHORS:20100510-112640133
Authors: {'items': [{'id': 'Leong-Derek', 'name': {'family': 'Leong', 'given': 'Derek'}}, {'id': 'Dimakis-A-G', 'name': {'family': 'Dimakis', 'given': 'Alexandros G.'}}, {'id': 'Ho-Tracey', 'name': {'family': 'Ho', 'given': 'Tracey'}}]}
Year: 2009
DOI: 10.1109/NETCOD.2009.5191399
We investigate the problem of using
several storage nodes to store a data object, subject
to an aggregate storage budget or redundancy constraint.
It is challenging to find the optimal allocation
that maximizes the probability of successful recovery
by the data collector because of the large space of possible
symmetric and nonsymmetric allocations, and
the nonconvexity of the problem. For the special case
of probability-l recovery, we show that the optimal
allocation that minimizes the required budget is symmetric.
We further explore several storage allocation
and access models, and determine the optimal symmetric
allocation in the high-probability regime for a
case of interest. Based on our experimental investigation,
we make a general conjecture about a phase
transition on the optimal allocation.https://authors.library.caltech.edu/records/rg56j-5qn29Symmetric Allocations for Distributed Storage
https://resolver.caltech.edu/CaltechAUTHORS:20110406-104359419
Authors: {'items': [{'id': 'Leong-Derek', 'name': {'family': 'Leong', 'given': 'Derek'}}, {'id': 'Dimakis-A-G', 'name': {'family': 'Dimakis', 'given': 'Alexandros G.'}}, {'id': 'Ho-Tracey', 'name': {'family': 'Ho', 'given': 'Tracey'}}]}
Year: 2010
DOI: 10.1109/GLOCOM.2010.5683962
We consider the problem of optimally allocating a given total storage budget in a distributed storage system.
A source has a data object which it can code and store over
a set of storage nodes; it is allowed to store any amount
of coded data in each node, as long as the total amount of
storage used does not exceed the given budget. A data collector subsequently attempts to recover the original data object by accessing each of the nodes independently with some constant probability. By using an appropriate code, successful recovery occurs when the total amount of data in the accessed nodes is at least the size of the original data object. The goal is to find an optimal storage allocation that maximizes the probability
of successful recovery. This optimization problem is challenging because of its discrete nature and nonconvexity, despite its simple formulation. Symmetric allocations (in which all nonempty nodes store the same amount of data), though intuitive, may be suboptimal; the problem is nontrivial even if we optimize over only symmetric allocations. Our main result shows that the symmetric allocation that spreads the budget maximally
over all nodes is asymptotically optimal in a regime of interest. Specifically, we derive an upper bound for the suboptimality of this allocation and show that the performance gap vanishes asymptotically in the specified regime. Further, we explicitly find the optimal symmetric allocation for a variety of cases. Our results can be applied to distributed storage systems and other problems dealing with reliability under uncertainty, including delay tolerant networks (DTNs) and content delivery networks
(CDNs).https://authors.library.caltech.edu/records/phzw6-pf739Distributed Storage Allocations for Optimal Delay
https://resolver.caltech.edu/CaltechAUTHORS:20120406-091523956
Authors: {'items': [{'id': 'Leong-Derek', 'name': {'family': 'Leong', 'given': 'Derek'}}, {'id': 'Dimakis-A-G', 'name': {'family': 'Dimakis', 'given': 'Alexandros G.'}}, {'id': 'Ho-Tracey', 'name': {'family': 'Ho', 'given': 'Tracey'}}]}
Year: 2011
DOI: 10.1109/ISIT.2011.6033779
We examine the problem of creating an encoded distributed storage representation of a data object for a network of mobile storage nodes so as to achieve the optimal recovery delay. A source node creates a single data object and disseminates an encoded representation of it to other nodes for storage, subject to a given total storage budget. A data collector node subsequently attempts to recover the original data object by contacting other nodes and accessing the data stored in them. By using an appropriate code, successful recovery is achieved when the total amount of data accessed is at least the size of the original data object. The goal is to find an allocation of the given budget over the nodes that optimizes the recovery delay incurred by the data collector; two objectives are considered: (i) maximization of the probability of successful recovery by a given deadline, and (ii) minimization of the expected recovery delay. We solve the problem completely for the second objective in the case of symmetric allocations (in which all nonempty nodes store the same amount of data), and show that the optimal symmetric allocation for the two objectives can be quite different. A simple data dissemination and storage protocol for a mobile delay-tolerant network is evaluated under various scenarios via simulations. Our results show that the choice of storage allocation can have a significant impact on the recovery delay performance, and that coding may or may not be beneficial depending on the circumstances.https://authors.library.caltech.edu/records/qy286-xv198Erasure Coding for Real-Time Streaming
https://resolver.caltech.edu/CaltechAUTHORS:20120828-151414949
Authors: {'items': [{'id': 'Leong-Derek', 'name': {'family': 'Leong', 'given': 'Derek'}}, {'id': 'Ho-Tracey', 'name': {'family': 'Ho', 'given': 'Tracey'}}]}
Year: 2012
DOI: 10.1109/ISIT.2012.6284055
We consider a real-time streaming system where messages are created sequentially at the source, and are encoded for transmission over a packet erasure channel. Each message must subsequently be decoded at the receiver within a given delay from its creation time. We consider code design and maximum message rates when all messages must be decodable by their respective deadlines under a specified set of erasure patterns (erasure model). Specifically, we provide a code construction that achieves the optimal rate for an asymptotic number of messages, under erasure models containing a limited number of erasures per coding window, per sliding window, and containing erasure bursts of a limited length.https://authors.library.caltech.edu/records/qbteb-6am20VIP: A Framework for Joint Dynamic Forwarding and Caching in Named Data Networks
https://resolver.caltech.edu/CaltechAUTHORS:20141117-151706662
Authors: {'items': [{'id': 'Yeh-Edmund', 'name': {'family': 'Yeh', 'given': 'Edmund'}}, {'id': 'Ho-Tracey', 'name': {'family': 'Ho', 'given': 'Tracey'}}, {'id': 'Cui-Ying', 'name': {'family': 'Cui', 'given': 'Ying'}}, {'id': 'Burd-M', 'name': {'family': 'Burd', 'given': 'Michael'}}, {'id': 'Liu-Ran', 'name': {'family': 'Liu', 'given': 'Ran'}}, {'id': 'Leong-Derek', 'name': {'family': 'Leong', 'given': 'Derek'}}]}
Year: 2014
DOI: 10.1145/2660129.2660151
Emerging information-centric networking architectures seek to optimally utilize both bandwidth and storage for efficient content distribution. This highlights the need for joint design of traffic engineering and caching strategies, in order to optimize network performance in view of both current traffic loads and future traffic demands. We present a systematic framework for joint dynamic interest request forwarding and dynamic cache placement and eviction, within the context of the Named Data Networking (NDN) architecture. The framework employs a virtual control plane which operates on the user demand rate for data objects in the network, and an actual plane which handles Interest Packets and Data Packets. We develop distributed algorithms within the virtual plane to achieve network load balancing through dynamic forwarding and caching, thereby maximizing the user demand rate that the NDN network can satisfy. Numerical experiments within a number of network settings demonstrate the superior performance of the resulting algorithms for the actual plane in terms of low user delay and high rate of cache hits.https://authors.library.caltech.edu/records/xh1dz-x8e63VIP: Joint Traffic Engineering and Caching in Named Data Networks
https://resolver.caltech.edu/CaltechAUTHORS:20160922-113810491
Authors: {'items': [{'id': 'Yeh-Edmund', 'name': {'family': 'Yeh', 'given': 'Edmund'}}, {'id': 'Ho-Tracey', 'name': {'family': 'Ho', 'given': 'Tracey'}}, {'id': 'Cui-Ying', 'name': {'family': 'Cui', 'given': 'Ying'}}, {'id': 'Burd-M', 'name': {'family': 'Burd', 'given': 'Michael'}}, {'id': 'Liu-Ran', 'name': {'family': 'Liu', 'given': 'Ran'}}, {'id': 'Leong-Derek', 'name': {'family': 'Leong', 'given': 'Derek'}}]}
Year: 2015
DOI: 10.1109/ICCNC.2015.7069430
Emerging information-centric networking architectures seek to optimally utilize both bandwidth and storage for efficient content distribution. This highlights the need for joint design of traffic engineering and caching strategies. We present a systematic framework for joint dynamic interest request forwarding and dynamic cache placement and eviction, within the context of the Named Data Networking (NDN) architecture. The framework employs a virtual control plane which operates on the user demand rate for data objects in the network, and an actual plane which handles Interest Packets and Data Packets. We develop distributed algorithms within the virtual plane to achieve network load balancing through dynamic forwarding and caching, thereby maximizing the user demand rate that the NDN network can satisfy. Numerical experiments demonstrate the superior performance of the resulting algorithms for the actual plane in terms of low user delay.https://authors.library.caltech.edu/records/neym8-cr453