17,893 research outputs found
Distributed Storage Allocations for Optimal Delay
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.Comment: Extended version of an IEEE ISIT 2011 paper. 10 pages, 4 figure
Symmetric Allocations for Distributed Storage
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).Comment: 7 pages, 3 figures, extended version of an IEEE GLOBECOM 2010 pape
Derandomized Distributed Multi-resource Allocation with Little Communication Overhead
We study a class of distributed optimization problems for multiple shared
resource allocation in Internet-connected devices. We propose a derandomized
version of an existing stochastic additive-increase and multiplicative-decrease
(AIMD) algorithm. The proposed solution uses one bit feedback signal for each
resource between the system and the Internet-connected devices and does not
require inter-device communication. Additionally, the Internet-connected
devices do not compromise their privacy and the solution does not dependent on
the number of participating devices. In the system, each Internet-connected
device has private cost functions which are strictly convex, twice continuously
differentiable and increasing. We show empirically that the long-term average
allocations of multiple shared resources converge to optimal allocations and
the system achieves minimum social cost. Furthermore, we show that the proposed
derandomized AIMD algorithm converges faster than the stochastic AIMD algorithm
and both the approaches provide approximately same solutions
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