Distributed Fault-Tolerant Backup-Placement in Overloaded Wireless Sensor Networks

Oren, Gal; Barenboim, Leonid; Levin, Harel

doi:10.1007/978-3-030-05195-2_21

Cited by 8 publications

(7 citation statements)

References 13 publications

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“…Thus, the problems of efficient data transfer, storage and backup become major topics of interest in IoT [26]. A specific topic of interest is backup placement in wireless sensor networks, which was considered in [23].…”

Section: The Importance Of Backup Placement In Wireless Networkmentioning

confidence: 99%

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Fast Distributed Backup Placement in Sparse and Dense Networks

Barenboim¹,

Oren²

2020

Symposium on Algorithmic Principles of Computer Systems

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We consider the Backup Placement problem in networks in the CON GEST distributed setting. Given a network graph G = (V, E), the goal of each vertex v ∈ V is selecting a neighbor, such that the maximum number of vertices in V that select the same vertex is minimized. The backup placement problem was introduced by Halldorsson, Kohler, Patt-Shamir, and Rawitz [12], who obtained an O(log n/ log log n) approximation with randomized polylogarithmic time. Their algorithm remained the state-of-the-art for general graphs, as well as specific graph topologies. In this paper we obtain significantly improved algorithms for various graph topologies. Specifically, we show that O(1)-approximation to optimal backup placement can be computed deterministically in O(1) rounds in graphs that model wireless networks, certain social networks, claw-free graphs, and more generally, in any graph with neighborhood independence bounded by a constant. At the other end, we consider sparse graphs, such as trees, forests, planar graphs and graphs of constant arboricity, and obtain a constant approximation to optimal backup placement in O(log n) deterministic rounds.Clearly, our constant-time algorithms for graphs with constant neighborhood independence are asymptotically optimal. Moreover, we show that our algorithms for sparse graphs are not far from optimal as well, by proving several lower bounds. Specifically, optimal backup placement of unoriented trees requires Ω(log n) time, and approximate backup placement with a polylogarithmic approximation factor requires Ω( log n/ log log n) time. Our results extend the knowledge regarding the question of "what can be computed locally?" [20], and reveal surprising gaps between complexities of distributed symmetry breaking problems.

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Section: The Importance Of Backup Placement In Wireless Networkmentioning

confidence: 99%

“…Finding a backup placement in a network while minimizing the load on the network vertices is a very important goal [12,13,23]. First of all, it allows each vertex to perform a backup to a neighboring node, rather than a more distant destination, and thus improves network performance.…”

Section: Introductionmentioning

confidence: 99%

Fast Distributed Backup Placement in Sparse and Dense Networks

Barenboim¹,

Oren²

2020

Symposium on Algorithmic Principles of Computer Systems

Self Cite

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“…The Distributed Backup Placement problem was introduced by Halldorsson, Kohler, Patt-Shamir, and Rawitz [10] in 2015. It is very well motivated by networks whose nodes may have memory faults, and wish to store backup copies of their data at neighboring nodes [17]. Unfortunately, neighboring nodes may incur faults as well, and so the number of nodes that select the same node as their backup should be minimized.…”

Section: The Backup Placement Problem In Networkmentioning

confidence: 99%

“…However, in practice, real-world attributes of the network and its nodes should be taken into account. Therefore, those algorithms should be extended to include other important aspects [17], such as:…”

Section: The Backup Placement Problem In Networkmentioning

confidence: 99%

Distributed K-Backup Placement and Applications to Virtual Memory in Real-World Wireless Networks

Oren¹,

Barenboim²

2020

Preprint

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The Backup Placement problem in networks in the CON GE ST distributed setting considers a network graph G = (V, E), in which the goal of each vertex v ∈ V is selecting a neighbor, such that the maximum number of vertices in V that select the same vertex is minimized [10]. Previous backup placement algorithms suffer from obliviousness to main factors of real-world heterogeneous wireless network. Specifically, there is no consideration of the nodes memory and storage capacities, and no reference to a case in which nodes have different energy capacity, and thus can leave (or join) the network at any time. These parameters are strongly correlated in wireless networks, as the load on different parts of the network can differ greatly, thus requiring more communication, energy, memory and storage. In order to fit the real-world attributes of wireless networks, this work addresses a generalized version of the original problem, namely K-Backup Placement, in which each vertex selects K neighbors, for a positive parameter K. Our K-Backup Placement algorithm terminates within just one round. In addition we suggest two complementary algorithms which employ K-Backup-Placement to obtain efficient virtual memory schemes for wireless networks. The first algorithm divides the memory of each node to many small parts. Each vertex is assigned the memories of a large subset of its neighbors. Thus more memory capacity for more vertices is gained, but with much fragmentation. The second algorithm requires greater round-complexity, but produces larger virtual memory for each vertex without any fragmentation.

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“…It was introduced by Halldorsson, Kohler, Patt-Shamir, and Rawitz [8]. It is very well motivated by computer networks whose nodes may have memory faults, and wish to store backup copies of their data at neighboring nodes [11]. But neighboring nodes may incur faults as well, and so the number of nodes that select the same backup-node should be minimized.…”

Section: Introductionmentioning

confidence: 99%

Distributed Backup Placement in One Round and its Applications to Maximum Matching Approximation and Self-Stabilization

Barenboim¹,

Oren²

2020

Symposium on Simplicity in Algorithms

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In the distributed backup-placement problem [8] each node of a network has to select one neighbor, such that the maximum number of nodes that make the same selection is minimized. This is a natural relaxation of the perfect matching problem, in which each node is selected just by one neighbor. Previous (approximate) solutions for backup placement are non-trivial, even for simple graph topologies, such as dense graphs [5]. In this paper we devise an algorithm for dense graph topologies, including unit disk graphs, unit ball graphs, line graphs, graphs with bounded diversity, and many more. Our algorithm requires just one round, and is as simple as the following operation. Consider a circular list of neighborhood IDs, sorted in an ascending order, and select the ID that is next to the selecting vertex ID. Surprisingly, such a simple one-round strategy turns out to be very efficient for backup placement computation in dense networks. Not only that it improves the number of rounds of the solution, but also the approximation ratio is improved by a multiplicative factor of at least 2.Our new algorithm has several interesting implications. In particular, it gives rise to a (2 + ǫ)approximation to maximum matching within O(log * n) rounds in dense networks. The resulting algorithm is very simple as well, in sharp contrast to previous algorithms that compute such a solution within this running time. Moreover, these algorithms are applicable to a narrower graph family than our algorithm. For the same graph family, the best previously-known result has O(log ∆ + log * n) running time [4]. Another interesting implication is the possibility to execute our backup placement algorithm as-is in the self-stabilizing setting. This makes it possible to simplify and improve other algorithms for the self-stabilizing setting, by employing helpful properties of backup placement.

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Distributed Fault-Tolerant Backup-Placement in Overloaded Wireless Sensor Networks

Cited by 8 publications

References 13 publications

Fast Distributed Backup Placement in Sparse and Dense Networks

Fast Distributed Backup Placement in Sparse and Dense Networks

Distributed K-Backup Placement and Applications to Virtual Memory in Real-World Wireless Networks

Distributed Backup Placement in One Round and its Applications to Maximum Matching Approximation and Self-Stabilization

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