Analysis of optical networks in presence of nodes noise and crosstalk

2022 ◽  
Vol 0 (0) ◽  
Author(s):  
Rahul Deo Shukla ◽  
Ajay Pratap ◽  
Raghuraj Singh Suryavanshi

Abstract Optical packet switching has gained lot of momentum in last decade due to the advantages of optical fiber over copper cables. Optical switching is beneficial in optical networks which form connections of links and switching nodes. In these high speed networks minimum delay and high throughput are two important parameters which are considered. To minimize network delay shortest path algorithm is used for route selections. In previous studies while choosing shortest path distance among various nodes is considered. In this work we have shown that it is necessary to consider both distance and number of hops while choosing path from source to destination to minimize power per bit used for the transmission.

1995 ◽  
Vol 05 (04) ◽  
pp. 369-395 ◽  
Author(s):  
ESTHER M. ARKIN ◽  
JOSEPH S.B. MITCHELL ◽  
SUBHASH SURI

We develop a data structure for answering link distance queries between two arbitrary points in a simple polygon. The data structure requires O(n3) time and space for its construction and answers link distance queries in O(log n) time, after which a minimum-link path can be reported in time proportional to the number of links. Here, n denotes the number of vertices of the polygon. Our result extends to link distance queries between pairs of segments or polygons. We also propose a simpler data structure for computing a link distance approximately, where the error is bounded by a small additive constant. Finally, we also present a scheme for approximating the link and the shortest path distance simultaneously.


2020 ◽  
Author(s):  
Jun Liu ◽  
Yicheng Pan ◽  
Qifu Hu

Abstract Shortest path distance query is one of the most fundamental problems in graph theory and applications. Nowadays, the scale of graphs becomes so large that traditional algorithms for shortest path are not available to answer the exact distance query quickly. Many methods based on two-hop labeling have been proposed to solve this problem. However, they cost too much either in preprocessing or query phase to handle large networks containing as many as tens of millions of vertices. In this paper, we propose a novel $k$-hub labeling method to address this problem in large networks with less preprocessing cost while keeping the query time in the microsecond level on average. Technically, two types of labels are presented in our construction, one for distance queries when the actual distance is at most $k-2$, which we call local label, and the other for further distance queries, which we call hub label. Our approach of $k$-hub labeling is essentially different from previous widely used two-hop labeling framework since we construct labels by using hub network structure. We conduct extensive experiments on large real-world networks and the results demonstrate the higher efficiency of our method in preprocessing phase and the much smaller space size of constructed index compared to previous efficient two-hop labeling method, with a comparatively fast query speed.


Author(s):  
Moonyoung Chung ◽  
Woong-Kee Loh

AbstractIn spatial database and road network applications, the search for the nearest neighbor (NN) from a given query object q is the most fundamental and important problem. Aggregate nearest neighbor (ANN) search is an extension of the NN search with a set of query objects $$Q = \{ q_0, \dots , q_{M-1} \}$$ Q = { q 0 , ⋯ , q M - 1 } and finds the object $$p^*$$ p ∗ that minimizes $$g \{ d(p^*, q_i), q_i \in Q \}$$ g { d ( p ∗ , q i ) , q i ∈ Q } , where g (max or sum) is an aggregate function and d() is a distance function between two objects. Flexible aggregate nearest neighbor (FANN) search is an extension of the ANN search with the introduction of a flexibility factor $$\phi \, (0 < \phi \le 1)$$ ϕ ( 0 < ϕ ≤ 1 ) and finds the object $$p^*$$ p ∗ and the set of query objects $$Q^*_\phi $$ Q ϕ ∗ that minimize $$g \{ d(p^*, q_i), q_i \in Q^*_\phi \}$$ g { d ( p ∗ , q i ) , q i ∈ Q ϕ ∗ } , where $$Q^*_\phi $$ Q ϕ ∗ can be any subset of Q of size $$\phi |Q|$$ ϕ | Q | . This study proposes an efficient $$\alpha $$ α -probabilistic FANN search algorithm in road networks. The state-of-the-art FANN search algorithm in road networks, which is known as IER-$$k\hbox {NN}$$ k NN , used the Euclidean distance based on the two-dimensional coordinates of objects when choosing an R-tree node that most potentially contains $$p^*$$ p ∗ . However, since the Euclidean distance is significantly different from the actual shortest-path distance between objects, IER-$$k\hbox {NN}$$ k NN looks up many unnecessary nodes, thereby incurring many calculations of ‘expensive’ shortest-path distances and eventually performance degradation. The proposed algorithm transforms road network objects into k-dimensional Euclidean space objects while preserving the distances between them as much as possible using landmark multidimensional scaling (LMDS). Since the Euclidean distance after LMDS transformation is very close to the shortest-path distance, the lookup of unnecessary R-tree nodes and the calculation of expensive shortest-path distances are reduced significantly, thereby greatly improving the search performance. As a result of performance comparison experiments conducted for various real road networks and parameters, the proposed algorithm always achieved higher performance than IER-$$k\hbox {NN}$$ k NN ; the performance (execution time) of the proposed algorithm was improved by up to 10.87 times without loss of accuracy.


2017 ◽  
Vol 92 ◽  
pp. 41-48 ◽  
Author(s):  
Cencheng Shen ◽  
Joshua T. Vogelstein ◽  
Carey E. Priebe

2018 ◽  
Vol 0 (0) ◽  
Author(s):  
Himanshi Saini ◽  
Amit Kumar Garg

AbstractFailures and malicious attacks in high-speed optical networks lead to huge data as well as revenue loss. In this paper, a survivability technique called Dynamic and Hybrid with Multiple Backup selection Criteria technique for high-speed networks has been proposed with the objective to minimize network resource utilization, blocking probability, End-to-End delay and maximize throughput. The proposed model decides the type of backup provisioning technique based on the location of failure in the network. Protection has been provisioned to selective links near to destination node and other links are restored. The simulation results indicate that proposed survivability technique is efficient as compared to conventional techniques in terms of various network performance measuring parameters. This technique inherits merits of protection as well as restoration. It can be practically implemented to provide resilience in future high-speed networks.


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