Peer-to-peer (P2P) networking is a distributed architecture that partitions tasks or data between peer nodes. In this paper, an efficient Hypercube Sequential Matrix Partition (HS-MP) for efficient data sharing in P2P...Peer-to-peer (P2P) networking is a distributed architecture that partitions tasks or data between peer nodes. In this paper, an efficient Hypercube Sequential Matrix Partition (HS-MP) for efficient data sharing in P2P Networks using tokenizer method is proposed to resolve the problems of the larger P2P networks. The availability of data is first measured by the tokenizer using Dynamic Hypercube Organization. By applying Dynamic Hypercube Organization, that efficiently coordinates and assists the peers in P2P network ensuring data availability at many locations. Each data in peer is then assigned with valid ID by the tokenizer using Sequential Self-Organizing (SSO) ID generation model. This ensures data sharing with other nodes in large P2P network at minimum time interval which is obtained through proximity of data availability. To validate the framework HS-MP, the performance is evaluated using traffic traces collected from data sharing applications. Simulations conducting using Network simulator-2 show that the proposed framework outperforms the conventional streaming models. The performance of the proposed system is analyzed using energy consumption, average latency and average data availability rate with respect to the number of peer nodes, data size, amount of data shared and execution time. The proposed method reduces the energy consumption 43.35% to transpose traffic, 35.29% to bitrev traffic and 25% to bitcomp traffic patterns.展开更多
Some rank equalities are established for anti-involutory matrices. In particular, we get the formulas for the rank of the difference, the sum and the commutator of anti-involutory matrices.
We consider the rank-constrained subset selection problem (RCSS): Given a matrix A and an integer p ≤ rank(A), find the largest submatrix A0 consisting of some columns of A with rank(A0) = p. The RCSS problem is gene...We consider the rank-constrained subset selection problem (RCSS): Given a matrix A and an integer p ≤ rank(A), find the largest submatrix A0 consisting of some columns of A with rank(A0) = p. The RCSS problem is generally NP- hard. This paper focuses on a divide-and-conquer (DC) algorithm for solving the RCSS problem: partition the matrix A into several small column blocks: A = [Al,……) Ak] with a certain column permutation II and decompose p to p1 + p2 + ……+ pk such that solutions of the RCSS problems for smaller couples form a solution of the original RCSS problem. We show that the optimal solution of the RCSS problem can be found by DC algorithm for each p ≤ rank(A), if and only if A is column-partitionable, i. e., rank(A) = Σ rank(Ai). Based upon QR decomposition, a fast algorithm for determining the column partition is offered. Our divide-and-conquer algorithm is also quite efficient even A is approkimately column-partitionable.展开更多
文摘Peer-to-peer (P2P) networking is a distributed architecture that partitions tasks or data between peer nodes. In this paper, an efficient Hypercube Sequential Matrix Partition (HS-MP) for efficient data sharing in P2P Networks using tokenizer method is proposed to resolve the problems of the larger P2P networks. The availability of data is first measured by the tokenizer using Dynamic Hypercube Organization. By applying Dynamic Hypercube Organization, that efficiently coordinates and assists the peers in P2P network ensuring data availability at many locations. Each data in peer is then assigned with valid ID by the tokenizer using Sequential Self-Organizing (SSO) ID generation model. This ensures data sharing with other nodes in large P2P network at minimum time interval which is obtained through proximity of data availability. To validate the framework HS-MP, the performance is evaluated using traffic traces collected from data sharing applications. Simulations conducting using Network simulator-2 show that the proposed framework outperforms the conventional streaming models. The performance of the proposed system is analyzed using energy consumption, average latency and average data availability rate with respect to the number of peer nodes, data size, amount of data shared and execution time. The proposed method reduces the energy consumption 43.35% to transpose traffic, 35.29% to bitrev traffic and 25% to bitcomp traffic patterns.
文摘Some rank equalities are established for anti-involutory matrices. In particular, we get the formulas for the rank of the difference, the sum and the commutator of anti-involutory matrices.
文摘We consider the rank-constrained subset selection problem (RCSS): Given a matrix A and an integer p ≤ rank(A), find the largest submatrix A0 consisting of some columns of A with rank(A0) = p. The RCSS problem is generally NP- hard. This paper focuses on a divide-and-conquer (DC) algorithm for solving the RCSS problem: partition the matrix A into several small column blocks: A = [Al,……) Ak] with a certain column permutation II and decompose p to p1 + p2 + ……+ pk such that solutions of the RCSS problems for smaller couples form a solution of the original RCSS problem. We show that the optimal solution of the RCSS problem can be found by DC algorithm for each p ≤ rank(A), if and only if A is column-partitionable, i. e., rank(A) = Σ rank(Ai). Based upon QR decomposition, a fast algorithm for determining the column partition is offered. Our divide-and-conquer algorithm is also quite efficient even A is approkimately column-partitionable.
