Catálogo de publicaciones - libros

Security and reliability are major concerns in Grid computing systems. Trust mechanism has been focus of much research in recent years providing a safety and reliable Grid computing environment. Based on EigenTrust model, in this paper, we extend the traditional job scheduling strategies and present a new algorithm named Trust-Oriented Sufferage algorithm. Simulations are performed to evaluate the performance of the new algorithm.

To construct an evolutionary tree is an important topic in computational biology. An evolutionary tree can symbolize the relationship and histories for a set of species. There are many models had been proposed to resolve these problems. However, most of them are NP-hard problem. Ultrametric tree is one of the most popular models, it is used by a well-accepted tree construction method–Unweighted Pair Group Method with Arithmetic Mean, which is widely used by biologists to observe the relationship among species. However, it is a heuristic algorithm. In this paper, we proposed a 3-Points relationship (3PR) based parallel algorithm to solve this problem. 3PR is a relationship between distance matrix and constructed evolutionary trees. The main concept is for any triplet species, two species closer to each other in distance matrix should be closer to each other in evolutionary tree. Then we combined this property and branch-and-bound strategy to reduce the computation time to obtain the optimal solution. Moreover, we put the lower ranked path which is determined by3PR to delay bound pool (DBP) to accelerate the algorithm execution. DBP is a mechanism which can store the lower ranked path and can be helping algorithm to find a better bounding values speedily. The experimental results show that our proposed algorithm can reduce the computation time compared with algorithm without 3PR. Moreover, it also shows 3PR can reduce the computation time when number of computing nodes increasing.

Association rules mining from transaction-oriented databases is an important issue in data mining. Frequent pattern is crucial for association rules generation, time series analysis, classification, etc. There are two categories of algorithms that had been proposed, candidate set generate-and-test approach (Apriori-like) and Pattern growth approach. Many methods had been proposed to solve the association rules mining problem based on FP-tree instead of Apriori-like, since apriori-like algorithm scans the database many times. However, the computation time is costly when the database size is large with FP-tree data structure. Parallel and distributed computing is a good strategy to solve this circumstance. Some parallel algorithms had been proposed, however, most of them did not consider the load balancing issue. In this paper, we proposed a parallel and distributed mining algorithm based on FP-tree structure, Load Balancing FP-Tree (LFP-tree). The algorithm divides the item set for mining by evaluating the tree’s width and depth. Moreover, a simple and trusty calculate formulation for loading degree is proposed. The experimental results show that LFP-tree can reduce the computation time and has less idle time compared with Parallel FP-Tree (PFP-tree). In addition, it has better speed-up ratio than PFP-tree when number of processors grow. The communication time can be reduced by preserving the heavy loading items in their local computing node.