Fault localization, a central part of network fault management, is a process of deducing the exact source of a failure from a set of observed failure indications. in the network, end systems and hosts communicate through routers and links connecting them. When a link or a router faces with a fault, the information sent through these components will be damaged. Hence, faulty components in a network need to be detected and repaired to sustain the health of the network. In this paper we introduce an end to end method that detect and repair the faulty components in the network. The proposed method is a heuristic algorithm that uses the embedded information retrieved from disseminated data over the network to detect and repair faulty components. Simulation results show that our heuristic scheme only requires testing a very small set of network components to detect and repair all faults in the network.

Palette re-ordering is a class of pre-processing methods aiming at finding a permutation of color palette such that the resulting image of indexes is more amenable for compression. The efficiency of lossless compression algorithms for fixed-palette images (indexed images) may change if a different indexing scheme is adopted. Obtaining an optimal re-indexing scheme is suspected to be a hard problem and only approximate solutions have been provided in literature. In this paper, we explore a heuristic method to improve the performances on compression ratio. The results indicate that the proposed approach is very effective, acceptable and proved.

Scheduling is the process of improving the performance of a parallel and distributed system. Parallel systems are part of distributed systems. Parallel systems refers to the concept of run parallel jobs that can be run simultaneously on several processors. Load balancing and scheduling are very important and complex problems in multiprocessor systems. So that problems are an NP-Complete problems. In this paper, we introduce a method based on genetic algorithms for scheduling and laod balancing in parallel heterogeneous multi-processor systems. The results of the simulations indicate Genetic algorithm for scheduling at in systems is better than LPT, SPT and FIFO. Simualation results indicate Genetic Algorithm reduce total response time and also it increase utilization.