Routing protocol selection of VANET is crucial for its network performance, which undertakes task to transfer important information. On the basis of comparison and analysis on existing VANET routing protocols, an VANET routing technology based on link quality and velocity vector (LQ-VV-GPSR) was brought out. The method aims at selecting reasonable relay node to convenient forwarding message intelligently. It comprehensively takes into account velocity vector information and underlying link status to determine which next hop data packet will be forwarded. The performance of improved algorithm and GPSR was compared with simulation on NS2. Simulations were performed with different network scenarios and different simulation strategies and results were also provided. The result shows that LQ-VV-GPSR has better robustness and scalability, which is suitable for large scale and heavy traffic network scenarios.
Load balancing technology can effectively exploit potential enormous compute power available on distributed systems and achieve scalability. Communication delay overhead on distributed system, which is time-varying and is usually ignored or assumed to be deterministic for traditional load balancing strategies, can greatly degrade the load balancing performance. Considering communication delay overhead and its time-varying feature, a hierarchical load balancing strategy based on generalized neural network (HLBSGNN) is presented for large distributed systems. The novelty of the HLBSGNN is threefold: (1) the hierarchy with optimized communication is employed to reduce load balancing overhead for large distributed computing systems, (2) node computation rate and communication delay randomness imposed by the communication medium are considered, and (3) communication and migration overheads are optimized via forecasting delay. Comparisons with traditional strategies, such as centralized, distributed, and random delay strategies, indicate that the HLBSGNN is more effective and efficient.
The task allocation problem (TAP) generally aims to minimize total execution cost and internode communication cost in traditional parallel computing systems. New TAP (NTAP) considering additive intranode communication cost in emerging multicore cluster systems is investigated in this paper. We analyze the complexity of NTAP with network flow method and conclude that the intranode communication cost is a key to the complexity of NTAP, and prove that (1) the NTAP can be cast as a generalized linear network minimum cost flow problem and can be solved inO(m2n4)time if the intranode communication cost equals the internode communication cost, and (2) the NTAP can be cast as a generalized convex cost network minimum cost flow problem and can be solved in polynomial time if the intranode communication cost is more than the internode communication cost. More in particular, the uniform cost NTAP can be cast as a convex cost flow problem and can be solved inO(m2n2log(m+n))time. Furthermore, solutions to the NTAP are also discussed. Our work extends currently known theoretical results and the theorems and conclusions presented in this paper can provide theoretical basis for task allocating strategies on multicore clusters.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.