RESUMO
The usage of multiple flow tables (MFT) has significantly extended the flexibility and applicability of software-defined networking (SDN). However, the size of MFT is usually limited due to the use of expensive ternary content addressable memory (TCAM). Moreover, the pipeline mechanism of MFT causes long flow processing time. In this paper a novel approach called Agg-ExTable is proposed to efficiently manage the MFT. Here the flow entries in MFT are periodically aggregated by applying pruning and the Quine-Mccluskey algorithm. Utilizing the memory space saved by the aggregation, a front-end ExTable is constructed, keeping popular flow entries for early match. Popular entries are decided by the Hidden Markov model based on the match frequency and match probability. Computer simulation reveals that the proposed scheme is able to save about 45% of space of MFT, and efficiently decrease the flow processing time compared to the existing schemes.
RESUMO
Load Balancing (LB) is one of the most important tasks required to maximize network performance, scalability and robustness. Nowadays, with the emergence of Software-Defined Networking (SDN), LB for SDN has become a very important issue. SDN decouples the control plane from the data forwarding plane to implement centralized control of the whole network. LB assigns the network traffic to the resources in such a way that no one resource is overloaded and therefore the overall performance is maximized. The Ant Colony Optimization (ACO) algorithm has been recognized to be effective for LB of SDN among several existing optimization algorithms. The convergence latency and searching optimal solution are the key criteria of ACO. In this paper, a novel dynamic LB scheme that integrates genetic algorithm (GA) with ACO for further enhancing the performance of SDN is proposed. It capitalizes the merit of fast global search of GA and efficient search of an optimal solution of ACO. Computer simulation results show that the proposed scheme substantially improves the Round Robin and ACO algorithm in terms of the rate of searching optimal path, round trip time, and packet loss rate.
RESUMO
A content-matched (CM) rangemonitoring query overmoving objects continually retrieves the moving objects (i) whose non-spatial attribute values are matched to given non-spatial query values; and (ii) that are currently located within a given spatial query range. In this paper, we propose a new query indexing structure, called the group-aware query region tree (GQR-tree) for efficient evaluation of CMrange monitoring queries. The primary role of the GQR-tree is to help the server leverage the computational capabilities of moving objects in order to improve the system performance in terms of the wireless communication cost and server workload. Through a series of comprehensive simulations, we verify the superiority of the GQR-tree method over the existing methods.