CDLB: A Cross-Domain Load Balancing Mechanism for Software Defined Networks in Cloud Data Center

Currently, cross-domain load balancing is one of the core issues for software defined networks (SDN) in cloud data centre, which can optimise resource allocation. In this paper, we propose a cross-domain load balancing mechanism, CDLB, based on Extensive Messaging and Presence Protocol (XMPP) for SDN in cloud data centre. Different from poll method, XMPP based push model is introduced in the proposed scheme, which can avoid wasting network and computing resources in large-scale distributed network environment. The proposed scheme enables all the controllers in the flat distributed control plane to share the same consistent global-view network information in real time through XMPP and XMPP publish/subscribe extension. Thus, the problem of non-real time information synchronisation can be resolved and cross-domain load balancing can be realised. The simulations show the efficiency of the proposed scheme

Efficient algorithms for mining clickstream patterns using pseudo-IDLists

Sequential pattern mining is an important task in data mining. Its subproblem, clickstream pattern mining, is starting to attract more research due to the growth of the Internet and the need to analyze online customer behaviors. To date, only few works are dedicately proposed for the problem of mining clickstream patterns. Although one approach is to use the general algorithms for sequential pattern mining, those algorithms’ performance may suffer and the resources needed are more than would be necessary with a dedicated method for mining clickstreams. In this paper, we present pseudo-IDList, a novel data structure that is more suitable for clickstream pattern mining. Based on this structure, a vertical format algorithm named CUP (Clickstream pattern mining Using Pseudo-IDList) is proposed. Furthermore, we propose a pruning heuristic named DUB (Dynamic intersection Upper Bound) to improve our proposed algorithm. Four real-life clickstream databases are used for the experiments and the results show that our proposed methods are effective and efficient regarding runtimes and memory consumption. © 2020 Elsevier B.V.Vietnam National Foundation for Science and Technology Development (NAFOSTED)National Foundation for Science & Technology Development (NAFOSTED) [02/2019/TN