Pub/Sub systems permit users to submit subscriptions and notify interested users of the events detected in a distributed way. Moving a Pub/Sub system to a cloud infrastructure is for high performance and scalability. This paper describes how to migrate two Pub/Sub systems i.e. PADRES and OncePubSub to Xen Cloud Platform, especially proposes black-box method, grey-box method and white-box method so as to take full advantage of cloud mechanisms. This paper then conducts a series of experiments on the Pub/Sub systems in the cloud to evaluate benefits and costs. The experimental results indicate that the black-box method does not always take effect although it can be implemented easily, the grey-box method is more appropriate to a Pub/Sub system if its workload features and brokers' roles are known in advance. Further, the experimental results show the white-box method, combined the load balance mechanism both in the cloud and in a Pub/Sub system, can achieve satisfying performance and scalability especially facing the workload with unidentified distribution.
In Pub/Sub systems, channel-based approaches to routing the subscriptions and events have many advantages such as fewer routing messages, lower costs for subscription management, etc. But a potential issue embedded in this kind of approach, i.e. loadings on different event brokers are apt to unbalancing, is ignored more or less. In this paper, we design a load balancing mechanism and integrate it into a channel-based approach in a Pub/Sub system. In particular, we define a balancing state in a Pub/Sub system, and then propose the balancing control initiation algorithm which decides not only whether to perform load balancing among event brokers but also whether to adjust the number of event brokers. Also we present the load scheduling algorithm which can achieve load balancing by channel splitting, merging and migration. We conduct the experiments by taking loads with different distributions as input to reveal the capability of dealing with changing loads. The experimental data prove that our mechanism can help balance the system loads efficiently and dynamically start or shut down event brokers when facing overloads or insufficient loads.
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