This chapter introduces PADRES, the publish/subscribe model with the capability to correlate events, uniformly access data produced in the past and future, balance the traffic load among brokers, and handle network failures. The new model can filter, aggregate, correlate and project any combination of historic and future data. A flexible architecture is proposed consisting of distributed and replicated data repositories that can be provisioned in ways to tradeoff availability, storage overhead, query overhead, query delay, load distribution, parallelism, redundancy and locality. This chapter gives a detailed overview of the PADRES content-based publish/subscribe system. Several applications are presented in detail that can benefit from the content-based nature of the publish/subscribe paradigm and take advantage of its scalability and robustness features. A list of example applications are discussed that can benefit from the content-based nature of publish/subscribe paradigm and take advantage of its scalability and robustness features.

Distributed content-based publish/subscribe systems suffer from performance degradation and poor scalability caused by uneven load distributions typical in real-world applications. The reason for this shortcoming is the lack of a load balancing scheme. This article proposes a load balancing solution specifically tailored to the needs of content-based publish/subscribe systems that is distributed, dynamic, adaptive, transparent, and accommodates heterogeneity. The solution consists of three key contributions: a load balancing framework, a novel load estimation algorithm, and three offload strategies. A working prototype of our solution is built on an open-sourced contentbased publish/subscribe system and evaluated on PlanetLab, a cluster testbed, and in simulations. Real-life experiment results show that the proposed load balancing solution is efficient with less than 0.2% overhead; effective in distributing and balancing load originating from a single server to all available servers in the network; and capable of preventing overloads to preserve system stability, availability, and quality of service.

We develop a content-based publish/subscribe platform, called PADRES, which is a distributed middleware platform with features inspired by the requirements of workflow management and business process execution. These features constitute original additions to publish/subscribe systems and include an expressive subscription language, historic, query-based data access, composite subscription processing, a rule-based matching and routing mechanism, and the support for the decentralized execution of service-oriented applications.

Abstract. Distributed content-based publish/subscribe systems to date suffer from performance degradation and poor scalability caused by uneven load distributions typical in real-world applications. The reason for this shortcoming is due to the lack of a load balancing solution, which have rarely been studied in the context of publish/subscribe. This paper proposes a load balancing solution specific to distributed content-based publish/subscribe systems that is distributed, dynamic, adaptive, transparent, and accommodates heterogeneity. The solution consists of three key contributions: a load balancing framework, a novel load estimation algorithm, and three offload strategies. Experimental results show that the proposed load balancing solution is efficient with less than 1.5% overhead, effective with at least 91% load estimation accuracy, and capable of distributing all of the system's load originating from an edge point of the network.

Abstract-A popular trend in large enterprises today is the adoption of green IT strategies that use resources as efficiently as possible to reduce IT operational costs. With the publish/subscribe middleware playing a vital role in seamlessly integrating applications at large enterprises including Google and Yahoo, our goal is to search for resource allocation algorithms that enable publish/subscribe systems to use system resources as efficiently as possible. To meet this goal, we develop methodologies that minimize system-wide message rates, broker load, hop count, and the number of allocated brokers, while maximizing the resource utilization of allocated brokers to achieve maximum efficiency. Our contributions consist of developing a bit vector supported resource allocation framework, designing and comparing four different classes with a total of ten variations of subscription allocation algorithms, and developing a recursive overlay construction algorithm. A compelling feature of our work is that it works under any arbitrary workload distribution and is independent of the publish/subscribe language, which makes it easily applicable to any topic and content-based publish/subscribe system. Experiments on a cluster testbed and a high performance computing platform show that our approach reduces the average broker message rate by up to 92% and the number of allocated brokers by up to 91%.