Abstract—This paper outlines an autonomic approach to business process management using goal-oriented principles and autonomous agent technology employing the well-known BDI method. We discuss how the structure of common business processes can be mapped onto goal-oriented models where achievement points are represented by goals and the task structures used to reach them are represented by plans. The flexibility afforded by this approach implies that autonomic feedback loops are created between the business process incarnation and its autonomous controller, which may be extended to the multiple process case where several controllers interact to form extended feedback relationships. We introduce a set of technologies designed to deliver these features in real-world applications and support the position with a case study in the domain of Engineering Change Management. I.

Abstract—Service-oriented architecture (SOA) paradigm for orchestrating large-scale distributed applications offers significant cost savings by reusing existing services. However, the high irregularity of client requests and the distributed nature of the approach may deteriorate service response time and availability. Static replication of components in datacenters for accommodating load spikes requires proper resource planning and underutilizes the cloud infrastructure. Moreover, no service availability guarantees are offered in case of datacenter failures. In this paper, we propose a cost-efficient approach for dynamic and geographically-diverse replication of components in a cloud computing infrastructure that effectively adapts to load variations and offers service availability guarantees. In our virtual economy, components rent server resources and replicate, migrate or delete themselves according to selfoptimizing strategies. We experimentally prove that such an approach outperforms in response time even full replication of the components in all servers, while offering service availability guarantees under failures. Keywords-component; net benefit; replication; agent; web service I.

Autonomic control managers can remove the need for manual system configuration in order to achieve good performance and efficient resource utilization. However, simple controllers based on reconfiguration actions tied to thresholds, or ’if-then ’ rules, themselves need to be configured and tuned in order to adapt the controller behavior to the expected workload characteristic. In this paper we present an experimental study of zero-configuration policies that can be automatically tuned based on analytical models of the system under control. In particular, we have designed and implemented a threshold-free self-configuration policy for a distributed workflow execution engine and compared it with a standard PID controller. The experimental results included in the paper show that using such a policy the controller can tune itself in addition to reconfiguring the distributed engine and the proposed policy out-performs simpler policies that require manual and error-prone tuning of their parameters. 1.

Abstract—Significant achievements have been made for automated allocation of cloud resources. However, the performance of applications may be poor in peak load periods, unless their cloud resources are dynamically adjusted. Moreover, although cloud resources dedicated to different applications are virtually isolated, performance fluctuations do occur because of resource sharing, and software or hardware failures (e.g. unstable virtual machines, power outages, etc.). In this paper, we propose a decentralized economic approach for dynamically adapting the cloud resources of various applications, so as to statistically meet their SLA performance and availability goals in the presence of varying loads or failures. According to our approach, the dynamic economic fitness of a Web service determines whether it is replicated or migrated to another server, or deleted. The economic fitness of a Web service depends on its individual performance constraints, its load, and the utilization of the resources where it resides. Cascading performance objectives are dynamically calculated for individual tasks in the application workflow according to the user requirements. By fully implementing our framework, we experimentally proved that our adaptive approach statistically meets the performance objectives under peak load periods or failures, as opposed to static resource settings. Index Terms—cost-efficiency, replication, migration, net benefit, performance elasticity, web services I.