Artin Avanes

Adaptive workflow scheduling under resource allocation constraints and network dynamics

Workflow concepts are well suited for scenarios where many distributed entities work collaboratively together to achieve a common goal. Today, workflows are mostly used as computerized model for business processes executed in instances in commercial Workflow Management Systems. However, there are many other application domains where computer-supported cooperative work can be captured and organized by workflows. In this paper, we investigate the task of scheduling workflows in self-organizing wireless networks for disaster scenarios. Most research work in the field of workflow scheduling has been driven by temporal and causality constraints. We present an adaptive scheduling algorithm that finds a suitable execution sequence for workflow activities by additionally considering resource allocation constraints and dynamic topology changes. Our approach utilizes a multi-stage distribution algorithm which we extend with techniques to cope with network dynamics.

Service composition and deployment for a smart items infrastructure

Smart items technologies, like RFID and sensor networks, are the next big step in business process automation We are currently developing a dedicated Smart Items Infrastructure (SII) that supports the development and deployment of distributed ubiquitous computing applications Decomposing these applications into individual services and deploying them at different devices poses new technical challenges In particular, we address service composition for heterogeneous device landscapes and mapping of service implementations to devices for execution.

Distributed Service Deployment in Mobile Ad-Hoc Networks

Todays applications are increasingly composed out of services. Standardized protocols, such as SOAP, WSDL, and UDDI are used to discover and invoke remotely located services. Nowadays, improved resource capabilities of mobile devices, e.g. PDAs, smart phones, and sensor devices allow the execution of services even on these smaller computing devices. In comparison to traditional centralized process management, a decentralized, cooperative execution of services on embedded real-time systems leads to higher system scalability, better system response time and higher data accuracy. In this paper we describe an efficient way to deploy services onto highly distributed, mobile, and unreliable devices. To achieve an efficient resource tracking we utilize different group-based data retrieval strategies. Furthermore, we present a prototype system that implements our distributed service deployment algorithm and that evaluates our approach in terms of scalability for different network topologies.

Flexible failure handling for cooperative processes in distributed systems

Distributed systems will be increasingly built on top of wireless networks, such as sensor networks or hand-held devices with advanced sensing and computational abilities. Supporting cooperative processes executed by such unreliable and dynamic system components poses a various number of new technical challenges. In terms of recovery, limited resource capabilities have be considered during re-scheduling of failed process activities. In terms of concurrency, a non-blocking protocol is required to allow a high degree of parallelism. In this paper, we introduce a flexible and resource-oriented failure handling mechanism for cooperative processes in hierarchical and distributed systems. The objective is to ensure both - transactional semantics as well as the selection of suitable nodes with respect to available resource capabilities. Based on a nested execution model, we develop a multi-stage algorithm that uses constraint solving techniques in a parallel fashion thus achieving a more efficient recovery. We evaluate our proposed techniques in a prototype implementation, and demonstrate significant performance gains by using a parallel re-scheduling.