Non-functional Data Collection for Adaptive Business Processes and Decision Making

International audienceMonitoring application services becomes more and more a transverse key activity in SOA. Beyond traditional human system administration and load control, new activities such as autonomic management as well as SLA enforcement raise the stakes over monitoring requirements. In this paper, we address a new monitoring-based activity which is selecting among competitive service offers based on their currently measured QoS. Starting from this use case, the late binding of service calls in SOA given the current QoS of a set of candidate services, we first elicit the requirements and then describe M4ABP (Monitoring for Adaptive Business Process), a middleware component for monitoring services and delivering monitoring data to business processes wishing to call them. M4ABP provides solutions for general requirements: flexibility as well as performance in data access for clients, coherency of data sets and network usage optimization. Lessons learned from this first use case can be applied to similar monitoring scenario, as well as to the larger field of context-aware computing

Self-Tuning, Bandwidth-Aware Monitoring for Dynamic Data Streams

... monitoring system that maximizes result precision of continuous aggregate queries over dynamic data streams. While prior approaches minimize bandwidth cost under fixed precision constraints, they may still overload a monitoring system during traffic bursts. To facilitate practical deployment of monitoring systems, SMART therefore bounds the worst-case bandwidth cost for overload resilience. The primary challenge for SMART is how to dynamically select updates at each node to maximize query precision while keeping per-node monitoring bandwidth below a specified budget. To address this challenge, SMART’s hierarchical algorithm (1) allocates bandwidth budgets in a near-optimal manner to maximize global precision and (2) selftunes bandwidth settings to improve precision under dynamic workloads. Our prototype implementation of SMART provides key solutions to (a) prioritize pending updates for multi-attribute queries, (b) build bounded fan-in, load-aware aggregation trees to improve accuracy, and (c) combine temporal batching with arithmetic filtering to reduce load and to quantify result staleness. Our evaluation using simulations and a network monitoring application shows that SMART incurs low overheads, improves accuracy by up to an order of magnitude compared to uniform bandwidth allocation, and performs close to the optimal algorithm under modest bandwidth budgets

Collaborative Planning and Event Monitoring Over Supply Chain Network

The shifting paradigm of supply chain management is manifesting increasing reliance on automated collaborative planning and event monitoring through information-bounded interaction across organizations. An end-to-end support for the course of actions is turning vital in faster incident response and proactive decision making. Many current platforms exhibit limitations to handle supply chain planning and monitoring in decentralized setting where participants may divide their responsibilities and share computational load of the solution generation. In this thesis, we investigate modeling and solution generation techniques for shared commodity delivery planning and event monitoring problems in a collaborative setting. In particular, we first elaborate a new model of Multi-Depot Vehicle Routing Problem (MDVRP) to jointly serve customer demands using multiple vehicles followed by a heuristic technique to search near-optimal solutions for such problem instances. Secondly, we propose two distributed mechanisms, namely: Passive Learning and Active Negotiation, to find near-optimal MDVRP solutions while executing the heuristic algorithm at the participant's side. Thirdly, we illustrate a collaboration mechanism to cost-effectively deploy execution monitors over supply chain network in order to collect in-field plan execution data. Finally, we describe a distributed approach to collaboratively monitor associations among recent events from an incoming stream of plan execution data. Experimental results over known datasets demonstrate the efficiency of the approaches to handle medium and large problem instances. The work has also produced considerable knowledge on the collaborative transportation planning and execution event monitoring