Adaptive service discovery on service-oriented and spontaneous sensor systems

Service-oriented architecture, Spontaneous networks, Self-organisation, Self-configuration, Sensor systems, Social patternsNatural and man-made disasters can significantly impact both people and environments. Enhanced effect can be achieved through dynamic networking of people, systems and procedures and seamless integration of them to fulfil mission objectives with service-oriented sensor systems. However, the benefits of integration of services will not be realised unless we have a dependable method to discover all required services in dynamic environments. In this paper, we propose an Adaptive and Efficient Peer-to-peer Search (AEPS) approach for dependable service integration on service-oriented architecture based on a number of social behaviour patterns. In the AEPS network, the networked nodes can autonomously support and co-operate with each other in a peer-to-peer (P2P) manner to quickly discover and self-configure any services available on the disaster area and deliver a real-time capability by self-organising themselves in spontaneous groups to provide higher flexibility and adaptability for disaster monitoring and relief

Towards More Data-Aware Application Integration (extended version)

Although most business application data is stored in relational databases, programming languages and wire formats in integration middleware systems are not table-centric. Due to costly format conversions, data-shipments and faster computation, the trend is to "push-down" the integration operations closer to the storage representation. We address the alternative case of defining declarative, table-centric integration semantics within standard integration systems. For that, we replace the current operator implementations for the well-known Enterprise Integration Patterns by equivalent "in-memory" table processing, and show a practical realization in a conventional integration system for a non-reliable, "data-intensive" messaging example. The results of the runtime analysis show that table-centric processing is promising already in standard, "single-record" message routing and transformations, and can potentially excel the message throughput for "multi-record" table messages.Comment: 18 Pages, extended version of the contribution to British International Conference on Databases (BICOD), 2015, Edinburgh, Scotlan

Responsible Composition and Optimization of Integration Processes under Correctness Preserving Guarantees

Enterprise Application Integration deals with the problem of connecting heterogeneous applications, and is the centerpiece of current on-premise, cloud and device integration scenarios. For integration scenarios, structurally correct composition of patterns into processes and improvements of integration processes are crucial. In order to achieve this, we formalize compositions of integration patterns based on their characteristics, and describe optimization strategies that help to reduce the model complexity, and improve the process execution efficiency using design time techniques. Using the formalism of timed DB-nets - a refinement of Petri nets - we model integration logic features such as control- and data flow, transactional data storage, compensation and exception handling, and time aspects that are present in reoccurring solutions as separate integration patterns. We then propose a realization of optimization strategies using graph rewriting, and prove that the optimizations we consider preserve both structural and functional correctness. We evaluate the improvements on a real-world catalog of pattern compositions, containing over 900 integration processes, and illustrate the correctness properties in case studies based on two of these processes.Comment: 37 page

A Multi-Skilled Approach to Property Maintenance Considering Temporal, Spatial and Resource Constraints

With the continued increase in age of the United States housing and building stock, as well as the continued need to maintain properties across the U.S., the need for timely, cost-optimal maintenance is ever more critical. This paper proposes the application of a mathematical model to aid in the scheduling and assignment of construction and maintenance tasks, considering the multi-skilled workforce. The benefit of this approach is to take advantage of the economies of scale that can be developed using cross-functional skilled workers with varying levels of competence and efficiency. This approach schedules and assigns tasks using data from maintenance task software datasets, using the least-cost, competent worker available for the job while also considering the trade-off between skilled labor cost and travel costs, both in terms of travel wage and vehicle wear and tear. The model is enhanced to include pairing between a mentor and an apprentice, where combined efficiency and pairing costs are considered at the same time as travel costs. Due to the practical nature of this research, a case organization was used and data from that firm was analyzed so that operational insights into the necessity of such a model could be considered. The mathematical backbone of the optimization approach to multi-skilled resource allocation considers the temporal and spatial demands of a geographically dispersed property management program. Actual, as opposed to sample, data allows us to evaluate the real financial implications on the case firm, if such an approach to scheduling is used. The generalization of this data provides excellent fit for a model that can be used to assign the best capable worker to the most cost-efficient task, considering deadlines, priorities and availability. Results of this scheduling approach provide significant cost and resource reductions over the historical firm performance, though practical considerations should temper that expectation. The above approach offers exceptional scalability and adaptability with the continued advancement of algorithm approaches to network-distribution and peer-to-peer work platforms

Deep Reinforcement Learning Framework with Q Learning For Optimal Scheduling in Cloud Computing

Cloud computing is an emerging technology that is increasingly being appreciated for its diverse uses, encompassing data processing, The Internet of Things (IoT) and the storing of data. The continuous growth in the number of cloud users and the widespread use of IoT devices have resulted in a significant increase in the volume of data being generated by these users and the integration of IoT devices with cloud platforms. The process of managing data stored in the cloud has become more challenging to complete. There are numerous significant challenges that must be overcome in the process of migrating all data to cloud-hosted data centers. High bandwidth consumption, longer wait times, greater costs, and greater energy consumption are only some of the difficulties that must be overcome. Cloud computing, as a result, is able to allot resources in line with the specific actions made by users, which is a result of the conclusion that was mentioned earlier. This phenomenon can be attributed to the provision of a superior Quality of Service (QoS) to clients or users, with an optimal response time. Additionally, adherence to the established Service Level Agreement further contributes to this outcome. Due to this circumstance, it is of utmost need to effectively use the computational resources at hand, hence requiring the formulation of an optimal approach for task scheduling. The goal of this proposed study is to find ways to allocate and schedule cloud-based virtual machines (VMs) and tasks in such a way as to reduce completion times and associated costs. This study presents a new method of scheduling that makes use of Q-Learning to optimize the utilization of resources.The algorithm's primary goals include optimizing its objective function, building the ideal network, and utilizing experience replay techniques