Cloud Ready Applications Composed via HTN Planning

Modern software applications are increasingly deployed and distributed on infrastructures in the Cloud, and then offered as a service. Before the deployment process happens, these applications are being manually - or with some predefined scripts - composed from various smaller interdependent components. With the increase in demand for, and complexity of applications, the composition process becomes an arduous task often associated with errors and a suboptimal use of computer resources. To alleviate such a process, we introduce an approach that uses planning to automatically and dynamically compose applications ready for Cloud deployment. The industry may benefit from using automated planning in terms of support for product variability, sophisticated search in large spaces, fault tolerance, near-optimal deployment plans, etc. Our approach is based on Hierarchical Task Network (HTN) planning as it supports rich domain knowledge, component modularity, hierarchical representation of causality, and speed of computation. We describe a deployment using a formal component model for the Cloud, and we propose a way to define and solve an HTN planning problem from the deployment one. We employ an existing HTN planner to experimentally evaluate the feasibility of our approach

A Synthesis of Automated Planning and Model Predictive Control Techniques and its Use in Solving Urban Traffic Control Problem

Most desired applications for planning and scheduling typically have the characteristics of a continuous changing world. Unfortunately, traditional classical planning does not possess this characteristic. This drawback is because most real-world situations involve quantities and numeric values, which cannot be adequately represented in classical planning. Continuous planning in domains that are represented with rich notations is still a great challenge for AI. For instance, changes occurring due to fuel consumption, continuous movement, or environmental conditions may not be adequately modelled through instantaneous or even durative actions; rather these require modelling as continuously changing processes. The development of planning tools that can reason with domains involving continuous and complex numeric fluents would facilitate the integration of automated planning in the design and development of complex application models to solve real world problems. Traditional urban traffic control (UTC) approaches are still not very efficient during unforeseen situations such as road incidents when changes in traffic are requested in a short time interval. For such anomalies, we need systems that can plan and act effectively in order to restore an unexpected road traffic situation into a normal order. In the quest to improve reasoning with continuous process within the UTC domain, we investigate the role of Model Predictive Control (MPC) approach to planning in the presence of mixed discrete and continuous state variables within a UTC problem. We explore this control approach and show how it can be embedded into existing, modern AI Planning technology. This approach preserves the many advantages of the AI Planning approach, to do with domain independence through declarative modelling, and explicit reasoning while leveraging the capability of MPC to deal with continuous processes. We evaluate the possibility of reasoning with the knowledge of UTC structures to optimise traffic flow in situations where a given road within a network of roads becomes unavailable due to unexpected situations such as road accidents. We specify how to augment the standard AI planning engine with the incorporation of MPC techniques into the central reasoning process of a continuous domain. This approach effectively utilises the strengths of search-based and model-simulation-based methods. We create a representation that can be used to capture declaratively, the definitions of processes, actions, events, resources resumption and the structure of the environment in a UTC scenario. This representation is founded on world states modelled by mixed discrete and continuous state variables. We create a planner with a hybrid algorithm, called UTCPLAN that combines both AI planning and MPC approach to reason with traffic network and control traffic signal at junctions within the network. The experimental objective of minimising the number of vehicles in a queue is implemented to validate the applicability and effectiveness of the algorithm. We present an experimental evaluation showing that our approach can provide UTC plans in a reasonable time. The result also shows that the UTCPLAN approach can perform well in dealing with heavy traffic congestion problems, which might result from heavy traffic flow during rush hours

Coordinating services embedded everywhere via hierarchical planning

The spaces we live in are provided with different devices and technologies, such as sensors for recognising our presence. The aim of such spaces is to improve our comfort, productivity, and even reduce our energy bills. The problem with fulfilling the aim is that devices alone cannot do much to achieve such difficult goals. People would also have problems in manually searching for the best situation accomplishing their needs. A way to deal with this problem is to coordinate devices automatically. For example, our home can autonomously figure out that some lamps can be turned off because the living room has enough natural light and the activity we are currently doing requires a low light level. The benefits are improved comfort and a reasonable amount of energy saved. We therefore explore the possibilities of using a system based on automated planning. This planning produces a set of device services, such as turn off a lamp, that achieves a given goal. We use a method, called hierarchical planning, which enables us to organise the knowledge we have about spaces and devices in hierarchical forms. We show that planning is suitable for this kind of problems by using hierarchical planning to save energy in the Bernoulliborg building at the University of Groningen. The results show energy and money savings, and that people are satisfied with our system. We also improve the system and show that even more money can be saved without sacrificing the well-being of people if we can buy energy from several energy providers

HTN Planning for the Composition of Stream Processing Applications

Goal-driven automated composition of software components is an important problem with applications in Web service composition and stream processing systems. The popular approach to address this problem is to build the composition automatically using AI planning. However, it is shown that some of these planning approaches may neither be feasible nor scalable for many large-scale flow-based applications. Recent advances have proven that the automated composition problem can take advantage of expert knowledge describing the many ways in which different reusable components can be composed. This knowledge can be represented using an extensible composition template or pattern. In prior work, a flow pattern language called Cascade and its corresponding specialized planner have shown the best performance in these domains. In this paper, we propose the use of Hierarchical Task Network (HTN) planning for the composition of stream processing applications. To this end, we propose an automated approach of creating an HTN-based problem from the Cascade representation of the flow patterns. The resulting technique not only allows us to use the HTN planning paradigm and its many advantages including added expressivity but also enables optimization and customization of composition with respect to preferences and constraints. Further, we propose and develop a lookahead heuristic and show that it significantly reduces the planning time. We have performed extensive experimentation with stream processing applications and evaluated applicability and performance of our approach