Scheduling Problems

Scheduling is defined as the process of assigning operations to resources over time to optimize a criterion. Problems with scheduling comprise both a set of resources and a set of a consumers. As such, managing scheduling problems involves managing the use of resources by several consumers. This book presents some new applications and trends related to task and data scheduling. In particular, chapters focus on data science, big data, high-performance computing, and Cloud computing environments. In addition, this book presents novel algorithms and literature reviews that will guide current and new researchers who work with load balancing, scheduling, and allocation problems

An interactive design environment for coal piping system

The design of coal piping system of a coal-fired power plant is a complex and time-consuming engineering task that involves meeting of several design objectives and constraints. The distribution of coal particles in a pneumatic pipeline can be highly inhomogeneous. Current coal piping design technology relies on empirical model and does not consider particle distribution characteristics in the pipe. In this thesis, a design tool which couples a validated detailed pipe model and an interactive optimization algorithm is developed. This new design tool uses evolutionary algorithms (EAs) as the optimization algorithm, and computational fluid dynamics (CFD) as the evaluation mechanism. The process uses an iterative approach that allows design to be evaluated using CFD analysis automatically to optimize several criteria. The proposed design change is then re-meshed and displayed. Three fundamentally different techniques from traditional optimization methods were considered in order to reduce computation time. Firstly, the tool has been implemented in a virtual engineering environment using VE-Suite. Secondly, the system is integrated with a general interface to allow users to set up the design procedure and interact or guide the searching path as the design evolves. Thirdly, a fast calculation approach is used to reduce the time for single CFD case. The proposed interactive design tool is analyzed and enhanced so that it is usable by the general engineering community. A real coal pipe application was carried out using this design tool. The main objective is to distribute coal flow to its two branches as uniform as possible. The results of this work suggested that the optimum coal pipe can be found relatively fast even when using high-fidelity CFD solver as the analysis method, and the optimum pipe can greatly reduce the coal flow unbalance. This indicates that the tool presented in this thesis can be used as a new and efficient design environment for coal pipe

Management of Temporally and Spatially Correlated Failures in Federated Message Oriented Middleware for Resilient and QoS-Aware Messaging Services.

PhDMessage Oriented Middleware (MOM) is widely recognized as a promising solution for the communications between heterogeneous distributed systems. Because the resilience and quality-of-service of the messaging substrate plays a critical role in the overall system performance, the evolution of these distributed systems has introduced new requirements for MOM, such as inter domain federation, resilience and QoS support. This thesis focuses on a management frame work that enhances the Resilience and QoS-awareness of MOM, called RQMOM, for federated enterprise systems. A common hierarchical MOM architecture for the federated messaging service is assumed. Each bottom level local domain comprises a cluster of neighbouring brokers that carry a local messaging service, and inter domain messaging are routed through the gateway brokers of the different local domains over the top level federated overlay. Some challenges and solutions for the intra and inter domain messaging are researched. In local domain messaging the common cause of performance degradation is often the fluctuation of workloads which might result in surge of total workload on a broker and overload its processing capacity, since a local domain is often within a well connected network. Against performance degradation, a combination of novel proactive risk-aware workload allocation, which exploits the co-variation between workloads, in addition to existing reactive load balancing is designed and evaluated. In federated inter domain messaging an overlay network of federated gateway brokers distributed in separated geographical locations, on top of the heterogeneous physical network is considered. Geographical correlated failures are threats to cause major interruptions and damages to such systems. To mitigate this rarely addressed challenge, a novel geographical location aware route selection algorithm to support uninterrupted messaging is introduced. It is used with existing overlay routing mechanisms, to maintain routes and hence provide more resilient messaging against geographical correlated failures

Multi-agent based architecture for digital libraries

Digital Libraries (DL) generally contain a collection of independently maintained data sets, in different formats, which may be queried by geographically dispersed users. The general problem of managing such large digital data archives is particularly challenging when the system must cope with data which is processed on demand. This dissertation proposes a Multi-Agent System (MAS) architecture for the utilisation of an active DL that provides computing services in addition to data-retrieval services, so that users can initiate computing jobs on remote supercomputers for processing, mining, and filtering of the data in the library. The system architecture is based on a collaborative set of agents, where each agent undertakes a pre-defined role, and is responsible for offering a particular type of service. The integration of services is based on a user defined query which can range in complexity from simple queries, to specialised algorithms which are transmitted to image processing archives as mobile agents. The proposed architecture enables new information sources and services to be integrated into the system dynamically, supports autonomous and dynamic on-demand data processing based on collaboration between agents, capable of handling a large number of concurrent users. Focus is based on the management of mobile agents which roam through the servers that constitute the DL to serve user queries. A new load balancing scheme is proposed for managing agent load among the available servers, based on the system state information and predictions about lifetime of agent tasks and server status. The system architecture is further extended by defining a gateway to provide interoperability with other heterogeneous agent-based systems. Interoperability in this sense enables agents from different types of platforms to communicate between themselves and use services provided by other systems. The novelty of the proposed gateway approach lies in the ability to adapt an existing legacy system for use with the agent-based approach (and one that adheres to FIPA standards). A prototype has been developed as a proof-of-concept to outline the principles and ideas involved, with reference to the Synthetic Aperture Radar Atlas (SARA) DL composed of multi-spectral remote-sensing imagery of the Earth. Although, the work presented in this dissertation has been evaluated in the context of SARA DL, the proposed techniques suggest useful guidelines that may be employed by other active archival systems

CHANGE-READY MPC SYSTEMS AND PROGRESSIVE MODELING: VISION, PRINCIPLES, AND APPLICATIONS

The last couple of decades have witnessed a level of fast-paced development of new ideas, products, manufacturing technologies, manufacturing practices, customer expectations, knowledge transition, and civilization movements, as it has never before. In today\u27s manufacturing world, change became an intrinsic characteristic that is addressed everywhere. How to deal with change, how to manage it, how to bind to it, how to steer it, and how to create a value out of it, were the key drivers that brought this research to existence. Change-Ready Manufacturing Planning and Control (CMPC) systems are presented as the first answer. CMPC characteristics, change drivers, and some principles of Component-Based Software Engineering (CBSE) are interwoven to present a blueprint of a new framework and mind-set in the manufacturing planning and control field, CMPC systems. In order to step further and make the internals of CMPC systems/components change-ready, an enabling modeling approach was needed. Progressive Modeling (PM), a forward-looking multi-disciplinary modeling approach, is developed in order to modernize the modeling process of today\u27s complex industrial problems and create pragmatic solutions for them. It is designed to be pragmatic, highly sophisticated, and revolves around many seminal principles that either innovated or imported from many disciplines: Systems Analysis and Design, Software Engineering, Advanced Optimization Algorisms, Business Concepts, Manufacturing Strategies, Operations Management, and others. Problems are systemized, analyzed, componentized; their logic and their solution approaches are redefined to make them progressive (ready to change, adapt, and develop further). Many innovations have been developed in order to enrich the modeling process and make it a well-assorted toolkit able to address today\u27s tougher, larger, and more complex industrial problems. PM brings so many novel gadgets in its toolbox: function templates, advanced notation, cascaded mathematical models, mathematical statements, society of decision structures, couplers--just to name a few. In this research, PM has been applied to three different applications: a couple of variants of Aggregate Production Planning (APP) Problem and the novel Reconfiguration and Operations Planning (ROP) problem. The latest is pioneering in both the Reconfigurable Manufacturing and the Operations Management fields. All the developed models, algorithms, and results reveal that the new analytical and computational power gained by PM development and demonstrate its ability to create a new generation of unmatched large scale and scope system problems and their integrated solutions. PM has the potential to be instrumental toolkit in the development of Reconfigurable Manufacturing Systems. In terms of other potential applications domain, PM is about to spark a new paradigm in addressing large-scale system problems of many engineering and scientific fields in a highly pragmatic way without losing the scientific rigor

An agile and adaptive holonic architecture for manufacturing control

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. 2004. Faculdade de Engenharia. Universidade do Port

On Solving Some Issues in Cloud Computing

In past few years, cloud computing has emerged as one of the fastest growing segment in IT industry. It delivers infrastructure, platform, and software as a service on demand basis. Cloud provides several data centers at different geographical locations for service reliability and availability. Users can deploy applications and subscribe services from any location at competitive cost. However, this system doesn’t support mechanism and policies for dynamically coordinating load distribution among different cloud-based data centers. Further, cloud providers are unable to predict geographical distribution of users availing this services. There exist many challenging issues but few of them such as load balancing, event matching, and real-time data analysis have been addressed in the thesis. First three contributions in this thesis are dedicated to load balancing using evolutionary techniques. In the first contribution, a genetic algorithm based load balancing (LBGA) has been proposed with real value coded GA with a new encoding mechanism. Similarly, a particle swarm optimization based load balancing (LBPSO) is suggested. Both the schemes are simulated in cloud analyst, and performance comparisons are made with the competitive schemes.Consequently, both the schemes are grouped together to form a hybrid load balancing algorithm (HLBA). HLBA based central load balancer balances the load among virtual machines in cloud data center. HLBA utilizes the benefits of both genetic algorithm and particle swarm optimization. Different measures such as average response time, data center request service time, virtual machine cost, and data transfer cost are considered to evaluate the performance of the proposed algorithm. Suggested approach achieves better load balancing in large scale cloud computing environment as compared to other competitive approaches. In another contribution, an event matching algorithm has been developed for content-based event dissemination in publish/subscribe system. Proposed modified rapid match (MRM) algorithm has been compared with existing heuristics in the cloud system. Finally, a framework for the sensor-cloud environment for patient monitoring has been suggested. A prototype model has been developed for the purpose to validate the framework. This integrated system helps in monitoring, analyzing, and delivering real-time information on the fly