Flush communication channels: Effective implementation and verification

Flush communication channels, or F-channels, generalize more conventional asynchronous communication paradigms. A distributed system which uses an F-channel allows a programmer to define the delivery order of each message in relation to other messages transmitted on the channel. Unreliable datagrams and FIFO (first-in-first-out) communication channels have strictly defined delivery semantics. No restrictions are allowed on message delivery order with unreliable datagrams--message delivery is completely unordered. FIFO channels, on the other hand, insist messages are delivered in the order of their transmission. Flush channels can provide either of these delivery order semantics; in addition, F-channels allow the user to define the delivery of a message to be after the delivery of all messages previously transmitted or before the delivery of all messages subsequently transmitted or both. A system which communicates with a flush channel has a message delivery order that is a partial order.;Dynamically specifying a partial message delivery order complicates many aspects of how we implement and reason about the communication channel. From the system\u27s perspective, we develop a feasible implementation protocol and prove its correctness. The protocol effectively handles the partially ordered message delivery. From the user\u27s perspective, we derive an axiomatic verification methodology for flush applications. The added flexibility of defining the delivery order dynamically slightly increases the complexity for the application programmer. Our verification work helps the user effectively deal with the partially ordered message delivery in flush communication

Stochastic bounds in fork-join queueing systems under full and partial mapping

In a Fork-Join (FJ) queueing system an upstream fork station splits incoming jobs into N tasks to be further processed by N parallel servers, each with its own queue; the response time of one job is determined, at a downstream join station, by the maximum of the corresponding tasks’ response times. This queueing system is useful to the modelling of multi-service systems subject to synchronization constraints, such as MapReduce clusters or multipath routing. Despite their apparent simplicity, FJ systems are hard to analyze. This paper provides the first computable stochastic bounds on the waiting and response time distributions in FJ systems under full (bijective) and partial (injective) mapping of tasks to servers. We consider four practical scenarios by combining 1a) renewal and 1b) non-renewal arrivals, and 2a) non-blocking and 2b) blocking servers. In the case of non-blocking servers we prove that delays scale as O(log N), a law which is known for first moments under renewal input only. In the case of blocking servers, we prove that the same factor of log N dictates the stability region of the system. Simulation results indicate that our bounds are tight, especially at high utilizations, in all four scenarios. A remarkable insight gained from our results is that, at moderate to high utilizations, multipath routing “makes sense” from a queueing perspective for two paths only, i.e., response times drop the most when N = 2; the technical explanation is that the resequencing (delay) price starts to quickly dominate the tempting gain due to multipath transmissions

Flush communication channels: Effective implementation and verification

Flush communication channels, or F-channels, generalize more conventional asynchronous communication paradigms. A distributed system which uses an F-channel allows a programmer to define the delivery order of each message in relation to other messages transmitted on the channel. Unreliable datagrams and FIFO (first-in-first-out) communication channels have strictly defined delivery semantics. No restrictions are allowed on message delivery order with unreliable datagrams--message delivery is completely unordered. FIFO channels, on the other hand, insist messages are delivered in the order of their transmission. Flush channels can provide either of these delivery order semantics; in addition, F-channels allow the user to define the delivery of a message to be after the delivery of all messages previously transmitted or before the delivery of all messages subsequently transmitted or both. A system which communicates with a flush channel has a message delivery order that is a partial order.;Dynamically specifying a partial message delivery order complicates many aspects of how we implement and reason about the communication channel. From the system\u27s perspective, we develop a feasible implementation protocol and prove its correctness. The protocol effectively handles the partially ordered message delivery. From the user\u27s perspective, we derive an axiomatic verification methodology for flush applications. The added flexibility of defining the delivery order dynamically slightly increases the complexity for the application programmer. Our verification work helps the user effectively deal with the partially ordered message delivery in flush communication

Overview on: sequencing in mixed model flowshop production line with static and dynamic context

In the present work a literature overview was given on solution techniques considering basic as well as more advanced and consequently more complex arrangements of mixed model flowshops. We first analyzed the occurrence of setup time/cost; existing solution techniques are mainly focused on permutation sequences. Thereafter we discussed objectives resulting in the introduction of variety of methods allowing resequencing of jobs within the line. The possibility of resequencing within the line ranges from 1) offline or intermittent buffers, 2) parallel stations, namely flexible, hybrid or compound flowshops, 3) merging and splitting of parallel lines, 4) re-entrant flowshops, to 5) change job attributes without physically interchanging the position. In continuation the differences in the consideration of static and dynamic demand was studied. Also intermittent setups are possible, depending on the horizon and including the possibility of resequencing, four problem cases were highlighted: static, semi dynamic, nearly dynamic and dynamic case. Finally a general overview was given on existing solution methods, including exact and approximation methods. The approximation methods are furthermore divided in two cases, know as heuristics and methaheuristic

Manufacturing System and Supply Chain Analyses Related to Product Complexity and Sequenced Parts Delivery

Mixed model assembly has been widely used in many industries. It is applied in order to effectively deal with increasing product complexity. Sequencing and resequencing on a mixed-model assembly line is also complicated by high product complexity. To improve the performance of a mixed-model assembly system and the supply chain, one can develop efficient sequencing rules to address sequencing problems, and manage product complexity to reduce its negative impact on the production system. This research addresses aspects of sequence alteration and restoration on a mixed-model assembly line for the purpose of improving the performance of a manufacturing system and its supply chain, and addresses product complexity analysis. This dissertation is organized into Parts 1, 2, and 3 based on three submitted journal papers. Part 1. On a mixed-model assembly line, sequence alteration is generally used to intentionally change the sequence to the one desired by the downstream department; and sequence restoration is generally applied to achieve sequence compliance by restoring to the original sequence that has been unintentionally changed due to unexpected reasons such as rework. Rules and methods for sequence alteration using shuffling lines or sorting lines were developed to accommodate the sequence considerations of the downstream department. A spare units system based on queuing analysis was proposed to restore the unintentionally altered sequence in order to facilitate sequenced parts delivery. A queuing model for the repairs of defective units in the spare units system was developed to estimate the number of spare units needed in this system. Part 2. Research was conducted on product complexity analysis. Data envelopment analysis (DEA) was first applied to compare product complexity related to product variety among similar products in the same market, two DEA models including their respective illustrative models considering various product complexity factors and different comparison objectives were developed. One of these models compared the product complexity factors in conjunction with sales volume. The third DEA model was developed to identify product complexity reduction opportunities by ranking various product attributes. A further incremental economic analysis considering the changes in costs and market impact by an intended complexity change was presented in order to justify a product complexity reduction opportunity identified by the DEA model. Part 3. Two extended DEA models were developed to compare the relative complexity levels of similar products specifically in automobile manufacturing companies. Some automobile product attributes that have significant cost impact on manufacturing and the supply chain were considered as inputs in the two extended DEA models. An incremental cost estimation approach was developed to estimate the specific cost change in various categories of production activities associated with a product complexity change. A computational tool was developed to accomplish the cost estimation. In each of the above stated parts, a case study was included to demonstrate how these developed rules, models, or methods could be applied at an automobile assembly plant. These case studies showed that the methodologies developed in this research were useful for better managing mixed-model assembly and product complexity in an automobile manufacturing system and supply chain

Theoretical and algorithmic approaches to field-programmable gate array partitioning

Many practical problems dealing with the design of Very Large Scale Integrated (VLSI) circuits can be modeled as graphs in which vertices represent components of the circuit and edges represent a relationship between these components. When expressed as graphs, these problems can then often be solved using graph theoretic methods. Unfortunately, many such problems are NP-complete, hence no practical exact solutions are known to exist. In this dissertation, we study NP-complete problems taken from the realm of partitioning for Field-Programmable Gate Arrays (FPGAs). We adopt a two-pronged approach of theory and practice, developing practical heuristics driven by theoretical study. The theoretical approach is motivated by well-quasi-order (WQO) theory, which can be used to show, among other things, that when some hard problems have fixed parameters, polynomial-time solutions exist. This is of significance in the area of FPGA partitioning, in which practical problems are often characterized by fixed parameter instances. WQO techniques are not generally practical, however, and we develop new methods to solve several important problems in VLSI that are not even amenable to WQO techniques. We begin with a representative partitioning problem, Min Degree Graph Partition (MDGP), the fixed-parameter version of which is closed under the immersion order. \Ve show that the obstruction set ( set of immersion minimal elements) for this problem is computable; we prove both upper and lower bounds on the obstruction set size; and we completely characterize all fixed-parameter MDGP simple tree obstructions. WQO theory tells us only that fixed-parameter MDGP is solvable in (high-degree) polynomial time. We attack the problem using what we refer to as kd-candidate subsets, culminating in linear-time decision and search algorithms. The kd-candidate subset method also paves the way for an efficient heuristic for the FPGA Minimization problem. We then broaden our scope to incorporate delay minimization into FPGA partitioning. We develop, analyze and test a novel method called critical path compression, inspired in part by compiler optimization techniques. We then look at a variety of generalizations of MDGP. Some of these problems are not immersion closed; others are not even defined in a way that WQO theory applies. However, almost all of them are efficiently solvable via the kd-candidate subset approach. Interspersed in these results are many refinements of what is known about the complexity of these problems. We also discuss a few other solution strategies, and present many open problems

Ant Colony Optimisation for Dynamic and Dynamic Multi-objective Railway Rescheduling Problems

Recovering the timetable after a delay is essential to the smooth and efficient operation of the railways for both passengers and railway operators. Most current railway rescheduling research concentrates on static problems where all delays are known about in advance. However, due to the unpredictable nature of the railway system, it is possible that further unforeseen incidents could occur while the trains are running to the new rescheduled timetable. This will change the problem, making it a dynamic problem that changes over time. The aim of this work is to investigate the application of ant colony optimisation (ACO) to dynamic and dynamic multiobjective railway rescheduling problems. ACO is a promising approach for dynamic combinatorial optimisation problems as its inbuilt mechanisms allow it to adapt to the new environment while retaining potentially useful information from the previous environment. In addition, ACO is able to handle multi-objective problems by the addition of multiple colonies and/or multiple pheromone and heuristic matrices. The contributions of this work are the development of a junction simulator to model unique dynamic and multi-objective railway rescheduling problems and an investigation into the application of ACO algorithms to solve those problems. A further contribution is the development of a unique two-colony ACO framework to solve the separate problems of platform reallocation and train resequencing at a UK railway station in dynamic delay scenarios. Results showed that ACO can be e ectively applied to the rescheduling of trains in both dynamic and dynamic multi-objective rescheduling problems. In the dynamic junction rescheduling problem ACO outperformed First Come First Served (FCFS), while in the dynamic multi-objective rescheduling problem ACO outperformed FCFS and Non-dominated Sorting Genetic Algorithm II (NSGA-II), a stateof- the-art multi-objective algorithm. When considering platform reallocation and rescheduling in dynamic environments, ACO outperformed Variable Neighbourhood Search (VNS), Tabu Search (TS) and running with no rescheduling algorithm. These results suggest that ACO shows promise for the rescheduling of trains in both dynamic and dynamic multi-objective environments.Engineering and Physical Sciences Research Council (EPSRC

Methods in intelligent transportation systems exploiting vehicle connectivity, autonomy and roadway data

Intelligent transportation systems involve a variety of information and control systems methodologies, from cooperative systems which aim at traffic flow optimization by means of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, to information fusion from multiple traffic sensing modalities. This thesis aims to address three problems in intelligent transportation systems, one in optimal control of connected automated vehicles, one in discrete-event and hybrid traffic simulation model, and one in sensing and classifying roadway obstacles in smart cities. The first set of problems addressed relates to optimally controlling connected automated vehicles (CAVs) crossing an urban intersection without any explicit traffic signaling. A decentralized optimal control framework is established whereby, under proper coordination among CAVs, each CAV can jointly minimize its energy consumption and travel time subject to hard safety constraints. A closed-form analytical solution is derived while taking speed, control, and safety constraints into consideration. The analytical solution of each such problem, when it exists, yields the optimal CAV acceleration/deceleration. The framework is capable of accommodating for turns and ensures the absence of collisions. In the meantime, a measurement of passenger comfort is taken into account while the vehicles make turns. In addition to the first-in-first-out (FIFO) ordering structure, the concept of dynamic resequencing is introduced which aims at further increasing the traffic throughput. This thesis also studies the impact of CAVs and shows the benefit that can be achieved by incorporating CAVs to conventional traffic. To validate the effectiveness of the proposed solution, a discrete-event and hybrid simulation framework based on SimEvents is proposed, which facilitates safety and performance evaluation of an intelligent transportation system. The traffic simulation model enables traffic study at the microscopic level, including new control algorithms for CAVs under different traffic scenarios, the event-driven aspects of transportation systems, and the effects of communication delays. The framework spans multiple toolboxes including MATLAB, Simulink, and SimEvents. In another direction, an unsupervised anomaly detection system is developed based on data collected through the Street Bump smartphone application. The system, which is built based on signal processing techniques and the concept of information entropy, is capable of generating a prioritized list of roadway obstacles, such that the higher-ranked entries are most likely to be actionable bumps (e.g., potholes) requiring immediate attention, while those lower-ranked are most likely to be nonactionable bumps(e.g., flat castings, cobblestone streets, speed bumps) for which no immediate action is needed. This system enables the City to efficiently prioritize repairs. Results on an actual data set provided by the City of Boston illustrate the feasibility and effectiveness of the system in practice