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In this paper, we investigate the dynamic task scheduling problem with stochastic task arrival times and due dates in the UAV-based intelligent on-demand meal delivery system (UIOMDS) to improve the efficiency. The objective is to minimize the total tardiness. The new constraints and characteristics introduced by UAVs in the problem model are fully studied. An iterated heuristic framework SES (Stochastic Event Scheduling) is proposed to periodically schedule tasks, which consists of a task collection and a dynamic task scheduling phases. Two task collection strategies are introduced and three Roulette-based flight dispatching approaches are employed. A simulated annealing based local search method is integrated to optimize the solutions. The experimental results show that the proposed algorithm is robust and more effective compared with other two existing algorithms.

Changes in network state are a common source of instability in networks. An update event typically involves multiple flows that compete for network resources at the cost of rescheduling and migrating some existing flows. Previous network updating schemes tackle such flows independently, rather than as the entity of an update event. They only optimize the flow-level metrics for the flows involved in an update event. In this paper, we present an event-level abstraction of network update that groups flows of an update event and schedules them together to minimize the event completion time (ECT). We then study the scheduling problem of multiple update events for achieving high scheduling efficiency and preserving fairness. The designed least migration traffic first (LMTF) method schedules all update events in the FIFO order, but it avoids head-of-line blocking by randomly fine-tuning the queue order of some events. It can considerably reduce the update cost, the average, and tail ECTs of update events. In addition, we design a general parallel-LMTF (P-LMTF) method to guarantee fairness and further improve scheduling efficiency among update events. This improves the LMTF method by opportunistically updating multiple events simultaneously. The comprehensive evaluation results indicate that the average ECT of our approach is up to $10 \times $ faster than the flow-level scheduling method for network update events, and its tail ECT is up to $6 \times $ faster. Our P-LMTF method incurs a 75% reduction in the average ECT compared with FIFO when the network utilization exceeds 70%, and it achieves a 42% reduction in tail ECT.

School institutions in Batam have problems in how the events they conduct can support the school's operational effectiveness. The solution model for these problems is to build web-based event systems using the dynamic systems development (DSDM) method. This system aims to solve problems that occur in event scheduling at activities carried out at school institutions in Batam. The school representative used is a school in Batam. The method is done by learning and the feasibility of case studies, model functionality, design, and implementation. This implementation produces a system that is very helpful in processing data and reports during the event. Starting from the registration of the event, the continuity of the event, until the completion of the event, all can be stored in detail. Besides users, there are also managers and administrators. Managers have a role to manage the data entered by registrants, while administrators give access to each user account and maintain system security. This system is divided into 2 roles with one as a user and one acting as an administrator. Registrants to participate in this event act as users. The results of this study are that school operational effectiveness increases after implementing this web-based event system.

Discrete event simulation is the most important and essential part in network simulation. The node-oriented model of discrete event scheduling is a model that allocates computing resources as nodes and makes the discrete event simulation as a simulation task on nodes. In this article the reason of low performance in large-scale network simulation is analyzed, and an ideal node-oriented model of discrete event scheduling is presented and a resource-limited node-oriented model of discrete event scheduling by adding some restrictions on network resources is proposed. Then, the authors complete contrast experiments of the resource-limited node-oriented model of discrete event scheduling and NS2. Finally, packet loss in resource-limited node-oriented model of discrete event scheduling is examined. Also, NS2 is discussed in this article and the authors have proposed an improved method for the packet loss algorithm in a resource-limited node-oriented model of discrete event scheduling.

The large size and complexity of the modern digital hardware impose great challenges to design and validation. Hardware Description Languages (HDLs) and System-Level Description Languages (SLDLs) rely on sequential discrete event semantics. Parallel Discrete Event Simulation (PDES) has recently gained extensive attention for parallelizing these languages due to the ever-increasing complexity of embedded and cyber physical systems. However, PDES application has not yet reached acceptable maturity and pervasiveness for accelerating computer architecture problems. This is due to inherent complexity of hardware components that require using different advanced PDES techniques. In this paper, we look at the main problem from a radically different angle. First, we suppose there is only a single universal discrete event model of computation for simulation purely defined by distributed optimistic PDES, i.e., logical-process-based event scheduling worldview. Second, we construct a new parallel system-level simulation language called OSML for ESL. Third, we propose a unified Cloud-based CAD tool called Troodon to automatically parallelize existing hardware languages atop OSML. To the best of our knowledge, OSML is the first work on optimistic synchronization applied to hardware-specific SLDLs and hardware models at different levels of abstraction in ESL, which contain complex data structures by proposing a hybrid checkpointing scheme.

Brain-inspired computing aims to understand the cognitive mechanisms of a brain and apply them to advance various areas in computer science. Deep learning is an example to greatly improve the field of pattern recognition and classification by utilizing an artificial neural network (ANN). To exploit advanced mechanisms of a brain and thus make more great advances, researchers need a methodology that can simulate neural networks with higher computational capabilities such as advanced spiking neural networks (SNNs) with two-stage neurons and synaptic delays. However, existing SNN simulation methodologies are too slow and energy-inefficient due to their software-based simulation or hardware-based but time-driven execution mechanisms. In this paper, we present NeuroEngine, a fast and energy-efficient hardware-based system to efficiently simulate advanced SNNs. The key idea is to design an accelerator to enable event-driven simulations of the SNNs at a minimum cost. NeuroEngine achieves high speed and energy efficiency by carefully architecting its datapath and memory units to take the best advantage of the event-driven mechanism while satisfying all the important requirements to simulate our target SNNs. For high performance and energy efficiency, NeuroEngine applies a simpler datapath, multi-queue scheduler, and lazy update to minimize its neuron computation and event scheduling overhead. Then, we build an end-to-end simulation system by implementing a programming interface and a compilation toolchain for NeuroEngine hardware. Our evaluations show that NeuroEngine greatly improves the harmonic mean performance and energy efficiency by 4.30× and 2.60×, respectively, over the state-of-the-art time-driven simulator.

Modern smartphones are having a large number of applications for personal event scheduling. The main mode of input to those applications are text, voice-input from the user or E-Mail updates provided by certain service providers. For certain events, the details of the event would be available as hard copies of different forms like paper print outs, banners and various modes of advertisements. In such cases, the user needs to manually locate the vital information from the image then needs to update his event schedule. To minimize the load on the user, an intelligent personal scheduler has been developed which takes an image input, extracts necessary information from the image and updates the schedule of the user with appropriate alert settings. This paper discusses about an event planner based on image processing and uses Google Calendar for scheduling the events. The application makes use of Optical Character Recognition (OCR) algorithm for identification of text in the images which acts as an input to the scheduler and Google Calendar is used to update the event planner.

During the verification, the important strategy is to have better understanding of the delays and event scheduling. The verification team needs to focus much on the various coverage goals and the use of the various delays, threads and processes. In this scenario, the chapter discusses about the event scheduler, fork-join styles, loops used during the testbenches and other robust verification architecture and techniques.

Event-based social networks (EBSNs), are platforms that provide users with event scheduling and publishing. In recent years, the number of users and events on such platforms has increased dramatically, and interactions have become more complicated, which has made modeling heterogeneous networks more difficult. Moreover, the requirement of real-time matching between users and events becomes urgent because of the significant dynamics brought by the widespread use of mobile devices on such platforms. Therefore, we proposed a graph embedding based real-time social event matching model called GERM. We first model heterogeneous EBSNs into heterogeneous graphs, and use graph embedding technology to represent the nodes and their relationships in the graph which can more effectively reflect the hidden features of nodes and mine user preferences. Then a real-time social event matching algorithm is proposed, which matches users and events on the premise of fully considering user preferences and spatio-temporal characteristics, and recommends suitable events to users in real-time efficiently. We conducted experiments on the Meetup dataset to verify the effectiveness of our method by comparison with the mainstream algorithms. The results show that the proposed algorithm has a good improvement on the matching success rate, user satisfaction, and user waiting time.

Discrete-event modelling and simulation languages can be classified based on their world view: event scheduling, activity scanning, or process interaction. To study the semantics of these languages one may investigate the relationship between them, and in particular translate models between languages in different world views. A translation approach also lets one re-use all the simulation tooling available for the target language. We describe a translation of the classic process interaction language GPSS developed by Gordon in the early 1960s onto DEVS, a modular discrete-event modelling and simulation language with precise semantics developed by Zeigler in the late 1970s. We specify and implement a translation that produces, for each GPSS model, a behaviourally equivalent DEVS model. As GPSS has no formal semantics, there is no proof of equivalence. Rather, we describe the structure of the translation, starting from Gordon's informal description, centered around the main data structures called chains and the scanning algorithm. We build a working prototype for a representative subset of GPSS blocks found in most tools implementing the language. Finally, we exhaustively test the translation by comparing simulation results of the generated DEVS model with a those obtained by the GPSS World simulator. GPSS World is a popular GPSS variant. We also demonstrate our approach on a small but representative example from the manufacturing domain.

Classical Parallel Discrete Event Simulation (PDES) increases simulation performance by distributing the computational load of a simulation application across multiple computing platforms. A burden is placed on the developer to partition the simulation. This work focuses instead on identifying available parallelism within the simulation executive, thus removing the burden from the developer. Event scheduling and event execution have natural parallelism with each other; future events may be scheduled with the simulation executive while the currently executing event continues. Execution and scheduling are dealt with as multiple threads, taking advantage of multicore architectures. This paper identifies the available parallelism between event scheduling and execution, highlights points of contention between the two, provides an algorithm to take advantage of the parallelism and presents timing results demonstrating the performance benefits.

The wide adoption of smartphones and mobile applications has brought significant changes to not only how individuals behave in the real world, but also how groups of users interact with each other when organizing group events. Understanding how users make event decisions as a group and identifying the contributing factors can offer important insights for social group studies and more effective system and application design for group event scheduling. In this work, we have designed a new mobile application called OutWithFriendz, which enables users of our mobile app to organize group events, invite friends, suggest and vote on event time and venue. We have deployed OutWithFriendz at both Apple App Store and Google Play, and conducted a large-scale user study spanning over 500 users and 300 group events. Our analysis has revealed several important observations regarding group event planning process including the importance of user mobility, individual preferences, host preferences, and group voting process., accepted for publication in ACM IMWUT (Ubicomp) 2018

Electric utilities have historically treated power demand as an uncontrollable input, requiring generation and transmission resources to maintain the supply-demand balance. In recent years, demand response (DR) has emerged as a means to manage customer loads to balance the grid. This paper presents analytic solutions to enable utilities to optimize DR programs to serve as operational resources for the grid. We developed two sets of analytics. First, we developed a clustering-based method to accurately estimate the load curtailments expected from customers during DR events. Then, we used an option value-based optimal DR event scheduling method to compute a dynamic threshold value that the utility can use to make daily decisions for triggering DR events. In extensive tests, the proposed methods show superior performance over existing approaches. We implemented these analytics in the General Electric (GE) PowerOn™ Precision Demand Response Management System, which GE offered from 2011 to 2015.

International audience; Event-scheduling algorithms can compute in continuous time the next occurrence of points (as events) of a counting process based on their current conditional intensity. In particular event-scheduling algorithms can be adapted to perform the simulation of finite neuronal networks activity. These algorithms are based on Ogata's thinning strategy [17], which always needs to simulate the whole network to access the behaviour of one particular neuron of the network. On the other hand, for discrete time models, theoretical algorithms based on Kalikow decomposition can pick at random influencing neurons and perform a perfect simulation (meaning without approximations) of the behaviour of one given neuron embedded in an infinite network, at every time step. These algorithms are currently not computationally tractable in continuous time. To solve this problem, an event-scheduling algorithm with Kalikow decomposition is proposed here for the sequential simulation of point processes neuronal models satisfying this decomposition. This new algorithm is applied to infinite neuronal networks whose finite time simulation is a prerequisite to realistic brain modeling.

This paper describes Ardestan, a visual programming language (VPL) for Arduino currently under development. The language is designed with art and design students in mind as novice programmers. Although multitasking and event scheduling are essential for interactive prototypes, novices often face difficulty in implementing these features. By borrowing the language design from Pure Data, a VPL for interactive music, Ardestan facilitates the implementation of multitasking and event scheduling, while generating C++ code for a standalone Arduino system. Such features would be beneficial to support prototyping activity by art and design students in undergraduate interaction design courses.

International audience; The growing use of models for separating concerns in complex systems has lead to a proliferation of model composition operators. These composition operators have traditionally been defined from scratch following various approaches differing in formality, level of detail, chosen paradigm, and styles. Due to the lack of proper foundations for defining model composition (concepts, abstractions, or frameworks), it is difficult to compare or reuse composition operators. In this paper , we stipulate the existence of a unifying framework that reduces all structural composition operators to structural merging, and all composition operators acting on discrete behaviors to event scheduling. We provide convincing evidence of this hypothesis by discussing how structural and behavioral homogeneous model composition operators (i.e., weavers) can be mapped onto this framework. Based on this discussion, we propose a conceptual model of the framework, and identify a set of research challenges, which, if addressed, lead to the realization of this framework to support rigorous and efficient engineering of model composition operators for homogeneous and eventually heterogeneous modeling languages.

This chapter describes a new approach to the activity scanning worldview of discrete-event simulation. With this new approach it becomes possible to realize activity scanning simulations with a parallel computer while resolving simultaneous events in a repeatable manner. The motivation for this approach is the two-phase simulation procedure intrinsic to cellular automata, coupled difference equations, and the discrete-event system specification. This new approach is introduced via an intuitively appealing construction of a new simulation procedure for activity scanning models, and this introduction is followed by an embedding of the new approach into the discrete-event system specification. In doing so, we illustrate a practical approach to distributed simulation and demonstrate how the proposed approach can be applied to the event scheduling and process-oriented worldviews.

Web applications are gradually shifting toward resource-constrained mobile devices. As a result, the Web runtime system must simultaneously address two challenges: responsiveness and energy-efficiency. Conventional Web runtime systems fall short due to their reactive nature: they react to a user event only after it is triggered. The reactive strategy leads to local optimizations that schedule event executions one at a time, missing global optimization opportunities. This paper proposes Proactive Event Scheduling (PES). The key idea of PES is to proactively anticipate future events and thereby globally coordinate scheduling decisions across events. Specifically, PES predicts events that are likely to happen in the near future using a combination of statistical inference and application code analysis. PES then speculatively executes future events ahead of time in a way that satisfies the QoS constraints of all the events while minimizing the global energy consumption. Fundamentally, PES unlocks more optimization opportunities by enlarging the scheduling window, which enables coordination across both outstanding events and predicted events. Hardware measurements show that PES reduces the QoS violation and energy consumption by 61.2% and 26.5%, respectively, over the Android default Interactive CPU governor. It also reduces the QoS violation and energy consumption by 63.1% and 17.9%, respectively, compared to EBS, a state-of-the-art reactive scheduler., Comment: International Symposium on Computer Architecture (ISCA). 2019

This paper presents a new method for autonomous computer-aided process planning (A-CAPP) in an intelligent manufacturing system, in which the related input and output of the system are discussed on the basis of comparative analysis of traditional CAPP. The crucial functional components of the A-CAPP system, such as event scheduling management, manufacturing process planning, operation process/step planning, numerical control machining program planning, process simulation and evaluation, are introduced; and the methods of process knowledge management, including process feature knowledge, manufacturing resource knowledge and process method knowledge, are discussed as well. A-CAPP applied for intelligent manufacturing system can effectively support the production line reconstruction dynamically; shorten the time of production line configuration modification in accordance with customers’ requirement change or market requirement fluctuation, and furthermore to balance the production lines load.

Inter-organizational collaborative simulation requires covering the knowledge of agent, flow and process to qualifiedly represent the supply chain network operation. This paper proposes a multi-methodological collaborative simulation framework for inter-organizational supply chain networks. This framework integrates the agent-based, flow-centric and process-oriented methodologies. This framework establishes a collaborative knowledge representation approach for simulation modeling. In the approach, a multi-agent system is adopted to represent the inter-organizational structure of a supply chain network; the three flows of material, information and time are enabled to represent the operational mechanisms; and the processes are used to represent the micro behaviors of agents. This approach integrates the knowledge of agent and process with that of flow and solves the problems regarding the integration of the agent-based, flow-centric and process-oriented methodologies. To implement the inter-organizational collaborative simulation, a collaborative framework is proposed. This framework integrates multiple simulation formalisms, such as time series increments, event scheduling, policy control, process interaction and activity scanning; it promotes the unification of the three different methodologies. A case of a five-level manufacturing supply chain network is studied using the proposed framework. The findings indicate that the proposed framework is particularly qualified in the knowledge representation of a supply chain network and is effective in implementing the inter-organizational collaborative simulation in a decentralized manner; in addition, it is well contributive to collaborative decision making through KPI analysis.

This paper outlines the fundamentals of a consistent theory of numerical modelling of a population system under study. The focus is on the systematic work to construct an executable simulation model. There are six fundamental choices of model category and model constituents to make. These choices have a profound impact on how the model is structured, what can be studied, possible introduction of bias, lucidity and comprehensibility, size, expandability, performance of the model, required information about the system studied and its range of validity. The first choice concerns a discrete versus a continuous description of the population system under study—a choice that leads to different model categories. The second choice is the model representation (based on agents, entities, compartments or situations) used to describe the properties and behaviours of the objects in the studied population. Third, incomplete information about structure, transitions, signals, initial conditions or parameter values in the system under study must be addressed by alternative structures and statistical means. Fourth, the purpose of the study must be explicitly formulated in terms of the quantities used in the model. Fifth, irrespective of the choice of representation, there are three possible types of time handling: Event Scheduling, Time Slicing or Micro Time Slicing. Sixth, start and termination criteria for the simulation must be stated. The termination can be at a fixed end time or determined by a logical condition. Population models can thereby be classified within a unified framework, and population models of one type can be translated into another type in a consistent way. Understanding the pros and cons for different choices of model category, representation, time handling etc. will help the modeller to select the most appropriate type of model for a given purpose and population system under study. By understanding the rules for consistent population modelling, an appropriate model can be created in a systematic way and a number of pitfalls can be avoided.

In recent years, when carrying out educational activities, modern information technology and Internet technology have been integrated with each other to form a variety of educational information platforms, which have gradually replaced the traditional education and teaching methods. Using Internet thinking to improve the application level of education informatization is helpful to improve the quality of education and is of great significance to comprehensively promote the quality education of students. English is an important subject in student learning activities. English learning can improve the comprehensive ability of students, promote the healthy development of students, and enable students to better meet the needs of society. In order to improve learning efficiency, optimize the learning platform, and meet the needs of users, it is imperative to establish a 5G English education information platform. 80% of people in the current society are expecting the arrival of 5G platforms. Considering that in a high-density scenario, the number of terminals is large and the traffic is intensive. If the event scheduling method is still used to process data packets, there will be problems of too many event generations and too long processing time. Computing packet processing architecture. This architecture divides the time axis into multiple time slots. Based on the length and generation time of each data packet, the source terminal number of each data packet is mapped to the corresponding time slot position. By calculating the effective signals and interference in each time slot The strength of the signal is used to obtain performance indicators such as system capacity and throughput.

This paper focuses on the concept and description specification of Real-Time Discrete Event System(RT-DEVS) simulation, and proposes a frame-based real-time event scheduling strategy and multi-thread real-time event scheduling algorithm in RT-DEVS simulation. Based on the JMASE platform discrete event scheduling engine, the real-time event scheduling module of discrete event system simulation is designed to increase the function of real-time event scheduling service, and the implementation method and steps of real-time synchronization behavior are described in detail. Finally, the function and real-time performance of real-time event scheduling module are tested through the simulation experiment. The results show that the RT-DEVS simulation event scheduling algorithm implemented in this paper can meet the requirements of real-time simulation.

Conventional machine learning systems are trained-once-used-everywhere. The Achilles heel of such systems is that in its highly specialized scope of tasks, the system's knowledge learnt with one single and static training dataset can hardly keep up with the demand from a changing and dynamic environment. Using a calendar system as an example, it may be equipped with initial knowledge of a user's preferences. But as a user encounters different types of conflicting events when scheduling her daily activities, the calendar system must continuously learn to refine its scheduling knowledge to adapt to the user's dynamic and changing circumstances and preferences. It turns out that none of the existing calendar systems is capable of detecting any type of conflicts, say a temporal inconsistency between two events, not to mention how to handle those conflicts. Inspired by inconsistency-induced learning, in this paper, we propose a calendar system that can continuously improve its scheduling performance through overcoming temporal inconsistencies. With Interval Temporal Logic, we can identity and detect different types of temporal inconsistencies. We propose distance-based heuristics for solving temporal inconsistencies between events. Knowledge will continuously be refined so as to incrementally improve the performance of event scheduling. We also compare our approach with related work.

Android GUI testing is an important research field to maintain software quality of Android apps. Although many GUI testing schemes have been investigated in the past, the discussion of exploration event scheduling has not been investigated comprehensively for the event replay process. Based on the component priority relationship, this study proposes a novel exploration event scheduling scheme called CPR to reduce the number of the testing events in the replay process. Compared with the breadth-first traversal scheme, the proposed CPR scheme can reduce up to 62% of testing events for achieving the same component coverage, and up to 69% of testing events for layout traversal. Compared with the depth-first traversal scheme, CPR can reduce up to 15% of testing events for achieving the same component coverage, and up to 42% of testing events for layout traversal. With respect to the testing time, CPR can achieve the best performance for most of the AUTs in the empirical study. The results of the empirical experiments show that the proposed CPR scheduling scheme can have the benefits in improving the testing performance.

[EN] We consider a special workflow scheduling problem in a hybrid-cloud-based workflow management system in which tasks are linearly dependent, compute-intensive, stochastic, deadline-constrained and executed on elastic and distributed cloud resources. This kind of problems closely resemble many real-time and workflow-based applications. Three optimization objectives are explored: number, usage time and utilization of rented VMs. An iterated heuristic framework is presented to schedule jobs event by event which mainly consists of job collecting and event scheduling. Two job collecting strategies are proposed and two timetabling methods are developed. The proposed methods are calibrated through detailed designs of experiments and sound statistical techniques. With the calibrated components and parameters, the proposed algorithm is compared to existing methods for related problems. Experimental results show that the proposal is robust and effective for the problems under study., This work is sponsored by the National Natural Science Foundations of China (Nos. 71401079, 61572127, 61472192), the National Key Research and Development Program of China (No. 2017YFB1400801) and the Collaborative Innovation Center of Wireless Communications Technology. Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project "SCHEYARD-Optimization of Scheduling Problems in Container Yards" (No. DPI2015-65895-R) financed by FEDER funds.

This paper presents a report from Global Game Jam site in Hong Kong. Global Game Jam Hong Kong has been among the largest jam sites of this international event for several years and grown steadily since its initiation in 2013. This report introduces the practical arrangements and participation statistics from the past 6 years focusing on 2018 and reflects on industry collaboration and event scheduling.

A major challenge for social event organizers (e.g., event planning and marketing companies, venues) is attracting the maximum number of participants, since it has great impact on the success of the event, and, consequently, the expected gains (e.g., revenue, artist/brand publicity). In this paper, we introduce the Social Event Scheduling (SES) problem, which schedules a set of social events considering user preferences and behavior, events' spatiotemporal conflicts, and competing vents, in order to maximize the overall number of attendees. We show that SES is strongly NP-hard, even in highly restricted instances. To cope with the hardness of the SES problem we design a greedy approximation algorithm. Finally, we evaluate our method experimentally using a dataset from the Meetup event-based social network., This paper appears in 34th IEEE International Conference on Data Engineering (ICDE 2018)

This book promotes the use of mathematical optimization and operations research methods in rail transportation. The editors assembled thirteen contributions from leading scholars to present a unified voice, standardize terminology, and assess the state-of-the-art. There are three main clusters of articles, corresponding to the classical stages of the planning process: strategic, tactical, and operational. These three clusters are further subdivided into five parts which correspond to the main phases of the railway network planning process: network assessment, capacity planning, timetabling, resource planning, and operational planning. Individual chapters cover: Simulation Capacity Assessment Network Design Train Routing Robust Timetabling Event Scheduling Track Allocation Blocking Shunting Rolling Stock Crew Scheduling Dispatching Delay Propagation

The rapid development of Internet of Things (IoT) attracts growing attention from both industry and academia. IoT seamlessly connects the real world and cyberspace via various business process applications hosted on the IoT devices, especially on smart sensors. Due to the discrete distribution and complex sensing environment, multiple coordination patterns exist in the heterogeneous sensor networks, making modeling and analysis particularly difficult. In addition, massive sensing events need to be routed, forwarded and processed in the distributed execution environment. Therefore, the corresponding sensing event scheduling algorithm is highly desired. In this paper, we propose a novel modeling methodology and optimization algorithm for collaborative business process towards IoT applications. We initially extend the traditional Petri nets with sensing event factor. Then, the formal modeling specification is investigated and the existing coordination patterns, including event unicasting pattern, event broadcasting pattern, and service collaboration pattern, are defined. Next, we propose an optimization algorithm based on Dynamic Priority First Response (DPFR) to solve the problem of sensing event scheduling. Finally, the approach presented in this paper has been validated to be valid and implemented through an actual development system.

This chapter describes how a user can write control functions for a simulation. The chapter describes how the control functions have to be written as Python files and specified in the simulation parameter spreadsheet. Every control function will have an interface to the simulation in terms of inputs and outputs, and this interface is described by a spreadsheet called a descriptor. Besides inputs and outputs, every control function can use certain types of variables that perform special actions. The chapter describes the importance of each type of control variable and how they are implemented in the simulator. The chapter describes how control functions are scheduled by the simulator using time events, and with an example, it is described how the simulator ensures that the control functions execute at the desired time instant. A simple example has been provided to describe how control functions can be interfaced with the simulation and also with each other.

LHCb is planning major changes for its data processing and analysis workflows for LHC Run 3. Removing the hardware trigger, a software only trigger at 30 MHz will reconstruct events using final alignment and calibration information provided during the triggering phase. These changes pose a major strain on the online software framework which needs to improve significantly. The foreseen changes in the area of the core framework include a re-design of the event scheduling, introduction of concurrent processing, optimizations in processor cache accesses and code vectorization. Furthermore changes in the areas of event model, conditions data and detector description are foreseen. The changes in the data processing workflow will allow an unprecedented amount of signal events to be selected and therefore increase the load on the experiments simulation needs. Several areas of improvement for fast simulation are currently being investigated together with improvements needed in the area of distributed computing. Finally the amount of data stored needs to be reflected in the analysis computing model where individual user analysis on distributed computing resources will become inefficient. This contribution will give an overview of the status of those activities and future plans in the different areas from the perspective of the LHCb computing project.

Today, numerous research support the growing scheduling problems that exist globally in competitive businesses. Scheduling needs to become efficient in order to remain relevant against competitors. Simulations need to provide results in short periods of time so that adjustments can be made and unnecessary costs can be avoided. Scheduling problems have become larger in size and greater in complexity given the rising product variations and increase in variety for manufacturing equipment. Hence, there is a practical need for genetic algorithms solving scheduling problems to be fast and versatile. This thesis introduces an event-scheduling/time-advance algorithm for the decoder to reduce the load on the genetic algorithm with a smaller global search space. Consequently, convergence can be reached sooner and larger problems can be tackled easily. The structure of this heuristic algorithm allows metrics to be easily implemented in order to give the user performance measures on the scheduling problem. NSERC, Integrated Production and Manpower Scheduling and MERSEN Canada Toronto Inc.

Knowledge of user movement in mobile environments paves the way for intelligent resource allocation and event scheduling for a variety of applications. Existing schemes for estimating user mobility are limited in their scope as they rely on repetitive patterns of user movement. Such patterns neither exist not easy to recognize in soft-real time, in open environments such as parks, malls, or streets. We propose a novel scheme for Real-time Mobility and Orientation Estimation for Mobile Environments (MOEME). MOEME employs the concept of temporal distances and uses logistic regression to make real time estimations about user movement. MOEME is also used to make predictions about the absolute orientation of users. MOEME relies only on opportunistic message exchange and is fully distributed, scalable, and requires neither a central infrastructure nor Global Positioning System. MOEME has been tested on real world and synthetic mobility traces—makes predictions about direction and count of users with up to 90% accuracy, enhances successful video downloads in shared environments.

Many organizations use scheduling software to assist in the organizing of events. One popular example is Microsoft Outlook. Such software typically requires event participants to vote on time slot allocations or to negotiate with one another on time slot allocations, until event attendees are appropriately satisfied. The process of scheduling events into time slots can be automated by requiring users to apply soft constraints on their availability for different time slots. In a soft constraint system, users assign values to time-slots, which denote the relative importance of each event. The problem can then be formalized as an optimization problem where the objective is to increase the efficiency in the scheduling of events versus availability in participants' schedules. Since knowledge of all events is not known initially, the optimal placement of events can change over time. Therefore, there is a need for on-demand re-optimization of schedules. This can be a timely process as the scheduler has to account for the best placement of all events across all users' time slots. One method which deals with problems that require a high volume of processing is to split and distribute the problem across machines. However, since the optimal solution depends on a maximization of all events across all time-slots, an event may need to be ‘aware’ of other events' placements, as this can affect which slot is best. This paper presents an efficient way of distributing users, and by extension, events to distribute work while keeping relevant events together.

Cyber-Physical System is a novel intelligent system integrating computing and physical procedures, which combines computing, communication and control technologies. Event is the key element to link the cyber world and the physical world. However, the time stamp of the event is imprecise in practical applications because of data loss, mismatching of the time granularities of events from different monitoring systems, or asynchronism of events in the distributed systems and so on. We improve the Low Water Mark algorithm originally designed for the event scheduling with deterministic timestamps by leveraging tailored pruning techniques considering the time uncertainty. Specifically a scheduling algorithm of events with uncertain timestamps is proposed, which can support effective scheduling of reading events and writing events in CPS. The experiments verify that the scheduling algorithm is more accurate, which guarantees providing correct feedback of the event sequences to CPS.

The set of events available for execution in a Parallel Discrete Event Simulation (PDES) are known as the pending event set. In a Time Warp synchronized simulation engine, these pending events are scheduled for execution in an aggressive manner that does not strictly enforce the causal relations between events. One of the key principles of Time Warp is that this relaxed causality will result in the processing of events in a manner that implicitly satisfies their causal order without paying the overhead costs of a strict enforcement of their causal order. On a shared memory platform the event scheduler generally attempts to schedule all available events in their Least TimeStamp First (LTSF) order to facilitate event processing in their causal order. By following an LTSF scheduling policy, a Time Warp scheduler can generally process events so that: (i) the critical path of the event timestamps is scheduled as early as possible, and (ii) causal violations occur infrequently. While this works effectively to minimize rollback (triggered by causal violations), as the number of parallel threads increases, the contention to the shared data structures holding the pending events can have significant negative impacts on overall event processing throughput. This work examines the application of profile data taken from Discrete-Event Simulation (DES) models to drive the simulation kernel optimization process. In particular, we take profile data about events in the schedule pool from three DES models to derive alternate scheduling possibilities in a Time Warp simulation kernel. Profile data from the studied DES models suggests that in many cases each Logical Process (LP) in a simulation will have multiple events that can be dequeued and executed as a set. In this work, we review the profile data and implement group event scheduling strategies based on this profile data. Experimental results show that event group scheduling can help alleviate contention and improve performance. However, the size of the event groups matters, small groupings can improve performance, larger groupings can trigger more frequent causal violations and actually slow the parallel simulation.