Exact admission-control for integrated aperiodic and periodic tasks

Admission controllers are used to prevent overload in systems with dynamically arriving tasks. Typically, these admission controllers are based on suÆcient (but not necessary) capacity bounds in order to maintain a low computational complexity. In this paper we present how exact admission-control for aperiodic tasks can be eÆciently obtained. Our rst result is an admission controller for purely aperiodic task sets where the test has the same runtime complexity as utilization-based tests. Our second result is an extension of the previous controller for a baseload of periodic tasks. The runtime complexity of this test is lower than for any known exact admission-controller. In addition to presenting our main algorithm and evaluating its performance, we also discuss some general issues concerning admission controllers and their implementation.

Online intra-task device scheduling for hard real-time systems

A large part of power dissipation in a system is generated by I/O devices. Increasingly these devices provide power saving mechanisms, inter alia to enhance battery life. While I/O device scheduling has been studied in the past for realtime systems, the use of energy resources by these scheduling algorithms may be improved. These approaches are crafted considering a very large overhead of device transitions. Technology enhancements have allowed the hardware vendors to reduce the device transition overhead and energy consumption. We propose an intra-task device scheduling algorithm for real time systems that allows to shut-down devices while ensuring system schedulability. Our results show an energy gain of up to 90% when compared to the techniques proposed in the state-of-the-art.

Minimizing total tardiness in a stochastic single machine scheduling problem using approximate dynamic programming

This paper addresses the non-preemptive single machine scheduling problem to minimize total tardiness. We are interested in the online version of this problem, where orders arrive at the system at random times. Jobs have to be scheduled without knowledge of what jobs will come afterwards. The processing times and the due dates become known when the order is placed. The order release date occurs only at the beginning of periodic intervals. A customized approximate dynamic programming method is introduced for this problem. The authors also present numerical experiments that assess the reliability of the new approach and show that it performs better than a myopic policy.

A flexible online apparatus for projectile launch experiments

In order to provide a more flexible learning environment in physics, the developed projectile launch apparatus enables students to determine the acceleration of gravity and the dependence of a set of parameters in the projectile movement. This apparatus is remotely operated and accessed via web, by first scheduling an access time slot. This machine has a number of configuration parameters that support different learning scenarios with different complexities.

Online Admission of Parallel Real-Time Tasks

6th Real-Time Scheduling Open Problems Seminar (RTSOPS 2015), Lund, Sweden.

Semi-Partitioned Scheduling of Fork-Join Tasks using Work-Stealing

13th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (EUC 2015). 21 to 23, Oct, 2015, Session W1-A: Multiprocessing and Multicore Architectures. Porto, Portugal.

Optimal scheduling with uncertainty in the numerical data on the basis of a stability analysis

Scheduling, job shop, uncertainty, mixed (disjunctive) graph, stability analysis

Perceptions of some teachers about the similarities and/or differences in teaching online versus in a classroom environment /

The introduction of computer and communications technology, and particularly the internet, into education has opened up some new possibilities for teaching and learning. Courses designed and delivered in an online environment offer the possibility of highly interactive and individually focussed teaching and learning experiences. However, online courses also present new challenges for both teachers and students. A qualitative study was conducted to explore teachers' perceptions about the similarities and differences in teaching in the online and face-to-face (F2F) environments. Focus group discussions were held with 5 teachers; 2 teachers were interviewed in depth. The participants, 3 female and 2 male, were full-time teachers from a large College of Applied Arts & Technology in southern Ontario. Each of them had over 10 years of F2F teaching experience and each had been involved in the development and teaching of at least one online course. i - -; The study focussed on how teaching in the online environment compares with teaching in the F2F environment, what roles teachers and students adopt in each setting, what learning communities mean online and F2F and how they are developed, and how institutional policies, procedures, and infrastructure affect teaching and learning F2F and online. This study was emic in nature, that is the teachers' words determine the themes identified throughout the study. The factors identified as affecting teaching in an online environment included teacher issues such as course design, motivation to teach online, teaching style, role, characteristics or skills, and strategies. Student issues as perceived by the teachers included learning styles, role, and characteristics or skills. As well, technology issues such as a reliable infrastructure, clear role and responsibilities for maintaining the infrastructure, support, and multimedia capability affected teaching online. Finally, administrative policies and procedures, including teacher selection and training, registration and scheduling procedures, intellectual property and workload policies, and the development and communication of a comprehensive strategic plan were found to impact on teaching online. The teachers shared some of the benefits they perceived about teaching online as well as some of the challenges they had faced and challenges they perceived students had faced online. Overall, the teachers feh that there were more similarities than differences in teaching between the two environments, with the main differences being the change from F2F verbal interactions involving body language to online written interactions without body language cues, and the fundamental reliance on technology in the online environment. These findings support previous research in online teaching and learning, and add teachers' perspectives on the factors that stay the same and the factors that change when moving from a F2F environment to an online environment.

A novel approach for distributed application scheduling based on prediction of communication events

The evolution of commodity computing lead to the possibility of efficient usage of interconnected machines to solve computationally-intensive tasks, which were previously solvable only by using expensive supercomputers. This, however, required new methods for process scheduling and distribution, considering the network latency, communication cost, heterogeneous environments and distributed computing constraints. An efficient distribution of processes over such environments requires an adequate scheduling strategy, as the cost of inefficient process allocation is unacceptably high. Therefore, a knowledge and prediction of application behavior is essential to perform effective scheduling. In this paper, we overview the evolution of scheduling approaches, focusing on distributed environments. We also evaluate the current approaches for process behavior extraction and prediction, aiming at selecting an adequate technique for online prediction of application execution. Based on this evaluation, we propose a novel model for application behavior prediction, considering chaotic properties of such behavior and the automatic detection of critical execution points. The proposed model is applied and evaluated for process scheduling in cluster and grid computing environments. The obtained results demonstrate that prediction of the process behavior is essential for efficient scheduling in large-scale and heterogeneous distributed environments, outperforming conventional scheduling policies by a factor of 10, and even more in some cases. Furthermore, the proposed approach proves to be efficient for online predictions due to its low computational cost and good precision. (C) 2009 Elsevier B.V. All rights reserved.

Online and Hybrid Course Prioritization Guide: 10 Strategies for Promoting Enrollment Growth and Student Success

Institutions of all shapes and sizes are investing significant sums to expand their portfolio of online and hybrid courses without specific institutional priorities in mind, often resulting in a mix of arbitrary, sub-scale offerings. This creates an unsustainably expensive disconnect between the institution’s online portfolio (largely steered by unit-level interests and capacity) and its overarching interest in using technology to increase access, improve student success, and grow revenue. This guide is designed to help institutional leaders prioritize scarce resources devoted to online and hybrid course development toward the most promising available opportunities. By targeting specific curricular "gaps," institutions can improve retention, reduce time-to-degree, regain or expand their share of currently enrolled student credit hours, or even attract new students to existing programs.

Three time-based scale formulations for the two-stage lot sizing and scheduling in process industries

In this paper, we propose three novel mathematical models for the two-stage lot-sizing and scheduling problems present in many process industries. The problem shares a continuous or quasi-continuous production feature upstream and a discrete manufacturing feature downstream, which must be synchronized. Different time-based scale representations are discussed. The first formulation encompasses a discrete-time representation. The second one is a hybrid continuous-discrete model. The last formulation is based on a continuous-time model representation. Computational tests with state-of-the-art MIP solver show that the discrete-time representation provides better feasible solutions in short running time. On the other hand, the hybrid model achieves better solutions for longer computational times and was able to prove optimality more often. The continuous-type model is the most flexible of the three for incorporating additional operational requirements, at a cost of having the worst computational performance. Journal of the Operational Research Society (2012) 63, 1613-1630. doi:10.1057/jors.2011.159 published online 7 March 2012

An MILP-based heuristic for staff scheduling problems with acceptance levels

We present a real-world staff-assignment problem that was reported to us by a provider of an online workforce scheduling software. The problem consists of assigning employees to work shifts subject to a large variety of requirements related to work laws, work shift compatibility, workload balancing, and personal preferences of employees. A target value is given for each requirement, and all possible deviations from these values are associated with acceptance levels. The objective is to minimize the total number of deviations in ascending order of the acceptance levels. We present an exact lexicographic goal programming MILP formulation and an MILP-based heuristic. The heuristic consists of two phases: in the first phase a feasible schedule is built and in the second phase parts of the schedule are iteratively re-optimized by applying an exact MILP model. A major advantage of such MILP-based approaches is the flexibility to account for additional constraints or modified planning objectives, which is important as the requirements may vary depending on the company or planning period. The applicability of the heuristic is demonstrated for a test set derived from real-world data. Our computational results indicate that the heuristic is able to devise optimal solutions to non-trivial problem instances, and outperforms the exact lexicographic goal programming formulation on medium- and large-sized problem instances.

3D TerraFly: Quality of service management for online interactive 3D geographic information system

3D geographic information system (GIS) is data and computation intensive in nature. Internet users are usually equipped with low-end personal computers and network connections of limited bandwidth. Data reduction and performance optimization techniques are of critical importance in quality of service (QoS) management for online 3D GIS. In this research, QoS management issues regarding distributed 3D GIS presentation were studied to develop 3D TerraFly, an interactive 3D GIS that supports high quality online terrain visualization and navigation. ^ To tackle the QoS management challenges, multi-resolution rendering model, adaptive level of detail (LOD) control and mesh simplification algorithms were proposed to effectively reduce the terrain model complexity. The rendering model is adaptively decomposed into sub-regions of up-to-three detail levels according to viewing distance and other dynamic quality measurements. The mesh simplification algorithm was designed as a hybrid algorithm that combines edge straightening and quad-tree compression to reduce the mesh complexity by removing geometrically redundant vertices. The main advantage of this mesh simplification algorithm is that grid mesh can be directly processed in parallel without triangulation overhead. Algorithms facilitating remote accessing and distributed processing of volumetric GIS data, such as data replication, directory service, request scheduling, predictive data retrieving and caching were also proposed. ^ A prototype of the proposed 3D TerraFly implemented in this research demonstrates the effectiveness of our proposed QoS management framework in handling interactive online 3D GIS. The system implementation details and future directions of this research are also addressed in this thesis. ^