Using Machine Learning for Handover Optimization in Vehicular Fog Computing

Smart mobility management would be an important prerequisite for future fog computing systems. In this research, we propose a learning-based handover optimization for the Internet of Vehicles that would assist the smooth transition of device connections and offloaded tasks between fog nodes. To accomplish this, we make use of machine learning algorithms to learn from vehicle interactions with fog nodes. Our approach uses a three-layer feed-forward neural network to predict the correct fog node at a given location and time with 99.2 % accuracy on a test set. We also implement a dual stacked recurrent neural network (RNN) with long short-term memory (LSTM) cells capable of learning the latency, or cost, associated with these service requests. We create a simulation in JAMScript using a dataset of real-world vehicle movements to create a dataset to train these networks. We further propose the use of this predictive system in a smarter request routing mechanism to minimize the service interruption during handovers between fog nodes and to anticipate areas of low coverage through a series of experiments and test the models' performance on a test set

Energy-Efficient Thermal-Aware Scheduling for RT Tasks Using TCPN

This work leverages TCPNs to design an energy-efficient, thermal-aware real-time scheduler for a multiprocessor system that normally runs in a low state energy at maximum system utilization but its capable of increasing the clock frequency to serve aperiodic tasks, optimizing energy, and honoring temporal and thermal constraints. An off-line stage computes the minimum frequency required to run the periodic tasks at maximum CPU utilization, the proportion of each task''s job to be run on each CPU, the maximum clock frequency that keeps temperature under a limit, and the available cycles (slack) with respect to the system with minimum frequency. Then, a Zero-Laxity online scheduler dispatches the periodic tasks according to the offline calculation. Upon the arrival of aperiodic tasks, it increases clock frequency in such a way that all periodic and aperiodic tasks are properly executed. Thermal and temporal requirements are always guaranteed, and energy consumption is minimized

Energy-efficient thermal-aware multiprocessor scheduling for real-time tasks using TCPNs

We present an energy-effcient thermal-aware real-time global scheduler for a set of hard real-time (HRT) tasks running on a multiprocessor system. This global scheduler fulfills the thermal and temporal constraints by handling two independent variables, the task allocation time and the selection of clock frequency. To achieve its goal, the proposed scheduler is split into two stages. An off-line stage, based on a deadline partitioning scheme, computes the cycles that the HRT tasks must run per deadline interval at the minimum clock frequency to save energy while honoring the temporal and thermal constraints, and computes the maximum frequency at which the system can run below the maximum temperature. Then, an on-line, event-driven stage performs global task allocation applying a Fixed-Priority Zero-Laxity policy, reducing the overhead of quantum-based or interval-based global schedulers. The on-line stage embodies an adaptive scheduler that accepts or rejects soft RT aperiodic tasks throttling CPU frequency to the upper lowest available one to minimize power consumption while meeting time and thermal constraints. This approach leverages the best of two worlds: the off-line stage computes an ideal discrete HRT multiprocessor schedule, while the on-line stage manage soft real-time aperiodic tasks with minimum power consumption and maximum CPU utilization

A flexible framework for real-time thermal-aware schedulers using timed continuous petri nets

This work presents TCPN-ThermalSim, a software tool for testing Real-Time Thermal-Aware Schedulers1. This framework consists of four main modules. The first one helps the user to define the problem: Task set with periods, deadlines and worst case execution times in CPU cycles, along with the CPU characteristics, temperature and energy consumption. The second module is the Kernel simulation, which builds up a global simulation model according to the configuration module. In the third module, the user selects the scheduler algorithm. Finally the last module allows the execution of the simulation and present the results. The framework encompasses two modes: Manual and automatic. In manual mode the simulator uses the task set data provided in the first section. In automatic mode the task set is generated by parameterizing the integrated UUniFast algorithm

Real time scheduler for multiprocessor systems based on continuous control using timed continuous petri nets

This work exploits Timed Continuous Petri Nets (TCPN) to design and test a novel energy-efficient thermal-aware real-time global scheduler for a hard real-time (HRT) task set running on a multiprocessor system. The TCPN model encompasses both the system and task set, including thermal features. In previous work we calculated the share of each task that must be executed per time interval by solving off-line an Integer Programming Problem Problem (ILP). A subsequent on-line stage allocated jobs to processors. We now perform the allocation off-line too, including an allocation controller and an execution controller in the on-line stage. This adds robustness by ensuring that actual task allocation and execution honor the safe schedule provided off-line. Last, the on-line controllers allow the design of an improved soft RT aperiodic task manager. Also, ee experimentally prove that our scheduler yields fewer context switches and migrations on the HRT task set than RUN, a reference algorith

Thermal-aware real-time scheduling using timed continuous Petri Nets

We present a thermal-aware, hard real-time (HRT) global scheduler for a multiprocessor system designed upon three novel techinques. First, we present a modeling methodology based on Timed Continuous Petri nets (TCPN) that yields a complete state variable model, including job arrivals, CPU usage, power, and thermal behavior. The model is accurate and avoids the calibration stage of RC thermal models. Second, based on this model, a linear programming problem (LPP) determines the existence of a feasible HRT thermal-aware schedule. Last, a sliding-mode controller and an online discretization algorithm implement the global HRT scheduler, which is capable of managing thermal constraints, context switching, migrations, and disturbances

Third-order nonlinear optical response and photoluminescence characterization of tellurite glasses with different alkali metal oxides as network modifiers

Studies of the third-order nonlinear optical properties in TeO(2)-MO-R(2)O glasses with three different alkali metal oxides R(2)O (R - Li, Na, K) as network modifiers and two network intermediates MO (M - Zn, Mg) are reported. The influence of such modifiers and intermediates on the nonlinear optical properties of these glasses was investigated using the standard Z-scan and the thermally managed Z-scan techniques under femtosecond pulse excitation at 800 nm. For different modifiers and intermediates, the nonlinear refraction indices n(2) of these glasses varied in the range 1.31-2.81 (x10(-15) cm(2)/W). It was found that n(2) increases as the ionic radius of both network modifiers and intermediates decreases. Furthermore, the measurements show that the contribution from thermo-optical effects to the nonlinear refraction index is negligible for all of the studied glass compositions. In addition, the effect of modifiers and intermediates in the formation of localized states in the vicinity of the optical bandgap was also studied through photoluminescence experiments. These experiments revealed the presence of two emission bands (red and blue) originating from these localized states that can be populated after optical excitation and subsequent relaxation

Enhancing Optical Up-Conversion Through Electrodynamic Coupling with Ancillary Chromophores

In lanthanide-based optical materials, control over the relevant operating characteristics–for example transmission wavelength, phase and quantum efficiency–is generally achieved through the modification of parameters such as dopant/host combination, chromophore concentration and lattice structure. An alternative avenue for the control of optical response is through the introduction of secondary, codoped chromophores. Here, such secondary centers act as mediators, commonly bridging the transfer of energy between primary absorbers of externally sourced optical input and other sites of frequency-converted emission. Utilizing theoretical models based on experimentally feasible, three-dimensional crystal lattice structures; a fully quantized theoretical framework provides insights into the locally modified mechanisms that can be implemented within such systems. This leads to a discussion of how such effects might be deployed to either enhance, or potentially diminish, the efficiency of frequency up-conversion