A virtual time CSMA protocols for hard real-time communication

We study virtual time CSMA protocols for hard real time communication systems where messages have explicit deadlines. In this protocol, each node maintains two clocks; a real time clock and a virtual time clock. Whenever a node finds the channel to be idle, it resets its virtual clock to be equal to the real clock. The virtual clock then runs at a higher rate than the real clock. A node transmits a waiting message when the time on the virtual clock is equal to the latest time to send the message. This protocol implements the minimum-laxity-first transmission policy. We compare the performance of our protocol with two baseline protocols both of which transmit messages according to the minimum-laxity-first policy. While both use perfect state information about the nodes and channel, the first is an idealized protocol which obtains this information without paying any cost and the second one pays a reasonable price for it. The simulation study shows that in most cases, our protocol performs close to the first one and better than the second one

CLASSIFICATION OF THE STRUCTURE OF CITIES THROUGH MID-RESOLUTION SATELLITE IMAGERY AND PATCH BASED NEURAL NETWORKS

The studies in the classification of the urban spatial structure have been essential in deriving insights into the land cover and the built typology which helped in the estimation of energy consumption patterns, urban density, compactness, and hierarchy of settlements. However, the analysis and comparison of the physical forms of the cities have been attempted in a piecemeal fashion where the requirement of datasets and the computation power for analysis has been a major hindrance. With the advancement in machine learning based techniques, large datasets such as satellite imagery can be studied with advanced computer vision methods. These solutions may help in studying the intricate nature of human habitats in large extents of geographical areas including various urban areas. This study utilizes smaller patches of medium resolution Sentinel-2B Imagery of ten different cities in India to explore the urban forms present in these cities. This study uses Stacked Convolutional Autoencoder (CAE) to reduce the dimensionality of satellite imagery patches and unsupervised clustering techniques such as t-SNE and K-means to study the characteristics of similar patches. On analyzing the clusters through visual exploration, similar patches are delineated and provided with corresponding labels representing urban forms. Individual clusters are then studied with respect to each city. The motive of the study is to gain insights into the different types of morphological patterns present within and among cities

Programming Heterogeneous Parallel Machines Using Refactoring and Monte-Carlo Tree Search

Funding: This work was supported by the EU Horizon 2020 project, TeamPlay, Grant Number 779882, and UK EPSRC Discovery, Grant Number EP/P020631/1.This paper presents a new technique for introducing and tuning parallelism for heterogeneous shared-memory systems (comprising a mixture of CPUs and GPUs), using a combination of algorithmic skeletons (such as farms and pipelines), Monte–Carlo tree search for deriving mappings of tasks to available hardware resources, and refactoring tool support for applying the patterns and mappings in an easy and effective way. Using our approach, we demonstrate easily obtainable, significant and scalable speedups on a number of case studies showing speedups of up to 41 over the sequential code on a 24-core machine with one GPU. We also demonstrate that the speedups obtained by mappings derived by the MCTS algorithm are within 5–15% of the best-obtained manual parallelisation.Publisher PDFPeer reviewe

Optimizing Performance of Continuous-Time Stochastic Systems using Timeout Synthesis

We consider parametric version of fixed-delay continuous-time Markov chains (or equivalently deterministic and stochastic Petri nets, DSPN) where fixed-delay transitions are specified by parameters, rather than concrete values. Our goal is to synthesize values of these parameters that, for a given cost function, minimise expected total cost incurred before reaching a given set of target states. We show that under mild assumptions, optimal values of parameters can be effectively approximated using translation to a Markov decision process (MDP) whose actions correspond to discretized values of these parameters

Adding Robustness in Dynamic Preemptive Scheduling

In this paper we introduce a robust earliest deadline scheduling algorithm for deal ing with hard aperiodic tasks under overloads in a dynamic realtime environment The algorithm synergistically combines many features including dynamic guarantees graceful degradation in overloads deadline tolerance resource reclaiming and dy namic reguarantees A necessary and sucient schedulability test is presented and an ecient On guarantee algorithm is proposed The new algorithm is evaluated via simulation and compared to several baseline algorithms The experimental results show excellent performance of the new algorithm in normal and overload conditions Static realtime systems are designed for worst case situations Assuming that all the assumptions made in the design and analysis are correct we can say that the level of guarantee for these systems is absolute and all tasks will make their deadlines Unfortunately static systems are not always possible becaus

Allocation and scheduling of complex periodic tasks

A static algorithm for allocating and scheduling components of complex periodic tasks across sites in distributed systems is discussed. Besides dealing with the periodicity constraints (which have been the sole concern of many previous algorithms), this algorithm handles precedence, communication, and fault-tolerance requirements of subtasks of the tasks. The algorithm determines the allocation of subtasks of periodic tasks to sites, the scheduled start times of subtasks allocated to a site, and the schedule for communication along the communication channel(s). Experimental evaluation of the algorithm shows that the heuristics and search techniques incorporated in the algorithm are extremely effective. Specifically, they show that, if a task set can be feasibly allocated and scheduled, the algorithm is highly likely to find it without any backtracking during the search