scholarly journals An infrastructure-assisted job scheduling and task coordination in volunteer computing-based VANET

2021 ◽  
Author(s):  
Abdul Waheed ◽  
Munam Ali Shah ◽  
Abid Khan ◽  
Gwanggil Jeon
Keyword(s):  
Job Scheduling ◽  
Good Governance ◽  
Smart Cities ◽  
Transportation Systems ◽  
Coordination Mechanism ◽  
Volunteer Computing ◽  
Task Execution ◽  
Task Coordination ◽  
Task Replication ◽  
And Task

AbstractVehicular networks as the key enablers in Intelligent Transportation Systems (ITS) and the Internet of Things (IoT) are key components of smart sustainable cities. Vehicles as a significant component of smart cities have emerging in-vehicle applications that can assist in good governance for sustainable smart cities. Most of these applications are delay sensitive and demand high computational capabilities that are provided by emerging technologies. Utilizing the distributed computational resources of vehicles with the help of volunteer computing is an efficient method to fulfill the high computational requirements of vehicles itself and the other components of smart cities. Vehicle as a resource is an emerging concept that must be considered to address the future challenges of sustainable smart cities. In this paper, an infrastructure-assisted job scheduling and task coordination mechanism in volunteer computing-based VANET called RSU-based VCBV is proposed, which enhances the architecture of VANET to utilize the surplus resources of vehicles for task execution. We propose job scheduling and task coordination algorithms for different volunteer models. Further, we design and implement an adaptive task replication method to seek fault tolerance by avoiding task failures due to locations of vehicles. We propose a task replication algorithm called location-based task replication algorithm. Extensive simulations validate the performance of our proposed volunteer models while comparing average task execution time and weight ratios with existing work.

AN EFFICIENT JOB SCHEDULING ALGORITHM IN PARTITIONABLE MESH CONNECTED SYSTEMS

2001 ◽  
Vol 12 (06) ◽  
pp. 763-773 ◽  
Author(s):  
KEQIN LI
Keyword(s):  
Job Scheduling ◽  
Scheduling Algorithm ◽  
Random Variables ◽  
Multiprocessor Scheduling ◽  
Performance Ratio ◽  
Task Execution ◽  
Average Case ◽  
Mean And Variance ◽  
Special Case ◽  
And Task

In this paper, we consider the problem of scheduling independent jobs in partitionable mesh connected systems. The problem is NP-hard, since it includes the multiprocessor scheduling problem as a special case when all jobs request for one processor. We analyze a simple approximation algorithm called A m. In particular, we show that if the sizes of submeshes requested by jobs are independent and identically distributed (i.i.d.) random variables uniformly distributed in the range [1..M1]×[1..M2], where M1×M2 is the size of a partitionable mesh connected system, and task execution times are i.i.d. random variables with finite mean and variance, then the average-case performance ratio E( A m(L))/E( OPT (L)) is asymptotically bounded from above by 1.6637594…. The average-case performance ratio improves significantly when jobs request for square submeshes or small submeshes.

scholarly journals A Task Execution Scheme for Dew Computing with State-of-the-Art Smartphones

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 2006
Author(s):  
Matías Hirsch ◽  
Cristian Mateos ◽  
Alejandro Zunino ◽  
Tim A. Majchrzak ◽  
Tor-Morten Grønli ◽  
...  
Keyword(s):  
Citizen Participation ◽  
Ad Hoc ◽  
Job Scheduling ◽  
State Of The Art ◽  
Smart Cities ◽  
Fog Computing ◽  
Task Execution ◽  
Industry Applications ◽  
Hoc Networks ◽  
Dew Computing

The computing resources of today’s smartphones are underutilized most of the time. Using these resources could be highly beneficial in edge computing and fog computing contexts, for example, to support urban services for citizens. However, new challenges, especially regarding job scheduling, arise. Smartphones may form ad hoc networks, but individual devices highly differ in computational capabilities and (tolerable) energy usage. We take into account these particularities to validate a task execution scheme that relies on the computing power that clusters of mobile devices could provide. In this paper, we expand the study of several practical heuristics for job scheduling including execution scenarios with state-of-the-art smartphones. With the results of new simulated scenarios, we confirm previous findings and better comprehend the baseline approaches already proposed for the problem. This study also sheds some light on the capabilities of small-sized clusters comprising mid-range and low-end smartphones when the objective is to achieve real-time stream processing using Tensorflow object recognition models as edge jobs. Ultimately, we strive for industry applications to improve task scheduling for dew computing contexts. Heuristics such as ours plus supporting dew middleware could improve citizen participation by allowing a much wider use of dew computing resources, especially in urban contexts in order to help build smart cities.

Synergies Between Transportation Systems, Energy Hub and the Grid in Smart Cities

2021 ◽  
pp. 1-15
Author(s):  
Morteza Sheikh ◽  
Jamshid Aghaei ◽  
Hossein Chabok ◽  
Mahmoud Roustaei ◽  
Taher Niknam ◽  
...  
Keyword(s):  
Smart Cities ◽  
Transportation Systems ◽  
Energy Hub

Manipulation and Task Execution by Humanoids

2018 ◽  
pp. 1633-1655
Author(s):  
Kensuke Harada ◽  
Máximo A. Roa
Keyword(s):  
Task Execution ◽  
And Task

scholarly journals An Efficient Technique for Virtual Machine Clustering and Communications Using Task-Based Scheduling in Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
C. Saravanakumar ◽  
M. Geetha ◽  
S. Manoj Kumar ◽  
S. Manikandan ◽  
C. Arun ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
High Availability ◽  
Performance Parameters ◽  
Task Execution ◽  
User Tasks ◽  
Cloud User ◽  
Computing Models ◽  
Bucket Size ◽  
And Task

Cloud computing models use virtual machine (VM) clusters for protecting resources from failure with backup capability. Cloud user tasks are scheduled by selecting suitable resources for executing the task in the VM cluster. Existing VM clustering processes suffer from issues like preconfiguration, downtime, complex backup process, and disaster management. VM infrastructure provides the high availability resources with dynamic and on-demand configuration. The proposed methodology supports VM clustering process to place and allocate VM based on the requesting task size with bandwidth level to enhance the efficiency and availability. The proposed clustering process is classified as preclustering and postclustering based on the migration. Task and bandwidth classification process classifies tasks with adequate bandwidth for execution in a VM cluster. The mapping of bandwidth to VM is done based on the availability of the VM in the cluster. The VM clustering process uses different performance parameters like lifetime of VM, utilization of VM, bucket size, and task execution time. The main objective of the proposed VM clustering is that it maps the task with suitable VM with bandwidth for achieving high availability and reliability. It reduces task execution and allocated time when compared to existing algorithms.

scholarly journals An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments

2021 ◽  
Vol 13 (12) ◽  
pp. 306
Author(s):  
Ahmed Dirir ◽  
Henry Ignatious ◽  
Hesham Elsayed ◽  
Manzoor Khan ◽  
Mohammed Adib ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Weather Conditions ◽  
Research Area ◽  
Transportation Systems ◽  
Detection Accuracy ◽  
Adverse Weather ◽  
Object Counting ◽  
The Impact

Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.

scholarly journals In-Lecture Media Use and Academic Performance: Investigating Demographic and Intentional Moderators

2018 ◽  
Vol 30 (1) ◽  
Author(s):  
Douglas A. Parry ◽  
Daniel B. Le Roux
Keyword(s):  
Academic Performance ◽  
University Students ◽  
Digital Media ◽  
Empirical Investigation ◽  
Media Use ◽  
Significant Negative Correlation ◽  
Task Execution ◽  
Communication Processes ◽  
Undergraduate University Students ◽  
And Task

The growing prevalence of continuous media use among university students in lecture environments has potential for detrimental effects. In this study we investigate the relationships between in-lecture media use and academic performance. Previous studies have shown that students frequently engage with digital media whilst in university lectures. Moreover, multitasking imposes cognitive costs detrimental to learning and task execution. We propose, accordingly, that the constant distractions created by digital media, interrupt the thought and communication processes of students during lectures and, subsequently, obstruct their ability to learn. To test this proposition we conducted a survey-based empirical investigation of digital media use and academic performance among undergraduate university students. A significant negative correlation was found between the number of in-lecture media use instances and academic performance. Furthermore, this effect was found to be pervasive independent of individual demographic factors and the intention with which a medium was used.

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