Minimizing Deadline Misses and Total Run-time with Load Balancing for a Connected Car Systems in Fog Computing

Abstract

Cloud computing helps in providing the applications with a few number of resources that are used to unload the tasks. But there are certain applications like coordinated lane change assistance which are helpful in cars that connects to internet has strict time constraints, and it may not be possible to get the job done just by unloading the tasks to the cloud. Fog computing helps in reducing the latency i.e the computation is now done in local fog servers instead of remote datacentres and these fog servers are connected to the nearby distance to clients. To achieve better timing performance in fog computing load balancing in these fog servers is to be performed in an efficient manner.The challenges in the proposed application includes the number of tasks are high, client mobility and heterogeneous nature of fog servers. We use mobility patterns of connected cars and load balancing is done periodically among fog servers. The task model presented here in this paper solves scheduling problem and this is done at the server level and not on the device level. And at last, we present an optimization problem formulation for balancing the load and for reducing the misses in deadline, also the time required for running the task in these cars will be minimized with the help of fog computing. It also performs better than some common algorithms such as active monitoring, weighted round robin and throttled load balancer.

Load balancing techniques for fog computing environment: Comparison, taxonomy, open issues, and challenges

“…Naik and Naik 47 proposed a solution for LB for reducing misses in the deadline. The challenges they faced were maximum task, mixed nature of fog servers, and mobility of the car.…”

“…The servers, memory, and storage devices consume the most energy, which also leads to an increase in the cost of the system. As a result, these must be considered to reduce total energy use 14,15,47,68,69 …”

Fault-Tolerant indoor localization based on speed conscious recurrent neural network using Kullback–Leibler divergence

“…Also, some optimization techniques like EOMR [27], particle swarm optimization [28], and mathematical models of [29][30][31][32] were studied to gain an understanding of how they optimize in various networking scenarios and whether they can be applied to maximize localization strategies.…”

Cloud-Based Smart Traffic Lights Monitoring and Controlling Using IoT

“…We used Windows 10 on an Intel Core i5-6200U CPU (2.30 GHz and 2.30 GHz), with 16 GB of RAM and 256 GB SSD. For load scheduling, it was referred [13][14][15][16].…”