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

2020 ◽  
Vol 21 (1) ◽  
pp. 73-84
Author(s):  
K Jairam Naik ◽  
D Hanumanth Naik
Keyword(s):  
Load Balancing ◽  
Fog Computing ◽  
Problem Formulation ◽  
Mobility Patterns ◽  
Active Monitoring ◽  
Connected Cars ◽  
Time Required ◽  
Load Balancer ◽  
Optimization Problem Formulation ◽  
Better Than

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 somecommon algorithms such as active monitoring, weighted round robin and throttled load balancer.

scholarly journals Simulation and performance assessment of a modified throttled load balancing algorithm in cloud computing environment

2022 ◽  
Vol 12 (2) ◽  
pp. 2087
Author(s):  
Noha G. Elnagar ◽  
Ghada F. Elkabbany ◽  
Amr A. Al-Awamry ◽  
Mohamed B. Abdelhalim
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Processing Time ◽  
Virtual Machines ◽  
Active Monitoring ◽  
Load Fluctuation ◽  
Load Balancing Algorithm ◽  
And Performance ◽  
Load Balancer

<span lang="EN-US">Load balancing is crucial to ensure scalability, reliability, minimize response time, and processing time and maximize resource utilization in cloud computing. However, the load fluctuation accompanied with the distribution of a huge number of requests among a set of virtual machines (VMs) is challenging and needs effective and practical load balancers. In this work, a two listed throttled load balancer (TLT-LB) algorithm is proposed and further simulated using the CloudAnalyst simulator. The TLT-LB algorithm is based on the modification of the conventional TLB algorithm to improve the distribution of the tasks between different VMs. The performance of the TLT-LB algorithm compared to the TLB, round robin (RR), and active monitoring load balancer (AMLB) algorithms has been evaluated using two different configurations. Interestingly, the TLT-LB significantly balances the load between the VMs by reducing the loading gap between the heaviest loaded and the lightest loaded VMs to be 6.45% compared to 68.55% for the TLB and AMLB algorithms. Furthermore, the TLT-LB algorithm considerably reduces the average response time and processing time compared to the TLB, RR, and AMLB algorithms.</span>

Fuzzy Dynamic Load Balancing in Virtualized Data Centers of SaaS Cloud Provider

2015 ◽  
Vol 4 (3) ◽  
pp. 50-71 ◽  
Author(s):  
Md. S. Q. Zulkar Nine ◽  
Abul Kalam Azad ◽  
Saad Abdullah ◽  
Rashedur M. Rahman
Keyword(s):  
Load Balancing ◽  
Data Centers ◽  
Cloud Model ◽  
Cloud Provider ◽  
Active Monitoring ◽  
Computing Power ◽  
Novel Approach ◽  
Virtualized Data Centers ◽  
Load Balancer ◽  
Saas Cloud

Cloud computing provides a robust infrastructure that can facilitate computing power as a utility service. All the virtualized services are made available to end users in a pay-as-you-go basis. Serving user requests using distributed network of Virtualized Data Centers is a challenging task as response time increases significantly without a proper load balancing strategy. As the parameters involved in generating load in the Virtualized Data Center has imprecise effect on the overall load of Virtual Machine, a fuzzy load balancing strategy is required. This paper proposes two efficient fuzzy load balancing methods - Fuzzy Active Monitoring Load Balancer (FAM-LB) and Fuzzy Throttled Load Balancer (FT-LB) for the distributed SaaS cloud provider. The authors implemented a cloud model in simulation environment and compared the results of otheir novel approach with the existing techniques. Among them FT-LB has provided better performance compared to other scheduling algorithms.

Fuzzy Dynamic Load Balancing in Virtualized Data Centers of SaaS Cloud Provider

Fuzzy Systems ◽  
2017 ◽  
pp. 1643-1665
Author(s):  
Md. S. Q. Zulkar Nine ◽  
Abul Kalam Azad ◽  
Saad Abdullah ◽  
Rashedur M. Rahman
Keyword(s):  
Load Balancing ◽  
Data Centers ◽  
Cloud Model ◽  
Cloud Provider ◽  
Active Monitoring ◽  
Computing Power ◽  
Novel Approach ◽  
Virtualized Data Centers ◽  
Load Balancer ◽  
Saas Cloud

Cloud computing provides a robust infrastructure that can facilitate computing power as a utility service. All the virtualized services are made available to end users in a pay-as-you-go basis. Serving user requests using distributed network of Virtualized Data Centers is a challenging task as response time increases significantly without a proper load balancing strategy. As the parameters involved in generating load in the Virtualized Data Center has imprecise effect on the overall load of Virtual Machine, a fuzzy load balancing strategy is required. This paper proposes two efficient fuzzy load balancing methods - Fuzzy Active Monitoring Load Balancer (FAM-LB) and Fuzzy Throttled Load Balancer (FT-LB) for the distributed SaaS cloud provider. The authors implemented a cloud model in simulation environment and compared the results of otheir novel approach with the existing techniques. Among them FT-LB has provided better performance compared to other scheduling algorithms.

A Reliable Trust Computing Mechanism in Fog Computing

2021 ◽  
Vol 11 (1) ◽  
pp. 1-20
Author(s):  
Vijay Lingaraddi Hallappanavar ◽  
Mahantesh N. Birje
Keyword(s):  
Fog Computing ◽  
Multiple Sources ◽  
Individual Interaction ◽  
Punishment Mechanism ◽  
Iot Devices ◽  
Time Required ◽  
Subjective Trust ◽  
Trust Computing ◽  
Better Than

Due to the lack of trust on IoT devices, the integration of fog computing and IoT devices is hindered. Trust is considered to have two notions: subjective trust where the user puts his individual interests to the interactions and objective trust which depends only on individual interaction experiences. This paper proposes a reliable trust computing mechanism based on subjective and objective trust. The subjective trust is calculated from feedback of multiple sources. The incentive and punishment mechanism is applied to the subjective trust to avoid malicious devices. The objective trust is calculated based on quality of services. The overall trust helps the IoT devices to determine the trustworthiness of other IoT devices and in turn helps to establish a trusted environment. The experimental results show that the performance is better than existing methods in terms of time required to calculate the overall trust, reliability, and trustworthiness of IoT devices.

Virtualized Fog Network with Load Balancing for IoT based Fog-to-Cloud

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Istabraq M. Al-Joboury ◽  
Emad H. Al-Hemiary
Keyword(s):  
Load Balancing ◽  
Packet Loss ◽  
Fog Computing ◽  
Transport Protocol ◽  
Network Simulator ◽  
Huge Number ◽  
Message Queue ◽  
Limited Bandwidth ◽  
Cloud Architecture ◽  
Fog Network

Fog Computing is a new concept made by Cisco to provide same functionalities of Cloud Computing but near to Things to enhance performance such as reduce delay and response time. Packet loss may occur on single Fog server over a huge number of messages from Things because of several factors like limited bandwidth and capacity of queues in server. In this paper, Internet of Things based Fog-to-Cloud architecture is proposed to solve the problem of packet loss on Fog server using Load Balancing and virtualization. The architecture consists of 5 layers, namely: Things, gateway, Fog, Cloud, and application. Fog layer is virtualized to specified number of Fog servers using Graphical Network Simulator-3 and VirtualBox on local physical server. Server Load Balancing router is configured to distribute the huge traffic in Weighted Round Robin technique using Message Queue Telemetry Transport protocol. Then, maximum message from Fog layer are selected and sent to Cloud layer and the rest of messages are deleted within 1 hour using our proposed Data-in-Motion technique for storage, processing, and monitoring of messages. Thus, improving the performance of the Fog layer for storage and processing of messages, as well as reducing the packet loss to half and increasing throughput to 4 times than using single Fog server.

M2FBalancer: A mist-assisted fog computing-based load balancing strategy for smart cities

2021 ◽  
pp. 1-15
Author(s):  
Subhranshu Sekhar Tripathy ◽  
Diptendu Sinha Roy ◽  
Rabindra K. Barik
Keyword(s):  
Load Balancing ◽  
Resource Utilization ◽  
Smart City ◽  
Load Transfer ◽  
Smart Cities ◽  
Fog Computing ◽  
Round Robin ◽  
Healthcare Management ◽  
Optimization Strategy ◽  
Allocation Method

Nowadays, cities are intended to change to a smart city. According to recent studies, the use of data from contributors and physical objects in many cities play a key element in the transformation towards a smart city. The ‘smart city’ standard is characterized by omnipresent computing resources for the observing and critical control of such city’s framework, healthcare management, environment, transportation, and utilities. Mist computing is considered a computing prototype that performs IoT applications at the edge of the network. To maintain the Quality of Service (QoS), it is impressive to employ context-aware computing as well as fog computing simultaneously. In this article, the author implements an optimization strategy applying a dynamic resource allocation method based upon genetic algorithm and reinforcement learning in combination with a load balancing procedure. The proposed model comprises four layers i.e. IoT layer, Mist layer, Fog layer, and Cloud layer. Authors have proposed a load balancing technique called M2F balancer which regulates the traffic in the network incessantly, accumulates the information about each server load, transfer the incoming query, and disseminate them among accessible servers equally using dynamic resources allocation method. To validate the efficacy of the proposed algorithm makespan, resource utilization, and the degree of imbalance (DOI) are considered as the scheduling parameter. The proposed method is being compared with the Least count, Round Robin, and Weighted Round Robin. In the end, the results demonstrate that the solutions enhance QoS in the mist assisted cloud environment concerning maximization resource utilization and minimizing the makespan. Therefore, M2FBalancer is an effective method to utilize the resources efficiently by ensuring uninterrupted service. Consequently, it improves performance even at peak times.

scholarly journals Optimization-Based Formulations for Short-Circuit Studies with Inverter-Interfaced Generation in PowerModelsProtection.jl

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2160
Author(s):  
Arthur K. Barnes ◽  
Jose E. Tabarez ◽  
Adam Mate ◽  
Russell W. Bent
Keyword(s):  
Optimization Problem ◽  
Short Circuit ◽  
Problem Formulation ◽  
Power Delivery ◽  
Fault Current ◽  
Protective Device ◽  
Protective Relaying ◽  
Overcurrent Protection ◽  
Short Circuits ◽  
Optimization Problem Formulation

Protecting inverter-interfaced microgrids is challenging as conventional time-overcurrent protection becomes unusable due to the lack of fault current. There is a great need for novel protective relaying methods that enable the application of protection coordination on microgrids, thereby allowing for microgrids with larger areas and numbers of loads while not compromising reliable power delivery. Tools for modeling and analyzing such microgrids under fault conditions are necessary in order to help design such protective relaying and operate microgrids in a configuration that can be protected, though there is currently a lack of tools applicable to inverter-interfaced microgrids. This paper introduces the concept of applying an optimization problem formulation to the topic of inverter-interfaced microgrid fault modeling, and discusses how it can be employed both for simulating short-circuits and as a set of constraints for optimal microgrid operation to ensure protective device coordination.

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