A Priority-Based Message Response Time Aware Job Scheduling Model for the Internet of Things (IoT)

2019 ◽  
Vol 1 (1) ◽  
pp. 1-14
Author(s):  
Sumit Kumar ◽  
Zahid Raza
Keyword(s):  
Internet Of Things ◽  
Response Time ◽  
Job Scheduling ◽  
The Internet ◽  
Message Delivery ◽  
Execution Costs ◽  
Message Scheduling ◽  
Scheduling Model ◽  
Underlying Network ◽  
The Internet Of Things

The Internet of Things is seen as the progressive version of internet involving the transmission of information between things/objects with the aim of context-aware processing. The IoT can be anything ranging from home appliances, vehicles, almost anything networked and fitted with sensors, actuators or embedded computers. The IoT aims to make the internet sensory while maintaining a minimum quality of service (QoS) guarantee. In such an environment, job scheduling becomes very important, ensuring the minimum response time for message transfer. This work proposes an SCM based scheduling model for the IoT with the aim of minimization of the response time to optimize the scheduling performance of the underlying network and minimize the execution costs. After being serviced by a given node with its queue acting as a server for the message, appropriate next node for message forwarding is selected offering the least response time until the message reaches the destination. The effect of message scheduling to account for both the prioritized and non-prioritized message delivery has been studied.

scholarly journals Management of Software-Defined Networking Powered by Artificial Intelligence

2021 ◽  
Author(s):  
Jehad Ali ◽  
Byeong-hee Roh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Internet Of Things ◽  
Heterogeneous Networks ◽  
Software Defined Networking ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Underlying Network ◽  
And Control ◽  
The Internet Of Things

Separating data and control planes by Software-Defined Networking (SDN) not only handles networks centrally and smartly. However, through implementing innovative protocols by centralized controllers, it also contributes flexibility to computer networks. The Internet-of-Things (IoT) and the implementation of 5G have increased the number of heterogeneous connected devices, creating a huge amount of data. Hence, the incorporation of Artificial Intelligence (AI) and Machine Learning is significant. Thanks to SDN controllers, which are programmable and versatile enough to incorporate machine learning algorithms to handle the underlying networks while keeping the network abstracted from controller applications. In this chapter, a software-defined networking management system powered by AI (SDNMS-PAI) is proposed for end-to-end (E2E) heterogeneous networks. By applying artificial intelligence to the controller, we will demonstrate this regarding E2E resource management. SDNMS-PAI provides an architecture with a global view of the underlying network and manages the E2E heterogeneous networks with AI learning.

scholarly journals Message Queuing Telemetry Transport dalam Internet of Things menggunakan ESP-32

2019 ◽  
Vol 3 (3) ◽  
pp. 159
Author(s):  
Moh Noor Al-Azam ◽  
Darian Rizaludin ◽  
Yulius Satmoko Raharjo ◽  
Aryo Nugroho
Keyword(s):  
Internet Of Things ◽  
Design Principles ◽  
The Internet ◽  
Message Delivery ◽  
M2m Communication ◽  
Resource Requirements ◽  
Basic Functions ◽  
Bandwidth Capacity ◽  
The Internet Of Things ◽  
Delivery Protocol

Message Queuing Telemetry Transport (MQTT) is a connectivity protocol between machines or now better known as the Internet of Things (IoT). This protocol recognizes two basic functions of M2M communication, namely publish and subscribe (pub/sub). The MQTT protocol is designed as a very simple and very lightweight message delivery protocol, designed for devices that are limited and with low bandwidth capacity, high latency or on an unreliable network. The design principles are to minimize bandwidth requirements and device resource requirements, and keep trying to ensure reliability and guaranteed delivery rates. In this paper, VerneMQ performance reliability is tested - one of the MQTT brokers, with several stressing levels using ESP-32 as a publisher and notebook with the python application as a subscriber.

scholarly journals Load Balancing of Fog Computing Centers: Minimizing Response Time of High Priority Requests

2019 ◽  
Vol 8 (11) ◽  
pp. 2713-2716
Keyword(s):  
Internet Of Things ◽  
Load Balancing ◽  
Response Time ◽  
Fog Computing ◽  
The Internet ◽  
Computing Technology ◽  
Computing Center ◽  
Computing Centers ◽  
The Internet Of Things ◽  
High Priority Request

Fog computing is one of the enabling computing technology which primarily aims to fulfill the requirements of the Internet of Things (IoT). IoT is fast-growing networking and computing sector. The scalability of users, devices, and application is crucial for the success of IoT systems. The load balancing is an approach to distribute the load among computing nodes so that the computing nodes are not overloaded. In this paper, we propose the priority-based request servicing at fog computing centers. We particularly address the situation when the fog node in fog computing center (FCC) receives more workload than their capacity to handle it. The increased workload is shifted to nearby fog nodes rather than to the remote cloud. The proposed approach is able to minimize the offloading the high priority request to other nodes by 11% which proves the novelty of our proposed.

scholarly journals EFFICIENT AND SECURE DATA UTILIZATION IN MOBILE EDGE COMPUTING BY DATA REPLICATION

2020 ◽  
Vol 2 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Dr. Bhalaji N.
Keyword(s):  
Internet Of Things ◽  
Response Time ◽  
Data Flow ◽  
Edge Computing ◽  
The Internet ◽  
Mobile Edge Computing ◽  
Data Utilization ◽  
Huge Data ◽  
Secure Data ◽  
The Internet Of Things

The technological improvement at a rapid pace in the information and the communication fields has made the internet of things inevitable in our day today activities and takes a significant role in the every part of our regular schedule. The seamless communication through the internet of things is made possible, by connecting the tangible things around resulting in the numerous of advantages such as timely information delivery, servicing and monitoring. The inbuilt benefits of the IOT has made it more prominent among a wide range of application resulting in a huge data flow, though the congestion in the dataflow are managed using the cloud computing and the alternative sources such as the edge computing , the security of the data that are used are still under research. To manage the huge data flow and have secure data utilization in the internet of things, the paper has put forth the mobile edge computing integrated with the data duplication process taking into consideration the power utilization and the response time. The proposed method is simulated using the Network Simulator-2 and results obtained shows that the duplication process provides an enhancement in the bandwidth utilization along with the cut down in the power consumption and the response time.

scholarly journals Internet of Things Platform for Manage Multiple Message Queuing Telemetry Transport Broker Server

2019 ◽  
pp. 197-206 ◽  
Author(s):  
Aulia Arif Wardana ◽  
Andrian Rakhmatsyah ◽  
Agus Eko Minarno ◽  
Dhika Rizki Anbiya
Keyword(s):  
Internet Of Things ◽  
Response Time ◽  
Open Source ◽  
Business Process ◽  
Usability Testing ◽  
Recording System ◽  
The Other ◽  
The Internet ◽  
The Internet Of Things ◽  
Monitoring Platform

This study proposed the Internet of Things (IoT) monitoring platform model to manage multiple Message Queuing Telemetry Transport (MQTT) broker server. The Broker is a part of the MQTT protocol system to deliver the message from publisher to subscriber. The single MQTT protocol that setup in a server just have one broker system. However, many users used more than one broker to develop their system. One of the problems with the user that use more than one MQTT broker to develop their system is no recording system that helps users to record configurations from multi brokers and connected devices. This can cause to slow the deployment process of the device because the configuration of the device and broker not properly managed. The platform built is expected to solve the problem. This proposed platform can manage multiple MQTT broker server and device configuration from different product or vendor. The platform also can manage the topic that connects to a registered broker on the platform. The other advantages of this platform are open source and can modify to a specific business process. After usability testing and response time testing, the proposed platform can manage multiple MQTT broker server, functional to use, and an average of response time from the platform page is not more than 10 seconds.

scholarly journals User preference–based QoS-aware service function placement in IoT-Edge cloud

2021 ◽  
Vol 17 (5) ◽  
pp. 155014772110199
Author(s):  
Briytone Mutichiro ◽  
Younghan Kim
Keyword(s):  
Quality Of Service ◽  
Internet Of Things ◽  
Response Time ◽  
User Preference ◽  
Mixed Integer ◽  
The Internet ◽  
Service Function ◽  
Cost Constraints ◽  
The Internet Of Things

In the Internet of Things-Edge cloud, service provision presents a challenge to operators to satisfy user service-level agreements while meeting service-specific quality-of-service requirements. This is because of inherent limitations in the Internet of Things-Edge in terms of resource infrastructure as well as the complexity of user requirements in terms of resource management in a heterogeneous environment like edge. An efficient solution to this problem is service orchestration and placement of service functions to meet user-specific requirements. This work aims to satisfy user quality of service through optimizing the user response time and cost by factoring in the workload variation on the edge infrastructure. We formulate the service function placement at the edge problem. We employ user service request patterns in terms of user preference and service selection probability to model service placement. Our framework proposal relies on mixed-integer linear programming and heuristic solutions. The main objective is to realize a reduced user response time at minimal overall cost while satisfying the user service requirements. For this, several parameters, and factors such as capacity, latency, workload, and cost constraints, are considered. The proposed solutions are evaluated based on different metrics and the obtained results show the gap between the heuristic user preference placement algorithm and the optimal solution to be minimal.

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