scholarly journals Dynamic Application Partitioning and Task-Scheduling Secure Schemes for Biosensor Healthcare Workload in Mobile Edge Cloud

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2797
Author(s):  
Abdullah Lakhan ◽  
Jin Li ◽  
Tor Morten Groenli ◽  
Ali Hassan Sodhro ◽  
Nawaz Ali Zardari ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Task Scheduling ◽  
Search Algorithm ◽  
Healthcare Applications ◽  
Mobile Healthcare ◽  
Application Partitioning ◽  
Algorithm Framework ◽  
Node Search ◽  
Min Cut ◽  
And Task

Currently, the use of biosensor-enabled mobile healthcare workflow applications in mobile edge-cloud-enabled systems is increasing progressively. These applications are heavyweight and divided between a thin client mobile device and a thick server edge cloud for execution. Application partitioning is a mechanism in which applications are divided based on resource and energy parameters. However, existing application-partitioning schemes widely ignore security aspects for healthcare applications. This study devises a dynamic application-partitioning workload task-scheduling-secure (DAPWTS) algorithm framework that consists of different schemes, such as min-cut algorithm, searching node, energy-enabled scheduling, failure scheduling, and security schemes. The goal is to minimize the energy consumption of nodes and divide the application between local nodes and edge nodes by applying the secure min-cut algorithm. Furthermore, the study devises the secure-min-cut algorithm, which aims to migrate data between nodes in a secure form during application partitioning in the system. After partitioning the applications, the node-search algorithm searches optimally to run applications under their deadlines. The energy and failure schemes maintain the energy consumption of the nodes and the failure of the system. Simulation results show that DAPWTS outperforms existing baseline approaches by 30% in terms of energy consumption, deadline, and failure of applications in the system.

scholarly journals A Novel Cost-Efficient Framework for Critical Heartbeat Task Scheduling Using the Internet of Medical Things in a Fog Cloud System

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 441 ◽  
Author(s):  
Qurat-ul-ain Mastoi ◽  
Teh Ying Wah ◽  
Ram Gopal Raj ◽  
Abdullah Lakhan
Keyword(s):  
Health Care ◽  
Task Scheduling ◽  
Minimum Cost ◽  
Cloud Services ◽  
Cloud System ◽  
Scheduling Problems ◽  
Healthcare Applications ◽  
Algorithm Framework ◽  
Internet Of Medical Things ◽  
Cost Efficient

Recently, there has been a cloud-based Internet of Medical Things (IoMT) solution offering different healthcare services to wearable sensor devices for patients. These services are global, and can be invoked anywhere at any place. Especially, electrocardiogram (ECG) sensors, such as Lead I and Lead II, demands continuous cloud services for real-time execution. However, these services are paid and need a lower cost-efficient process for the users. In this paper, this study considered critical heartbeat cost-efficient task scheduling problems for healthcare applications in the fog cloud system. The objective was to offer omnipresent cloud services to the generated data with minimum cost. This study proposed a novel health care based fog cloud system (HCBFS) to collect, analyze, and determine the process of critical tasks of the heartbeat medical application for the purpose of minimizing the total cost. This study devised a health care awareness cost-efficient task scheduling (HCCETS) algorithm framework, which not only schedule all tasks with minimum cost, but also executes them on their deadlines. Performance evaluation shows that the proposed task scheduling algorithm framework outperformed the existing algorithm methods in terms of cost.

scholarly journals Computation Offloading and Scheduling in Edge-Fog Cloud Computing

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Dadmehr Rahbari ◽  
Mohsen Nickray
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Task Scheduling ◽  
Fog Computing ◽  
Edge Computing ◽  
Computation Offloading ◽  
Test Environment ◽  
Iot Devices ◽  
And Task

Resource allocation and task scheduling in the Cloud environment faces many challenges, such as time delay, energy consumption, and security. Also, executing computation tasks of mobile applications on mobile devices (MDs) requires a lot of resources, so they can offload to the Cloud. But Cloud is far from MDs and has challenges as high delay and power consumption. Edge computing with processing near the Internet of Things (IoT) devices have been able to reduce the delay to some extent, but the problem is distancing itself from the Cloud. The fog computing (FC), with the placement of sensors and Cloud, increase the speed and reduce the energy consumption. Thus, FC is suitable for IoT applications. In this article, we review the resource allocation and task scheduling methods in Cloud, Edge and Fog environments, such as traditional, heuristic, and meta-heuristics. We also categorize the researches related to task offloading in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC), and Mobile Fog Computing (MFC). Our categorization criteria include the issue, proposed strategy, objectives, framework, and test environment. 

Task Scheduling in cloud using ACO

2020 ◽  
Vol 13 ◽  
Author(s):  
Yuvaraj Natarajan ◽  
Srihari Kannan ◽  
Gaurav Dhiman
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Limited Resource ◽  
Cloud Environment ◽  
Clear Cut ◽  
Strategic Approach ◽  
User Demand ◽  
Scheduling Strategies ◽  
Cloud Users ◽  
And Task

Background: Cloud computing is a multi-tenant model for computation that offers various features for computing and storage based on user demand. With increasing cloud users, the usage increases that highlights the problem of load balancing with limited resource availability based on dynamic cloud environment. In such cases, task scheduling creates fundamental issue in cloud environment. Introduction: Certain problems such as, inefficiencies in load balancing latency, throughput ratio, proper utilization of the cloud resources, better energy consumption and response time have been observed. These drawbacks can be efficiently resolved through the incorporation of efficient load balancing and task scheduling strategies. Method: In this paper, we develop an efficient co-operative method to solve the most recent approaches against load balancing and task scheduling have been proposed using Ant Colony Optimization (ACO). These approaches enables in the clear cut identification of the problems associated with the load balancing and task scheduling strategies in the cloud environment. Results: The simulation is conducted to find the efficacy of the improved ACO system for load balancing in cloud than the other methods. The result shows that the proposed method obtains reduced execution time, reduced cost and delay. Conclusion: A unique strategic approach is developed in this paper, Load Balancing, which works with the ACO in relation to the cloud workload balancing task through the incorporation of the ACO technique. The strategy for determining the applicant nodes is based on which the load balancing approach would essentially depend. By incorporating two different approaches: the maximum minute rules and the forward-backward ant, this reliability task can be established. This method is intended to articulate the initialization of the pheromone and thus upgrade the relevant cloud-based physical properties.

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