Reliability and mobility aware task offloading strategy and scheduling algorithm in wisdom medical scenario

2020 ◽  
pp. 1-19
Author(s):  
Ping Qi ◽  
Hong Shu ◽  
Qiang Zhu
Keyword(s):  
Energy Consumption ◽  
Mobile Device ◽  
Evaluation Model ◽  
Scheduling Algorithm ◽  
Cognitive Model ◽  
Computation Offloading ◽  
Task Execution ◽  
Computing Paradigm ◽  
Energy Consumption Optimization ◽  
Moving Path

Computation offloading is a key computing paradigm used in mobile edge computing. The principle of computation offloading is to leverage powerful infrastructures to augment the computing capability of less powerful devices. However, the most existing computation offloading algorithms assume that the mobile device is not moving, and these algorithms do not take into account the reliability of task execution. In this paper, we firstly present the formalized description of the workflow, the wireless signal, the wisdom medical scenario and the moving path. Then, inspired by the Bayesian cognitive model, a trust evaluation model is presented to reduce the probability of failure for task execution based on the reliable behaviors of multiply computation resources. According to the location and the velocity of the mobile device, the execution time and the energy consumption model based on the moving path are constructed, task deferred execution and task migration are introduced to guarantee the service continuity. On this basis, considering the whole scheduling process from a global viewpoint, the genetic algorithm is used to solve the energy consumption optimization problem with the constraint of response time. Experimental results show that the proposed algorithm optimizes the workflow under the mobile edge environment by increasing 20.4% of successful execution probability and decreasing 21.5% of energy consumption compared with traditional optimization algorithms.

scholarly journals Modeling Multi-Site Computation Offloading in Unreliable Cloud Environments

2021 ◽  
Author(s):  
Marzieh Ranjbar Pirbasti
Keyword(s):  
Energy Consumption ◽  
Response Time ◽  
Mobile Device ◽  
Fault Injection ◽  
Computation Offloading ◽  
Failure Rates ◽  
Recovery Mechanisms ◽  
Efficient Allocations ◽  
Cloud Servers ◽  
Time And Energy

Offloading heavy computations from a mobile device to cloud servers can reduce the power consumption of the mobile device and improve the response time of mobile applications. However, the gains of offloading can be significantly affected by failures of cloud servers and network links. In this thesis, we propose a fault-aware, multi-site computation offloading model capable of finding efficient allocations of tasks to resources. Our model reduces both response time and energy consumption by incorporating the effect of failures and recovery mechanisms for various offloading allocations. In addition, we create a fault-injection framework to evaluate an allocation under various failure rates and recovery mechanisms. The experiments carried out with our fault-injection framework demonstrate that our fault-aware model can determine an allocation—based on the type of failures, failure rates, and the employed recovery mechanisms—that improves both response time and lower energy consumption compared to model without failures.

scholarly journals Modeling Multi-Site Computation Offloading in Unreliable Cloud Environments

2021 ◽  
Author(s):  
Marzieh Ranjbar Pirbasti
Keyword(s):  
Energy Consumption ◽  
Response Time ◽  
Mobile Device ◽  
Fault Injection ◽  
Computation Offloading ◽  
Failure Rates ◽  
Recovery Mechanisms ◽  
Efficient Allocations ◽  
Cloud Servers ◽  
Time And Energy

Offloading heavy computations from a mobile device to cloud servers can reduce the power consumption of the mobile device and improve the response time of mobile applications. However, the gains of offloading can be significantly affected by failures of cloud servers and network links. In this thesis, we propose a fault-aware, multi-site computation offloading model capable of finding efficient allocations of tasks to resources. Our model reduces both response time and energy consumption by incorporating the effect of failures and recovery mechanisms for various offloading allocations. In addition, we create a fault-injection framework to evaluate an allocation under various failure rates and recovery mechanisms. The experiments carried out with our fault-injection framework demonstrate that our fault-aware model can determine an allocation—based on the type of failures, failure rates, and the employed recovery mechanisms—that improves both response time and lower energy consumption compared to model without failures.

scholarly journals Computation offloading to edge cloud and dynamically resource-sharing collaborators in Internet of Things

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Siqi Mu ◽  
Zhangdui Zhong
Keyword(s):  
Energy Consumption ◽  
Internet Of Things ◽  
Resource Sharing ◽  
Optimization Theory ◽  
Wireless Channel ◽  
Computation Offloading ◽  
Total Energy Consumption ◽  
Markov Decision Problem ◽  
Computing Paradigm ◽  
Cooperative Computing

AbstractWith the diversity of the communication technology and the heterogeneity of the computation resources at network edge, both the edge cloud and peer devices (collaborators) can be scavenged to provide computation resources for the resource-limited Internet-of-Things (IoT) devices. In this paper, a novel cooperative computing paradigm is proposed, in which the computation resources of IoT device, opportunistically idle collaborators and dedicated edge cloud are fully exploited. Computation/offloading assistance is provided by collaborators at idle/busy states, respectively. Considering the channel randomness and opportunistic computation resource share of collaborators, we study the stochastic offloading control for an IoT device, regarding how much computation load is processed locally, offloaded to the edge cloud and a collaborator. The problem is formulated into a finite horizon Markov decision problem with the objective of minimizing the expected total energy consumption of the IoT device and the collaborator, subject to satisfying the hard computation deadline constraint. Optimal offloading policy is derived based on the stochastic optimization theory, which demonstrates that the energy consumption can be reduced by a proportional factor through the cooperative computing. More energy saving is achieved with better wireless channel condition or higher computation energy efficiency of collaborators. Simulation results validate the optimality of the proposed policy and the efficiency of the cooperative computing between end devices and edge cloud, compared to several other offloading schemes.

scholarly journals A Multilevel Optimization Framework for Computation Offloading in Mobile Edge Computing

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Nanliang Shan ◽  
Yu Li ◽  
Xiaolong Cui
Keyword(s):  
Mobile Devices ◽  
Language Processing ◽  
Scheduling Algorithm ◽  
Edge Computing ◽  
Computation Offloading ◽  
Potential Game ◽  
Mobile Edge Computing ◽  
Computing Paradigm ◽  
Channel Interference ◽  
Selection Stage

Mobile edge computing is a new computing paradigm that can extend cloud computing capabilities to the edge network, supporting computation-intensive applications such as face recognition, natural language processing, and augmented reality. Notably, computation offloading is a key technology of mobile edge computing to improve mobile devices’ performance and users’ experience by offloading local tasks to edge servers. In this paper, the problem of computation offloading under multiuser, multiserver, and multichannel scenarios is researched, and a computation offloading framework is proposed that considering the quality of service (QoS) of users, server resources, and channel interference. This framework consists of three levels. (1) In the offloading decision stage, the offloading decision is made based on the beneficial degree of computation offloading, which is measured by the total cost of the local computing of mobile devices in comparison with the edge-side server. (2) In the edge server selection stage, the candidate is comprehensively evaluated and selected by a multiobjective decision based on the Analytic Hierarchy Process based on Covariance (Cov-AHP) for computation offloading. (3) In the channel selection stage, a multiuser and multichannel distributed computation offloading strategy based on the potential game is proposed by considering the influence of channel interference on the user’s overall overhead. The corresponding multiuser and multichannel task scheduling algorithm is designed to maximize the overall benefit by finding the Nash equilibrium point of the potential game. Amounts of experimental results show that the proposed framework can greatly increase the number of beneficial computation offloading users and effectively reduce the energy consumption and time delay.

scholarly journals Computation Offloading Optimization in Mobile Edge Computing Based on HIBSA

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Yang Liu ◽  
Jin Qi Zhu ◽  
Jinao Wang
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problem ◽  
Scheduling Algorithm ◽  
Computation Offloading ◽  
Multiobjective Optimization Problem ◽  
Mobile Edge Computing ◽  
Task Execution ◽  
The Core ◽  
5G Network ◽  
Task Offloading

Multiaccess edge computation (MEC) is a hotspot in 5G network. The problem of task offloading is one of the core problems in MEC. In this paper, a novel computation offloading model which partitions tasks into subtasksis proposed. This model takes communication and computing resources, energy consumption of intelligent mobile devices, and weight of tasks into account. We then transform the model into a multiobjective optimization problem based on Pareto that balances the task weight and time efficiency of the offloaded tasks. In addition, an algorithm based on hybrid immune and bat scheduling algorithm (HIBSA) is further designed to tackle the proposed multiobjective optimization problem. The experimental results show that HIBSA can meet the requirements of both the task execution deadline and the weight of the offloaded tasks.

scholarly journals Energy-Efficient Collaborative Task Computation Offloading in Cloud-Assisted Edge Computing for IoT Sensors

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1105 ◽  
Author(s):  
Fagui Liu ◽  
Zhenxi Huang ◽  
Liangming Wang
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mixed Integer ◽  
Integer Programming Problem ◽  
Efficient Computation ◽  
Collaborative Task ◽  
Computing Paradigm ◽  
Task Dependency

As an emerging and promising computing paradigm in the Internet of things (IoT),edge computing can significantly reduce energy consumption and enhance computation capabilityfor resource-constrained IoT devices. Computation offloading has recently received considerableattention in edge computing. Many existing studies have investigated the computation offloadingproblem with independent computing tasks. However, due to the inter-task dependency in variousdevices that commonly happens in IoT systems, achieving energy-efficient computation offloadingdecisions remains a challengeable problem. In this paper, a cloud-assisted edge computing frameworkwith a three-tier network in an IoT environment is introduced. In this framework, we first formulatedan energy consumption minimization problem as a mixed integer programming problem consideringtwo constraints, the task-dependency requirement and the completion time deadline of the IoT service.To address this problem, we then proposed an Energy-efficient Collaborative Task ComputationOffloading (ECTCO) algorithm based on a semidefinite relaxation and stochastic mapping approachto obtain strategies of tasks computation offloading for IoT sensors. Simulation results demonstratedthat the cloud-assisted edge computing framework was feasible and the proposed ECTCO algorithmcould effectively reduce the energy cost of IoT sensors.

Agent Mediated Code Comprehension: A Cognitive Challenge

2019 ◽  
Vol 11 (2) ◽  
Author(s):  
Ram Gopal Gupta ◽  
Bireshwar Dass Mazumdar ◽  
Kuldeep Yadav
Keyword(s):  
Information Systems ◽  
Software Design ◽  
Business Processes ◽  
Software Agents ◽  
Cognitive Model ◽  
Cognitive Models ◽  
Software Market ◽  
Computing Paradigm ◽  
Cognitive Challenge ◽  
Software Code

The rapidly changing needs and opportunities of today’s global software market require unprecedented levels of code comprehension to integrate diverse information systems to share knowledge and collaborate among organizations. The combination of code comprehension with software agents not only provides a promising computing paradigm for efficient agent mediated code comprehension service for selection and integration of inter-organizational business processes but this combination also raises certain cognitive issues that need to be addressed. We will review some of the key cognitive models and theories of code comprehension that have emerged in software code comprehension. This paper will propose a cognitive model which will bring forth cognitive challenges, if handled properly by the organization would help in leveraging software design and dependencies.

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