scholarly journals A Computational Offloading Method for Edge Server Computing and Resource Allocation Management

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Muna Al-Razgan ◽  
Taha Alfakih ◽  
Mohammad Mehedi Hassan
Keyword(s):  
Resource Allocation ◽  
Response Time ◽  
Real Time ◽  
Mobile Devices ◽  
Mapping Function ◽  
Mobile User ◽  
Computation Offloading ◽  
Efficient Resource ◽  
And Performance ◽  
Cloud Servers

The emerging technology of mobile cloud is introduced to overcome the constraints of mobile devices. We can achieve that by offloading resource intensive applications to remote cloud-based data centers. For the remote computing solution, mobile devices (MDs) experience higher response time and delay of the network, which negatively affects the real-time mobile user applications. In this study, we proposed a model to evaluate the efficiency of the close-end network computation offloading in MEC. This model helps in choosing the adjacent edge server from the surrounding edge servers. This helps to minimize the latency and increase the response time. To do so, we use a decision rule based Heuristic Virtual Value (HVV). The HVV is a mapping function based on the features of the edge server like the workload and performance. Furthermore, we propose availability of a virtual machine resource algorithm (AVM) based on the availability of VM in edge cloud servers for efficient resource allocation and task scheduling. The results of experiment simulation show that the proposed model can meet the response time requirements of different real-time services, improve the performance, and minimize the consumption of MD energy and the resource utilization.

scholarly journals Mobility-Included DNN Partition Offloading from Mobile Devices to Edge Clouds

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 229
Author(s):  
Xianzhong Tian ◽  
Juan Zhu ◽  
Ting Xu ◽  
Yanjun Li
Keyword(s):  
Neural Networks ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Wireless Network ◽  
Deep Neural Networks ◽  
Mobile User ◽  
Computation Offloading ◽  
Long Latency ◽  
Total Latency ◽  
And Performance

The latest results in Deep Neural Networks (DNNs) have greatly improved the accuracy and performance of a variety of intelligent applications. However, running such computation-intensive DNN-based applications on resource-constrained mobile devices definitely leads to long latency and huge energy consumption. The traditional way is performing DNNs in the central cloud, but it requires significant amounts of data to be transferred to the cloud over the wireless network and also results in long latency. To solve this problem, offloading partial DNN computation to edge clouds has been proposed, to realize the collaborative execution between mobile devices and edge clouds. In addition, the mobility of mobile devices is easily to cause the computation offloading failure. In this paper, we develop a mobility-included DNN partition offloading algorithm (MDPO) to adapt to user’s mobility. The objective of MDPO is minimizing the total latency of completing a DNN job when the mobile user is moving. The MDPO algorithm is suitable for both DNNs with chain topology and graphic topology. We evaluate the performance of our proposed MDPO compared to local-only execution and edge-only execution, experiments show that MDPO significantly reduces the total latency and improves the performance of DNN, and MDPO can adjust well to different network conditions.

scholarly journals Flexible computation offloading in a fuzzy-based mobile edge orchestrator for IoT applications

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
VanDung Nguyen ◽  
Tran Trong Khanh ◽  
Tri D. T. Nguyen ◽  
Choong Seon Hong ◽  
Eui-Nam Huh
Keyword(s):  
Cloud Computing ◽  
Augmented Reality ◽  
Mobile Devices ◽  
Computing System ◽  
Mobile User ◽  
Computation Offloading ◽  
Context Aware ◽  
Computing Systems ◽  
Iot Applications ◽  
Service Times

AbstractIn the Internet of Things (IoT) era, the capacity-limited Internet and uncontrollable service delays for various new applications, such as video streaming analysis and augmented reality, are challenges. Cloud computing systems, also known as a solution that offloads energy-consuming computation of IoT applications to a cloud server, cannot meet the delay-sensitive and context-aware service requirements. To address this issue, an edge computing system provides timely and context-aware services by bringing the computations and storage closer to the user. The dynamic flow of requests that can be efficiently processed is a significant challenge for edge and cloud computing systems. To improve the performance of IoT systems, the mobile edge orchestrator (MEO), which is an application placement controller, was designed by integrating end mobile devices with edge and cloud computing systems. In this paper, we propose a flexible computation offloading method in a fuzzy-based MEO for IoT applications in order to improve the efficiency in computational resource management. Considering the network, computation resources, and task requirements, a fuzzy-based MEO allows edge workload orchestration actions to decide whether to offload a mobile user to local edge, neighboring edge, or cloud servers. Additionally, increasing packet sizes will affect the failed-task ratio when the number of mobile devices increases. To reduce failed tasks because of transmission collisions and to improve service times for time-critical tasks, we define a new input crisp value, and a new output decision for a fuzzy-based MEO. Using the EdgeCloudSim simulator, we evaluate our proposal with four benchmark algorithms in augmented reality, healthcare, compute-intensive, and infotainment applications. Simulation results show that our proposal provides better results in terms of WLAN delay, service times, the number of failed tasks, and VM utilization.

Cost efficient resource allocation for real-time tasks in embedded systems

2019 ◽  
Vol 48 ◽  
pp. 101523 ◽  
Author(s):  
Nasro Min-Allah ◽  
Muhammad Bilal Qureshi ◽  
Saleh Alrashed ◽  
Omer F. Rana
Keyword(s):  
Resource Allocation ◽  
Embedded Systems ◽  
Real Time ◽  
Efficient Resource ◽  
Cost Efficient

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 MB-CNN: Memristive Binary Convolutional Neural Networks for Embedded Mobile Devices

2018 ◽  
Vol 8 (4) ◽  
pp. 38 ◽  
Author(s):  
Arjun Pal Chowdhury ◽  
Pranav Kulkarni ◽  
Mahdi Nazm Bojnordi
Keyword(s):  
Neural Networks ◽  
Mobile Devices ◽  
Convolutional Neural Networks ◽  
Mobile User ◽  
Power Performance ◽  
Performance Requirements ◽  
System Energy ◽  
And Performance ◽  
The Cost

Applications of neural networks have gained significant importance in embedded mobile devices and Internet of Things (IoT) nodes. In particular, convolutional neural networks have emerged as one of the most powerful techniques in computer vision, speech recognition, and AI applications that can improve the mobile user experience. However, satisfying all power and performance requirements of such low power devices is a significant challenge. Recent work has shown that binarizing a neural network can significantly improve the memory requirements of mobile devices at the cost of minor loss in accuracy. This paper proposes MB-CNN, a memristive accelerator for binary convolutional neural networks that perform XNOR convolution in-situ novel 2R memristive data blocks to improve power, performance, and memory requirements of embedded mobile devices. The proposed accelerator achieves at least 13.26 × , 5.91 × , and 3.18 × improvements in the system energy efficiency (computed by energy × delay) over the state-of-the-art software, GPU, and PIM architectures, respectively. The solution architecture which integrates CPU, GPU and MB-CNN outperforms every other configuration in terms of system energy and execution time.

scholarly journals Security and Cost-Aware Computation Offloading via Deep Reinforcement Learning in Mobile Edge Computing

2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Binbin Huang ◽  
Yangyang Li ◽  
Zhongjin Li ◽  
Linxuan Pan ◽  
Shangguang Wang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Mobile Devices ◽  
Mobile Applications ◽  
Mobile User ◽  
Edge Computing ◽  
Computation Offloading ◽  
Security Threats ◽  
Mobile Edge Computing ◽  
Computing Environment ◽  
Markov Decision

With the explosive growth of mobile applications, mobile devices need to be equipped with abundant resources to process massive and complex mobile applications. However, mobile devices are usually resource-constrained due to their physical size. Fortunately, mobile edge computing, which enables mobile devices to offload computation tasks to edge servers with abundant computing resources, can significantly meet the ever-increasing computation demands from mobile applications. Nevertheless, offloading tasks to the edge servers are liable to suffer from external security threats (e.g., snooping and alteration). Aiming at this problem, we propose a security and cost-aware computation offloading (SCACO) strategy for mobile users in mobile edge computing environment, the goal of which is to minimize the overall cost (including mobile device’s energy consumption, processing delay, and task loss probability) under the risk probability constraints. Specifically, we first formulate the computation offloading problem as a Markov decision process (MDP). Then, based on the popular deep reinforcement learning approach, deep Q-network (DQN), the optimal offloading policy for the proposed problem is derived. Finally, extensive experimental results demonstrate that SCACO can achieve the security and cost efficiency for the mobile user in the mobile edge computing environment.

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.

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