scholarly journals Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 779
Author(s):  
Shichao Chen ◽  
Qijie Li ◽  
Mengchu Zhou ◽  
Abdullah Abusorrah
Keyword(s):  
Problem Formulation ◽  
Computing System ◽  
High Energy ◽  
Edge Computing ◽  
Computation Offloading ◽  
Future Research ◽  
Research Issues ◽  
Computing Paradigm ◽  
Collaborative Scheduling ◽  
And Storage

In edge computing, edge devices can offload their overloaded computing tasks to an edge server. This can give full play to an edge server’s advantages in computing and storage, and efficiently execute computing tasks. However, if they together offload all the overloaded computing tasks to an edge server, it can be overloaded, thereby resulting in the high processing delay of many computing tasks and unexpectedly high energy consumption. On the other hand, the resources in idle edge devices may be wasted and resource-rich cloud centers may be underutilized. Therefore, it is essential to explore a computing task collaborative scheduling mechanism with an edge server, a cloud center and edge devices according to task characteristics, optimization objectives and system status. It can help one realize efficient collaborative scheduling and precise execution of all computing tasks. This work analyzes and summarizes the edge computing scenarios in an edge computing paradigm. It then classifies the computing tasks in edge computing scenarios. Next, it formulates the optimization problem of computation offloading for an edge computing system. According to the problem formulation, the collaborative scheduling methods of computing tasks are then reviewed. Finally, future research issues for advanced collaborative scheduling in the context of edge computing are indicated.

scholarly journals A Survey on Mobile Edge Computing: Focusing on Service Adoption and Provision

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Kai Peng ◽  
Victor C. M. Leung ◽  
Xiaolong Xu ◽  
Lixin Zheng ◽  
Jiabin Wang ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Mobile Networks ◽  
Edge Computing ◽  
Computation Offloading ◽  
Battery Life ◽  
Mobile Edge Computing ◽  
Service Adoption ◽  
Comprehensive Survey ◽  
And Storage ◽  
Execution Delay

Mobile cloud computing (MCC) integrates cloud computing (CC) into mobile networks, prolonging the battery life of the mobile users (MUs). However, this mode may cause significant execution delay. To address the delay issue, a new mode known as mobile edge computing (MEC) has been proposed. MEC provides computing and storage service for the edge of network, which enables MUs to execute applications efficiently and meet the delay requirements. In this paper, we present a comprehensive survey of the MEC research from the perspective of service adoption and provision. We first describe the overview of MEC, including the definition, architecture, and service of MEC. After that we review the existing MUs-oriented service adoption of MEC, i.e., offloading. More specifically, the study on offloading is divided into two key taxonomies: computation offloading and data offloading. In addition, each of them is further divided into single MU offloading scheme and multi-MU offloading scheme. Then we survey edge server- (ES-) oriented service provision, including technical indicators, ES placement, and resource allocation. In addition, other issues like applications on MEC and open issues are investigated. Finally, we conclude the paper.

Two-tier end-edge collaborative computation offloading for edge computing

2021 ◽  
pp. 1-12
Author(s):  
Yong Xiao ◽  
Ling Wei ◽  
Junhao Feng ◽  
Wang En
Keyword(s):  
Computing System ◽  
End Users ◽  
Edge Computing ◽  
Computation Offloading ◽  
Iot Applications ◽  
Delay Sensitive ◽  
Task Processing ◽  
Queue Lengths ◽  
The Stability ◽  
The Cost

Edge computing has emerged for meeting the ever-increasing computation demands from delay-sensitive Internet of Things (IoT) applications. However, the computing capability of an edge device, including a computing-enabled end user and an edge server, is insufficient to support massive amounts of tasks generated from IoT applications. In this paper, we aim to propose a two-tier end-edge collaborative computation offloading policy to support as much as possible computation-intensive tasks while making the edge computing system strongly stable. We formulate the two-tier end-edge collaborative offloading problem with the objective of minimizing the task processing and offloading cost constrained to the stability of queue lengths of end users and edge servers. We perform analysis of the Lyapunov drift-plus-penalty properties of the problem. Then, a cost-aware computation offloading (CACO) algorithm is proposed to find out optimal two-tier offloading decisions so as to minimize the cost while making the edge computing system stable. Our simulation results show that the proposed CACO outperforms the benchmarked algorithms, especially under various number of end users and edge servers.

Toward Agent-Based Grid Computing

2011 ◽  
pp. 173-188
Author(s):  
Lizhe Wang
Keyword(s):  
Grid Computing ◽  
Research Trends ◽  
Future Research ◽  
Research Issues ◽  
Agent Based ◽  
Computing Paradigm ◽  
Natural Choice ◽  
Key Technologies ◽  
Computing Grid ◽  
Multi Agent

This chapter discusses research issues related to agent-based Grid computing. Grid computing now becomes an innovative computing paradigm and helps build non-traditional computing infrastructures and applications. Multiple-agent systems and algorithms, on the other hand, mainly focus on solving corporative problems among multiple participants, mainly from theoretical aspects. It is thus a natural choice to combine these two key technologies together and benefit both research communities. This chapter first reviews background for multi-agent system, agent-based computing, and Grid computing. Research challenges and issues are characterized and identified together with possible solutions. After the investigation of current research efforts of agent-based Grid computing, future research trends are presented and studied.

scholarly journals Latency-Aware Computation Offloading for 5G Networks in Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xianwei Li ◽  
Baoliu Ye
Keyword(s):  
Poor Quality ◽  
Edge Computing ◽  
Computation Offloading ◽  
5G Networks ◽  
Time Data ◽  
Fine Grained ◽  
Computing Paradigm ◽  
Speed Up ◽  
Real Time Data Processing

With the development of Internet of Things, massive computation-intensive tasks are generated by mobile devices whose limited computing and storage capacity lead to poor quality of services. Edge computing, as an effective computing paradigm, was proposed for efficient and real-time data processing by providing computing resources at the edge of the network. The deployment of 5G promises to speed up data transmission but also further increases the tasks to be offloaded. However, how to transfer the data or tasks to the edge servers in 5G for processing with high response efficiency remains a challenge. In this paper, a latency-aware computation offloading method in 5G networks is proposed. Firstly, the latency and energy consumption models of edge computation offloading in 5G are defined. Then the fine-grained computation offloading method is employed to reduce the overall completion time of the tasks. The approach is further extended to solve the multiuser computation offloading problem. To verify the effectiveness of the proposed method, extensive simulation experiments are conducted. The results show that the proposed offloading method can effectively reduce the execution latency of the tasks.

scholarly journals Dependable and Secure Voting Mechanism in Edge Computing

2019 ◽  
Vol 11 (12) ◽  
pp. 262
Author(s):  
Pedro A.R.S. Costa ◽  
Marko Beko
Keyword(s):  
Middle Layer ◽  
Edge Computing ◽  
Cloud Environment ◽  
Computing Paradigm ◽  
Edge Network ◽  
And Storage ◽  
The Internet Of Things ◽  
Multi Cloud ◽  
Voting Mechanism ◽  
Open Issues

Edge computing is a distributed computing paradigm that encompasses data computing and storage and is performed close to the user, efficiently guaranteeing faster response time. This paradigm plays a pivotal role in the world of the Internet of Things (IoT). Moreover, the concept of the distributed edge cloud raises several interesting open issues, e.g., failure recovery and security. In this paper, we propose a system composed of edge nodes and multiple cloud instances, as well as a voting mechanism. The multi-cloud environment aims to perform centralized computations, and edge nodes behave as a middle layer between edge devices and the cloud. Moreover, we present a voting mechanism that leverages the edge network to validate the performed computation that occurred in the centralized environment.

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