Auction-Based VM Allocation for Deadline-Sensitive Tasks in Distributed Edge Cloud

Gao, Guoju; Xiao, Mingjun; Wu, Jie; Huang, He; Wang, Shengqi; Chen, Guoliang

doi:10.1109/tsc.2019.2902549

Cited by 67 publications

(29 citation statements)

References 36 publications

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“…A very few works concern the QoE as a metric directly. energy Gao et al [49] independent full cost Chen et al [50] independent full cost Chen et al [51] independent full profit Yuan et al [52] independent full profit Lin et al [53] independent full performance, energy Du et al [54] independent full performance, energy Duan et al [55] independent full performance, energy Mahmud et al [56] independent full performance, profit Li et al [57] independent full Performance, cost Sun et al [58] independent full performance, cost Adhikari et al [59] independent full performance, utilization Ma et al [60] independent full QoE, cost Miao et al [61] independent partial performance Kai et al [62] independent partial performance Guo et al [63] independent partial performance Meng et al [64], [65] independent partial performance hop-e Cui et al [66], [67] independent partial performance hop-d, hop-e Sarkar et al [68] independent partial performance hop-e Ouyang et al [69] independent partial performance Y Cheng et al [70] independent partial energy Xia et al [71] independent partial energy Zhang et al [72] independent partial cost Chabbouh et al [73] independent partial performance, balance Y Wang et al [74] independent partial performance, cost Zhao et al [75] independent partial performance, cost Khayyat et al [76] independent partial performance, energy Alshahrani et al [77] independent partial performance, energy Chen et al [78] independent partial performance, cost, energy Hong et al [16] independent partial performance, energy hop-d Sun et al [79] independent partial performance, energy Long et al [80] independent partial performance, energy Nguyen et al…”

Section: Optimization Objectivementioning

confidence: 99%

“…b: Cost/Profit Optimization Gao et al [49] transform the task offloading problem as periodical auctions by seeing servers and users as the resource sellers and buyers, respectively, where the cloud is seen as a server, and formulate it as a constrained total profit optimization problem. To solve the problem, they model is as a weighted bipartite graph matching problem with capacity and deadline constraints, and adopt the heuristic method which iteratively selects the edge with the best profits from the bipartite graph.…”

Section: ) All Offloading A: Response Time Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

et al. 2020

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Edge-cloud computing, combining the benefits of both edge computing and cloud computing, is one of the most promising ways to address the resource insufficiency of smart devices. Task offloading is an important challenge must be addressed for edge-cloud computing in practice, which decides the place and the time for performing each task. Even though there is existing research focusing on the task offloading in edge-cloud computing, a lot of problems should be solved before the application of these offloading technologies. Thus, in this article, we first propose a taxonomy of task offloading in edge-cloud environments to investigate and classify related research articles, and then summarize several challenges which have not been addressed for future research directions on this area to promote the development of edge-cloud market.

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Section: Optimization Objectivementioning

confidence: 99%

Section: ) All Offloading A: Response Time Optimizationmentioning

confidence: 99%

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

et al. 2020

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“…In the following, it is shown that G-TRAP is actually an incentive mechanism to solve the distribution problem defined above and it satisfies the economic properties include truthfulness, individual rationality, budget balance, and computing efficiency [29]. Definition 1.…”

Section: Properties Of G-trapmentioning

confidence: 99%

Double Auction-Based Two-Level Resource Allocation Mechanism for Computation Offloading in Mobile Blockchain Application

2021

Mobile Information Systems

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It is increasingly popular that platforms integrate various services into mobile applications due to the high usage and convenience of mobile devices, many of which demand high computational capacities and energy, such as cryptocurrency services based on blockchain. However, it is hard for mobile devices to run these services due to the limited storage and computational capacity. In this paper, the problem of computation offloading that requires sufficient computing resources with high utilization in large-scale users and multiprovider MEC system was investigated. A mechanism based on the combinatorial double auction, G-TRAP, is proposed in this paper to solve the above problem. In the mechanism, resources are provided both in the cloud and at the edge of the network. Mobile users compete for these resources to offload computing tasks by the rule that the edge-level resources will be allocated at first while cloud-level resources could be the supplement for the edge level. Given that the proof-of-work (PoW), the core issue of blockchain application, is resource-expensive to implement in mobile devices, we provide resource allocation service to users of blockchain application as experimental subjects. Simulation results show that the proposed mechanism for serving large-scale users in a short execution time outperforms two existing algorithms in terms of social utility and resource utilization. Consequently, our proposed system can effectively solve the intensive computation offloading problem of mobile blockchain applications.

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“…In practice, when a user rents a virtual machine to use an edge cloud server in the MEC environment, it creates a request and then submits the request to the platform. The request includes the user's maximum allowable latency (i.e., deadline), the size of the input task, and the number of resources required by the virtual machine [2]. We use the deadline value of these.…”

Section: Task Analysismentioning

confidence: 99%

“…By applying distributed computing technology to wireless base stations, MEC technology can dramatically reduce delay time and overall network traffic: in MEC, various services are provided with caching content at the wireless base station closest to a target user device. A typical MEC framework is composed of many mobile devices, a smaller number of edge cloud servers, and one central cloud server [2]. For example, when a user executes a task on his/her device, if the processing power of the device is insufficient to perform the task, it is offloaded to an edge server.…”

Section: Introductionmentioning

confidence: 99%

Latency-Classification-Based Deadline-Aware Task Offloading Algorithm in Mobile Edge Computing Environments

Choi

Lee

2019

Applied Sciences

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In this study, we consider an edge cloud server in which a lightweight server is placed near a user device for the rapid processing and storage of large amounts of data. For the edge cloud server, we propose a latency classification algorithm based on deadlines and urgency levels (i.e., latency-sensitive and latency-tolerant). Furthermore, we design a task offloading algorithm to reduce the execution time of latency-sensitive tasks without violating deadlines. Unlike prior studies on task offloading or scheduling that have applied no deadlines or task-based deadlines, we focus on a comprehensive deadline-aware task scheduling scheme that performs task offloading by considering the real-time properties of latency-sensitive tasks. Specifically, when a task is offloaded to the edge cloud server due to a lack of resources on the user device, services could be provided without delay by offloading latency-tolerant tasks first, which are presumed to perform relatively important functions. When offloading a task, the type of the task, weight of the task, task size, estimated execution time, and offloading time are considered. By distributing and offloading latency-sensitive tasks as much as possible, the performance degradation of the system can be minimized. Based on experimental performance evaluations, we prove that our latency-based task offloading algorithm achieves a significant execution time reduction compared to previous solutions without incurring deadline violations. Unlike existing research, we applied delays with various network types in the MEC (mobile edge computing) environment for verification, and the experimental result was measured not only by the total response time but also by the cause of the task failure rate.

show abstract

Auction-Based VM Allocation for Deadline-Sensitive Tasks in Distributed Edge Cloud

Cited by 67 publications

References 36 publications

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

Double Auction-Based Two-Level Resource Allocation Mechanism for Computation Offloading in Mobile Blockchain Application

Latency-Classification-Based Deadline-Aware Task Offloading Algorithm in Mobile Edge Computing Environments

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