Tasks scheduling and resource allocation in heterogeneous cloud for delay-bounded mobile edge computing

Zhao, Tianchu; Zhou, Sheng; Guo, Xueying; Niu, Zhisheng

doi:10.1109/icc.2017.7996858

Cited by 102 publications

(68 citation statements)

References 15 publications

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“…The computing time is known as the service time in the queueing model. As other works on edge and cloud computing in [13], [14], [33]- [38], we assume the computing time to be exponentially distributed for the ith type task with a service rate µ h,i , ∀h = {c, m}. 7 Clearly, when there are I types of users in the system, the overall distribution of the service time τ h , ∀h ∈ {c, m}, at the CS and a MEC server are given by…”

Section: B Proposed Task Offloading Modelmentioning

confidence: 99%

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Edge Computing-Enabled Cell-Free Massive MIMO Systems

Mukherjee

Lee

2020

IEEE Trans. Wireless Commun.

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Mobile edge computing (MEC) has been introduced to provide additional computing capabilities at network edges in order to improve performance of latency critical applications. In this paper, we consider the cell-free (CF) massive MIMO framework with implementing MEC functionalities. We consider multiple types of users with different average time requirements for computing/processing the tasks, and consider access points (APs) with MEC servers and a central server (CS) with the cloud computing capability. After deriving successful communication and computing probabilities using stochastic geometry and queueing theory, we present the successful edge computing probability (SECP) for a target computation latency. Through numerical results, we also analyze the impact of the AP coverage and the offloading probability to the CS on the SECP. It is observed that the optimal probability of offloading to the CS in terms of the SECP decreases with the AP coverage. Finally, we numerically characterize the minimum required energy consumption for guaranteeing a desired level of SECP. It is observed that for any desired level of SECP, it is more energy efficient to have larger number of APs as compared to having more number of antennas at each AP with smaller AP density.

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Section: B Proposed Task Offloading Modelmentioning

confidence: 99%

“…Note that in the last line of (51), we have L(s) ∆ = E e −sI , the Laplace transform of the total interference power, I. Using (51) and (52) in (50) we obtain (13).…”

Section: A Proof Of Theoremmentioning

confidence: 99%

Edge Computing-Enabled Cell-Free Massive MIMO Systems

Mukherjee

Lee

2020

IEEE Trans. Wireless Commun.

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Section: Introductionmentioning

confidence: 99%

“…Once a fog network is formed, the next step is to share resources and tasks among fog nodes as studied in [17]- [25]. For instance, the work in [17] investigates the problem of scheduling tasks over heterogeneous cloud servers in different scenarios in which multiple users can offload their tasks to the cloud and fog layers. The work in [18] studies the joint optimization of radio and computing resources using a game-theoretic approach in which mobile cloud service providers can decide to cooperate in resource pooling.…”

Section: Introductionmentioning

confidence: 99%

An Online Optimization Framework for Distributed Fog Network Formation With Minimal Latency

Lee

Saad

Bennis

2019

IEEE Trans. Wireless Commun.

103

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Fog computing is emerging as a promising paradigm to perform distributed, low-latency computation by jointly exploiting the radio and computing resources of end-user devices and cloud servers. However, the dynamic and distributed formation of local fog networks is highly challenging due to the unpredictable arrival and departure of neighboring fog nodes. Therefore, a given fog node must properly select a set of neighboring nodes and intelligently offload its computational tasks to this set of neighboring fog nodes and the cloud in order to achieve low-latency transmission and computation. In this paper, the problem of fog network formation and task distribution is jointly investigated while considering a hybrid fog-cloud architecture. The overarching goal is to minimize the maximum communication and computation latency by enabling a given fog node to form a suitable fog network and optimize the task distribution, under uncertainty on the arrival process of neighboring fog nodes. To solve this problem, a novel online optimization framework is proposed in which the neighboring nodes are selected by using a threshold-based online algorithm that uses a target competitive ratio, defined as the ratio between the latency of the online algorithm and the offline optimal latency. The proposed framework repeatedly updates its target competitive ratio and optimizes the distribution of the fog node's computational tasks in order to minimize latency. Simulation results show that, for specific settings, the proposed framework can successfully select a set of neighboring nodes while reducing latency by up to 19.25% compared to a baseline approach based on the well-known online secretary framework. The results also show how, using the proposed framework, the computational tasks can be properly offloaded between the fog network and a remote cloud server in different network settings.

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“…With the various demand for users [14], users can choose to store data to the local edge computing or send it to the cloud computing. In this case, resource allocation and scheduling are user oriented, so task-oriented scheduling algorithms are not applicable.…”

Section: Introductionmentioning

confidence: 99%

Resource Scheduling Based on Improved Spectral Clustering Algorithm in Edge Computing

et al. 2018

Scientific Programming

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With the development of Internet of Things (IoT), the massive data generated by it forms big data, and the complexity of dealing with big data brings challenges to resource scheduling in edge computing. In order to solve the problem of resource scheduling and improve the satisfaction of users in edge computing environment, we propose a user-oriented improved spectral clustering scheduling algorithm (ISCM) in this paper. Based on the improved k-means algorithm, the ISCM algorithm solves the problem that the clustering result is sensitive to the initial value and realizes the reclustering, which makes the obtained clustering results more stable. Finally, the edge computing resource scheduling scheme is obtained based on the clustering results. The experimental results show that the resource scheduling scheme based on improved spectral clustering algorithm is superior to traditional spectral clustering algorithm in edge computing environment.

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Tasks scheduling and resource allocation in heterogeneous cloud for delay-bounded mobile edge computing

Cited by 102 publications

References 15 publications

Edge Computing-Enabled Cell-Free Massive MIMO Systems

Edge Computing-Enabled Cell-Free Massive MIMO Systems

An Online Optimization Framework for Distributed Fog Network Formation With Minimal Latency

Resource Scheduling Based on Improved Spectral Clustering Algorithm in Edge Computing

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