Joint Optimization of Task Scheduling and Image Placement in Fog Computing Supported Software-Defined Embedded System

Zeng, Deze; Gu, Lin; Guo, Song; Cheng, Zixue; Yu, Shui

doi:10.1109/tc.2016.2536019

Cited by 388 publications

(163 citation statements)

References 19 publications

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“…26,27 Besides, there are some early works that try to deal with task scheduling problem regarding fog computing. Zeng et al 28 target at minimizing the task completion time while providing a better user experience by designing an efficient resource management strategy in a fog computing supported software-defined embedded system. Meanwhile, Nan et al 29 consider a three-tier cloud of things (CoT) system and design an adaptive decision-making algorithm called unit-slot optimization for distributing the incoming data to the corresponding tiers that can provide cost-effective processing while guaranteeing average response time.…”

Section: Related Workmentioning

confidence: 99%

A cost- and performance-effective approach for task scheduling based on collaboration between cloud and fog computing

Pham

Man

Tri

et al. 2017

International Journal of Distributed Sensor Networks

127

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The rapid development of Internet of Things applications, along with the limitations of cloud computing due mainly to the far distance between Internet of Thing devices and cloud-based platform, has promoted a newly distributed computing platform based on collaboration between cloud computing and fog computing. Fog computing helps to reduce transmission latency and monetary cost for cloud resources, while cloud computing helps to fulfill the increasing demands of large-scale compute-intensive offloading applications. In this article, we study the tradeoff issue between the makespan and cloud cost when scheduling large-scale applications in such a platform. We propose a scheduling algorithm called Cost-Makespan aware Scheduling heuristic whose major objective is to achieve the balance between the performance of application execution and the mandatory cost for the use of cloud resources. Additionally, an efficient task reassignment strategy based on the critical path of the directed acyclic graph modeling the applications is also proposed to refine the output schedules of the Cost-Makespan aware Scheduling algorithm to satisfy the user-defined deadline constraints or quality of service of the system. We also verify our proposal by extensive simulations, and the experimental results show that our scheduling approach is more cost-effective and achieves better performance compared to others.

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Section: Related Workmentioning

confidence: 99%

A cost- and performance-effective approach for task scheduling based on collaboration between cloud and fog computing

Pham

Man

Tri

et al. 2017

International Journal of Distributed Sensor Networks

127

View full text Add to dashboard Cite

show abstract

“…Furthermore, the costs for fog resources should have been compared to cloud resources as these resources were also shared by multiple clients. Zeng [2] proposed a joint optimization of task scheduling and image placement in fog computing supported software-defined network embedded system. Computational resources were provided from two sources: embedded clients and fog nodes represented by computation servers.…”

Section: B On Effects Of Computation Service Ratementioning

confidence: 99%

“…Nevertheless, heavy computation tasks still need to be processed at a remote cloud due to the limitation of fog's resources. Research on computation offloading has been explored on methods and in frameworks over the last few years [1][2][3]. These approaches showed that processing requests at local fog platforms results in faster response times in general compared to handling them at centralized clouds.…”

Section: Introductionmentioning

confidence: 99%

FBRC: Optimization of task Scheduling in Fog-Based Region and Cloud

Hoang

Dang

2017

2017 IEEE Trustcom/BigDataSE/Icess

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Abstract-Fog computing preserves benefits of cloud computing and is strategically positioned to address effectively many local and performance issues because its resources and specific services are virtualized and located at the edge of the customer premises. Resource management is a critical issue affecting system performance significantly. Due to the complex distribution and high mobility of fog devices, computation resources still experience high latencies in fog's large coverage area. This paper considers a Fog-based Region and Cloud (FBRC) in which requests are locally handled not just by a region but multiple regions when additional resources are needed. An efficient task scheduling mechanism is thus essential to minimize the completion time of tasks and improve user experiences. To this end, two issues are investigated in the paper: 1) designing a fog-based region architecture to provide nearby computing resources; 2) investigating efficient scheduling algorithms to distribute tasks among regions and remote clouds. To deal with the complexity of scheduling tasks, a heuristic-based algorithm is proposed based on our formulation and validated by extensive simulations.

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“…When compared to cloud computing networks, F‐RANs shorten the transmission distance and reduce the network latency by colocating core network functions and localized mobile data content . The scalability and flexibility of F‐RANs can be increased by coordinating the technologies used in the air interface, network architecture, and core network . With these advantages, F‐RAN architectures have been selected for use in the forthcoming 5G network system .…”

Section: Introductionmentioning

confidence: 99%

Genetic algorithm‐based content distribution strategy for F‐ RAN architectures

Wang

Sun

et al. 2019

ETRI Journal

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Fog radio access network (F‐RAN) architectures provide markedly improved performance compared to conventional approaches. In this paper, an efficient genetic algorithm‐based content distribution scheme is proposed that improves the throughput and reduces the transmission delay of a F‐RAN. First, an F‐RAN system model is presented that includes a certain number of randomly distributed fog access points (F‐APs) that cache popular content from cloud and other sources. Second, the problem of efficient content distribution in F‐RANs is described. Third, the details of the proposed optimal genetic algorithm‐based content distribution scheme are presented. Finally, simulation results are presented that show the performance of the proposed algorithm rapidly approaches the optimal throughput. When compared with the performance of existing random and exhaustive algorithms, that of the proposed method is demonstrably superior.

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Joint Optimization of Task Scheduling and Image Placement in Fog Computing Supported Software-Defined Embedded System

Cited by 388 publications

References 19 publications

A cost- and performance-effective approach for task scheduling based on collaboration between cloud and fog computing

A cost- and performance-effective approach for task scheduling based on collaboration between cloud and fog computing

FBRC: Optimization of task Scheduling in Fog-Based Region and Cloud

Genetic algorithm‐based content distribution strategy for F‐ RAN architectures

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