Data and Task Offloading in Collaborative Mobile Fog-Based Networks

Ciobanu, Radu-Ioan; Dobre, Ciprian; Bălănescu, Mihaela; Suciu, George

doi:10.1109/access.2019.2929683

Cited by 15 publications

(10 citation statements)

References 31 publications

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“…Some scholars use D2D collaboration to implement collaborative edge computing. Ciobanu et al [21] propose a computational offloading solution that can improve user QoE, reduce application and service developers' cost, and reduce battery consumption. In [3], the D2D is utilized to offload tasks to other smart devices for collaborative execution.…”

Section: Related Workmentioning

confidence: 99%

Cooperative Edge Computing Task Offloading Strategy for Urban Internet of Things

Wang

2021

Wireless Communications and Mobile Computing

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With the continuous progress of edge computing technology and the development of the Internet of Things technology, scenarios such as smart transportation, smart home, and smart medical care enable people to enjoy the smart era’s convenience. Simultaneously, with the addition of many smart devices, a large number of tasks are submitted to the edge server, making the edge server unable to meet the needs of completing tasks submitted by the smart device. Besides, if the task is submitted to the remote cloud data center, it increases the user’s additional delay and cost. Therefore, it is necessary to improve the task offloading strategy and resource allocation scheme to solve these problems. This paper first proposes a new task offloading mechanism and then proposes a two-stage Stackelberg game model to solve each participant’s interaction problem in the task offloading mechanism and ensure the maximization of their respective interests. Finally, a theoretical analysis proves the equilibrium of the two-stage Stackelberg game. Experiments are used to prove the effectiveness of the proposed mechanism. Comparative experimental results show that the proposed model can achieve better results regarding delay and energy consumption.

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Section: Related Workmentioning

confidence: 99%

Cooperative Edge Computing Task Offloading Strategy for Urban Internet of Things

Wang

2021

Wireless Communications and Mobile Computing

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“…The advantages of MECs include data locality, as data is usually produced at the edge, and of low network latencies afforded by local WiFi networks. Moreover, recently proposed a hybrid of MEC and cloud architectures are also considered in simulation frameworks like EdgeCloudSim (Suryavansh et al, 2019 ) or MobEmu (Ciobanu et al, 2019 ).…”

Section: Related Workmentioning

confidence: 99%

A Self-Aware and Scalable Solution for Efficient Mobile-Cloud Hybrid Robotics

2020

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Backed by the virtually unbounded resources of the cloud, battery-powered mobile robotics can also benefit from cloud computing, meeting the demands of even the most computationally and resource-intensive tasks. However, many existing mobile-cloud hybrid (MCH) robotic tasks are inefficient in terms of optimizing trade-offs between simultaneously conflicting objectives, such as minimizing both battery power consumption and network usage. To tackle this problem we propose a novel approach that can be used not only to instrument an MCH robotic task but also to search for its efficient configurations representing compromise solution between the objectives. We introduce a general-purpose MCH framework to measure, at runtime, how well the tasks meet these two objectives. The framework employs these efficient configurations to make decisions at runtime, which are based on: (1) changing of the environment (i.e., WiFi signal level variation), and (2) itself in a changing environment (i.e., actual observed packet loss in the network). Also, we introduce a novel search-based multi-objective optimization (MOO) algorithm, which works in two steps to search for efficient configurations of MCH applications. Analysis of our results shows that: (i) using self-adaptive and self-aware decisions, an MCH foraging task performed by a battery-powered robot can achieve better optimization in a changing environment than using static offloading or running the task only on the robot. However, a self-adaptive decision would fall behind when the change in the environment happens within the system. In such a case, a self-aware system can perform well, in terms of minimizing the two objectives. (ii) The Two-Step algorithm can search for better quality configurations for MCH robotic tasks of having a size from small to medium scale, in terms of the total number of their offloadable modules.

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“…Block reordering is another method used in [24] to reduce the amount of communication required in Strassen's algorithm. Reducing the communication traffic has also been used to speed up applications in large scale distributed systems such as mobile networks [25].…”

Section: Related Workmentioning

confidence: 99%

Fine Tuning the Performance of Parallel Codes

Gheibi¹,

Banerjee²,

Ranka³

et al. 2020

Adv. sci. technol. eng. syst. j.

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We propose a multilevel method to speed highly optimized parallel codes whose runtime increases faster than their workload. This method requires the ability to solve large instances by decomposing them into smaller instances. Using a simple parallel computing model, we derive a mathematical model that predicts whether or not our method can improve performance and also predicts the amount of improvement attainable. Our method is tested and shown to be effective on three highly optimized BLAS (Basic Linear Algebra Subprograms) routines from Intel's Math Kernel Library (MKL). Those routines are cblas dgemm, cblas dtrmm and cblas dsymm. On the Intel Knights Landing (KNL) platform our method speeds cblas dgemm by 33%, cblas dtrmm by 50% and cblas dsymm by 49% on double-precision matrices of size 16K × 16K, when the KNL's default memory-clustering configuration (cache-quadrant) is used.

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Data and Task Offloading in Collaborative Mobile Fog-Based Networks

Cited by 15 publications

References 31 publications

Cooperative Edge Computing Task Offloading Strategy for Urban Internet of Things

Cooperative Edge Computing Task Offloading Strategy for Urban Internet of Things

A Self-Aware and Scalable Solution for Efficient Mobile-Cloud Hybrid Robotics

Fine Tuning the Performance of Parallel Codes

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