Improving the performance of mobile applications using cloud computing

Elgendy, Ibrahim A.; Elkawkagy, Mohamed; Keshk, Arabi

doi:10.1109/infos.2014.7036687

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…where η i,j and δ i,j refer the CPU cycles needed for encrypting and decrypting the data at user i and edge server, respectively [37], [38]. Moreover, regarding the security, computation and communication models the total consumption of time and energy for processing a tasks j of the user i can be defined as:…”

Section: Security Modelmentioning

confidence: 99%

Security-Aware Data Offloading and Resource Allocation For MEC Systems: A Deep Reinforcement Learning

Elgendy¹,

Muthanna²,

Hammoudeh³

et al. 2021

Preprint

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The Internet of Things (IoT) is permeating our daily lives where it can provide data collection tools and important measurement to inform our decisions. In addition, they are continually generating massive amounts of data and exchanging essential messages over networks for further analysis. The promise of low communication latency, security enhancement and the efficient utilization of bandwidth leads to the new shift change from Mobile Cloud Computing (MCC) towards Mobile Edge Computing (MEC). In this study, we propose an advanced deep reinforcement resource allocation and securityaware data offloading model that considers the computation and radio resources of industrial IoT devices to guarantee that shared resources between multiple users are utilized in an efficient way. This model is formulated as an optimization problem with the goal of decreasing the consumption of energy and computation delay. This type of problem is NP-hard, due to the curseof-dimensionality challenge, thus, a deep learning optimization approach is presented to find an optimal solution. Additionally, an AES-based cryptographic approach is implemented as a security layer to satisfy data security requirements. Experimental evaluation results show that the proposed model can reduce offloading overhead by up to 13.2% and 64.7% in comparison with full offloading and local execution while scaling well for large-scale devices.

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Section: Security Modelmentioning

confidence: 99%

Security-Aware Data Offloading and Resource Allocation For MEC Systems: A Deep Reinforcement Learning

Elgendy¹,

Muthanna²,

Hammoudeh³

et al. 2021

Preprint

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“…There have been various attempts to offload data and intensive portions of computation from mobile devices to the cloud. Lin et al, 24 Elgendy et al, 25 and Kolb et al 26 have proposed frameworks for offloading computation from a mobile device to a server. These frameworks consider different patterns to determine when battery use can be reduced by offloading.…”

Section: Related Workmentioning

confidence: 99%

CUDA offloading for energy‐efficient and high‐frame‐rate simulations using tablets

Martinez‐Noriega

Yazaki

Narumi

2019

Concurrency and Computation

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The multiple sensors and touch capabilities of mobile devices are defining new methods of computer interaction. However, the computing power of such devices is not currently sufficient for new applications that require compute-intensive applications. Using graphics processing units (GPUs) for general-purpose computing with GPU programming models such as Compute Unified Device Architecture (CUDA) has been proved to accelerate simulations in supercomputers. Although, CUDA-capable chips such as the Tegra K1 have been released on tablets can accelerate computer simulations, their absolute computing power and performance per watt are not comparable with ordinary GPUs. In this paper, we analyze a heterogeneous system composed of both of a tablet (client) and notebook with a low-power GPU (server). Intensive computations on a tablet device are offloaded to a notebook GPU using the rCUDA middleware. Molecular dynamics (MD) simulations are performed using our test system, and the computing speed and performance per watt are reported. Implementing dynamic parallelism (DP) reduced the latency, doubling the total frames per second in some cases. Our system achieves better computational performance, and higher performance per watt than a tablet powered by a CUDA-capable GPU. We achieved 21.7 Gflops/W by combining multiple client tablets and server, compared with 21.3 Gflops/W from the server itself. KEYWORDS energy-efficient computing, GPGPU offloading, mobile cloud computing, mobile computing, real-time simulations 1 INTRODUCTION General-purpose computing on graphics processing units (GPGPU) has become a standard method of acceleration in high-performance computing (HPC), with supercomputers such as those listed in the Top500 and Green500 lists 1 typically using this kind of acceleration. Programming models that handle GPUs and specific architectures have arisen from major GPU vendors such as OpenCL 2 and Compute Unified Device Architecture (CUDA). 3 Developed by NVIDIA, CUDA has quickly gained popularity among computer scientists, who use it to accelerate many kinds of simulations across various fields. 4 Post-PC devices such as tablets and smartphones have become part of our daily lives. These mobile devices with touch screens and sensors are changing the way users interact with computers and view data, and are defining how new software is created. Using such technology, interactive modeling, such as interactive molecular dynamics (MD) simulations, 5,6 enables the artificial acceleration of simulations through manual interaction. Mobile devices are suitable for such simulations because they have touch capability and multiple sensors. Nevertheless, mobile devices require more computational power to deliver the best user experience for such intensive computational tasks, because simulations like these are characterized by high frame rates and processor-intensive routines. Cloud computing is another approach that can complement the low computing power of mobile devices. This is achieved by offloading intensive computations to...

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“…To address the contradiction between the requirements of these applications and the limitations of resource-constrained vehicles, a computational offloading concept has been introduced in which resource-intensive computations are migrated from the vehicles to a resource-rich server for remote execution, and the results are returned [4]- [8]. Vehicular cloud computing (VCC) was initially developed to provide vehicles with flexibility in computing, storage and service capabilities, thus reducing power consumption and enhancing application performance.…”

Section: Introductionmentioning

confidence: 99%

Advanced Deep Learning-Based Computational Offloading for Multilevel Vehicular Edge-Cloud Computing Networks

et al. 2020

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The promise of low latency connectivity and efficient bandwidth utilization has driven the recent shift from vehicular cloud computing (VCC) towards vehicular edge computing (VEC). This paper presents an advanced deep learning-based computational offloading algorithm for multilevel vehicular edge-cloud computing networks. To conserve energy and guarantee the efficient utilization of shared resources among multiple vehicles, an integration model of computational offloading, and resource allocation is formulated as a binary optimization problem to minimize the total cost of the entire system in terms of time and energy. However, this problem is considered NP-hard and it is computationally prohibitive to solve this type of problem, particularly for large-scale vehicles, due to the curse-of-dimensionality problem. Therefore, an equivalent reinforcement learning form is generated and we propose a distributed deep learning algorithm to find the near-optimal computational offloading decisions in which a set of deep neural networks are used in parallel. Finally, simulation results show that the proposed algorithm can exhibit fast convergence and significantly reduce the overall consumption of an entire system compared to the benchmark solutions.INDEX TERMS Computation offloading, vehicular edge-cloud computing, autonomous vehicles, 5G, resource allocation, deep reinforcement learning.

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Improving the performance of mobile applications using cloud computing

Cited by 7 publications

References 12 publications

Security-Aware Data Offloading and Resource Allocation For MEC Systems: A Deep Reinforcement Learning

Security-Aware Data Offloading and Resource Allocation For MEC Systems: A Deep Reinforcement Learning

CUDA offloading for energy‐efficient and high‐frame‐rate simulations using tablets

Advanced Deep Learning-Based Computational Offloading for Multilevel Vehicular Edge-Cloud Computing Networks

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