Resource Sharing of a Computing Access Point for Multi-User Mobile Cloud Offloading with Delay Constraints

Chen, Meng-Hsi; Dong, Min; Liang, Ben

doi:10.1109/tmc.2018.2815533

Cited by 143 publications

(56 citation statements)

References 48 publications

(111 reference statements)

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“…An example of such efforts was presented by Miluzzo et al [40] where a vision of mobile devices as a core for the cloudlet-distributed processing was proposed and evaluated. Another study proposed by Chen et al [42] tackled the problem of the mobile devices energy consumption optimization during task offloading processes. The processing and communication problem is formulated as a cost optimization problem, considering the effect of delay on offloaded tasks.…”

Section: (B) Fog Computing and Distributed Processingmentioning

confidence: 99%

Optimum resource allocation in optical wireless systems with energy-efficient fog and cloud architectures

Alsulami

Alahmadi

Saeed

et al. 2020

Phil. Trans. R. Soc. A.

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Optical wireless communication (OWC) is a promising technology that can provide high data rates while supporting multiple users. The optical wireless (OW) physical layer has been researched extensively, however, less work was devoted to multiple access and how the OW front end is connected to the network. In this paper, an OWC system which employs a wavelength division multiple access (WDMA) scheme is studied, for the purpose of supporting multiple users. In addition, a cloud/fog architecture is proposed for the first time for OWC to provide processing capabilities. The cloud/fog-integrated architecture uses visible indoor light to create high data rate connections with potential mobile nodes. These OW nodes are further clustered and used as fog mini servers to provide processing services through the OW channel for other users. Additional fog-processing units are located in the room, the building, the campus and at the metro level. Further processing capabilities are provided by remote cloud sites. Two mixed-integer linear programming (MILP) models were proposed to numerically study networking and processing in OW systems. The first MILP model was developed and used to optimize resource allocation in the indoor OWC systems, in particular, the allocation of access points (APs) and wavelengths to users, while the second MILP model was developed to optimize the placement of processing tasks in the different fog and cloud nodes available. The optimization of tasks placement in the cloud/fog-integrated architecture was analysed using the MILP models. Multiple scenarios were considered where the mobile node locations were varied in the room and the amount of processing and data rate requested by each OW node was varied. The results help to identify the optimum colour and AP to use for communication for a given mobile node location and OWC system configuration, the optimum location to place processing and the impact of the network architecture. This article is part of the theme issue ‘Optical wireless communication’.

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Section: (B) Fog Computing and Distributed Processingmentioning

confidence: 99%

Optimum resource allocation in optical wireless systems with energy-efficient fog and cloud architectures

Alsulami

Alahmadi

Saeed

et al. 2020

Phil. Trans. R. Soc. A.

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“…The AP may serve its conventional networking function and forward tasks to the remote cloud server, or be a CAP with built-in computation capability to directly process some received tasks by itself. been studied in [32]- [38]. Compared with two-tier systems, the three-tier system adds extra flexibility for task offloading.…”

Section: B Three-tier Offloading Systemmentioning

confidence: 99%

“…We have previously studied the scheduling of computation and communication resources in a CAP for a single mobile user [36] and multiple mobile users each with a single task only [37], [38], showing substantial system performance improvement under such simplified system models. In this work, we focus on the joint optimization problem for a general multi-user multi-task scenario.…”

Section: B Three-tier Offloading Systemmentioning

confidence: 99%

Multi-User Multi-Task Offloading and Resource Allocation in Mobile Cloud Systems

Chen

Liang

Dong

2018

IEEE Trans. Wireless Commun.

Self Cite

128

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We consider a general multi-user Mobile Cloud Computing (MCC) system where each mobile user has multiple independent tasks. These mobile users share the computation and communication resources while offloading tasks to the cloud. We study both the conventional MCC where tasks are offloaded to the cloud through a wireless access point, and MCC with a computing access point (CAP), where the CAP serves both as the network access gateway and a computation service provider to the mobile users. We aim to jointly optimize the offloading decisions of all users as well as the allocation of computation and communication resources, to minimize the overall cost of energy, computation, and delay for all users. The optimization problem is formulated as a non-convex quadratically constrained quadratic program, which is NP-hard in general. For the case without a CAP, an efficient approximate solution named MUMTO is proposed by using separable semidefinite relaxation (SDR), followed by recovery of the binary offloading decision and optimal allocation of the communication resource. To solve the more complicated problem with a CAP, we further propose an efficient three-step algorithm named MUMTO-C comprising of generalized MUMTO SDR with CAP, alternating optimization, and sequential tuning, which always computes a locally optimal solution. For performance benchmarking, we further present numerical lower bounds of the minimum system cost with and without the CAP. By comparison with this lower bound, our simulation results show that the proposed solutions for both scenarios give nearly optimal performance under various parameter settings, and the resultant efficient utilization of a CAP can bring substantial cost benefit.

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“…In a multiple-input multipleoutput (MIMO) multicell system, [10] jointly optimized the precoding matrices of multiple wireless devices (WDs) and the CPU frequency assigned to each device with fixed binary offloading decisions, in order to minimize the overall users' energy consumption. A multi-user MCC system with a computing access point (CAP), which can serve as both a network gateway connecting to the cloud and an edge cloudlet, was studied in [11] to find the binary offloading decisions. Joint optimization of (partial) offloaded data length and offloading time/subcarrier allocation was studied in a multi-user single-server MEC system based on time-division multiple access (TDMA) and orthogonal frequency-division multiple access (OFDMA), respectively, in [13].…”

Section: Introductionmentioning

confidence: 99%

Joint Task Assignment and Resource Allocation for D2D-Enabled Mobile-Edge Computing

Xing

Liu

et al. 2019

IEEE Trans. Commun.

177

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With the proliferation of computation-extensive and latency-critical applications in the 5G and beyond networks, mobile-edge computing (MEC) or fog computing, which provides cloud-like computation and/or storage capabilities at the network edge, is envisioned to reduce computation latency as well as to conserve energy for wireless devices (WDs). This paper studies a novel device-to-device (D2D)-enabled multi-helper MEC system, in which a local user solicits its nearby WDs serving as helpers for cooperative computation. We assume a time division multiple access (TDMA) transmission protocol, under which the local user offloads the tasks to multiple helpers and downloads the results from them over orthogonal pre-scheduled time slots. Under this setup, we minimize the computation latency by optimizing the local user's task assignment jointly with the time and rate for task offloading and results downloading, as well as the computation frequency for task execution, subject to individual energy and computation capacity constraints at the local user and the helpers. However, the formulated problem is a mixed-integer nonlinear program (MINLP) that is difficult to solve. To tackle this challenge, we propose an efficient algorithm by first relaxing the original problem into a convex one, and then constructing a suboptimal task assignment solution based on the obtained optimal one. Next, we consider a benchmark scheme that endows the WDs with their maximum computation capacities. To further reduce the implementation complexity, we also develop a heuristic scheme based on the greedy task assignment. Finally, numerical results validate the effectiveness of our proposed algorithm, as compared against the heuristic scheme and other benchmark ones without either joint optimization of radio and computation resources or task assignment design.

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Resource Sharing of a Computing Access Point for Multi-User Mobile Cloud Offloading with Delay Constraints

Cited by 143 publications

References 48 publications

Optimum resource allocation in optical wireless systems with energy-efficient fog and cloud architectures

Optimum resource allocation in optical wireless systems with energy-efficient fog and cloud architectures

Multi-User Multi-Task Offloading and Resource Allocation in Mobile Cloud Systems

Joint Task Assignment and Resource Allocation for D2D-Enabled Mobile-Edge Computing

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