Computation Offloading Strategy in Heterogeneous Fog Computing with Energy and Delay Constraints

Mukherjee, Mithun; Kumar, Vikas; Kumar, Suman; Matamy, Rakesh; Mavromoustakis, Constandinos X.; Zhang, Qi; Shojafar, Mohammad; Mastorakis, George

doi:10.1109/icc40277.2020.9148852

Cited by 22 publications

(15 citation statements)

References 11 publications

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“…In computing platforms, computation offloading strategies can be jointly exploited together with delay constraints towards energy savings. The authors in [22] proposed an offloading policy to find the optimal place where to offload and the amount of offloaded task data. In this work, the time taken for processing the offloaded task is reduced, at the same time consuming less energy.…”

Section: Methods For Energy Saving Within Computing Platformsmentioning

confidence: 99%

LSTM-Based Traffic Load Balancing and Resource Allocation for an Edge System

Dlamini

Vilakati

2020

Wireless Communications and Mobile Computing

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The massive deployment of small cell Base Stations (SBSs) empowered with computing capabilities presents one of the most ingenious solutions adopted for 5G cellular networks towards meeting the foreseen data explosion and the ultralow latency demanded by mobile applications. This empowerment of SBSs with Multi-access Edge Computing (MEC) has emerged as a tentative solution to overcome the latency demands and bandwidth consumption required by mobile applications at the network edge. The MEC paradigm offers a limited amount of resources to support computation, thus mandating the use of intelligence mechanisms for resource allocation. The use of green energy for powering the network apparatuses (e.g., Base Stations (BSs), MEC servers) has attracted attention towards minimizing the carbon footprint and network operational costs. However, due to their high intermittency and unpredictability, the adoption of learning methods is a requisite. Towards intelligent edge system management, this paper proposes a Green-based Edge Network Management (GENM) algorithm, which is an online edge system management algorithm for enabling green-based load balancing in BSs and energy savings within the MEC server. The main goal is to minimize the overall energy consumption and guarantee the Quality of Service (QoS) within the network. To achieve this, the GENM algorithm performs dynamic management of BSs, autoscaling and reconfiguration of the computing resources, and on/off switching of the fast tunable laser drivers coupled with location-aware traffic scheduling in the MEC server. The obtained simulation results validate our analysis and demonstrate the superior performance of GENM compared to a benchmark algorithm.

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Section: Methods For Energy Saving Within Computing Platformsmentioning

confidence: 99%

LSTM-Based Traffic Load Balancing and Resource Allocation for an Edge System

Dlamini

Vilakati

2020

Wireless Communications and Mobile Computing

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“…For normal services, the aim is to reduce energy consumption while the critical service target is to minimize the response time for critical service. The authors in [32] proposed an offloading scheme to minimize the delay and energy consumption in the FC architecture. The authors consider heterogeneous CPU frequencies in the proposed scheme to measure the mission.…”

Section: State-of-the-artmentioning

confidence: 99%

ARTNet: Ai-Based Resource Allocation and Task Offloading in a Reconfigurable Internet of Vehicular Networks

Akbar

Jan

et al. 2022

IEEE Trans. Netw. Sci. Eng.

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The convergence of Software-Defined Networking (SDN) and Internet of Vehicular (IoV) integrated with Fog Computing (FC), known as Software Defined Vehicular based FC (SDV-F), has recently been established to take advantage of both paradigms and efficiently control the wireless networks. SDV-F tackles numerous problems, such as scalability, load-balancing, energy consumption, and security. It lags, however, in providing a promising approach to enable ultra-reliable and delay-sensitive applications with high vehicle mobility over SDV-F. We propose ARTNet, an AI-based Vehicle-to-Everything (V2X) framework for resource distribution and optimized communication using the SDV-F architecture. ARTNet offers ultra-reliable and low-latency communications, particularly in highly dynamic environments, which is still a challenge in IoV. ARTNet is composed of intelligent agents/controllers, to make decisions intelligently about (i) maximizing resource utilization at the fog layer, and (ii) minimizing the average end-to-end delay of time-critical IoV applications. Moreover, ARTNet is designed to assign a task to fog nodes based on their states. Our experimental results show that considering a dynamic IoV environment, ARTNet can efficiently distribute the fog layer tasks while minimizing the delay.

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“…In online deployment, the computational offloading decision takes place at run-time and considers the current system status and process characteristics, such as the current waiting time and the current available computational resources, without prior knowledge of system inputs considered in the offline deployment. Several studies investigate computational offloading in offline deployment [ 8 , 11 , 12 , 13 , 14 , 18 , 19 , 20 , 21 , 22 , 23 ]. In [ 19 ], Wang et al investigated the optimized offloading problem to minimize task completion time given tolerable delay and energy constraints.…”

Section: Related Workmentioning

confidence: 99%

“…Comparing their scheme to Random, no offloading, and only offloading when considering only execution time, their simulation results showed an optimization of the execution time of tasks and energy consumption of mobile devices. Mukherjee et al [ 21 ] formulated the offloading problem as an optimization problem with the goal to minimize the total system cost, which is the sum of the total delay of end-users’ tasks and the total energy consumed at end-users’ devices due to local processing of tasks and uploading tasks to the fog environment for processing. Under delay and energy constraint, the optimization problem was transformed into a quadratically constraint quadratic programming problem and solved by semidefinite relaxation method.…”

Section: Related Workmentioning

confidence: 99%

Dynamically Controlling Offloading Thresholds in Fog Systems

Alenizi

Rana

2021

Sensors

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Fog computing is a potential solution to overcome the shortcomings of cloud-based processing of IoT tasks. These drawbacks can include high latency, location awareness, and security—attributed to the distance between IoT devices and cloud-hosted servers. Although fog computing has evolved as a solution to address these challenges, it is known for having limited resources that need to be effectively utilized, or its advantages could be lost. Computational offloading and resource management are critical to be able to benefit from fog computing systems. We introduce a dynamic, online, offloading scheme that involves the execution of delay-sensitive tasks. This paper proposes an architecture of a fog node able to adjust its offloading threshold dynamically (i.e., the criteria by which a fog node decides whether tasks should be offloaded rather than executed locally) using two algorithms: dynamic task scheduling (DTS) and dynamic energy control (DEC). These algorithms seek to minimize overall delay, maximize throughput, and minimize energy consumption at the fog layer. Compared to other benchmarks, our approach could reduce latency by up to 95%, improve throughput by 71%, and reduce energy consumption by up to 67% in fog nodes.

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Computation Offloading Strategy in Heterogeneous Fog Computing with Energy and Delay Constraints

Cited by 22 publications

References 11 publications

LSTM-Based Traffic Load Balancing and Resource Allocation for an Edge System

LSTM-Based Traffic Load Balancing and Resource Allocation for an Edge System

ARTNet: Ai-Based Resource Allocation and Task Offloading in a Reconfigurable Internet of Vehicular Networks

Dynamically Controlling Offloading Thresholds in Fog Systems

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