Energy-Optimal Dynamic Computation Offloading for Industrial IoT in Fog Computing
01 Jun 2020-Vol. 4, Iss: 2, pp 566-576
TL;DR: An energy-optimal dynamic computation offloading scheme (EDCO) for IIoT in a fog computing scenario that is superior to the local computing, full offloading and partial offloading with fixed computation speed schemes in terms of energy consumption and completion time and the convergence rate advantage of the accelerated algorithm.
Abstract: Fog computing is emerging as a promising mode to meet the stringent requirement of low latency in industrial Internet of Things (IIoT). By dynamically offloading part of the computation-intensive tasks from a fog node to a cloud server, the computation experience of users can be further improved in fog computing systems. In this paper, we develop an energy-optimal dynamic computation offloading scheme (EDCO) for IIoT in a fog computing scenario. The purpose is to minimize energy consumption when computation tasks are accomplished within a desired energy overhead and delay. Specifically, we first formulate an energy minimization computation offloading problem with delay, energy and other network resource constraints. To address this optimization problem, an accelerated gradient algorithm with joint optimization of the offloading ratio and transmission time is proposed; it can find the optimal value with a fast speed that improves the convergence speed of traditional methods. Subsequently, to better meet the stringent energy and latency requirements of IIoT applications, the dynamic voltage scaling (DVS) technique is integrated into the above solution, and we develop an alternating minimization algorithm to achieve energy-optimal fog computation offloading by jointly optimizing the offloading ratio, transmission power, local CPU computation speed and transmission time. Finally, the numerical results reveal that the proposed offloading scheme is superior to the local computing, full offloading and partial offloading with fixed computation speed schemes in terms of energy consumption and completion time. We also confirm the convergence rate advantage of the accelerated algorithm.
Citations
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TL;DR: In this paper , the authors present an overview of massive IoT and 6G-enabling technologies and discuss different energy-related challenges that arise while using fog computing in 6G enabled massive IoT.
Abstract: Fog computing is a promising technology that can provide storage and computational services to future 6G networks. To support the massive Internet-of-Things (IoT) applications in 6G, fog computing will play a vital role. IoT devices and fog nodes have energy limitations and hence, energy-efficient techniques are needed for storage and computation services. We present an overview of massive IoT and 6G-enabling technologies. We discuss different energy-related challenges that arise while using fog computing in 6G-enabled massive IoT. We categorize different energy-efficient fog computing solutions for IoT and describe the recent work done in these categories. Finally, we discuss future opportunities and open challenges in designing energy-efficient techniques for fog computing in the future 6G massive IoT network.
51 citations
TL;DR: This article surveys emerging technologies related to pervasive edge computing for industrial internet-of-things (IIoT) enabled by fifth-generation (5G) and beyond communication networks and reinforces the perspective that the PEC paradigm is an extremely suitable and important deployment model for industrial communication networks.
Abstract: This article surveys emerging technologies related to pervasive edge computing (PEC) for industrial internet-of-things (IIoT) enabled by fifth-generation (5G) and beyond communication networks. PEC encompasses all devices that are capable of performing computational tasks locally, including those at the edge of the core network (edge servers co-located with 5G base stations) and in the radio access network (sensors, actuators, etc.). The main advantages of this paradigm are core network offloading (and benefits therefrom) and low latency for delay-sensitive applications (e.g., automatic control). We have reviewed the state-of-the-art in the PEC paradigm and its applications to the IIoT domain, which have been enabled by the recent developments in 5G technology. We have classified and described three important research areas related to PEC—distributed artificial intelligence methods, energy efficiency, and cyber security. We have also identified the main open challenges that must be solved to have a scalable PEC-based IIoT network that operates efficiently under different conditions. By explaining the applications, challenges, and opportunities, our paper reinforces the perspective that the PEC paradigm is an extremely suitable and important deployment model for industrial communication networks, considering the modern trend toward private industrial 5G networks with local operations and flexible management.
37 citations
Cites background from "Energy-Optimal Dynamic Computation ..."
...The work in [78] addressed an industrial scenario where multiple IIoT devices are assisted by both edge and remote central servers....
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...Simulation studies in [78] showed that the proposed algorithms outperform the conventional approaches both in terms of energy consumption and convergence time....
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TL;DR: This article proposes a partially observable offloading scheme to enable the IoT device to make the optimal offloading decision with imperfect channel-state information, and an offline algorithm based on the deep recurrent $Q$ -network (DRQN) is developed.
Abstract: Driven by the growing popularity of mobile applications, such as the Internet of Things (IoT), fog computing has been envisioned as a promising approach to enhance the computation capability of mobile devices and reduce the energy consumption In this article, we aim to investigate the dynamic computation offloading problem in the IoT fog system under the fast time-varying wireless channel conditions Our work differs from the existing work, which is based on the assumption that the channel-state information can be perfectly obtained by the offloading agent (eg, the IoT device) In reality, due to hardware limitation, short sensing time, and network connectivity issues in IoT fog systems, it is difficult for the IoT device to have the perfect knowledge of a dynamic channel environment Therefore, in this article, we propose a partially observable offloading scheme to enable the IoT device to make the optimal offloading decision with imperfect channel-state information The optimization problem is formulated as a partially observable Markov decision process (POMDP) formulation, with the objective of minimizing the IoT device’s energy consumption while meeting its requirement on task processing delay To find the optimal offloading solution, an offline algorithm based on the deep recurrent $Q$ -network (DRQN) is developed Finally, extensive simulation experiments are performed to evaluate the effectiveness of the proposed offloading scheme
32 citations
TL;DR: In this paper, the authors provide a systematic and comprehensive study to evaluate fog offloading mechanisms' current and recent works and state and address the present potentialities and issues of offloading in a fog environment efficiently.
Abstract: Fog computing is considered a formidable next-generation complement to cloud computing. Nowadays, in light of the dramatic rise in the number of IoT devices, several problems have been raised in cloud architectures. By introducing fog computing as a mediate layer between the user devices and the cloud, one can extend cloud computing's processing and storage capability. Offloading can be utilized as a mechanism that transfers computations, data, and energy consumption from the resource-limited user devices to resource-rich fog/cloud layers to achieve an optimal experience in the quality of applications and improve the system performance. This paper provides a systematic and comprehensive study to evaluate fog offloading mechanisms' current and recent works. Each selected paper's pros and cons are explored and analyzed to state and address the present potentialities and issues of offloading mechanisms in a fog environment efficiently. We classify offloading mechanisms in a fog system into four groups, including computation-based, energy-based, storage-based, and hybrid approaches. Furthermore, this paper explores offloading metrics, applied algorithms, and evaluation methods related to the chosen offloading mechanisms in fog systems. Additionally, the open challenges and future trends derived from the reviewed studies are discussed.
22 citations
TL;DR: Two energy prediction techniques are presented, the first one is based on the Recursive Least Square (RLS) filter and the second one uses the Artificial Neural Network (ANN), both techniques use inputs such as the number of tasks and size of the tasks to predict the energy consumption at different fog nodes.
21 citations
References
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17 Aug 2012
TL;DR: This paper argues that the above characteristics make the Fog the appropriate platform for a number of critical Internet of Things services and applications, namely, Connected Vehicle, Smart Grid, Smart Cities, and, in general, Wireless Sensors and Actuators Networks (WSANs).
Abstract: Fog Computing extends the Cloud Computing paradigm to the edge of the network, thus enabling a new breed of applications and services. Defining characteristics of the Fog are: a) Low latency and location awareness; b) Wide-spread geographical distribution; c) Mobility; d) Very large number of nodes, e) Predominant role of wireless access, f) Strong presence of streaming and real time applications, g) Heterogeneity. In this paper we argue that the above characteristics make the Fog the appropriate platform for a number of critical Internet of Things (IoT) services and applications, namely, Connected Vehicle, Smart Grid, Smart Cities, and, in general, Wireless Sensors and Actuators Networks (WSANs).
4,440 citations
01 Jan 2019
TL;DR: This chapter argues that the above characteristics make the Fog the appropriate platform for a number of critical internet of things services and applications, namely connected vehicle, smart grid, smart cities, and in general, wireless sensors and actuators networks (WSANs).
Abstract: Fog computing extends the cloud computing paradigm to the edge of the network, thus enabling a new breed of applications and services. Defining characteristics of the Fog are 1) low latency and location awareness, 2) widespread geographical distribution, 3) mobility, 4) very large number of nodes, 5) predominant role of wireless access, 6) strong presence of streaming and real time applications, and 7) heterogeneity. In this chapter, the authors argue that the above characteristics make the Fog the appropriate platform for a number of critical internet of things (IoT) services and applications, namely connected vehicle, smart grid, smart cities, and in general, wireless sensors and actuators networks (WSANs).
2,384 citations
"Energy-Optimal Dynamic Computation ..." refers methods in this paper
...Fortunately, fog computing [6] has been developed to complement the shortcomings of cloud computing....
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TL;DR: This article first introduces deep learning for IoTs into the edge computing environment, and designs a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing.
Abstract: Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments Because of its multilayer structure, deep learning is also appropriate for the edge computing environment Therefore, in this article, we first introduce deep learning for IoTs into the edge computing environment Since existing edge nodes have limited processing capability, we also design a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing In the performance evaluation, we test the performance of executing multiple deep learning tasks in an edge computing environment with our strategy The evaluation results show that our method outperforms other optimization solutions on deep learning for IoT
1,270 citations
"Energy-Optimal Dynamic Computation ..." refers background in this paper
...These properties that inspire the fog computing paradigm can be applied in various fields, such as the IoT [8], vehicular networks [9], and information-centric networks [10]....
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TL;DR: This paper investigates partial computation offloading by jointly optimizing the computational speed of smart mobile device (SMD), transmit power of SMD, and offloading ratio with two system design objectives: energy consumption of ECM minimization and latency of application execution minimization.
Abstract: The incorporation of dynamic voltage scaling technology into computation offloading offers more flexibilities for mobile edge computing. In this paper, we investigate partial computation offloading by jointly optimizing the computational speed of smart mobile device (SMD), transmit power of SMD, and offloading ratio with two system design objectives: energy consumption of SMD minimization (ECM) and latency of application execution minimization (LM). Considering the case that the SMD is served by a single cloud server, we formulate both the ECM problem and the LM problem as nonconvex problems. To tackle the ECM problem, we recast it as a convex one with the variable substitution technique and obtain its optimal solution. To address the nonconvex and nonsmooth LM problem, we propose a locally optimal algorithm with the univariate search technique. Furthermore, we extend the scenario to a multiple cloud servers system, where the SMD could offload its computation to a set of cloud servers. In this scenario, we obtain the optimal computation distribution among cloud servers in closed form for the ECM and LM problems. Finally, extensive simulations demonstrate that our proposed algorithms can significantly reduce the energy consumption and shorten the latency with respect to the existing offloading schemes.
819 citations
"Energy-Optimal Dynamic Computation ..." refers background in this paper
...Since the CPU clock frequency of a fog node is usually linearly proportional to the voltage V [28], [31], equation (31) can be rewritten as...
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TL;DR: A theoretical framework of energy-optimal mobile cloud computing under stochastic wireless channel is provided, and numerical results suggest that a significant amount of energy can be saved for the mobile device by optimally offloading mobile applications to the cloud in some cases.
Abstract: This paper provides a theoretical framework of energy-optimal mobile cloud computing under stochastic wireless channel. Our objective is to conserve energy for the mobile device, by optimally executing mobile applications in the mobile device (i.e., mobile execution) or offloading to the cloud (i.e., cloud execution). One can, in the former case sequentially reconfigure the CPU frequency; or in the latter case dynamically vary the data transmission rate to the cloud, in response to the stochastic channel condition. We formulate both scheduling problems as constrained optimization problems, and obtain closed-form solutions for optimal scheduling policies. Furthermore, for the energy-optimal execution strategy of applications with small output data (e.g., CloudAV), we derive a threshold policy, which states that the data consumption rate, defined as the ratio between the data size (L) and the delay constraint (T), is compared to a threshold which depends on both the energy consumption model and the wireless channel model. Finally, numerical results suggest that a significant amount of energy can be saved for the mobile device by optimally offloading mobile applications to the cloud in some cases. Our theoretical framework and numerical investigations will shed lights on system implementation of mobile cloud computing under stochastic wireless channel.
754 citations
"Energy-Optimal Dynamic Computation ..." refers background or methods in this paper
...We define the received computation task of fog node i as being represented by a tuple (wi , ci ), where wi denotes the size (bits) of the computation task, and ci indicates the number of CPU cycles needed to complete a one bit computation [28]....
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...Since the CPU clock frequency of a fog node is usually linearly proportional to the voltage V [28], [31], equation (31) can be rewritten as...
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