Fog Computing Networks Research Articles

Migration Route Selection for a Microservice in a Fog Computing Network

Purpose. The new generation of communication networks has provided the basis for the realization of new classes of services, one of which are URLLC services. These services include concepts such as Tactile Internet. The transmission medium for URLLC requires high bandwidth and reliability of communication channels as well as ultra-low latency in the network. To cover these requirements, various technologies, such as fog computing and microservice architecture, are introduced into the architecture of networks. The latter is more often recommended to be used together with support for microservice migration between devices. A related goal is to determine the efficient route to the destination server. Methods. Conducting research on a model network with the collection of real load statistics from the network nodes and their analysis to determine the most efficient migration route for microservice according to the proposed algorithm based on these data. Novelty. The novelty of the presented method consists in adapting the classical algorithm of route selection in a graph to solve the actual problem of microservice migration route selection. Results. The parameters that need to be considered for efficient routing during microservice migration and the level of network architecture that should control the migration were defined. The results have shown that the proposed method is preferred over the AODV protocol. Practical relevance. It is quantitatively shown that using the proposed method the CPU resources consumption on the network nodes during microservice migration can be reduced total by 77 %, the RAM resources usage by 14 %, the percentage of lost packets on the used communication channels can be reduced total by 7 %, which would provide higher network reliability and increase its performance.

Agile Services Provisioning for Learning-Based Applications in Fog Computing Networks

Fog computing has emerged as a promising solution for service provisioning. To support the increasing number of smart end devices and their requirement for intelligent data processing, numerous learning-based applications will be deployed at the edge network to provide services with an acceptable level of latency. However, such applications assume that the training and the actual data are independent and identically distributed, which is impractical in the dynamic and heterogeneous environment of the real world. This makes service matchmaking a challenge because even if the service functionality matches user requirements, the mismatched actual data will inevitably result in the degradation of service quality. This paper targets the mismatching issue of learning-based applications and the high requirement of intelligent data processing at the network edge and proposes a novel service provisioning model. This model introduces a novel service description model to resolve data mismatch, a clustering algorithm that pre-processes requests to deal with high concurrency requirements, and a heuristic joint service searching method to reduce traffic costs. This work was evaluated through a case study and simulations. Evaluation results show that the proposed model can achieve a good quality of services and reduce response time as well as traffic costs.

Distributed Design of Wireless Powered Fog Computing Networks With Binary Computation Offloading

This paper investigates a multi-user wireless powered fog computing (FC) network, where multiple energy-limited wireless sensor devices (WSDs) first harvest energy from a nearby hybrid access point (HAP), and then compute their tasks locally (i.e., the local computing (LC) mode) or offload the tasks to the HAP (i.e., the FC mode) via a binary offloading policy. In order to pursue the green computing network design, an optimization problem is formulated to minimize the transmit power at the HAP by jointly optimizing the time allocation ratio and the computing mode selection vector, under the energy causality constraints and the WSDs’ computing rate requirements constraints. To efficiently solve the formulated non-convex problem in a distributed manner, it is first transformed into an approximate form, and then an alternating direction method of multipliers (ADMM)-based algorithm is designed to solve the transformed problem, based on which the successive convex approximation (SCA) is adopted to improve the approximating precision in an iterative way. With the proposed ADMM-based distributed algorithm, each WSD is able to optimize its computing mode and offloading time with local channel state information (CSI), which thus is more suitable for large-scale networks. For comparison, a channel-sorting-based (CSB) centralized algorithm with global CSI is also presented, and the computational complexities of the proposed ADMM-based algorithm and the CSB algorithm are analyzed. Simulation results show that the proposed distributed algorithm achieves a comparable performance with the CSB centralized algorithm and the exhaustive search method. It is also observed that to minimize the transmit power at the HAP, the WSDs with the better channel quality are inclined to select the LC mode, which is much different from traditional sum-computation-rate maximization design.

Joint Computational and Wireless Resource Allocation in Multicell Collaborative Fog Computing Networks

In 6G and future networks, joint optimization of communication and computational resources lays the foundation for various delay-sensitive intelligent IoT services in the fog computing architecture. In this paper, we present a multi-device collaborative computing architecture in the cell association environment to accelerate the processing procedure of data generated by smart IoT devices. In this scenario, a two-tier task scheduling scheme and an uplink and downlink power allocation factor are jointly optimized to reduce the data processing delay and improve fairness among different users, which is in nature a hard problem due to a series of non-convex constraints. To make the problem tractable, the problem is transformed into a smooth non-convex problem with the introduction of auxiliary variables and then decoupled into two subproblems based on the data transmission and processing procedure. Thereafter, different methods such as Successive Convex Approximation (SCA) and Block Successive Upperbound Minimization (BSUM) are employed to reconstruct several upper-bound convex optimization subproblems. Besides, a fast 0–1 binary offloading scheme is proposed based on the original algorithm. Finally, the simulation results depict the effectiveness of the proposed algorithms in detail, and the scalability of the system is also examined.

Collaborative Energy Beamforming for Wireless Powered Fog Computing Networks

Beam-based wireless power transfer and Fog/edge computing are promising dual technologies for realizing wireless powered Fog computing networks to support the upcoming B5G/6G IoT applications, which require latency-aware and intensive computing, with a limited energy supply. In such systems, IoT devices can either offload their computing tasks to the proximal Fog nodes or execute local computing with replenishing energy from the dedicated beamforming. However, effective integration of these techniques is still challenging, where two new issues arise: energy-aware task offloading and signal interferences from spillovers of wireless beamforming. In this paper, we observe that the beam-ripple phenomenon, which takes advantage of beamformer defects to transfer energy to IoT devices, is the key to jointly addressing these two issues. Different from traditional SWIPT technology, as in our approach the stream is not separately divided into data/energy streams, but target IoT devices can potentially harvest the whole stream. Inspired by this phenomenon, we treat the collaborative energy beamforming and edge computing design as a strongly <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {NP}$ </tex-math></inline-formula> -hard optimization problem. The proposed solution is an iterative algorithm to cascadingly integrate a polynomial-time <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\left({1 - \frac {1}{e}}\right)$ </tex-math></inline-formula> -approximation algorithm, which achieves the theoretical upper bound in approximation ratio unless <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {P} = \mathcal {NP}$ </tex-math></inline-formula> , and an optimal dynamic programming algorithm. The numerical results show that the energy minimization goal among IoT devices can achieve, and the developed harvest-when-interfered protocol is practical in the wireless powered Fog computing networks.

Towards Reliable Remote Health Monitoring in Fog Computing Networks

As the World is still facing the COVID-19 pandemic, several researchers and industry players have proposed technological solutions to help fight the pandemic and pave the way for post-pandemic era precautions. In this matter, the potential benefits of remote health monitoring have been brought back to the spotlight. Indeed, with current advances in wireless communications, core network virtualization, and computing architectures as enablers, consistently guaranteeing the stringent quality-of-service (QoS) requirements of remote health monitoring, e.g., ultra-low latency, may be achievable. Notably, the fog computing (FC) paradigm has been advocated as a potential solution for remote health monitoring. However, the unreliability of fog nodes in FC networks is a critical aspect often overlooked despite its significant impact on vital latency requirements. This paper proposes a reliable fog-based remote health monitoring framework operating under uncertain fog computing conditions. Specifically, we formulate the problem of assigning tasks of remote sensors attached to patients to their adequate applications deployed in fog nodes aiming to maximize the number of satisfied tasks with respect to the fog nodes’ availability and communication latency constraints. Due to the problem’s NP-hardness, we leverage a differential evolution-based algorithm enhanced by reinforcement learning to deploy applications in fog nodes. Numerical results demonstrate the superior reliability performance of our proposed solution, in terms of the average success ratio of tasks, compared to benchmarks. Specifically, our simulations show up to 60 % performance improvement compared to benchmarks in specific scenarios. Moreover, by investigating the impact of several key parameters, we identify a design trade-off between the number of fog nodes and the latter’s intrinsic failure rates.

Reliability Evaluation of a Cloud–Fog Computing Network Considering Transmission Mechanisms

A cloud–fog computing system is modeled as a network topology where each edge represents a transmission line, and each node represents a hub, an Internet of Things (IoT) device, a fog server, or a cloud server. Such a system with several capacities at each edge is called a stochastic-flow cloud–fog computing network (SCCN). There are three phases of transmitting data: the first is to transmit data from the IoT devices to the fog servers. The second and third phases are transmitting a part of the data from the fog servers to the cloud servers for the further operations and back to the IoT devices for the immediate operations, respectively. Based on the transmission mechanisms, two demands, including initial demands and processed demands, should be satisfied simultaneously, where an initial demand (a processed demand) is outing from an IoT device (a fog server). An algorithm is developed to evaluate reliability, the probability of successfully transmitting the data through the SCCN, by elucidating the flow relationship among the IoT devices, edge servers, and cloud servers. Furthermore, another approach to adjust the flow assignment is also proposed for the case that demand may not be an integer. A large real case is provided to validate the applicability and scalability of the proposed methodology.

Social Phenomena and Fog Computing Networks: A Novel Perspective for Future Networks

Fog computing is an emerging technology that aims at reducing the load on cloud data centers by migrating some computation and storage toward end-users. It leverages the intermediate servers for local processing and storage while making it possible to offload part of the computation and storage to the cloud. Inspired by the benefits of fog computing, we present a novel paradigm that considers the context of social phenomena. Online and off-line human interactions and the mobile social network’s relentless growth allowed real-world data and created users’ traces. We categorize social phenomena into two main groups to integrate with fog computing from social interactions’ continuous development. In this regard, the first contribution addresses the social relationship between the end-users and fog nodes based on personal benefits. The social relationship considers trust, reciprocity, incentives, and selfishness mechanisms. The second contribution describes the group-based social behavior, i.e., centrality, community, and colocation in fog computing networks (FCNs). We also discuss the impact of social phenomena on FCNs in network performance, resource allocations, security, and privacy. We present open challenges and highlight future directions on social perception to encourage follow-up work.

ICDRP-F-SDVN: An innovative cluster-based dual-phase routing protocol using fog computing and software-defined vehicular network

Over time, wireless technology is evolving impulsively. For the most part, studies presently focused on the telecommunication field. Vehicular Ad-hoc Network (VANET) is one of the leading genuine and appealing areas for Intelligent Transportation Systems (ITS) originators and organizations. Essentially, VANET is an ad-hoc network consisting of several vehicles that flow on the same road. The existence of such networks revolutionizes the wireless communication field. The immense increase in the number of smart vehicles throughout time is the reason behind the massive evolution in VANET technologies. VANETs requirements are evolving quickly, therefore, having more flexible, scalable, and well-connected routes in VANETs is becoming essential in any new routing protocol. Antiquated VANETs routing protocols and designs encounter very challenging technical, employment, and control problems with the evolution of VANETs requirements. This paper presents a new efficient routing protocol for VANETs, namely, Innovative Cluster-Based Dual-Phase Routing Protocol Using Fog Computing and Software-Defined Vehicular Network (ICDRP-F-SDVN) which utilizes state-of-the-art technologies to overcome the drawbacks of traditional VANETs routing protocols. The use of fog computing and Software-Defined Networks (SDN) combined in our protocol presents a robust new architecture that meets all the new requirements and overcomes challenges arising from technological development and rapid escalation in the number of smart vehicles. Additionally, we propose a novel clustering algorithm mainly for the reason of lessening the long-distance communications in each cluster. In conjunction with this clustering algorithm, we propose an efficient control overhead reduction mechanism, thereby lowering the messages' exchanges imposed. Interestingly, our proposed protocol is a dual-phase protocol with a 90% of SDN packet delivery ratio. In other words, when the SDN fails to deliver packets, traditional Ad-hoc On-Demand Distance Vector (AODV) handles them as a fallback mechanism. In this paper, the performance of ICDRP-F-SDVN is evaluated in terms of throughput and end-to-end delay and further compared with four closely related routing protocols, that are, Intersection Dynamic VANET Routing (IDVR), Vehicle Density and Load Aware (VDLA), Intelligent Routing Protocol Using Real-Time Traffic Information in Urban Vehicular Environment (IRTIV), and Greedy Perimeter Coordinator Routing (GPCR). In terms of control overhead, the comparison is conducted with Control Overhead Reduction Algorithm (CORA), Moving Zone-Based Routing Protocol (MoZo), Beaconless Routing Algorithm for Vehicular Environment (BRAVE), and Cluster-Based Directional Routing Protocol (CBDRP). Our proposed protocol has achieved impressive simulation results compared to previously mentioned protocols. In particular, the throughput is increased by 8277%, 64309%, 76235%, and 96053% while the end-to-end delay is reduced by 77.5%, 82.5%, 87.2%, 95.6% over IDVR, VDLA, IRTIV, and GPCR, respectively. Finally, the control overhead is lowered by 42.3%, 87%, 93.5%, 96.7% over that of CORA, MoZo, BRAVE, and CBDRP, respectively.

Evaluation of Performance of Cloud of Things (CoT) for Transferring Multimedia and Bulk-set Data

As the number of context-aware and unique data extracting and computing has grown leaps and bounds, paving way to their application in many platforms, Internet of Things (IoT) has gained a lot of importance in recent years. Using IoT, it is possible to connect any number of objects and in any context, giving rise to a diverse range of services. Hence power management, data storage, service discovery, service management, service creation and resource management need a more sophisticated mechanism and a better infrastructure. However, a single IoT that is power-constrained will not be able to tackle all the data that is generated. Hence cloud computing plays a crucial role by integrating with the IoT in such a way that the future internet and envisioned IoT is achievable. There are a number of challenges involved of which data trimming is common challenge. Due to unnecessary communication, the data this sent to the cloud may be interrupted. Hence to prevent this, data is initially preprocessed following which it is trimmed and then transferred to the cloud. This aspect of data processing can be carried out through Fog Computing or Smart Network that serves as a smart gateway. In this paper, we have introduced a novel concept Fog computing as the smart gateway. The results have been analysed and tested with respect to bulk-data synchronizstion Delay, bulk-data upload delay, jitter, synchronization delay and upload delay.

Optimal Energy Efficiency With Delay Constraints for Multi-Layer Cooperative Fog Computing Networks

We develop a joint offloading and resource allocation framework for a multi-layer cooperative fog computing network, aiming to minimize the total energy consumption of multiple mobile devices subject to their service delay requirements. The resulting optimization involves both binary (offloading decisions) and real variables (resource allocations), making it an NP-hard and computationally intractable problem. To tackle it, we first propose an improved branch-and-bound algorithm (IBBA) that is implemented in a centralized manner. However, due to the large size of the cooperative fog computing network, the computational complexity of the proposed IBBA is relatively high. To speed up the optimal solution searching as well as to enable its distributed implementation, we then leverage the unique structure of the underlying problem and the parallel processing at fog nodes. To that end, we propose a distributed framework, namely feasibility finding Benders decomposition (FFBD), that decomposes the original problem into a master problem for the offloading decision and subproblems for resource allocation. The master problem (MP) is then equipped with powerful cutting-planes to exploit the fact of resource limitation at fog nodes. The subproblems (SP) for resource allocation can find their closed-form solutions using our fast solution detection method. These (simpler) subproblems can then be solved in parallel at fog nodes. The numerical results show that the FFBD always returns the optimal solution of the problem with significantly less computation time (e.g., compared with the centralized IBBA approach). The FFBD with the fast solution detection method, namely FFBD-F, can reduce up to 60% and 90% of computation time, respectively, compared with those of the conventional FFBD, namely FFBD-S, and IBBA.

Blockchain‐Enabled Task Offloading and Resource Allocation in Fog Computing Networks

To address the data security and user privacy issues in the task offloading process and resource allocation of the fog computing network, a blockchain‐enabled fog computing network task offloading model is proposed in this paper. Furthermore, to reduce the network utility which is defined as the total energy consumption of the fog computing network and the total delay of the blockchain network, a blockchain‐enabled fog computing network task offloading and resource allocation algorithm (TR‐BFCN) is proposed to jointly optimize the task offloading decision and resource allocation. Finally, the original nonconvex optimization problem is converted into two suboptimization problems, namely, task offloading decisions and computational resource allocations. Moreover, a two‐stage Stackelberg game model is designed to obtain the optimal amount of purchased resource and the optimal resource pricing. Simulation results show that the proposed TR‐BFCN algorithm can effectively reduce the network utility compared with other algorithms.

A Resource Management Model for Distributed Multi-Task Applications in Fog Computing Networks

While the effectiveness of fog computing in Internet of Things (IoT) applications has been widely investigated in various studies, there is still a lack of techniques to efficiently utilize the computing resources in a fog platform to maximize Quality of Service (QoS) and Quality of Experience (QoE). This paper presents a resource management model for service placement of distributed multitasking applications in fog computing through mathematical modeling of such a platform. Our main design goal is to reduce communication between the candidate nodes hosting different task modules of an application by selecting a group of nodes near each other and as close to the source of the data as possible. We propose a method based on a greedy principle that demonstrates a highly scalable and near-optimal performance for resource mapping problems for multitasking applications in fog computing networks. Compared with the commercial Gurobi optimizer, our proposed algorithm provides a mapping solution that obtains 93% of the performance, attributed to a higher communication cost, while outperforming the reference method in terms of the computing speed, cutting the mapping execution time to less than 1% of that of the Gurobi optimizer.

Learning-in-the-Fog (LiFo): Deep Learning Meets Fog Computing for the Minimum-Energy Distributed Early-Exit of Inference in Delay-Critical IoT Realms

Fog Computing (FC) and Conditional Deep Neural Networks (CDDNs) with early exits are two emerging paradigms which, up to now, are evolving in a standing-alone fashion. However, their integration is expected to be valuable in IoT applications in which resource-poor devices must mine large volume of sensed data in real-time. Motivated by this consideration, this article focuses on the optimized design and performance validation of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Learning-in-the-Fog</i> (LiFo), a novel virtualized technological platform for the minimum-energy and delay-constrained execution of the inference-phase of CDDNs with early exits atop multi-tier networked computing infrastructures composed by multiple hierarchically-organized wireless Fog nodes. The main research contributions of this article are threefold, namely: (i) we design the main building blocks and supporting services of the LiFo architecture by explicitly accounting for the multiple constraints on the per-exit maximum inference delays of the supported CDNN; (ii) we develop an adaptive algorithm for the minimum-energy distributed joint allocation and reconfiguration of the available computing-plus-networking resources of the LiFo platform. Interestingly enough, the designed algorithm is capable to self-detect (typically, unpredictable) environmental changes and quickly self-react them by properly re-configuring the available computing and networking resources; and, (iii) we design the main building blocks and related virtualized functionalities of an Information Centric-based networking architecture, which enables the LiFo platform to perform the aggregation of spatially-distributed IoT sensed data. The energy-vs.-inference delay LiFo performance is numerically tested under a number of IoT scenarios and compared against the corresponding ones of some state-of-the-art benchmark solutions that do not rely on the Fog support.

A secure anonymous tracing fog-assisted method for the Internet of Robotic Things

PurposeEmploying a fog computing (FC) network system in the robotic network system is an effective solution to support robotic application issues. The interconnection between robotic devices through an FC network can be referred as the Internet of Robotic Things (IoRT). Although the FC network system can provide number of services closer to IoRT devices, it still faces significant challenges including real-time tracing services and a secure tracing services. Therefore, this paper aims to provide a tracking mobile robot devices in a secure and private manner, with high efficiency performance, is considered essential to ensuring the success of IoRT network applications.Design/methodology/approachThis paper proposes a secure anonymous tracing (SAT) method to support the tracing of IoRT devices through a FC network system based on the Counting Bloom filter (CBF) and elliptic curve cryptography techniques. With the proposed SAT mechanism, a fog node can trace a particular robot device in a secure manner, which means that the fog node can provide a service to a particular robot device without revealing any private data such as the device's identity or location.FindingsAnalysis shows that the SAT mechanism is both efficient and resilient against tracing attacks. Simulation results are provided to show that the proposed mechanism is beneficial to support IoRT applications over an FC network system.Originality/valueThis paper represents a SAT method based on CBF and elliptic curve cryptography techniques as an efficient mechanism that is resilient against tracing attacks.

An Intelligent Adaptive Algorithm for Servers Balancing and Tasks Scheduling over Mobile Fog Computing Networks

With the increasing popularity of terminals and applications, the corresponding requirements of services have been growing significantly. In order to improve the quality of services in resource restrained user devices and reduce the large latency of service migration caused by long distance in cloud computing, mobile fog computing (MFC) is presented to provide supplementary resources by adding a fog layer with several servers near user devices. Focusing on cloud-aware MFC networks with multiple servers, we formulate a problem with the optimization objective to improve the quality of service, relieve the restrained resource of user device, and balance the workload of participant server. In consideration of the data size of remaining task, the power consumption of user device, and the appended workload of participant server, this paper designs a machine learning-based algorithm which aims to generate intelligent adaptive strategies related with load balancing of collaborative servers and dynamic scheduling of sequential tasks. Based on the proposed algorithm and software-defined networking technology, the tasks can be executed cooperatively by the user device and the servers in the MFC network. Besides, we conducted some experiments to verify the algorithm effectiveness under different numerical parameters including task arrival rate, avaliable server workload, and wireless channel condition. The simulation results show that the proposed intelligent adaptive algorithm achieves a superior performance in terms of latency and power consumption compared to candidate algorithms.

A Multilevel Mobile Fog Computing Offloading Model Based on UAV-Assisted and Heterogeneous Network

The mobile fog computing (MFC) network that integrates unmanned aerial vehicles (UAV) fully exerts its advantages of flexible deployment, load balance, and rapid response. Under complex network environment, proposing a reasonable offloading model and according resource optimization of the MFC network is important to satisfy high-requirement offloading standard. In this paper, a multilevel MFC offloading model where UAV and fog nod undertake relay nodes and offloading computing nodes are established for computation-intensive and latency-critical tasks, considering heterogeneous network selection , dynamic channel quality and central cloud access . With the total system utility optimality function including reward function maximization as the goal, the MDP algorithm is applied to solve the best offloading decision of the computing task and the balanced load mode of the MFC network. Finally, the simulation section verifies the excellent performance of the proposed multilevel MFC offloading model in network resource utilization. Simulation results show that the model can optimize the relative position of service nodes in MFC network and ensure the offloading reliability of terminal equipment.

An energy harvesting solution for computation offloading in Fog Computing networks

Fog Computing is a promising networking paradigm enabling the nodes at the edge to share computational and storage resources. Being pervasively distributed, Fog Nodes are often battery powered and, for this reason, an efficient energy management should be considered to prolong network lifetime. In this paper, we introduce a smart energy management solution able to exploit information about the predicted harvested and consumed energy by Fog Nodes, equipped with small solar panels. The smart energy management is applied on a cluster based Fog Computing environment where computation offloading operations are performed. In the experimental section the effect of the smart energy management is explored in terms of network lifetime by considering variable battery size and Fog Nodes density in a realistic solar-panel harvesting-model and Fog Nodes setting.

Mobile fog computing security: A user-oriented smart attack defense strategy based on DQL

Each fog node interacts with data from multiple end-users in mobile fog computing (MFC) networks. Malicious users can use a variety of programmable wireless devices to launch different modes of smart attacks such as impersonation attack, jamming attack, and eavesdropping attack between fog servers and legitimate users. The existing research in MFC lacks in the contributions of defense of smart attack and also requires in the discussions of subjective decision making by participants. Therefore, we propose a smart attack defense scheme for authorized users in MFC in this paper. First, we construct a static zero-sum game model between smart attackers and legitimate users based on prospect theory. Second, the double Q-learning (DQL) is proposed to restrain the attack motive of smart attackers in the dynamic environment. The proposed DQL method generates the optimum defense choice of legitimate users against smart attacks so that they can efficiently determine whether to use only physical layer security (PLS) to avoid those smart attacks. We use our scheme to contrast with the basic schemes, i.e., Q-learning scheme, the Sarsa scheme, and the greedy strategy. Experiment results prove that the proposed scheme can enhance the utility of legitimate users, restrain the attack motive of smart attackers, and further provide better security protection in the MFC environment.

Multi-Objective Computation Sharing in Energy and Delay Constrained Mobile Edge Computing Environments

In a mobile edge computing (MEC) network, mobile devices, also called edge clients, offload their computations to multiple edge servers that provide additional computing resources. Since the edge servers are placed at the network edge, e.g., cell-phone towers, transmission delays between edge servers and edge clients are shorter compared to those of cloud computing. In addition, edge clients can offload their tasks to other nearby edge clients with available computing resources by exploiting the Fog Computing (FC) paradigm. A major challenge in MEC and FC networks is to assign the tasks from edge clients to edge servers, as well as to other edge clients, in such a way that their tasks are completed with minimum energy consumption and minimum processing delay. In this paper, we model task offloading in MEC as a constrained multi-objective optimization problem (CMOP) that minimizes both the energy consumption and task processing delay of the mobile devices. To solve the CMOP, we design an evolutionary algorithm that can efficiently find a representative sample of the best trade-offs between energy consumption and task processing delay, i.e., the Pareto-optimal front. Compared to existing approaches for task offloading in MEC, we see that our approach finds offloading decisions with lower energy consumption and task processing delay.