Mobile Edge Computing Server Research Articles

Joint Optimization of Computation, Communication and Caching in D2D-Assisted Caching-Enhanced MEC System

In the era of intelligent applications, Mobile Edge Computing (MEC) is emerging as a promising technology that provides abundant resources for mobile devices. However, establishing a direct connection to the MEC server is not always feasible for certain devices. This paper introduces a novel Device-to-Device (D2D)-assisted system to address this challenge. The system leverages idle helper devices to execute and offload tasks to the MEC server, thereby enhancing resource utilization and reducing offload time. To further minimize offloading time for latency-sensitive tasks, this paper incorporates edge caching. The problem is formulated by jointly optimizing computation, communication and caching, and a novel Joint Multiple Decision Optimization Algorithm (JMDOA) is proposed to solve the minimum-energy-consumption problem. Specifically, the JMDOA algorithm decomposes the integer-mixed non-convex optimization problem into two subproblems based on distinct properties of discrete variables. These subproblems are solved separately and optimized iteratively, ensuring convergence to a suboptimal solution. Simulations demonstrate the effectiveness and superiority of JMDOA, exhibiting lower energy consumption and reduced time compared to other baseline algorithms, approaching the optimum. This work contributes to the field by presenting a novel approach to optimizing resource allocation in MEC systems, with potential implications for the future development of intelligent applications.

Enhanced multi-objective gorilla troops optimizer for real-time multi-user dependent tasks offloading in edge-cloud computing

This research paper investigates Mobile Edge Computing (MEC) networks, which involve numerous wireless devices transferring their computation tasks to various edge servers and a single cloud server. The study considers various real-time dependent computation tasks composing an application running on each connected device. This study aims to determine whether to process each user task locally on the device or to offload and process it on one of the nearby MEC servers or the central cloud server. Despite the continued efforts to address the issue of task offloading, it remains a difficult area of research, especially in the edge-cloud environment. Improving the Quality of Service (QoS) and achieving optimized performance and resource utilization are still necessary goals. New approaches either focus on a single objective, are computationally demanding, depend only on the cloud or MEC for offloading, not both or disregard the interdependence between tasks. This paper proposes an enhanced multi-objective version of the Gorilla Algorithm (EMGA) for offloading interdependent tasks in edge-cloud environments, with three main objectives: 1-reducing the application's execution latency, 2-lowering energy consumption for connected devices, and 3-minimizing imposed cost for using servers' resources. The proposed approach assumes that each MEC and cloud server can support multiple cost levels to increase system flexibility. A customized mutation operation was introduced in EMGA, which extends the functionality of the Standard Gorilla algorithm to improve search strategies. Comparative analyses were conducted between EMGA and other optimizers in the problem domain, and the results indicate the superiority of EMGA.

Latency Minimization for IRS-Aided NOMA MEC Systems With WPT-Enabled IoT Devices

Mobile edge computing (MEC) and intelligent reflecting surface (IRS) are envisioned as two promising technologies that enable massive connectivity in the future Internet of Things (IoT) networks. MEC allows IoT devices (IDs) to offload their computation intensive tasks, and thus, can prolong their lifespan. In contrast, the IRS can enhance the channel condition between IDs and the access points (APs), which are colocated with the MEC server. Wireless power transfer technique enabling energy harvesting for IDs helps realizing sustainable IoT network. This paper applies IRS in a multi-ID MEC system for better latency performance. We first propose a multiple access scheme with hybrid frequency division and non-orthogonal access technologies and then design a timing protocol for the IDs. Based on the above design, we study the latency optimization problem with joint optimization of power allocation, the IRS phase shift matrix, and uplink and downlink beamformer under maximum power constraint for the IDs and AP. To tackle the formulated multi-variable non-convex problem, we split the target problem into several sub-problems and provide a nearoptimal low-complexity ID clustering scheme. Afterwards, we derive optimal solutions to these sub-problems, and a lowcomplexity fast-convergence alternating algorithm is proposed to minimize the overall latency. Presented simulation results verify the convergence of the alternating algorithm, and its superiority over the benchmarks.

Proof of Evaluation-based energy and delay aware computation offloading for Digital Twin Edge Network

The increasing availability of Internet of Things (IoT) applications has led to the development of new technologies. Specifically, the deployment of edge servers close to IoT devices has strengthened the edge computing paradigm. With the collaboration of Mobile Edge Computing (MEC) and cloud computing, delay-sensitive and computation-intensive tasks can be offloaded to the edge/cloud servers to improve system performance in terms of the delay and energy consumption of IoT devices. However, there is a need to schedule the computation tasks for an efficient management. More importantly, the task scheduling strategy can face data tampering attacks to deliberately modify, destroy or manipulate the decisions. To solve the above problems, in this paper, we newly propose to integrate digital twin and blockchain into the edge networks. However, it is unclear (i) how energy and delay-aware computation should be combined, and (ii) which mining computations should be executed for a secure task scheduling. The state-of-the-art focuses on task scheduling and blockchain mining, separately. Therefore, we propose a novel blockchain-based digital twin-edge network architecture where our proposed algorithm solves these two challenges at the same execution. We design a three-layer system architecture, composed of physical entity layer, digital twin edge layer and blockchain layer. In the physical entity layer, we formulate an energy and delay-aware task scheduling problem. In the digital twin edge layer, we propose a novel Proof of Evaluation (PoE)-based secure energy and delay-aware task scheduling algorithm where optimization is executed by the genetic algorithm implementation of Warehouse Location Problem (WLP). In the blockchain layer, the best-found solutions are shared with the topology in a blockchain. Here, each block includes the hash of the previous block, a genetic algorithm-based solution, nonce value, and a hash of whole blocks in the blockchain. Thus, we aim to execute the computation tasks with an acceptable delay in an energy-efficient manner and prevent data tampering attacks against the optimal computation decisions. We validate the outcomes of our PoE-based secure digital twin-edge network model with extensive evaluations. Since the proposed model distributes the task not only to the local device but also to the MEC and cloud server for delay awareness, it reduces the delay but consumes more energy. Nevertheless, the additional energy consumption can be neglected against the delay reduction. The proposed scheme is also more scalable to compare with the conventional solution. The numerical results clearly show that the proposed model provides energy and delay awareness, maintaining both data integrity and trustworthiness at the same execution of algorithm.

Energy-efficient task scheduling for mobile edge computing with virtual machine I/O interference

Mobile edge computing (MEC) is expected to support the computation-intensive and delay-sensitive applications of mobile internet users. In this paper, we investigate the resource allocation of MEC with the effect of I/O interference among parallel virtual machines (VMs) while satisfying the quality of service (QoS) of tasks. Different from existing works, we propose a flexible task scheduling approach that combining parallel and sequential computing to minimize the computing energy consumption of MEC server. We formulate the task scheduling problem as a mixed-integer nonlinear programming (MINLP) and decompose it as a CPU resource allocation subproblem, a computing time slot subproblem, and a VM selection subproblem. We show the first subproblem is a convex problem and propose a CPU frequency allocation (CFA) algorithm based on the Karush–Kuhn–Tucker (KKT) conditions to obtain the optimal CPU frequency resource allocation. For the time slot allocation and VM selection subproblems, we propose the three step allocation (TSA) and urgency based adjusting (UBA) algorithms to obtain the near-optimal solutions, respectively. Simulation results show that compared with several time slot allocations and VM selections, the proposed TSA and UBA algorithms can save up to 21.7% and 95.8% of energy consumption, respectively.

Energy-Efficient Resource Allocation for Backscatter-Assisted Wireless Powered MEC

In this paper, we study the energy-efficient resource allocation for a backscatter-assisted wireless powered mobile edge computing (MEC) network. Considering the binary offloading mode and the limited computation capacity of the MEC server, we propose to minimize the MEC server's energy consumption by jointly optimizing the binary offloading decision of each IoT node, the computing frequencies and time at the MEC server and IoT nodes, the backscatter time and reflection coefficients of IoT nodes, as well as the MEC server's transmit power and time. It is formulated into a non-convex mixed integer programming problem. To solve this problem, we first obtain the optimal computing time and frequency for the MEC server and the optimal computing time of each IoT node based on the proof by contradiction and the monotonicity of the objective function, then we decouple the resulting simplified problem into two subproblems and transform each subproblem into a convex one. On this basis, we propose a low-complexity iterative algorithm to solve the simplified problem by solving the two convex subproblems alternately. Simulation results verify the fast convergence of the proposed iterative algorithm and its superiority over the baseline schemes in terms of the MEC server's energy consumption.

IRS-Based MEC for Delay-Constrained QoS Over RF-Powered 6G Mobile Wireless Networks

Mobile-edge computing (MEC) and intelligent reflecting surface (IRS) have been recognized as two key technologies for 6G mobile networks. Consequently, we propose the IRS-based MEC schemes for the delay-constrained quality-of-service (QoS) provisioning over radio frequency (RF) powered 6G mobile networks with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">non-linear</i> energy harvesting (EH) model. Using the IRS technique, multiple mobile users (MUs) first harvest energy from a multi-antenna base station (BS) equipped with an MEC server and then transmit their data to the BS for data-processing. We first formulate a delay minimization problem for MUs under their QoS requirements, by jointly optimizing the IRS's phase-shift matrices, the MEC server's finite computation resource allocation, the MIMO based BS's multi-MU detection (MUD) coefficients, and the energy/data transmission time and task allocation coefficient of each MU. We define the total delay of each MU as the sum of its EH time, data-transmission time, and data-processing time. Since our formulated joint-optimization problem is non-convex with multiple coupled variables, we apply the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">block coordinate descending</i> (BCD) method to decompose it into several subproblems which then can be iteratively solved by a low complexity algorithm. Moreover, we also extend our proposed scheme to IRS-based MEC over Terahertz (THz) wideband mobile networks. Finally, we validate and evaluate our developed delay minimization schemes through numerical analyses, which show that the total delay of the RF-powered MUs can be significantly reduced by using our proposed schemes.

Online Optimal Service Selection, Resource Allocation and Task Offloading for Multi-Access Edge Computing: A Utility-Based Approach

Multi-access edge computing promises satisfactory user experience by offloading tasks to the MEC server deployed at the network edge. However, since the MEC server is often resource-limited as compared to the cloud infrastructure, how to efficiently utilize its resources for system performance optimization becomes a challenge. In this paper, we study this problem with the aim at maximizing user's QoE through jointly optimizing service selection, computation resource allocation and task offloading decision, which is less studied in existing literature. We formulate a mixed-integer nonlinear programming problem (MINLP) for the task and propose a utility-based approach together with a low-complexity resource-efficiency based heuristic to address the problem. We consider realistic settings, where centralized solutions may not apply and an optimal mechanism needs to adapt as system operates. A distributed algorithm based on the Lagrangian-dual based decomposition theory is proposed, and we prove all sub-problems derived can be efficiently solved. In line with the current VM technology, we develop a cost-aware online algorithm that explicitly incorporates the cost of service switches into service selection and resource allocation. We evaluate our mechanism through both synthetic and trace-driven simulations, and results indicate they are effective as compared to representative baseline algorithms.

Impact of Service Function Aging on the Dependability for MEC Service Function Chain

The Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) integrated architecture is a key enabling platform for 5G to run multiple customized services in the form of service function chain (SFC) configured as an ordered set of service functions (SFs). However, memory-related software aging in the SF that can be exploited by attackers becomes a new threat to the dependability of MEC-SFC services. To provide dependable MEC-SFC services, proactive rejuvenation techniques to counteract the SF aging problem are essential. In this paper, we develop a semi-Markov model to quantitatively investigate the transient availability and steady-state dependability (availability and reliability) of MEC-SFC services. Our model enables the analysis of a MEC-SFC with any number of SFs, and can capture complex time-dependent behaviors of aging, failure, and recovery. The approximate accuracies of the presented model on dependability measures are comprehensively evaluated through comparative studies with simulation experiments. We then detect potential bottlenecks for a MEC-SFC system through sensitivity analysis and further analyze the impact of event-time interval distributions on steady-state dependability. Finally, we investigate the transient behaviors of a MEC-SFC service when varying system parameters during MEC-SFC operation.

DDPG-Based Joint Resource Management for Latency Minimization in NOMA-MEC Networks

In this letter, we consider the latency minimization problem in NOMA-MEC networks. Each user offloads partial tasks to the MEC server for remote execution and processes the remaining tasks locally. An iterative two-user NOMA scheme is proposed for task offloading. Obviously, users’ tasks partition ratios and offloading power impose a great effect on system performance, which can be further optimized based on the deep deterministic policy gradient (DDPG) method. Specifically, we derive an upper bound for users’ offloading power, and all the power variables can be normalized to fall between zero and one. In this way, the ratio and power variables lie in the same range, which can be obtained via a single DDPG actor network. Moreover, the optimization objective and constraints are both incorporated into the design of the reward function, which assists DDPG to learn desired strategies. Simulation results show that the proposed algorithm can effectively reduce the task processing latency.

Red Deer Optimisation-Based Delay Minimisation for mmWave Communication System Enabled with Mobile Edge Computing

Mobile edge computing (MEC)-oriented solutions are essential for various 5G wireless communication systems. It is considered a key technology for future communication systems because of its capability for fulfilling a broad variety of necessities of the developing wireless terminals in the form of Intelligent Vehicles, augmented reality and virtual reality devices like huge computation, low latency and high data rate. Further, the resource package has made the research on mobile data offloading. One probable novel spectrum in the subsequent generation networks is the millimetre wave (mmWave) communication systems. It attains important attention because of its high rate. This research work, this area focuses on the delay of minimisation strategy in mmWave MEC by jointly optimising the hybrid beam-forming and also resource allocation. Here, the utilisation of a well-performing Red Deer Algorithm (RDA) is the ultimate aim of the suggested model that intends to optimise the analogue beam-forming vectors at the users, the analogue and digital beam-forming matrices at the Base Station (BS), the computation task offloading ratios and resource allocation at the MEC server. Here, the minimisation of the delay or latency is attained. The comparative analysis of the proposed model over the other models demonstrates the superiority of the proposed algorithm in assisting the mmWave MEC system.

Multi-MEC collaboration for VR video transmission: Architecture and cache algorithm design

Virtual Reality (VR) is becoming an important use case for 5G wireless networks, and Mobile Edge Computing (MEC) servers are being explored as a way to reduce VR video latency. To address the limited cache space of a single MEC server, this paper proposes dividing geographically close MEC servers into collaborative domains. A new VR video transmission architecture is designed after analyzing video compression mechanisms.The proposed architecture splits the VR video into equal-sized tile files to simplify the cache problem and improve caching efficiency. However, the large number of different tile files presents a challenge. To address this, the paper proposes an optimized k-shortest paths (OKSP) algorithm with a time complexity of O((K⋅M+N)⋅M⋅logN), where K is the number of tiles that all M MEC servers can cache in the collaboration domain and N is the number of tile files. For extremely large-scale data cases, a greedy-based approximation algorithm is also proposed. The numerical results demonstrate the OKSP algorithm’s excellent performance in solving large-scale data, outperforming other caching algorithms in experiments.

Device association and trajectory planning for UAV-assisted MEC in IoT: a matching theory-based approach

Unmanned aircraft vehicles (UAVs)-enabled mobile edge computing (MEC) can enable Internet of Things devices (IoTD) to offload computing tasks to them. Considering this, we study how multiple aerial service providers (ASPs) compete with each other to provide edge computing services to multiple ground network operators (GNOs). An ASP owning multiple UAVs aims to achieve the maximum profit from providing MEC service to the GNOs, while a GNO operating multiple IoTDs aims to seek the computing service of a certain ASP to meet its performance requirements. To this end, we first quantify the conflicting interests of the ASPs and GNOs by using different profit functions. Then, the UAV scheduling and resource allocation is formulated as a multi-objective optimization problem. To address this problem, we first solve the UAV trajectory planning and resource allocation problem between one ASP and one GNO by using the Lagrange relaxation and successive convex optimization (SCA) methods. Based on the obtained results, the GNOs and ASPs are then associated in the framework based on the matching theory, which results in a weak Pareto optimality. Simulation results show that the proposed method achieves the considerable performance.

Wireless-Powered OFDMA-MEC Networks With Hybrid Active–Passive Communications

In this article, we propose a novel system model for a wireless powered mobile edge computing (MEC) network, where the Internet-of-Things (IoT) nodes perform partial offloading to the MEC server via hybrid backscatter communication (BackCom) and active radio (AR) following an OFDMA protocol, and maximize the system computation bits (SCB). For the case of the system having more subchannels than IoT nodes, we formulate the SCB maximization problem that requires the joint optimisation of the transmit power and time, subchannel allocation, computation frequency and time of the MEC server, as well as the IoT nodes’ BackCom time and reflection coefficients, transmit power and time for AR-based offloading, local computing time and frequencies, subject to the MEC server’s computation capacity and the quality-of-service (QoS) and energy-causality constraints of each IoT node. By applying the proof by contradiction and time sharing relaxation, we transform the formulated problem into a convex one and then solve it by using the existing convex tools. For the case of the system having less subchannels than IoT nodes, we propose a dynamic subchannel allocation scheme that allows each IoT node to choose one task-offloading mode from three modes: HAPR, BackCom only, and AR only, while ensuring that no more than one IoT node occupies a subchannel at any time. The SCB are maximized by first determining the subchannel allocation and mode selection of each IoT node and then optimising the remaining resource allocation for the MEC server and all IoT nodes under the obtained subchannel assignment and mode selection. Simulations validate the superior performance of the proposed schemes over several benchmark schemes from the SCB perspective.

Energy-Efficient Task Offloading for Three-Tier Wireless-Powered Mobile-Edge Computing

Mobile edge computing (MEC) is envisioned to address the computation demands of Internet of Things (IoT) devices. However, it is crucial for the MEC to operate in coordination with the cloud tier to achieve a highly scalable IoT system. In addition, IoT devices require regular maintenance to either recharge or replace their batteries which may not always be feasible. Wireless energy transfer (WET) can provide IoT devices with a stable source of energy. Nonetheless, proper scheduling of energy harvesting and efficient allocation of computing resources are the key to the sustainable operation of these devices. In this paper, we introduce a three-tier wireless powered mobile edge computing (WPMEC) consisting of cloud, MEC servers, and IoT devices. We first formulate a combinatorial optimization problem that aims to minimize the wireless energy transmission. To tackle the complexity of the problem, we use bipartite graph matching and propose a harvest-then-offload mechanism for IoT devices. We also exploit parallel processing to increase the performance of the proposed algorithm. Through numerical experiments, we evaluate the performance of our proposed mechanism. Our results show that the proposed mechanism significantly reduces the required energy for the operation of IoT devices comparing to different offloading policies. We further show that WiEnTM results in up to 34% less wireless energy transmission in comparison to an existing work in the literature.

Joint Optimization of Energy and Task Scheduling in Wireless-Powered IRS-Assisted Mobile-Edge Computing Systems

This paper studies the design of an intelligent reflecting surface (IRS)-assisted mobile edge computing (MEC) system, which consists of a mobile user, an IRS equipped with radio-frequency (RF) energy harvesting (EH) circuits, and a hybrid access point (HAP) connected with an MEC server. The IRS is deployed to reflect the users task offloading signals to enhance the received power at HAP, and it needs to harvest energy from the RF signals emitted by the HAP before reflecting signals. To save energy consumption at the user, we first propose a novel MEC protocol, in which the system is enabled to operate in three modes, namely an EH mode, an IRS-assisted task offloading mode, and an IRS-inactive task offloading mode, so that the energy at IRS and the tasks generated at user can be flexibly scheduled within a finite time horizon, depending on channel conditions, IRS energy states and users task queue states. Under the protocol, we optimize the operation mode selection and resource allocation in each mode with a task execution delay constraint. Due to the randomness of wireless channels and task arrivals, the optimization problem is a stochastic programming. To solve this problem, we first transform it into a deterministic one by assuming that noncausal channel state information (CSI) and task state information (TSI) are available, and then derive a practical algorithm where only causal CSI and TSI are required. Simulation results verify that our proposed design can save at most 80% energy consumption as compared with existing baseline schemes.

Multi-agent deep reinforcement learning for collaborative task offloading in mobile edge computing networks

As is widely accepted, mobile edge computing (MEC) is a promising technology to enable wireless devices (WDs) to process computation-intensive tasks. Due to the mutual influence among different WDs, collaborative task offloading is needed in multi-agent environments. In this paper, a multi-agent MEC network with delay-sensitive and non-partitionable tasks is considered, as well as the load on MEC servers. The joint optimization problem of offloading decision and resource allocation is formulated to minimize the average delay. To realize the collaborative decision-making, a multi-agent deep reinforcement learning based algorithm is proposed based on the framework of centralized training and decentralized execution. The centralized deep neural networks (DNN) learn from the past experience and the WDs learn policies from the evaluation of the actions from the centralized DNNs. Based on the learned policies, WDs can make offloading decisions with only local information. Simulation results show that the proposed algorithm achieves near-optimal performance and has the advantage of high stability in varying multi-agent environments.

DTN-Assisted Dynamic Cooperative Slicing for Delay-Sensitive Service in MEC-Enabled IoT via Deep Deterministic Policy Gradient With Variable Action

Network Slicing (NS) provides customized services to users of the Internet of Things (IoT) by creating logical virtual networks, and NS combined with Multi-access Edge Computing (MEC) can significantly minimize the latency for delay-sensitive service. Therefore, it is important to research how to employ NS to achieve low latency for delay-sensitive service in MEC-enabled IoT. In this paper, we propose a paradigm of dynamic cooperative slicing based on Digital Twin Network (DTN) to achieve low latency for delay-sensitive service. Specifically, we first build a DTN for the MEC-enabled IoT, and build basic models and function models including prediction and decision-making in DTN. Then we realize dynamic cooperative slicing through the built basic models and function models. Secondly, with the assistance of the ubiquitous computing resources in MEC-enabled IoT based on DTN, we construct joint optimization problem of communication resources, computing resources, and collaboration proportion with the objective of ensuring low delay of delay-sensitive service while maximizing the long-term utility of operators. Thirdly, considering that the different MEC servers participating in the cooperation in each time slot lead to different action spaces in different time slots, we propose a Deep Deterministic Policy Gradient algorithm with Variable Action space, called VADDPG, which draws on the idea of action masking and introduces the action adjustor to realize the hard control of action space. Finally, a large number of simulations demonstrate that the proposed algorithm outperforms the benchmark algorithms in terms of both the long-term utility of operators and the delay obtained by slicing.

Computation Offloading and Resource Allocation Based on Game Theory in Symmetric MEC-Enabled Vehicular Networks

Due to the rocketing development of the Internet of Vehicles (IoV), the growth of computing-intensive and latency-sensitive applications brings a challenge to individual vehicles with limited computing resources. The computation offloading technology provides a feasible solution to this issue. In this paper, a multi-tier symmetric Vehicle-to-Everything (V2X) network framework is proposed, which consists of vehicle nodes (VNs), mobile edge computing (MEC) servers and a cloud server to provide computation offloading services for user vehicles. In this symmetric system, besides local computation, tasks can be offloaded to VNs or MEC servers or cloud servers for processing. The computation offloading problem in this network framework is considered as a game based on game theory. Then, in order to achieve the Nash equilibrium (NE) in this game, a joint optimization of computation offloading and resource allocation (JOCORA) algorithm is proposed. The numerical simulations show that the JOCORA algorithm can improve the success probability of offloading and reduce the total latency. The JOCORA algorithm has a better performance compared to other methods.

Delay Minimization Using Hybrid RSMA-TDMA for Mobile Edge Computing

Rate-splitting multiple access (RSMA) has recently received attention due to its benefits in both spectral and energy efficiencies. In this paper, we propose a hybrid RSMA-time-division multiple access (TDMA) scheme for a mobile edge computing (MEC) system, where two edge users need to offload their task data to a MEC server. In the proposed scheme, the offloading time is divided into two time phases. Specifically, we design a cognitive radio (CR)-inspired RSMA scheme, in which two users, namely the primary user and secondary user, offload their task data to the MEC server in the first time phase, while only a single user can offload task data in the second time phase. With the aim of minimizing the overall offloading delay, we formulate the offloading delay minimization problem subject to the transmit power and total energy constraints. We transform the original fractional programming non-convex problem to a convex one by using the Dinkelbach transform and propose Dinkelbach and Newton iterative algorithms to determine the optimal transmit power allocation. Specifically, we establish the optimization criteria for the three offloading schemes and derive the corresponding closed-form expressions for the optimal power allocation. Compared to the existing offloading schemes, the numerical results show that the proposed hybrid RSMA-TDMA scheme in scenarios where having a limited energy budget is superior in offloading delay compared to other offloading schemes and the sum offloading delay tends to a constant with the increase in the energy budget.