Computation Resource Allocation Problem Research Articles

Joint Computation Partitioning and Resource Allocation for Latency Sensitive Applications in Mobile Edge Clouds

The proliferation of mobile devices and ubiquitous access of the wireless network enable many new mobile applications such as augmented reality, mobile gaming and so on. As the applications are latency sensitive, researchers propose to offload the complex computations of these applications to the nearby edge cloud, in order to reduce the latency. Existing works mostly consider the problem of partitioning the computations between the mobile device and the traditional cloud that has abundant resources. The proposed approaches can not be applied in the context of mobile edge cloud, because both the resources in the mobile edge cloud and the wireless access bandwidth to the edge cloud are constrained. In this paper, we study <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">joint computation partitioning and resource allocation problem</i> for latency sensitive applications in mobile edge clouds. The problem is novel in that we combine the computation partitioning and the two-dimensional resource allocations in both the computation resources and the network bandwidth. We develop a new and efficient method, namely Multi-Dimensional Search and Adjust (MDSA), which is an offline algorithm, to solve the problem. We compare MDSA with the classic list scheduling method and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SearchAdjust</i> algorithm via comprehensive simulations. The results show that MDSA outperforms the benchmark algorithms in terms of the overall application latency. Moreover, we also design an online method, named by Cooperative Online Scheduling (COS), which can be easily deployed in practical systems. By extensive evaluations, we show that COS outperforms the benchmark methods by 25 percent on average.

Joint computing and communication resource allocation for satellite communication networks with edge computing

Benefit from the enhanced onboard processing capacities and high-speed satellite-terrestrial links, satellite edge computing has been regarded as a promising technique to facilitate the execution of the computation-intensive applications for satellite communication networks (SCNs). By deploying edge computing servers in satellite and gateway stations, SCNs can achieve significant performance gains of the computing capacities at the expense of extending the dimensions and complexity of resource management. Therefore, in this paper, we investigate the joint computing and communication resource management problem for SCNs to minimize the execution latency of the computation-intensive applications, while two different satellite edge computing scenarios and local execution are considered. Furthermore, the joint computing and communication resource allocation problem for the computation-intensive services is formulated as a mixed-integer programming problem. A game-theoretic and many-to-one matching theory-based scheme (JCCRA-GM) is proposed to achieve an approximate optimal solution. Numerical results show that the proposed method with low complexity can achieve almost the same weight-sum latency as the Brute-force method.

Mobile edge computing resource allocation: An operating system view

In this paper, we study the mobile edge computing (MEC) resource allocation problem from the view of operating systems (OSs). To be more specific, we consider a multi-OS MEC resource allocation problem, where end users (EUs)/fog nodes (FNs) have different OSs. The main obstacle of the problem is the heterogeneity. First, for EUs, according to their OSs’ properties, EUs can be mainly categorized into soft real-time EUs and hard real-time EUs. Second, there exists different OSs for FNs. Thus, a unified FNs’ qualification constraint may be in need to develop. This constraint should be applicable for diverse FNs. Third, although the conventional proportional fairness is a universal fairness criterion for the service provider (SP) to allocate resources for different EUs, the specified proportions which guarantee the service quality are seemed hard to be developed. Therefore, a user satisfaction mechanism (USM) is proposed to first denote the different requirements between soft real-time and hard real-time applications of EUs, and second test whether FNs are qualified for the MEC service. In addition, a novel p-fairness is designed for the differentiated and quality-guaranteed services provided by SPs. Then, the USM and p-fairness driven MEC resource allocation problem is formulated as a mixed integer non-linear programming (MINLP) problem, where we propose a variable-substitution convexity transformation (VSCT) scheme to tackle it. In VSCT, we first utilize the variable substitution to transform the integer variables’ quadric constraints to linear constraints. Then, we relax several constraints and the problem is transformed as a convex optimization problem which is solved iteratively. Finally, we analyze the upper and lower bounds of the proposed VSCT. In the simulation, we discuss the performances of USM and p-fairness (i.e., user satisfaction). Moreover, we compare VSCT with the existing optimization method (i.e., branch & bound). Finally, we demonstrate some interesting properties of our proposed scheme.

Hybrid fog/cloud computing resource allocation: Joint consideration of limited communication resources and user credibility

In this paper, we study the communication and computation resource allocation problem with the assumption of enough computation resources but limited communication resources. This assumption is indeed practical in a hybrid fog/cloud computing system, when there exists a large amount of data to be executed. More specifically, a powerful cloud computing center can help fog nodes (FNs) release the heavy computation burden. Namely, with the assistance of cloud computing, the system usually has enough computation resources to execute such computation-intensive applications. However, since the system has a certain amount of subchannels, the communication resources of the system may be limited at times, especially when the number of subchannels is insufficient. Therefore, with the aim of handling tasks in an energy efficient way, we propose a communication resource-aware cooperated with computation resources (CRACCR) scheme which has two components. The one is called spectral multiplexing computation consideration, where the system multiplexes communication resources under the consideration of computation resource allocation. The other is called FN scale adjustment (FNSA), where the number of FNs in use is influenced by the communication resource allocation. Furthermore, to develop a user-aware CRACCR scheme, we also design a mechanism to sketch users’ credibility. Then a limited communication resource allocation problem with the consideration of user credibility is formulated as a mixed integer non-linear programming problem (MINLP). After transforming the problem by ℓp-box constraints and scale conversion, the problem is tackled by the alternating direction multiplier method. Simulation results prove the improvement of energy efficiency achieved by the proposed scheme, and show the variation of FNs’ number while considering the communication resource allocation.

Multi‐tier MEC offloading strategy based on dynamic channel characteristics

Comparing with the cloud computing, mobile edge computing (MEC) can further decrease the latency and improve the stability of the networks. However, it is challenging for the edge servers to deal with the large computation task due to the limited computing capacity. In this study, we design a novel three-layer network architecture consisting of mobile devices, edge cloudlets, and helper cloudlets, where the computing data can be partially processed at the edge cloudlet and helper cloudlet. Based on this, a joint communication, offloading, and computation resource allocation problem is formulated to minimise the computation cost and energy consumption. Due to its difficulty to directly solve the formulated problem, we first propose an offloading scheme to obtain the closed-form solutions for the optimal offloading data size. Next, we decompose the optimisation problem into two subproblems: (i) for the cloud execution, we dynamically adjust the data transmission rate according to the stochastic channel condition, (ii) for the mobile execution, the energy consumption can be further reduced by applying the dynamic voltage and frequency scaling technique. Finally, the numerical results demonstrate the efficiency of the proposed scheme, and show the performance gains in terms of delay, computation cost and energy consumption.

Edge Intelligence for Energy-Efficient Computation Offloading and Resource Allocation in 5G Beyond

5G beyond is an end-edge-cloud orchestrated network that can exploit heterogeneous capabilities of the end devices, edge servers, and the cloud and thus has the potential to enable computation-intensive and delay-sensitive applications via computation offloading. However, in multi user wireless networks, diverse application requirements and the possibility of various radio access modes for communication among devices make it challenging to design an optimal computation offloading scheme. In addition, having access to complete network information that includes variables such as wireless channel state, available bandwidth and computation resources, is a major issue. Deep Reinforcement Learning (DRL) is an emerging technique to address such an issue with limited and less accurate network information. In this paper, we utilize DRL to design an optimal computation offloading and resource allocation strategy for minimizing system energy consumption. We first present a multi-user end-edge-cloud orchestrated network where all devices and base stations have computation capabilities. Then, we formulate the joint computation offloading and resource allocation problem as a Markov Decision Process (MDP) and propose a new DRL algorithm to minimize system energy consumption. Numerical results based on a real-world dataset demonstrate that the proposed DRL-based algorithm significantly outperforms the benchmark policies in terms of system energy consumption. Extensive simulations show that learning rate, discount factor, and number of devices have considerable influence on the performance of the proposed algorithm.

HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning

Federated Learning (FL) has been proposed as an appealing approach to handle data privacy issue of mobile devices compared to conventional machine learning at the remote cloud with raw user data uploading. By leveraging edge servers as intermediaries to perform partial model aggregation in proximity and relieve core network transmission overhead, it enables great potentials in low-latency and energy-efficient FL. Hence we introduce a novel Hierarchical Federated Edge Learning (HFEL) framework in which model aggregation is partially migrated to edge servers from the cloud. We further formulate a joint computation and communication resource allocation and edge association problem for device users under HFEL framework to achieve global cost minimization. To solve the problem, we propose an efficient resource scheduling algorithm in the HFEL framework. It can be decomposed into two subproblems: resource allocation given a scheduled set of devices for each edge server and edge association of device users across all the edge servers. With the optimal policy of the convex resource allocation subproblem for a set of devices under a single edge server, an efficient edge association strategy can be achieved through iterative global cost reduction adjustment process, which is shown to converge to a stable system point. Extensive performance evaluations demonstrate that our HFEL framework outperforms the proposed benchmarks in global cost saving and achieves better training performance compared to conventional federated learning.

User Satisfaction Oriented Resource Allocation for Fog Computing: A Mixed-Task Paradigm

In this paper, we tackle the joint computation and communication resource allocation problem for mixed-task user-concerned fog computing. To deal with diverse kinds of computing tasks, we propose a mixed-task paradigm to support the co-existence of binary offloading and partial offloading. Considering the impact of users' satisfaction on fog computing service, we employ the user-weighted energy efficiency (UWEE) as the objective of resource allocation and develop a user-concerned mechanism (UCM) to sketch users' social features. Then, under the constraints of users' satisfaction, the resource allocation problem for UWEE maximization is formulated as a mixed integer nonlinear programming problem (MINLP), which cannot be properly solved by the traditional relaxation algorithms due to the co-existence of binary offloading and partial offloading. After transforming the problem with the replacement-based method, an augmented Lagrange method (ALM)-based resource allocation scheme is proposed to iteratively solve this joint optimization problem, in which AMSGrad is employed to accelerate the convergence. Simulation results demonstrate the superior performance of the ALM-based resource allocation scheme in terms of UWEE.

Joint DNN Partition Deployment and Resource Allocation for Delay-Sensitive Deep Learning Inference in IoT

Nowadays, the widely used Internet-of-Things (IoT) mobile devices (MDs) generate huge volumes of data, which need analyzing and extracting accurate information in real time by compute-intensive deep learning (DL) inference tasks. Due to its multilayer structure, the deep neural network (DNN) is appropriate for the mobile-edge computing (MEC) environment, and the DL tasks can be offloaded to DNN partitions deployed in MEC servers (MECSs) for speed-up inference. In this article, we first assume the arrival process of DL tasks as Poisson distribution and develop a tandem queueing model to evaluate the end-to-end (E2E) inference delay of DL tasks in multiple DNN partitions. To minimize the E2E delay, we develop a joint optimization problem model of partition deployment and resource allocation in MECSs (JPDRA). Since the JPDRA is a mixed-integer nonlinear programming (MINLP) problem, we decompose the original problem into a computing resource allocation (CRA) problem with fixed partition deployment decision and a DNN partition deployment (DPD) problem that optimizes the optimal-delay function related to the CRA problem. Next, we design a CRA algorithm based on Markov approximation and a low-complexity DPD algorithm to obtain the near-optimal solution in the polynomial time. The simulation results demonstrate that the proposed algorithms are more efficient and can reduce the average E2E delay by 25.7% with better convergence performance.

Computation Offloading in Hierarchical Multi-Access Edge Computing Based on Contract Theory and Bayesian Matching Game

Multi-access edge computing (MEC) has emerged as a promising paradigm because of its good performance for computation-intensive and latency-critical applications. However, the enormous computing requests from computation service subscribers (CSSs) still cannot be satisfied by pre-existing edge computation nodes (ECNs). To fully utilize the advantage of the MEC network, a hierarchical computation offloading framework is developed under network virtualization (NV) scenario. Accordingly, a two-step sequential process is designed to stimulate the proposed framework. In the first step, an incentive mechanism is proposed in which more temporary ECNs can be motivated by MEC operator and then join the MEC network. Without perfect ECN information, the optimal contract items (the ECN's CPU contribution and reward) between the MEC operator and ECNs can be achieved by taking account of individual rationality (IR) and incentive compatible (IC) constraints. After acquiring the ECNs’ CPU contributions, the computing resource allocation problem between the ECNs and CSSs is then considered in the second step. Since the CSSs have private information, a Bayesian matching game with externality is leveraged to model the problem. Whereas, the conventional resident-oriented Gale-Shapley (RGS) algorithm cannot ensure the stability. Hence, an iterative matching algorithm that can always converge to stable results is developed. Finally, simulation results demonstrate that our proposed two-step sequential decision process can significantly improve social welfare considering the practical scenarios, with reasonable computational complexity.

Resource Allocation for Energy-Efficient MEC in NOMA-Enabled Massive IoT Networks

Integrating mobile edge computing (MEC) into the Internet of Things (IoT) enables the IoT devices of limited computation capabilities and energy to offload their computation-intensive and delay-sensitive tasks to the network edge, thereby providing high quality of service to the devices. In this article, we apply non-orthogonal multiple access (NOMA) technique to enable massive connectivity and investigate how it can be exploited to achieve energy-efficient MEC in IoT networks. In order to maximize the energy efficiency for offloading, while simultaneously satisfying the maximum tolerable delay constraints of IoT devices, a joint radio and computation resource allocation problem is formulated, which takes both intra- and inter-cell interference into consideration. To tackle this intractable mixed integer non-convex problem, we first decouple it into separated radio and computation resource allocation problems. Then, the radio resource allocation problem is further decomposed into a subchannel allocation problem and a power allocation problem, which can be solved by matching and sequential convex programming algorithms, respectively. Based on the obtained radio resource allocation solution, the computation resource allocation problem can be solved by utilizing the Knapsack method. Numerical results validate our analysis and show that our proposed scheme can significantly improve the energy efficiency of NOMA-enabled MEC in IoT networks compared to the existing baselines.

Deep Reinforcement Learning for Stochastic Computation Offloading in Digital Twin Networks

The rapid development of industrial Internet of Things (IIoT) requires industrial production towards digitalization to improve network efficiency. Digital Twin is a promising technology to empower the digital transformation of IIoT by creating virtual models of physical objects. However, the provision of network efficiency in IIoT is very challenging due to resource-constrained devices, stochastic tasks, and resources heterogeneity. Distributed resources in IIoT networks can be efficiently exploited through computation offloading to reduce energy consumption while enhancing data processing efficiency. In this article, we first propose a new paradigm digital twin network to build network topology and the stochastic task arrival model in IIoT systems. Then, we formulate the stochastic computation offloading and resource allocation problem to minimize the long-term energy efficiency. As the formulated problem is a stochastic programming problem, we leverage Lyapunov optimization technique to transform the original problem into a deterministic per-time slot problem. Finally, we present asynchronous actor-critic algorithm to find the optimal stochastic computation offloading policy. Illustrative results demonstrate that our proposed scheme is able to significantly outperforms the benchmarks.

Deep reinforcement learning based joint edge resource management in maritime network

Due to the rapid development of the maritime networks, there has been a growing demand for computation-intensive applications which have various energy consumption, transmission bandwidth and computing latency requirements. Mobile edge computing (MEC) can efficiently minimize computational latency by offloading computation tasks by the terrestrial access network. In this work, we introduce a space-air-ground-sea integrated network architecture with edge and cloud computing components to provide flexible hybrid computing service for maritime service. In the integrated network, satellites and unmanned aerial vehicles (UAVs) provide the users with edge computing services and network access. Based on the architecture, the joint communication and computation resource allocation problem is modelled as a complex decision process, and a deep reinforcement learning based solution is designed to solve the complex optimization problem. Finally, numerical results verify that the proposed approach can improve the communication and computing efficiency greatly.

Computation Offloading in Multi-Access Edge Computing: A Multi-Task Learning Approach

Multi-access edge computing (MEC) has already shown great potential in enabling mobile devices to bear the computation-intensive applications by offloading some computing jobs to a nearby access point (AP) integrated with a MEC server (MES). However, due to the varying network conditions and limited computational resources of the MES, the offloading decisions taken by a mobile device and the computational resources allocated by the MES can be formulated as a mixed-integer nonlinear programming (MINLP) problem, which may not be optimized with the lowest cost. In this paper, we propose a novel offloading framework for the multi-server MEC network where each AP is equipped with an MES assisting mobile users (MUs) in executing computation-intensive jobs via offloading. Specifically, we formulate the offloading decision problem as a multiclass classification problem and formulate the MES computational resource allocation problem as a regression problem. Then a multi-task learning based feedforward neural network (MTFNN) model is designed and trained to jointly optimize the offloading decision and computational resource allocation. Numerical results show that the proposed MTFNN outperforms the conventional optimization method in terms of inference accuracy and computational complexity.

Intelligent Resource Allocation Algorithm for Multi-platform Offloading in Vehicular Networks

In order to reduce the delay of computing tasks and the total cost of the system, Mobile Eedge Computing (MEC) technology is applied to vehicular networks to improve further the service quality. The delay problem of vehicular networks is studied with the consideration of computing resources. In order to improve the performance of the next generation vehicular networks, a multi-platform offloading intelligent resource allocation algorithm is proposed to allocate the computing resources. In the proposed algorithm, the K-Nearest Neighbor (KNN) algorithm is used to select the offloading platform (i.e., cloud computing, mobile edge computing, local computing) for computing tasks. For the computing resource allocation problem and system complexity in non-local computing, reinforcement learning is used to solve the optimization problem of resource allocation in vehicular networks using the mobile edge computing technology. Simulation results demonstrate that compared with the baseline algorithms (i.e., all tasks offload to the local or MEC server), the proposed multi-platform offloading intelligent resource allocation algorithm achieves a significant reduction in latency cost, and the average system cost can be saved by 80%.

HIQCO: A Hierarchical Optimization Method for Computation Offloading and Resource Optimization in Multi-Cell Mobile-Edge Computing Systems

Mobile-Edge Computing (MEC) addresses the shortcomings of mobile devices in terms of computing performance and energy efficiency by offloading computation tasks to the edge of the network. In this paper, a multi-cell MEC system is considered, where each small cell connects to a common MEC server, and the problem of minimizing system overhead in the MEC system is solved by jointly optimizing the computation offloading decisions, communication and computational resources. This problem is a mixed integer nonlinear programming problem (MINLP). Owning to the combined nature and complexity of this problem, we adopt a method to transform the original problem into two sub-problems: (i) joint optimization computation offloading decision and channel assignment problem (JO-CODCA) and (ii) joint optimization transmission power and computational resource allocation problem (JO-TPCRA). For the transformed sub-problems, we propose a hierarchical optimization method (HIQCO) that combines immune algorithm (IA), quasi-convex optimization and convex optimization techniques. Experimental results show that the proposed HIQCO give better performance comparing with the other compared algorithms in terms of system overhead.

Joint Offloading and Charge Cost Minimization in Mobile Edge Computing

Mobile edge computing (MEC) brings a breakthrough for Internet of Things (IoT) for its ability of offloading tasks from user equipments (UEs) to nearby servers which have rich computation resource. 5G network brings a huge breakthrough on transmission rate. Together with MEC and 5G, both execution delay of tasks and time delay from downloading would be shorter and the quality of experience (QoE) of UEs can be improved. Considering practical conditions, the computation resource of an MEC server is finite to some extent. Therefore, how to prevent the abuse of MEC resource and further allocate the resource reasonably becomes a key point for an MEC system. In this paper, an MEC system with multi-user is considered where a base station (BS) with an MEC server, which can not only provide computation offloading service but also data cache service. Especially, we take the charge for both data transmission and task computation as one part of total cost of UEs, and then explore a joint optimization for downlink resource allocation, offloading decision and computation resource allocation to minimize the total cost in terms of the time delay and the charge to UEs. The proposed problem is formulated as a mixed integer programming (MIP) one which is NP-hard. Therefore, we decouple the original problem into two subproblems which are downlink resource allocation problem and joint offloading decision and computation resource allocation problem. Then we address these two subproblems by using convex and nonconvex optimization techniques, respectively. An iterative algorithm is proposed to obtain a suboptimal solution in polynomial time. Simulation results show that our proposed algorithm performs better than benchmark algorithms.

UCAA: User-Centric User Association and Resource Allocation in Fog Computing Networks

In recent years, with the eruptive popularity of mobile Internet and the emergence of various new IoT applications, fog computing is proposed to shift the cloud computing services towards the edge, making up for its lack of mobility support and high delay. Fog computing is customized for scenarios with scarce resources and unpredictable environments, but there is no user-centric joint optimization fog computing models designed for such scenarios. In this paper, we aim to maximize the user experience and overall system performance by jointly optimizing user association and resource allocation in the scenarios mentioned above, which can be formulated as a mix-integer non-linear programming problem. To solve the NP-hard problem, we propose a low-complexity two-step interactive optimal algorithm, named UCAA algorithm. For the user association problem, we propose a semi-definite programming based algorithm, and then further propose a Kuhn-Munkres algorithm based user association decision approximation algorithm. For the resource allocation problem, we first prove that it can be decoupled into two sub-problems, ie., transmission power selection problem and computing resource allocation problem, and solve them individually, in addition, we have given a rigorous proof that the optimal solution of the two sub-problems is the optimal solution to the original problem as well. The numerical results show that the proposed UCAA algorithm achieves better performance than conventional algorithms in terms of the value of average user-centric utility, especially in case of more user equipments (UEs), fewer fog nodes, limited computing capacity of fog nodes, lower delay tolerance, lower local computation capacity, etc., which presented to illustrate that the UCAA algorithm can significantly improve user experience and system performance in the considering fog computing scenarios.

Energy-Efficient Edge Offloading in Heterogeneous Industrial IoT Networks for Factory of Future

The ultra-reliable and low latency communication (URLLC) and massive machine type communication (mMTC) in 5G are envisioned to support intelligent automation in the heterogeneous Factory of Future (FoF) networks, and Mobile-edge computing (MEC) is considered to be a promising system for enabling real-time task processing at the edge of the network. In the future factory, production machines, and environmental monitoring devices will be endowed with the wireless connecting for mobility. These devices are deployed for running complicated real-time tasks. To make such mission-critical tasks being processed in time, parts of the tasks should be completed with the assistance of the edge server or even the cloud. In this work, we jointly investigate the partial task offloading, computation, and communication (licensed and unlicensed) resource allocation problem in the trade-off between overall power consumption and quality of service (QoS) satisfaction. A 2-tier MEC-cloud framework is provided, wherein the IoT mobile devices (MDs) are able to partition the tasks into segments and offload them to the MEC and the cloud server. Considering the limits of communication and computation resources, we proposed a mechanism call 5G and NR-U opportunity-cost-based offloading algorithm (5G/NR-U OCBOA) to optimize resource allocation. Within the mechanism, there are two proposed algorithms, 5G OCBOA is for the licensed-only case, and NR-U OCBOA dedicates on unlicensed one. We iteratively perform the two algorithms to get the final solution. The simulation results show that our low-complexity algorithms almost outperform the other benchmark greedy algorithms. The proposed algorithm is up to 59.3% MD blocking probability less, up to 58.7% power saving gain, and up to 47.6% more QoS gain.

Task Execution Cost Minimization-Based Joint Computation Offloading and Resource Allocation for Cellular D2D MEC Systems

The rapid proliferation of new applications poses great challenges to the computation capability of mobile devices. To tackle this problem, computation offloading was proposed as a promising paradigm. In this paper, we consider a cellular device-to-device (D2D) mobile edge computing (MEC) system consisting of one base station (BS) deployed with an MEC server and users. We assume that a portion of users have task computation requirements and may perform local computing, MEC offloading, or D2D offloading. Further assuming that tasks can be partitioned into small-sized data and can be executed simultaneously via various computation modes, we jointly study computation offloading and resource allocation problem. To achieve efficient information interaction and task management, we first propose a joint task management architecture. Defining task execution cost as the weighted sum of task execution latency and energy consumption, we then formulate the joint optimization problem as a task execution cost minimization problem. As the formulated problem is a mixed integer nonlinear problem, which cannot be solved conveniently, we propose a heuristic algorithm that successively solves computation offloading subproblem and resource allocation subproblem by Kuhn-Munkres algorithm and Lagrangian dual method, respectively. Numerical results demonstrate the effectiveness of the proposed algorithm.