Computation Resource Allocation Problem Research Articles

A QUBO model-based approach for solving the computational resource allocation problem and its application analysis

A QUBO model-based approach for solving the computational resource allocation problem and its application analysis

Online dynamic multi-user computation offloading and resource allocation for HAP-assisted MEC: an energy efficient approach

Nowadays, the paradigm of mobile computing is evolving from a centralized cloud model towards Mobile Edge Computing (MEC). In regions without ground communication infrastructure, incorporating aerial edge computing nodes into network emerges as an efficient approach to deliver Artificial Intelligence (AI) services to Ground Devices (GDs). The computation offloading and resource allocation problem within a HAP-assisted MEC system is investigated in this paper. Our goal is to minimize the energy consumption. Considering the randomness and dynamism of the task arrival of GDs and the quality of wireless communication, stochastic optimization techniques are utilized to transform the long-term dynamic optimization problem into a deterministic optimization problem. Subsequently, the problem is further decomposed into three sub-problems which can be solved in parallel. An online Energy Efficient Dynamic Offloading (EEDO) algorithm is proposed to address these problems. Then, we conduct the theoretical performance analysis for EEDO. Finally, we carry out parameter analysis and comparative experiments, demonstrating that the EEDO algorithm can effectively reduce system energy consumption while maintaining the stability of the system.

Robust Offloading for Edge Computing-Assisted Sensing and Communication Systems: A Deep Reinforcement Learning Approach.

In this paper, we consider an integrated sensing, communication, and computation (ISCC) system to alleviate the spectrum congestion and computation burden problem. Specifically, while serving communication users, a base station (BS) actively engages in sensing targets and collaborates seamlessly with the edge server to concurrently process the acquired sensing data for efficient target recognition. A significant challenge in edge computing systems arises from the inherent uncertainty in computations, mainly stemming from the unpredictable complexity of tasks. With this consideration, we address the computation uncertainty by formulating a robust communication and computing resource allocation problem in ISCC systems. The primary goal of the system is to minimize total energy consumption while adhering to perception and delay constraints. This is achieved through the optimization of transmit beamforming, offloading ratio, and computing resource allocation, effectively managing the trade-offs between local execution and edge computing. To overcome this challenge, we employ a Markov decision process (MDP) in conjunction with the proximal policy optimization (PPO) algorithm, establishing an adaptive learning strategy. The proposed algorithm stands out for its rapid training speed, ensuring compliance with latency requirements for perception and computation in applications. Simulation results highlight its robustness and effectiveness within ISCC systems compared to baseline approaches.

Reverse Auction-Based Computation Offloading and Resource Allocation in Mobile Cloud-Edge Computing

This paper proposes a novel Reverse Auction-based Computation Offloading and Resource Allocation Mechanism, named RACORAM for the mobile Cloud-Edge computing. The basic idea is that the Cloud Service Center (CSC) recruits edge server owners to replace it to accommodate offloaded computation from nearby resource-constraint Mobile Devices (MDs). In RACORAM, the reverse auction is used to stimulate edge server owners to participate in the offloading process, and the reverse auction-based computation offloading and resource allocation problem is formulated as a Mixed Integer Nonlinear Programming (MINLP) problem, aiming to minimize the cost of the CSC. The original problem is decomposed into an equivalent master problem and subproblem, and low-complexity algorithms are proposed to solve the related optimization problems. Specifically, a Constrained Gradient Descent Allocation Method (CGDAM) is first proposed to determine the computation resource allocation strategy, and then a Greedy Randomized Adaptive Search Procedure based Winning Bid Scheduling Method (GWBSM) is proposed to determine the computation offloading strategy. Meanwhile, the CSC’s payment determination for the winning edge server owners is also presented. Simulations are conducted to evaluate the performance of RACORAM, and the results show that RACORAM is very close to the optimal method with significantly reduced computational complexity, and greatly outperforms the other baseline methods in terms of the CSC’s cost under different scenarios.

Computation Offloading and Resource Allocation in NOMA–MEC: A Deep Reinforcement Learning Approach

Multi-access edge computing (MEC) has emerged as a powerful paradigm for increasing the computation performance of mobile devices (MDs). Applying non-orthogonal multiple access (NOMA) to MEC can further improve the spectrum efficiency and reduce offloading delays caused by the upload congestion. In this paper, we examine the joint computation offloading and resource allocation problem in the NOMA-MEC system, which benefits from the combination of NOMA and MEC. Our optimization objective is to minimize the computational overhead (the weighted sum of the execution delay and the energy consumption) in dynamic environments with time-varying wireless fading channels. The optimization problem is formulated as a mixed integer programming (MIP), which involves jointly optimizing the task offloading decisions, channel assignment, and transmit power allocation. To solve such an optimization problem, we formalize the task offloading and the resource allocation as a Markov decision process (MDP). Then, we propose a deep reinforcement learning (DRL) based approach, which combines multiple deep neural networks (DNNs) to directly approximate different statistical models for continuous and discrete control. The simulation results demonstrate that the proposed approach can rapidly converge and efficiently decrease the total computational overhead compared to other baseline approaches in different scenarios.

Deep Reinforcement Learning Based Approach for Online Service Placement and Computation Resource Allocation in Edge Computing

Due to the urgent emergence of computation-intensive intelligent applications on end devices, edge computing has been put forward as an extension of cloud computing, to satisfy the low-latency requirements of these applications. To process heterogenous computation tasks on an edge node, the corresponding services should be placed in advance, including installing softwares and caching databases/libraries. Considering the limited storage space and computation resources on the edge node, services should be elaborately selected and deployed on the edge node and its computation resources should be carefully allocated to placed services, according to the arrivals of computation workloads. The joint service placement and computation resource allocation problem is particularly complicated, in terms of considering the stochastic arrivals of tasks, the additional latency incurred by service migration, and the waiting time of unprocessed tasks. Benefiting from deep reinforcement learning, we propose a novel approach based on parameterized deep Q networks to make the joint service placement and computation resource allocation decisions, with the objective of minimizing the total latency of tasks in a long term. Extensive simulations are conducted to evaluate the convergence and performance achieved by our proposed approach.

Optimal bandwidth and computing resource allocation in low earth orbit satellite constellation for earth observation applications

The next step in Earth Observation (EO) constellations will be leveraging Inter-Satellite Links (ISLs) to form a network where information generated by the EO application can be transmitted, in such a way that, by endowing spacecrafts with processing capacity, observation data may be processed directly in orbit by any satellite of the constellation. However, since bandwidth and on-board processing capacity are valuable resources, strategies to appropriately routing the information and deciding on which node it has to be processed shall be defined. In this work, we formalize and solve an optimal bandwidth and computing resource allocation problem in Low Earth Orbit (LEO) satellite constellation for EO applications. In order to deal with the complexity of the proposed optimization problem, we also present two heuristics requiring different computational effort. In the proposed problem formalization, processing can happen on any node of the network (i.e., either on the data source satellite, on any other satellite of the constellation or on ground station). After having validated the proposed heuristics by comparing their results to the optimization problem ones, we apply them to a real orbital scenario, showing their ability to reduce both total cost and data delivery delay to ground with respect to state-of-the-art solutions.

Application-aware computation offloading in edge computing networks

Edge computing involves distributive computation resources deployed at the network edge, unlike cloud computing, which has central computation resources in data centers. Edge computing is a complement of cloud computing because edge computing effectively reduces the computing response delay by processing computation tasks and data near terminals. Considering the dramatic increase of terminals connected to networks and data generated by terminals, computation tasks from different applications may require significantly different services with different computation requirements, storage requirements, and response delay requirements. Application-aware computation offloading and resource allocation in edge computation can provide efficient and guaranteed computation services to terminals. In this paper, an application-aware computation offloading and resource allocation problem is investigated in edge computing networks, where computation tasks from different applications have different requirements. A non-convex optimization problem of energy consumption minimization is formulated, where terminals, edge nodes, and a cloud are considered. We convert the original non-convex optimization problem into a lower-bound convex problem and an upper-bound convex problem. Then, an algorithm based on the branch-and-bound method is proposed to force the lower- and upper-bound solutions to approach the optimal solution. Finally, the performance of the algorithm is analyzed where the gap to the optimal solution is provided. Numerical results show that the proposed algorithm can provide guaranteed services for tasks of different application types, with improvements over application-unaware algorithms.

Joint Optimization of Request Assignment and Computing Resource Allocation in Multi-Access Edge Computing

With the development of multi-access edge computing (MEC), the cloudlet at the edge of the network can provide nearby high-performance computing services, thus reducing the computational consumption of user equipments (UEs). To provide more real-time computing services to UEs, service providers face the challenge of optimizing the assignment of requests and the allocation of cloudlets’ computing resources to achieve low latency while dealing with the large number of offloaded requests from UEs. Therefore, in this paper, we study the problem of minimizing the total latency to complete the requests in the MEC network by jointly optimizing request assignment and computing resource allocation. The problem is formulated as a mixed integer nonlinear programming (MINLP) problem which is NP-hard. To solve the problem, we decompose the problem into two subproblems which respectively optimize the request assignment and the computing resource allocation. We first deal with the computing resource allocation problem by utilizing the Lagrangian multiplier method, and the resulting solution is applied for the request assignment problem. Then a novel primal-dual based approximation algorithm is devised to address the request assignment problem. Finally, to verify the efficiency of the proposed algorithm, we provide an upper bound on the approximation ratio. The experiment results show that the proposed algorithm outperforms baseline algorithms in terms of total latency, loading balancing, and computational speed.

Joint computation offloading and resource allocation strategy for D2D-assisted and NOMA-empowered MEC systems

Multi-access edge computing (MEC) emerged as a promising network paradigm that provides computation, storage and networking features within the edge of the pervasive mobile radio access network. This paper jointly considers computation offloading and resource allocation problem in device-to-device (D2D)-assisted and non-orthogonal multiple access (NOMA)-empowered MEC systems, where each mobile device (MD) is allowed to execute its task in one of the three ways, i.e., local computing, MEC offloading or D2D offloading. We invoke orthogonal multiple access (OMA) and NOMA schemes for MDs that select D2D offloading mode, allowing them to assign tasks to their peers using OMA or NOMA. The original problem is formulated as an overall energy consumption minimization problem, which proves to be NP-hard, making it intractable to solve optimally. We start from a simple case, OMA case and transform the original problem into two sub-problems, i.e., resource allocation sub-problem and computation offloading sub-problem and propose two heuristic algorithms to obtain the sub-optimal solutions of both sub-problems. Then, for the MDs selecting D2D offloading mode, we conduct user pairing and apply the NOMA scheme. Finally, simulation results demonstrate the efficiency of the proposed scheme when compared with the related schemes.

Blockchain Assisted Federated Learning for Enabling Network Edge Intelligence

The recently emerging federated learning (FL) exploits massive data stored at multiple user nodes to train a global optimal learning model without leaking the privacy of user data. However, it is still inadequate to learn the global model safely at the centralized aggregator, which is an essential part for the traditional FL architecture. Specifically, when using FL in radio access networks to enable edge intelligence, it is difficult for a central server, which belongs to a third party, to guarantee its credibility. Moreover, because the central server may cause a single point of failure, its reliability is also difficult to guarantee. Besides, a malicious participating node of FL may send ill parameters for model aggregation. In this article, we develop a blockchain assisted federated learning (BC-FL) framework, with aim to overcome the single point of failure caused by central server. Meanwhile, we propose to use blockchain to implement auditing of individual involved nodes to ensure the reliability of learning process. To avoid privacy leakage during the audit process to the greatest extent, we design a matching audit mechanism to realize efficient random matching audit process. A cryptocurrency free delegated byzantine fault tolerant (CF-DBFT) consensus mechanism is also designed to realize the low-latency distributed consensus of all nodes in the FL proces. We apply the proposed BC-FL framework to resolve the computing resource allocation problem at the edger servers in MEC network. Simulation results demonstrate the effectiveness and performance superiority of the proposed BC-FL framework. Compared with legacy FL algorithm, the serving time of MEC servers and utilization of computing resource are increased by 35% and 48% respectively under our proposed BC-FL algorithm.

Multiaccess Edge Integrated Networking for Internet of Vehicles: A Blockchain-Based Deep Compressed Cooperative Learning Approach

Recently, Internet of Vehicles (IoV) and Machine Learning (ML) have attracted more and more attention. Considering inefficient real-time training and high requirements on computing capabilities of centralized data collection, performing Distributed Machine Learning (DML) in IoV has become an important research branch. However, the heterogeneity, mobility, and distrust among IoV nodes affect how to execute DML effectively, securely, and in a salable manner. In this paper, a blockchain-based Cooperative Learning framework combined with a Deep Compression method (CLDC) is proposed. First, we improve the local training efficiency of lightweight IoV nodes by using deep compression method. Meanwhile, we have introduced a blockchain system in CLDC, the significance of which is that we have completed the transformation from centralized architecture to distributed framework through the blockchain, and shared local training results in a verifiable manner. The framework uses non-tamperable features of the blockchain to ensure the security of local training results. Moreover, we propose a Learning-based Redundant Byzantine Fault Tolerance (L-RBFT) protocol, in which the primary node needs to confirm the loss percentage of learning in the transaction before forwarding the RBFT messages. The significance of L-RBFT is to ensure that IoV nodes obtain the best training results through the consensus of blockchain nodes. We use it to solve the computing and communication resource allocation problem in IoV to clarify the operating mechanism of the proposed framework. The experimental results prove that this scheme performs better when compared with the traditional centralized deep reinforcement learning method.

Energy-Efficient Resource Allocation in Multi-UAV-Assisted Two-Stage Edge Computing for Beyond 5G Networks

Unmanned aerial vehicle (UAV)-assisted multi-access edge computing (MEC) has become one promising solution for energy-constrained devices to run the applications with high computation demand and stringent delay requirement in beyond 5G era. In this work, we study a multi-UAV-assisted two-stage MEC system in which UAVs provide the computing and relaying services to the mobile devices. Due to the limited computing resources, each UAV executes only a portion of the offloaded tasks from its associated MDs in the first stage. Hence, in the second stage, each UAV relays the portions of the tasks to the terrestrial base station (TBS) which has rich computing resources enough to handle all the tasks relayed to it. In this regard, we formulate a joint task offloading, communication and computation resource allocation problem to minimize the energy consumption of MDs and UAVs by considering the limited resources of UAVs and the tolerable latency of the tasks. The formulated problem is a mixed-integer non-convex problem which is NP hard. To solve the formulated optimization problem, we apply the Block Successive Upper-bound Minimization (BSUM) method which guarantees to obtain the stationary points of the non-convex objective function. Finally, the extensive evaluation results are conducted to show the superior performance of our proposed framework.

Joint Communication and Computation Resource Allocation in Fog-Based Vehicular Networks

To satisfy the low-latency requirements of emerging computation-intensive vehicular services, offloading these services to edge or cloud servers has been recognized as an effective solution. Due to the limited resources of edge servers and the faraway distance of cloud servers, it is challenging to provide an efficient resource allocation strategy to balance the latency, throughput and the resource utilization. In this paper, an end–edge–cloud collaboration paradigm is presented for computation offloading in fog-based vehicular networks (FVNETs) by incorporating vehicles with idle resources as fog user equipments (F-UEs). To adaptively orchestrate end–edge–cloud resources in different load cases, a two-timescale resource reservation and allocation framework is proposed. Wherein, a Stackelberg-game-based dynamic F-UE incentive problem is first formulated with the cloud server as the leader and multiple F-UEs as the followers, and then an iterative algorithm is proposed to achieve the Stackelberg equilibrium of the computation resource pricing and reservation. On a small timescale, the joint communication and computation resource allocation problem is transferred into a multiagent stochastic game and a lenient multiagent deep-reinforcement-learning-based distributed algorithm is developed to minimize the sum latency. When latency performance deteriorates, F-UE incentive optimization will be triggered to reserve more resources of F-UEs. Simulation results show that the proposed end–edge–cloud orchestrated computation offloading scheme in FVNETs outperforms baselines in terms of average latency.

Computing resource allocation scheme based on edge computing under augmented reality application

Edge computing can place computing, storage, bandwidth, application and other resources on the edge of the network to reduce transmission delay, and bandwidth limitation for users. Therefore, task scheduling and resource allocation in edge computing have become a new research focus. In view of the dynamic nature of computing resources in edge computing and the computing power limitation of a single computing resource, this paper proposes a computing resource allocation scheme based on edge computing under augmented reality (AR) application. On the premise of ensuring the completion time of sub-tasks and the revenue of computing service devices, the method transforms the computing resource allocation problem in edge computing into the many-to-many matching problem between sub-tasks and computing service devices. The performance of the proposed scheme is analyzed through experiments, and the results show that the resource allocation time is relatively stable, and it is better than other state-of-the-art algorithms in terms of the edge calculation, task delay, and task violation rate.

Empirical Matching-Based Computation Offloading Optimization for 5G and Edge Computing-Integrated EIoT

Electric Internet of things (EIoT) that integrates 5G and edge computing can provide data transmission and processing guarantee for smart grid. However, computation offloading optimization including joint optimization of server selection and computation resource allocation still faces several challenges such as difficulty in tradeoff balance among various quality of service (QoS) parameters, coupling between server selection and computation resource allocation, and multi-device competition. To address these challenges, we propose an empirical matching-based computation offloading optimization algorithm for 5G and edge computing-integrated EIoT. The optimization objective is to minimize the computation offloading delay by jointly optimizing large timescale server selection and small timescale computation resource allocation. We first model the large timescale server selection problem as a many-to-one matching problem, which can be decoupled from small timescale computation resource allocation by establishing a matching preference list based on empirical performance. Then, the large timescale server selection problem is solved by pricing-based matching with a quota algorithm. Furthermore, based on the obtained suboptimal result of large timescale server selection, the small timescale computation resource allocation problem is subsequently solved by Lagrange dual decomposition, the result of which is used to update large timescale empirical performance. Finally, extensive simulations are carried out to demonstrate the superior performance of the proposed algorithm by comparing it with existing algorithms.

Joint Computation Offloading and Resource Allocation for D2D-Assisted Mobile Edge Computing

Computation offloading via device-to-device communications can improve the performance of mobile edge computing by exploiting the computing resources of user devices. However, most proposed optimization-based computation offloading schemes lack self-adaptive abilities in dynamic environments due to time-varying wireless environment, continuous-discrete mixed actions, and coordination among devices. The conventional reinforcement learning based approaches are not effective for solving an optimal sequential decision problem with continuous-discrete mixed actions. In this paper, we propose a hierarchical deep reinforcement learning (HDRL) framework to solve the joint computation offloading and resource allocation problem. The proposed HDRL framework has a hierarchical actor-critic architecture with a meta critic, multiple basic critics and actors. Specifically, a combination of deep Q-network (DQN) and deep deterministic policy gradient (DDPG) is exploited to cope with the continuous-discrete mixed action spaces. Furthermore, to handle the coordination among devices, the meta critic acts as a DQN to output the joint discrete action of all devices and each basic critic acts as the critic part of DDPG to evaluate the output of the corresponding actor. Simulation results show that the proposed HDRL algorithm can significantly reduce the task computation latency compared with baseline offloading schemes.

Joint Computation and Communication Resource Allocation With NOMA and OMA Offloading for Multi-Server Systems in F-RAN

Since mobile devices typically have limited computation resources, offloading computation tasks to fog access points (F-APs) is a promising approach to support delay-sensitive and computation-intensive applications. This paper considers joint computation and communication resource allocation for multiuser multi-server systems, which aims to maximize the number of users being served and minimize the total energy consumption subject to delay tolerance constraints. The joint computation and communication resource allocation problem is solved optimally for both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes. The joint user pairing and fog access point assignment problem for NOMA is proved to be NP-hard. For both NOMA and OMA, heuristic and optimal algorithms based on graph matching are designed. The optimal algorithms, though of high complexity, allow NOMA and OMA to be compared at their full potential and serve as benchmarks for evaluating the heuristic algorithms. Simulation results show that NOMA significantly outperforms OMA in terms of outage probability and energy consumption, especially for tight delay tolerance constraints and large computational tasks. Simulation results also demonstrate that our proposed NOMA and OMA schemes significantly outperform the swap-enabled matching algorithm widely used in the literature.

A SMDP Approach to Evaluate the Performance of a Vehicular Cloud Computing System with Prioritize Requests

In intelligent transportation systems, Vehicular Cloud Computing (VCC) is a new technology that can help ensure road security and transport efficiency. The study and evaluation of performances of a VCC is a topic of crucial interest in these environments. This paper presents a model of the computation resource allocation problem in VCC by considering heterogeneity and priority of service requests. We consider service requests from two classes, Primary service requests and Secondary service requests. We involve a Semi-Markov Decision Process (SMDP) to achieve the optimal policy that maximizes the performances of the VCC system taking into account the variability of resources, the income and the system cost. We utilize an iterative approach to achieve the optimal scheme that characterizes the action to be taken under each state. We validate our study by numerical results that show the effectiveness of the proposed SMDP-based scheme.

Code Caching-Assisted Computation Offloading and Resource Allocation for Multi-User Mobile Edge Computing

Utilizing the data caching technology to reduce data transmission is a promising technique for improving the performance of mobile edge computing (MEC), because the delay and energy consumption produced by data transmission constitute the dominant cost of task execution in MEC. Besides, computation tasks generally consist of input parameters, executive codes, and computation results. The executive codes are fixed and can output difference computation results under different input parameters. Motivated by this, we consider to proactively cache executive codes of tasks at the MEC server to reduce the weighted sum of task execution delay and users’ energy consumption. Aiming at establishing optimal system design, we formulate the problem as a non-linear programming problem which involves jointly optimizing the executive code caching strategy, computation offloading decision, wireless resource allocation, and computing resource allocation. We propose to find the optimal solution by employing an alternating optimization framework. The optimal wireless resource and computing resource allocation problem are firstly addressed by utilizing convex optimization technology. Then, a dynamic programming-based algorithm has been developed to achieve the optimal executive code caching and computation offloading strategies. Extensive simulation results show that the proposed scheme operates well and can substantially reduce the system cost over other benchmark schemes.