Resource Scheduling Problem Research Articles

Stochastic measurement-based multi-cloud consolidation for efficient resource distribution

In distributed cloud systems, due to high computational complexity, existing methods cannot infinitely reduce the measurement interval to obtain the mean demand. However, considering both the unpredictability of demand and the diversity of pricing adds significant complexity to the problem. In response, we introduce an efficient algorithm called Stochastic Demand-oriented Resource consolidation based on Meta-heuristics (SDRM). The algorithm introduces the stochastic demand model and formulates a cross-cloud demand consolidation and resource scheduling problem, and tries to solve it efficiently by combining differential evolution algorithm with dynamic programming optimization. The results are provided for experiments utilizing both simulated and real-world data. It demonstrates that compared with traditional algorithms, increasing gain by up to 53%, SDRM just incurs an affordable time expense (i.e., 1.10-2.51 times that of existing algorithms). Therefore, SDRM stands as a compelling solution for resource distribution in heterogeneous cloud environments.

AI Services-Oriented Dynamic Computing Resource Scheduling Algorithm Based on Distributed Data Parallelism in Edge Computing Network of Smart Grid

Massive computational resources are required by a booming number of artificial intelligence (AI) services in the communication network of the smart grid. To alleviate the computational pressure on data centers, edge computing first network (ECFN) can serve as an effective solution to realize distributed model training based on data parallelism for AI services in smart grid. Due to AI services with diversified types, an edge data center has a changing workload in different time periods. Selfish edge data centers from different edge suppliers are reluctant to share their computing resources without a rule for fair competition. AI services-oriented dynamic computational resource scheduling of edge data centers affects both the economic profit of AI service providers and computational resource utilization. This letter mainly discusses the partition and distribution of AI data based on distributed model training and dynamic computational resource scheduling problems among multiple edge data centers for AI services. To this end, a mixed integer linear programming (MILP) model and a Deep Reinforcement Learning (DRL)-based algorithm are proposed. Simulation results show that the proposed DRL-based algorithm outperforms the benchmark in terms of profit of AI service provider, backlog of distributed model training tasks, running time and multi-objective optimization.

Bilinear branch and check for unspecified parallel machine scheduling with shift consideration

This paper tackles the complex challenge of team formations, assignments, and job schedules within the static Unspecified Parallel Machine Flexible Resource Scheduling problem, specifically incorporating shift considerations. In existing literature, teams are often simplified as machines that operate continuously throughout the day without any interruptions. However, in reality, teams require breaks between shifts and cannot work continuously within a day. Therefore, we introduce shift considerations to ensure that teams do not work in consecutive shifts. We consider flexible workers, capable of performing any job, who are distributed among different teams in different shifts to undertake various jobs. The number of teams in each shift is a decision variable. The duration of each job is determined by the number of workers in a team assigned to it. The objective function is to minimize the makespan, representing the overall schedule completion time, while adhering to precedence constraints. We formulate an integer linear programming model for the proposed problem and develop a novel bilinear branch and check algorithm that introduces valid bilinear inequalities to accelerate convergence. The numerical results confirm that our algorithm’s performance optimally solves problems up to 35 jobs within a reasonable timeframe, surpassing the efficiency of the branch and cut method of IBM CPLEX and the classical branch and check algorithm.

New Methods for Optimal Power Allocation and Joint Resource Scheduling in 5G Network which Use Mobile Edge Computing

Mobile Edge Computing (MEC) is considered one of the enabling and promising technologies in 5G networks, especially with the massive data movement of various devices and the increased demand for computing. Here, computational offloading of tasks to edge clouds provides an effective, flexible, low-latency solution for mobile users in the network. However, the limited computing resources in edge clouds and the dynamic demands of mobile users make it difficult to schedule computing requests to appropriate edge clouds, and make the offloading process energetically expensive for devices. Therefore, it is very important to design an energy-efficient offloading strategy. To this end, we first formulate the transmission power allocation (PA) problem for mobile phone users to minimize power consumption. Using a quasi-convex technique, we address the (PA) problem using a (Hybrid GAPSO) algorithm resulting from combining the Genetic Algorithm (GA) with the Particle Swarm Optimization (PSO) algorithm. Next, we model the joint request offloading and resource scheduling (JRORS) problem as a mixed- nonlinear program to minimize the response delay of requests. The (JRORS) problem can be divided into two problems, namely the request offloading (RO) problem and the computing resource scheduling (RS) problem. Therefore, we analyze the JRORS problem as a dual decision problem and propose a multi-objective particle swarm optimization algorithm referred as (MO-PSO). The simulation results show that (HGAPSO) can save transmission power consumption and has good convergence property, and (MO-PSO) outperforms existing methods in terms of response rate and can maintain good performance in a dynamic network.

Client selection and resource scheduling in reliable federated learning for UAV-assisted vehicular networks

Federated Learning (FL), a promising deep learning paradigm extensively deployed in Vehicular Edge Computing Networks (VECN), allows a distributed approach to train datasets of nodes locally, e.g., for mobile vehicles, and exchanges model parameters to obtain an accurate model without raw data transmission. However, the existence of malicious vehicular nodes as well as the inherent heterogeneity of the vehicles hinders the attainment of accurate models. Moreover, the local model training and model parameter transmission during FL exert a notable energy burden on vehicles constrained in resources. In view of this, we investigate FL client selection and resource management problems in FL-enabled UAV-assisted Vehicular Networks (FLVN). We first devise a novel reputation-based client selection mechanism by integrating both data quality and computation capability metrics to enlist reliable high-performance vehicles. Further, to fortify the FL reliability, we adopt the consortium blockchain to oversee the reputation information, which boasts tamper-proof and interference-resistant qualities. Finally, we formulate the resource scheduling problem by jointly optimizing the computation capability, the transmission power, and the number of local training rounds, aiming to minimize the cost of clients while guaranteeing accuracy. To this end, we propose a reinforcement learning algorithm employing an asynchronous parallel network structure to achieve an optimized scheduling strategy. Simulation results show that our proposed client selection mechanism and scheduling algorithm can realize reliable FL with an accuracy of 0.96 and consistently outperform the baselines in terms of delay and energy consumption.

A Secure and Decentralized DLaaS Platform for Edge Resource Scheduling Against Adversarial Attacks

Edge Computing is promising for latency-sensitive applications. However, current edge resource scheduling is inefficient. Deep Learning as a Service (DLaaS) provides deep learning methods to optimize the resource scheduling problem, but faces great challenges of security and reliability. On one hand, DLaaS training agents and raw data are exposed to various adversarial attacks. On the other hand, dishonest DLaaS trainers can generate poisoned models to attack the DLaaS system. In this paper, we propose SAPE, a Secure and decentralized DLAaS Platform in Edge computing. SAPE allows users to submit their tasks, which will be scheduled to the appropriate edge clusters to minimize the task execution time. We formulate the resource scheduling problem and develop the federated deep reinforcement learning (DRL) method to optimize the problem and resist the adversarial attacks of DLaaS. We utilize blockchain and propose a consortium-based verification scheme to improve the reliability of the federated training process, protecting the DLaaS models from being poisoned and compromised. We conduct experiments to evaluate the latency and security performance of SAPE and the federated DRL scheduling policy. The results show that SAPE outperforms the traditional schemes when defending against adversarial attacks towards the DLaaS platform in edge computing.

Dual Resource Scheduling Problem of Machines and AGVs Based on Hybrid Discrete Salp Swarm Algorithm

Dual Resource Scheduling Problem of Machines and AGVs Based on Hybrid Discrete Salp Swarm Algorithm

Volunteer dispatch considering fatigue effect and satisfaction in emergency situation

Rescue organization scheduling is a hot issue in the field of emergency management, in which the participation of volunteer rescue organizations, a social force, is of great significance to improve the efficiency of emergency rescue. In the case of known rescue services required by the affected place and the information of rescue services provided by volunteer rescue organizations, the goal is to maximize the satisfaction of the victims and minimize the fatigue of the volunteer rescue organizations. The volunteer rescue organization scheduling problem is an NP-hard problem. To solve the studied problem model, two multi-objective optimization algorithms are applied in this article. With the goal of improving the overall rescue efficiency of volunteer rescue organizations implementing rescue at the disaster site during a single emergency, this study analyzes the practical problems related to emergency rescue, taking into account the effects of the time sensitivity of the disaster victims, the preference of the rescue services, the fatigue accumulation rate of the volunteer rescue organizations, and the matching of the rescue skills. Finally, this article distills some scheduling strategies applicable to emergency volunteer rescue organizations, hoping to provide theoretical basis and practical guidance for the Emergency Management Center and related emergency management departments to better configure and optimize emergency human resource scheduling problems.

Nature inspired algorithms in dynamic task scheduling: A review

The process of scheduling involves allocating shared resources gradually so that tasks can be completed effectively within the allotted time. In task scheduling and resource allocation, the terms are used independently for tasks and resources, respectively. In computer science and operational management, scheduling is a hot topic. Efficient schedules guarantee system effectiveness, facilitate sound decision-making, reduce resource waste and expenses, and augment total productivity. Selecting the most accurate resources to complete work items and schedules for computing and business process execution is typically a laborious task. Particularly in dynamic real-world systems, where scheduling different dynamic tasks involves multiple tasks, is a difficult problem. Emerging technology known as "nature inspired algorithms" has the ability to dynamically solve the problem of optimal task and resource scheduling. This review paper discusses a study that looked at algorithms inspired by nature and used them to schedule tasks dynamically. The Nature Inspired Algorithms used in dynamic task scheduling and a comparative analysis of those methods are used in this paper to address the study's findings.

Toward Intelligent Cross-Domain Resource Coordinate Scheduling for Satellite Networks

The new generation satellite network is a comprehensive service system that can provide communication, observation, navigation, and other functions. Typically, a system with a specific service function is defined as a domain and the supply and demand relationship of resources, such as communication, storage, and energy resources, are unbalanced among domains. Therefore, it is nontrivial to accurately characterize the cross-domain resource state and coordinate the data transmission policy of interrelated satellites from different domains to make full use of the resources in each domain aiming at improving the resource utilization ratio (RUR) of the whole network. To this end, this paper investigates the cross-domain resource scheduling (CDRS) problem in satellite networks aiming at maximizing the total amount of downloaded transmitted data. We start with the orbit motion law of satellites and construct the satellite orbit feature matrix of each domain to design a hierarchical sparse resource representation (HSRR) scheme to accurately characterize the resource state of each domain in real-time with low complexity. Further, based on the HSRR, we develop a cross-domain dynamic multi-resource scheduling algorithm to solve the CDRS problem by introducing the advantage factor and policy-oriented hyper-parameter. The algorithm can make full use of the resources in each domain to achieve efficient CDRS by dynamically adjusting the data transmission policy of satellites among domains. Simulation results show that the greater the service demand and resource difference among domains, the more significant the performance improvement brought by CDRS, and compared with the existing algorithms, the RUR and resource utilization efficiency have been significantly improved.

Global Resource Scheduling for Distributed Edge Computing

Recently, there has been a surge in interest surrounding the field of distributed edge computing resource scheduling. Notably, applications like intelligent traffic systems and Internet of Things (IoT) intelligent monitoring necessitate the effective scheduling and migration of distributed resources. In addressing this challenge, distributed resource scheduling must weigh the costs associated with resource scheduling, aiming to identify an optimal strategy amid various feasible solutions. Different application scenarios introduce diverse optimization objectives, including considerations such as cost, transmission delay, and energy consumption. While current research predominantly focuses on the optimization problem of local resource scheduling, there is a recognized need for increased attention to global resource scheduling. This paper contributes to the field by defining a global resource scheduling problem for distributed edge computing, demonstrating its NP-Hard nature through proof. To tackle this complex problem, the paper proposes a heuristic solution strategy based on the ant colony algorithm (ACO), with optimization of ACO parameters achieved through the use of particle swarm optimization (PSO). To assess the effectiveness of the proposed algorithm, an experimental comparative analysis is conducted. The results showcase the algorithm’s notable accuracy and efficient iteration cost performance, highlighting its potential applicability and benefits in the realm of distributed edge computing resource scheduling.

Task Scheduling for Federated Learning in Edge Cloud Computing Environments by Using Adaptive-Greedy Dingo Optimization Algorithm and Binary Salp Swarm Algorithm

With the development of computationally intensive applications, the demand for edge cloud computing systems has increased, creating significant challenges for edge cloud computing networks. In this paper, we consider a simple three-tier computational model for multiuser mobile edge computing (MEC) and introduce two major problems of task scheduling for federated learning in MEC environments: (1) the transmission power allocation (PA) problem, and (2) the dual decision-making problems of joint request offloading and computational resource scheduling (JRORS). At the same time, we factor in server pricing and task completion, in order to improve the user-friendliness and fairness in scheduling decisions. The solving of these problems simultaneously ensures both scheduling efficiency and system quality of service (QoS), to achieve a balance between efficiency and user satisfaction. Then, we propose an adaptive greedy dingo optimization algorithm (AGDOA) based on greedy policies and parameter adaptation to solve the PA problem and construct a binary salp swarm algorithm (BSSA) that introduces binary coding to solve the discrete JRORS problem. Finally, simulations were conducted to verify the better performance compared to the traditional algorithms. The proposed algorithm improved the convergence speed of the algorithm in terms of scheduling efficiency, improved the system response rate, and found solutions with a lower energy consumption. In addition, the search results had a higher fairness and system welfare in terms of system quality of service.

Time-cost-quality tradeoff considering resource-scheduling problems

Duration, cost, and resources are defined as project constraints. Consequently, in each project, one of these constraints is received attention compared with another. Thus, the abilities of construction project manager should include balancing these constraints in a way that all the project objectives are fulfilled. Achieving this balance is conducted by determining the best alternative for each activity, while minimizing project total cost and duration and satisfies resources constraints as well as smoothing resource histograms. For that purpose, this study aims at optimizing project schedule to provide this balance by using multi construction methods which is mainly based on multiskilling strategy. As will be discussed, to achieve this goal, a model was developed and genetic algorithm is used to preform optimization. This study contributes to the body of knowledge a model able to produce schedules with appropriate project duration, cost, and quality, also satisfy resource constraints with most leveled histograms.

Buffer Times Between Scheduled Events in Resource Assignment Problem: A Conflict-Robust Perspective

Problem definition: In many resource scheduling problems for services with scheduled starting and completion times (e.g., airport gate assignment), a common approach is to maintain appropriate buffer between successive services assigned to a common resource. With a large buffer, the chances of a “crossing” (i.e., a flight arriving later than the succeeding one at the gate) will be significantly reduced. This approach is often preferred over more sophisticated stochastic mixed-integer programming methods that track the arrival of all the flights to infer the number of “conflicts” (i.e., a flight arriving at a time when the assigned gate becomes unavailable). We provide a theoretical explanation, from the perspective of robust optimization for the good performance of the buffering approach in minimizing not only the number of crossings but also the number of conflicts in the operations. Methodology/results: We show that the buffering method inherently minimizes the worst-case number of “conflicts” under both robust and distributionally robust optimization models using down-monotone uncertainty sets. Interestingly, under down-monotone properties, the worst-case number of crossings is identical to the worst-case number of conflicts. Using this equivalence, we demonstrate how feature information from flight and historical delay information can be used to enhance the effectiveness of the buffering method. Managerial implications: The paper provides the first theoretical justification on the use of buffering method to control for the number of conflicts in resource assignment problem. Funding: This work was supported by the 2019 Academic Research Fund Tier 3 of the Ministry of Education-Singapore [Grant MOE-2019-T3-1-010] and the Research Grants Council of Hong Kong SAR, China [Grant PolyU 152240/17E]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0572 .

IOT (Internet Of Thinks) Resource Scheduling Problem Based on Two Imperialist Competitive and Genetic Algorithms

The Internet of Things (IoT) is an emerging concept in the world of Information Technologies and Communications. Using the IoT in the cloud computing platform is also feasible. Appropriate use of resources such as processors is one of the challenges of the IoT in the cloud computing platform. Hence, task scheduling and IoT resources are key issues. The IoT resource scheduling is the same as the selection of an appropriate resource to equally distribute loads in processors and maximize the efficiency of resources. This study proposes a method by which the Imperialist Competitive and Genetic algorithms are combined to resolve this problem. For this, several simulations are performed on the proposed method. Simulation results indicate that the proposed method can solve this scheduling problem at an appropriate time and computational load, which could reduce energy consumption and increase the efficiency of IoT resources in a cloud computing platform.

Energy Efficient Computation Offloading in Aerial Edge Networks With Multi-Agent Cooperation

With the high flexibility of supporting resource-intensive and time-sensitive applications, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is proposed as an innovational paradigm to support the mobile users (MUs). As a promising technology, digital twin (DT) is capable of timely mapping the physical entities to virtual models, and reflecting the MEC network state in real-time. In this paper, we first propose an MEC network with multiple movable UAVs and one DT-empowered ground base station to enhance the MEC service for MUs. Considering the limited energy resource of both MUs and UAVs, we formulate an online problem of resource scheduling to minimize the weighted energy consumption of them. To tackle the difficulty of the combinational problem, we formulate it as a Markov decision process (MDP) with multiple types of agents. Since the proposed MDP has huge state space and action space, we propose a deep reinforcement learning approach based on multi-agent proximal policy optimization (MAPPO) with Beta distribution and attention mechanism to pursue the optimal computation offloading policy. Numerical results show that our proposed scheme is able to efficiently reduce the energy consumption and outperforms the benchmarks in performance, convergence speed and utilization of resources.

Resource Scheduling of Time-Sensitive Services for B5G/6G Connected Automated Vehicles

Due to the shortage of spectrum resources in the Internet of vehicles, the diversified time-sensitive services for B5G/6G connected automated vehicles cannot be effectively transmitted and the existing researches lack efficient and intelligent resource scheduling methods. Therefore, this paper proposes a spectrum resource scheduling model for time-sensitive services, and designs a two-tier joint resource scheduling method based on the age of information and bandwidth requirements under macro base station (MBS) and Road Side Unit (RSU). In the first layer, we propose a Vickery-Clarke-Groves (VCG) enabled auction model to solve the time-sensitive services resource scheduling problem, in order to guarantee the authenticity of vehicle users (VUEs) bidding. In addition, the lagrange relaxation algorithm is utilized to obtain the optimal solution of spectrum resource allocation to ensure the VUEs service delay and achieve low complexity resource scheduling. In the second layer, we design a federated learning based MBS auxiliary communication method to alleviate RSU communication pressure. The communication links of differentiated time-sensitive services are predicted to improve the quality of perception services. Simulation results verify that the proposed algorithms can enhance the VUEs experience effectively compared with the conventional methods, in terms of the delay, throughput and packet loss ratio.

Edge Trusted Sharing: Task-Driven Decentralized Resources Collaborate in IoT

6G is committed to providing a fully connected world. The deployment and application of 6G technology in the Internet of Things (IoT) can efficiently collaborate IoT resources and realize resource sharing, which mainly encourages IoT development. However, due to the lack of trust between IoT resources, security and privacy become the main challenges. As an emerging technology, blockchain can solve the trust-absence issues and provide more benefits, but the introduction of blockchain also brings problems for IoT resource collaboration and sharing. This article first proposes a blockchain-enabled edge resource sharing (BEERS) architecture by combining blockchain and edge computing technology. Then, based on a typical resource sharing and collaboration scenario, the joint optimization problem of resource scheduling and task assignment (JRSTA) is constructed. We decompose JRSTA into a primal problem and a master problem and design a greedy-based task assignment (GBTA) algorithm to solve the primal problem. Based on the GBTA algorithm, the resource scheduling and task assignment (RSTA) algorithm is developed. Next, we design a layered parallel edge resource scheduling and task assignment (LPRSTA) mechanism to improve practicability. Finally, we analyze the security and the performance of our proposed architecture and algorithms. The results show that the proposed architecture can support the secure collaboration of IoT resources, and the proposed algorithm can achieve effective resource scheduling and task assignment.

Application of Improved Artificial Immune System Algorithm Based on Applied Mathematics for Optimization of Manpower Allocation in Construction Engineering

The outbreak of the COVID-19 pandemic has led construction companies to prioritize the intelligent and optimal scheduling of human resources in construction projects to reduce costs. This study addresses the problem of heterogeneity in human resource scheduling in construction projects, presents a mathematical model with generic human resources as an example, proposes an improved artificial immune system (NAIS) algorithm to solve the problem, and verifies its effectiveness. Experimental results show that the NAIS algorithm achieves the optimal duration of 9 days in just 2 s using the Matrix Laboratory (MATLAB), which is significantly faster than mathematical optimization technique software (CPLEX), thus confirming the feasibility of the NAIS algorithm. Additionally, the average PD values for the NAIS algorithm, calculated for different worker counts, skills, and the number of tasks, were lower compared to the comparison algorithm. Overall, the NAIS algorithm effectively addresses the heterogeneous problem of human resource scheduling in construction projects with multiple modes, thereby optimizing construction engineering labor allocation.

Routing and Resource Scheduling for Air-Ground Integrated Mesh Networks

Due to the advantage of achieving scalable connectivity and low-latency communications, air-ground integrated mesh networks (AGIMNs) will play an important role in the next generation wireless communication systems. However, due to the heterogeneous character of AGIMNs, it is challenging to manage the network and optimize the communication resources for improving the end-to-end (E2E) performance. Therefore, in this paper, we study a joint routing and time-frequency resource scheduling problem aiming at minimizing the total weighted E2E delay for heterogeneous AGIMNs. To capture the features of this complex system, we mathematically model the network constraints and formulate an optimization problem. To solve the original nonconvex problem, a suboptimal solution is proposed. First, we propose an optimal minimum-weight routing method, in which the hop count, conflict delay, and contention delay are taken into consideration. Then, we transform the time-frequency resource scheduling subproblem into a series of tractable problems for maximizing the number of active links through channel assignment in successive time slots. Finally, the successive convex approximation (SCA) technique is utilized to solve the channel assignment problem per time slot. Extensive simulation results show the performance superiority of the proposed solution compared to the benchmarks in terms of the total E2E delay.