Scheduling Allocation Research Articles

Energy-Efficient Service-Aware Multi-Connectivity Scheduler for Uplink Multi-Layer Non-Terrestrial Networks

This paper introduces the concept of energy efficiency (EE) in the uplink with the capability of multi-connectivity (MC) in a multi-orbit non-terrestrial network (NTN), where user terminals (UTs) can be simultaneously served by more than one satellite to achieve higher peak throughput at reduced energy consumption. This concept also considers the service classification of the users, so that network dimensioning is performed in order to satisfy the quality of service (QoS) requirement of users. MC can increase throughput, but this entails increased power consumption at user terminal for uplink transmissions. To this end, an energy-efficient service-aware multi-connectivity (EE-SAMC) scheduling algorithm is developed in this paper to improve the EE of uplink communications. EE-SAMC uses available radio resources and propagation information to intelligently define a dynamic resource allocation pattern, that optimally routes traffic so as to reduce the energy consumption at the UT while ensuring QoS is maximized. EE-SAMC is designed based on the formulation of a non-convex combinatorial problem, it is solved in two ways involving firstly an optimization solution and secondly a heuristic approach. The effectiveness of EE-SAMC is compared with random allocation, round robin and heuristic schedulers in terms of EE, throughput and delay; EE-SAMC outperforms all schedulers.

A DRL-Based Joint Scheduling and Resource Allocation Scheme for Mixed Unicast–Broadcast Transmission in 5G

With the ever-increasing demand for data services, the spectrum becomes precious resource. The cooperative transmission mechanism, which allows broadcast and unicast to share the same spectrum, can effectively alleviate this situation. The success of LDM in ATSC 3.0 has inspired many scholars to superimpose broadcast and unicast services with a fixed power injection ratio. To further improve radio resource utilization, A flexible LDM scheme with a variable power injection ratio is proposed. The scheduling policy in Medium Access Control layer is formulated as a sequential decision problem, and it proved to have Markov property. A DRL-based algorithm is proposed to obtain the optimal scheduling scheme. Considering the requirements of broadcasting service and unicast services, the Lyapunov optimization method is used to convert the problem to the Lyapunov penalty form that is more conducive for the agent to learn the optimal strategy. Simulation results show that the proposed scheme can achieve much improved performance in system throughput when guaranteeing the quality of the broadcast service.

Spectrum Allocation and User Scheduling Based on Combinatorial Multi-Armed Bandit for 5G Massive MIMO.

As a key 5G technology, massive multiple-input multiple-output (MIMO) can effectively improve system capacity and reduce latency. This paper proposes a user scheduling and spectrum allocation method based on combinatorial multi-armed bandit (CMAB) for a massive MIMO system. Compared with traditional methods, the proposed CMAB-based method can avoid channel estimation for all users, significantly reduce pilot overhead, and improve spectral efficiency. Specifically, the proposed method is a two-stage method; in the first stage, we transform the user scheduling problem into a CMAB problem, with each user being referred to as a base arm and the energy of the channel being considered a reward. A linear upper confidence bound (UCB) arm selection algorithm is proposed. It is proved that the proposed user scheduling algorithm experiences logarithmic regret over time. In the second stage, by grouping the statistical channel state information (CSI), such that the statistical CSI of the users in the angular domain in different groups is approximately orthogonal, we are able to select one user in each group and allocate a subcarrier to the selected users, so that the channels of users on each subcarrier are approximately orthogonal, which can reduce the inter-user interference and improve the spectral efficiency. The simulation results validate that the proposed method has a high spectral efficiency.

Optimizing teleconsultation scheduling to make healthcare greener

Some international organizations have called for greener healthcare in the post-pandemic era. Telemedicine has made an important contribution to achieving the goal of green healthcare by reducing the travel distance of patients. From the perspective of operation management, we put forward the teleconsultation room scheduling problem to get a better scheduling scheme to make telemedicine more low-carbon and green. First, we propose a deterministic teleconsultation room allocation scheduling model and a stochastic teleconsultation room allocation scheduling model. Then, we utilize some transformation techniques to simplify the models and solve them through numerical experiments. Next, we put the obtained scheduling schemes into the actual situation and compare them with the staff scheduling scheme. The results show that the stochastic programming model performs the best. In addition, the instability of the teleconsultation service duration has a great impact on the scheduling scheme. The telemedicine center should encourage experts from higher-level hospitals to provide more stable service time.

Resource allocation and maintenance scheduling for distributed multi-center renewable energy systems considering dynamic scope division

The continuity of activity and competition in the global renewable energy market has driven manufacturers to shift from centralized to decentralized structures and to develop multi-center service networks. Appropriate and timely preventive maintenance (PM) is essential for reducing the operation and maintenance (O&M) costs and promoting long-term stable operation for the equipment of distributed renewable energy systems. Traditionally, decision-makers separated the service network into regions and established regional centers for services. However, due to the dynamic characteristics of equipment degradation, the O&M requirement has shifted in real-time. This is an ambitious and challenging O&M task, which requires considering many aspects, including the equipment failure rate, the availability of maintenance resources, the variability of PM requirements, and the long-term horizons of renewable energy system operations. In this paper, we develop a novel multi-center collaborative maintenance (MCM) strategy to solve this resource allocation and maintenance scheduling problem (RAMSP). And a hybrid solution approach is developed by decomposing the original problem and solving two subproblems sequentially. Numerical examples are used to illustrate the economic and computational advantages of the proposed maintenance framework. By effectively integrating the computational efficiency of hierarchical modeling and the quality benefit of holistic modeling, the proposed MCM strategy emphasizes 13% average cost saving and 70% average computation time reduction. The cyclic maintenance schemes with minimal total O&M service cost are obtained to support the system's proper operation, maintenance resource management, and service route optimization.

IoT Orchestration-Based Optimal Energy Cost Decision Mechanism with ESS Power Optimization for Peer-to-Peer Energy Trading in Nanogrid

The Internet of things has revolutionized various domains, such as healthcare and navigation systems, by introducing mission-critical capabilities. However, the untapped potential of IoT in the energy sector is a topic of contention. Shifting from traditional mission-critical electric smart grid systems to IoT-based orchestrated frameworks has become crucial to improve performance by leveraging IoT task orchestration technology. Energy trading cost and ESS power optimization have long been concerns in the scientific community. To address these issues, our proposed architecture consists of two primary modules: (1) a nanogrid energy trading cost and ESS power optimization strategy that utilizes particle swarm optimization (PSO), with two objective functions, and (2) an IoT-enabled task orchestration system designed for improved peer-to-peer nanogrid energy trading, incorporating virtual control through orchestration technology. We employ IoT sensors and Raspberry Pi-based Edge technology to virtually operate the entire nanogrid energy trading architecture, encompassing the aforementioned modules. IoT task orchestration automates the interaction between components for service execution, involving five main steps: task generation, device virtualization, task mapping, task scheduling, and task allocation and deployment. Evaluating the proposed model using a real dataset from nanogrid houses demonstrates the significant role of optimization in minimizing energy trading cost and optimizing ESS power utilization. Furthermore, the IoT orchestration results highlight the potential for virtual operation in significantly enhancing system performance.

Stability measure for prefab balancing in prefabrication construction supply chain management

Effective management of the prefabrication construction supply chain management (PCSCM) requires careful consideration of rational resource allocation and Just-in-Time (JIT) delivery. The transportation and storage of materials can be a significant expense in the construction industry, making it essential to prioritize the efficient allocation of resources. When resource allocation and delivery schedules are not properly managed, it can result in additional costs and project delays. Current practices mainly depend on the foreman’s experience, leading to unpunctual delivery and insufficient distribution. Punctual delivery involves collaboration with many stakeholders, including main contractor, logistics companies, foreman, etc. This paper proposes a resource allocation model with a penalty mechanism to comprehensively consider the benefit of the main contractor under PCSCM, aiming to minimize costs and strategically allocate resources. Buffer space, as a crucial safety place, is beneficial for balancing the stability of PCSCM and controlling the cost among stakeholders. This research takes into account several practical considerations, including limitations on storage and buffer space, penalty of unpunctual delivery, and the availability of multiple transportation routes. Especially, research seldom includes the penalty mechanism with a soft time window related to the demand and supply of resources based on the storage limitation. The numerical analysis demonstrates that the result of the proposed PSO is similar to CPLEX in small-scale instances. The results show that implementing a penalty also significantly saves the CPU around 38% for the main contractor compared with the model without penalty mechanism in a practical case. These contributions allow stakeholders to optimize resource allocation and project timelines while minimizing waste through a rational penalty mechanism.

A Deep Reinforcement Learning-Based Model for Optimal Resource Allocation and Task Scheduling in Cloud Computing

The advent of cloud computing has dramatically altered how information is stored and retrieved. However, the effectiveness and speed of cloud-based applications can be significantly impacted by inefficiencies in the distribution of resources and task scheduling. Such issues have been challenging, but machine and deep learning methods have shown great potential in recent years. This paper suggests a new technique called Deep Q-Networks and Actor-Critic (DQNAC) models that enhance cloud computing efficiency by optimizing resource allocation and task scheduling. We evaluate our approach using a dataset of real-world cloud workload traces and demonstrate that it can significantly improve resource utilization and overall performance compared to traditional approaches. Furthermore, our findings indicate that deep reinforcement learning (DRL)-based methods can be potent and effective for optimizing cloud computing, leading to improved cloud-based application efficiency and flexibility.

EVALUASI PROGRAM BIMBINGAN DAN KONSELING BERBASIS CIPP PADA MASA PANDEMI COVID-19

The purpose of evaluating the Guidance and Counseling (BK) program at SMP Negeri 2 Ambarawa during the pandemic is to evaluate the context, input, process and product of the guidance and counseling program during the pandemic. This research is evaluative research. The principal provides full support in implementing counseling services during the pandemic by providing a schedule and budget allocation. The ratio of counseling teachers to students is fulfilled by the presence of school counselors with a background in counseling education. Facilities and infrastructure are in good condition, but do not yet have a special counseling room. BK services are carried out with the help of applications, namely Google classroom, and google forms used by school counselors for delivery systems in service activities with asynchronous types and in synchronous types using WhatsApp, Zoom or Google Meet depending on the agreement between teachers and students. The problems experienced in the implementation of online counseling services are less than optimal since not all students have devices. The essential material compiled by the MGBK is less appropriate to use, it is more appropriate to use a needs assessment of students to develop a BK program. School counselors provide time for learners to consult with flexible time, either through in-person or device-assisted.

Joint Optimal Data Collection Scheduling and Time Allocation in Full-Duplex WP-IoT Networks

This paper considers a full-duplex wireless powered internet of things (WP-IoT) network, supporting simultaneous wireless power transfer of a hybrid access point in the downlink and collected data transfer of devices in the uplink. Compared to existing works where the data collection process is mostly ignored, the problem of joint data collection scheduling and time allocation is investigated under two objectives, i.e., total throughput maximization subject to a total time constraint and total time minimization subject to minimum collected data constraints. For both objectives, the optimal solution is derived in closed-form. It is revealed that the proposed algorithms outperform various baseline algorithms in existing literature.

Wellness Wheel Clinics: A First Nation community-partnered care model improving healthcare access, from healthcare providers' perspectives.

First Nation people residing in rural and remote communities have limited primary healthcare access, which often affects chronic disease management leading to poor health outcomes. Individuals with lived experiences of chronic disease and substance use, along with health directors, advocated for improved services. Subsequently, an urban healthcare team in partnership with four First Nation communities developed an Outreach clinic to address healthcare access barriers. Established in 2016, this community-led clinic improves primary care access and chronic disease management in First Nation communities. Employing a qualitative research design, interviews were conducted with 15 clinic providers and 9 community members to explore the clinic's 1-year post-implementation impacts. Thematic data analysis indicated that engagement and approval by community leadership, support from Elders and community members and collaboration with existing community healthcare staff were crucial for establishing the Outreach clinic. Initial logistical challenges with space allocation, equipment, medical supplies, funding, staffing, medical records and appointment scheduling were resolved through community consultation and creative solutions. A nurse coordinator ensured continuity of care and was integral to ensuring clients receive seamless care. The commitment of the outreach team and the collective goal of providing client-centered care were instrumental in the clinic's success. In partnership with communities, access to healthcare in First Nation communities can be enhanced by coordinating Outreach clinics through existing community healthcare facilities.

Task Scheduling Mechanism Based on Reinforcement Learning in Cloud Computing

The explosive growth of users and applications in IoT environments has promoted the development of cloud computing. In the cloud computing environment, task scheduling plays a crucial role in optimizing resource utilization and improving overall performance. However, effective task scheduling remains a key challenge. Traditional task scheduling algorithms often rely on static heuristics or manual configuration, limiting their adaptability and efficiency. To overcome these limitations, there is increasing interest in applying reinforcement learning techniques for dynamic and intelligent task scheduling in cloud computing. How can reinforcement learning be applied to task scheduling in cloud computing? What are the benefits of using reinforcement learning-based methods compared to traditional scheduling mechanisms? How does reinforcement learning optimize resource allocation and improve overall efficiency? Addressing these questions, in this paper, we propose a Q-learning-based Multi-Task Scheduling Framework (QMTSF). This framework consists of two stages: First, tasks are dynamically allocated to suitable servers in the cloud environment based on the type of servers. Second, an improved Q-learning algorithm called UCB-based Q-Reinforcement Learning (UQRL) is used on each server to assign tasks to a Virtual Machine (VM). The agent makes intelligent decisions based on past experiences and interactions with the environment. In addition, the agent learns from rewards and punishments to formulate the optimal task allocation strategy and schedule tasks on different VMs. The goal is to minimize the total makespan and average processing time of tasks while ensuring task deadlines. We conducted simulation experiments to evaluate the performance of the proposed mechanism compared to traditional scheduling methods such as Particle Swarm Optimization (PSO), random, and Round-Robin (RR). The experimental results demonstrate that the proposed QMTSF scheduling framework outperforms other scheduling mechanisms in terms of the makespan and average task processing time.

Optimization of Quality of AI Service in 6G Native AI Wireless Networks

To comply with the trend of ubiquitous intelligence in 6G, native AI wireless networks are proposed to orchestrate and control communication, computing, data, and AI model resources according to network status, and efficiently provide users with quality-guaranteed AI services. In addition to the quality of communication services, the quality of AI services (QoAISs) includes multiple dimensions, such as AI model accuracy, overhead, and data privacy. This paper proposes a QoAIS optimization method for AI training services in 6G native AI wireless networks. To improve the accuracy and reduce the delay of AI services, we formulate an integer programming problem to obtain proper task scheduling and resource allocation decisions. To quickly obtain decisions that meet the requirements of each dimension of QoAIS, we further transform the single-objective optimization problem into a multi-objective format to facilitate the QoAIS configuration of network protocols. Considering the computational complexity, we propose G-TSRA and NSG-TSRA heuristic algorithms to solve the proposed problems. Finally, the feasibility and performance of QoAIS optimization are verified by simulation.

UDL: a cloud task scheduling framework based on multiple deep neural networks

Cloud task scheduling and resource allocation (TSRA) constitute a core issue in cloud computing. Batch submission is a common user task deployment mode in cloud computing systems. In this mode, it has been a challenge for cloud systems to balance the quality of user service and the revenue of cloud service provider (CSP). To this end, with multi-objective optimization (MOO) of minimizing task latency and energy consumption, we propose a cloud TSRA framework based on deep learning (DL). The system solves the TSRA problems of multiple task queues and virtual machine (VM) clusters by uniting multiple deep neural networks (DNNs) as task scheduler of cloud system. The DNNs are divided into exploration part and exploitation part. At each scheduling time step, the model saves the best outputs of all scheduling policies from each DNN to the experienced sample memory pool (SMP), and periodically selects random training samples from SMP to train each DNN of exploitation part. We designed a united deep learning (UDL) algorithm based on this framework. Experimental results show that the UDL algorithm can effectively solve the MOO problem of TSRA for cloud tasks, and performs better than benchmark algorithms such as heterogeneous distributed deep learning (HDDL) in terms of task scheduling performance.

Study on optimization and combination strategy of multiple daily runoff prediction models coupled with physical mechanism and LSTM

Accurate prediction of runoff is an important foundation for optimizing water resource allocation and reservoir scheduling operations. However, due to its complex characteristics such as time-varying and non-stationary, accurate prediction of the runoff process is very difficult. This paper proposes a novel approach for runoff forecasting by combining the physical mechanism models with the Long Short-Term Memory network (LSTM) method. Utilizing the simulation and description capabilities of physically based models, as well as the powerful nonlinear analysis provided by big data methods, a type of model selection and combination strategy is proposed incorporating 16 different physically based models with LSTM technology. Additionally, to accommodate the comprehensive analysis and evaluation of multi-model forecasting performance, this paper also proposed a comprehensive evaluation metric for runoff forecasting considering the characteristics of group models. The results of the case study demonstrate that this strategy can obtain model combinations suitable for different watershed characteristics and effectively improve the forecast accuracy of multiple models. Model combinations sorted by validation period RMSE and R2 should be a superior choice. When evaluating the runoff forecasting accuracy of obtained optimal model combination during the calibration period, the average reduction of Root Mean Square Error (RMSE) is 39.62%, and the average increase of Nash-Sutcliffe coefficient R2 is 7.49%. During the validation period, the average reduction of RMSE is 62.68% and the average increase of R2 is 24.24%. When the model combinations sorted by validation period F score, the obtained model combination increased RMSE by 22.7% and decreased R2 by 7.3% comparing to the model combination selected by RMSE and R2. It is indicated that the F score may not be suitable for evaluating model selection. The method proposed in this article can effectively improve the overall forecasting performance of a single forecasting model and has good practical value for promotion and application.

Study on Convex Resource Allocation Scheduling with a Time-Dependent Learning Effect

In classical schedule problems, the actual processing time of a job is a fixed constant, but in the actual production process, the processing time of a job is affected by a variety of factors, two of which are the learning effect and resource allocation. In this paper, single-machine scheduling problems with resource allocation and a time-dependent learning effect are investigated. The actual processing time of a job depends on the sum of normal processing times of previous jobs and the allocation of non-renewable resources. With the convex resource consumption function, the goal is to determine the optimal schedule and optimal resource allocation. Three problems arising from two criteria (i.e., the total resource consumption cost and the scheduling cost) are studied. For some special cases of the problems, we prove that they can be solved in polynomial time. More generally, we propose some accurate and intelligent algorithms to solve these problems.

A surrogate-assisted heuristic approach for the joint optimization of resource allocation and scheduling of an aircraft final assembly line

In an aircraft final assembly line, there exists flexibility in resource allocation in the sense that processing time of a task can be reduced by allocating more workers to the task. As task processing times change, assembly task scheduling needs to be adjusted accordingly, which in turn has an impact on resource allocation. This paper studies the joint optimization problem of resource allocation and task scheduling. Both resource requirement and starting time of each task need to be determined such that the total resource investment cost is minimized. We propose a surrogate-assisted heuristic approach to solve the problem by decomposing it into two phases. In the first phase, a multi-start iterative search algorithm is developed to perform a dedicated exploration on resource allocation. A surrogate model trained on numerical data is applied to evaluate the resource allocation strategy. In the second phase, a hybrid genetic algorithm embedded with a novel local search procedure is designed for task scheduling. Meanwhile, a fine search for resource allocation can also be achieved. Computational experiments demonstrate the superiority of our proposed approach and that the surrogate plays a significant role in terms of improving solution quality. A realistic case study provides valuable managerial insights for real production.

An intelligent approach of task offloading for dependent services in Mobile Edge Computing

With the growing popularity of Internet of Things (IoT), Mobile Edge Computing (MEC) has emerged for reducing the heavy workload at the multi-cloud core network by deploying computing and storage resources at the edge of network close to users. In IoT, services are data-intensive and event-driven, resulting in extensive dependencies among services. Traditional task offloading schemes face significant challenges in the IoT scenario with service dependencies. To this end, this paper proposes an intelligent approach for minimizing latency and energy consumption which jointly considers the task scheduling and resource allocation for dependent IoT services in MEC. Specifically, we establish the system model, communication model as well as computing model for performance evaluation by fully considering the dependent relationships among services, and an optimization problem is proposed for minimizing the delay and energy consumption simultaneously. Then, we design a layered scheme to deal with the service dependencies, and present detailed algorithms to intelligently obtain optimal task scheduling and resource allocation policies. Finally, simulation experiments are carried out to validate the effectiveness of the proposed scheme.

DBSCAN inspired task scheduling algorithm for cloud infrastructure

Cloud computing in today's computing environment plays a vital role, by providing efficient and scalable computation based on pay per use model. To make computing more reliable and efficient, it must be efficient, and high resources utilized. To improve resource utilization and efficiency in cloud, task scheduling and resource allocation plays a critical role. Many researchers have proposed algorithms to maximize the throughput and resource utilization taking into consideration heterogeneous cloud environments. This work proposes an algorithm using DBSCAN (Density-based spatial clustering) for task scheduling to achieve high efficiency. The proposed DBScan-based task scheduling algorithm aims to improve user task quality of service and improve performance in terms of execution time, average start time and finish time. The experiment result shows proposed model outperforms existing ACO and PSO with 13% improvement in execution time, 49% improvement in average start time and average finish time. The experimental results are compared with existing ACO and PSO algorithms for task scheduling.

A hybrid grey wolf optimizer using opposition-based learning, sine cosine algorithm and reinforcement learning for reliable scheduling and resource allocation

As the number of space debris in geosynchronous Earth orbits continues to grow, the threat posed by space debris to satellites surveillance is increasing, and the available orbital resources are also decreasing. Thus, reasonably scheduling and allocating the resources for space object tracking has become vital. This paper establishes an optimization model for the resource allocation and scheduling problem for space debris tracking. A fusion algorithm that combines the grey wolf optimizer, opposition-based learning, sine cosine search strategy, and reinforcement learning was proposed and used to solve the problem. Six groups of realistic data were selected based on the relevant background information of space debris tracking to test the validity and effectiveness of the proposed algorithm. The performance of the state-of-the-art optimization algorithms was compared with that of the proposed algorithms. The result of the experiment indicates that the proposed algorithm effectively solves the resource allocation and scheduling problem for space debris tracking.