Scheduling Allocation Research Articles

Robust cooperative planning of relief logistics operations under demand uncertainty: a case study on a possible earthquake in Tehran

Nowadays, the study of disasters such as earthquakes and their consequences, specifically in metropolises such as Tehran, has attracted significant attention. Allocating relief goods to distribution centres before the earthquake and providing relief services to the injured can reduce the fatality rate. Therefore, it seems quite necessary to present a certain routing and allocation scheduling model of relief vehicles to serve the injured. In this research, a mathematical model was proposed to minimise the unmet demands and transportation costs of relief vehicles and in the affected areas. Regarding the inherent uncertainties of disasters, we developed a robust scenario-based optimisation model for relief logistics networks to reduce the risk of fatality and provide near-optimal solutions for different possible disaster scenarios. We showed that the proposed cooperation plan produces a situation with additive supper property, which means that this type of cooperation will finally lead to a greater incentive for aid agencies to form larger coalitions. Furthermore, we proposed a set of cooperative game theory methods to distribute the cost-saving of the cooperation fairly.

Actor-critic learning-based energy optimization for UAV access and backhaul networks

In unmanned aerial vehicle (UAV)-assisted networks, UAV acts as an aerial base station which acquires the requested data via backhaul link and then serves ground users (GUs) through an access network. In this paper, we investigate an energy minimization problem with a limited power supply for both backhaul and access links. The difficulties for solving such a non-convex and combinatorial problem lie at the high computational complexity/time. In solution development, we consider the approaches from both actor-critic deep reinforcement learning (AC-DRL) and optimization perspectives. First, two offline non-learning algorithms, i.e., an optimal and a heuristic algorithms, based on piecewise linear approximation and relaxation are developed as benchmarks. Second, toward real-time decision-making, we improve the conventional AC-DRL and propose two learning schemes: AC-based user group scheduling and backhaul power allocation (ACGP), and joint AC-based user group scheduling and optimization-based backhaul power allocation (ACGOP). Numerical results show that the computation time of both ACGP and ACGOP is reduced tenfold to hundredfold compared to the offline approaches, and ACGOP is better than ACGP in energy savings. The results also verify the superiority of proposed learning solutions in terms of guaranteeing the feasibility and minimizing the system energy compared to the conventional AC-DRL.

Resilient operation planning of integrated electrical and natural gas systems in the presence of natural gas storages

In this paper, a new model is proposed for emergency response of Power Grid Systems (PGS) to severe events in the presence of Natural Gas (NG) storages. The PGS’s transmission lines outages as well as NG pipelines outages are integrated in this model. The optimal Network Reserve Requirement (NRR) allocation and NG storage scheduling are studied as emergency response strategies to relief sever events repercussion in PGS. The proposed method is linearized using the combination of piecewise linearization approach and big-M method. The effectiveness of the proposed MILP model to improve system resilience are verified through four case-studies on modified 24-bus IEEE test system integrated to 20-node NG network.

Greener Physical Layer Technologies for 6G Mobile Communications

Energy efficiency (EE) has been regarded as an essential design metric in 5G mobile communications. However, the massive antenna numbers, larger bandwidths, and higher density of base stations have been severely deteriorating the power consumption of 5G networks. In this article, the fundamental EE-spectrum efficiency (SE) joint design issues are thoughtfully considered. The relationships between EE and SE in single-input single-output (SISO), multiple-input multiple-output, and hybrid beamforming structures are first reviewed, where the increase of SE (EE) will unfortunately bring a reduction of EE (SE). To resolve this contradiction, a non-orthogonal multiple access (NOMA) scheme with energy-efficient user scheduling and resource allocation is investigated, which promises a simultaneous increase of EE and SE. Furthermore, a precoded waveform overlapping multiplexing scheme is presented, with significant improvements in EE and SE over the traditional Nyquist-criterion-based SISO systems. The waveform overlapping multiple access scheme is further proposed, which promises large performance improvements over the NOMA schemes discussed in 5G New Radio. The application of the waveform overlapping scheme in multiple-antenna systems is also investigated. Finally, the challenges of the waveform overlapping scheme and several other research topics are discussed. For sustainable development of the wireless communications industry, greener design in physical layer technologies should be second to none in the future 6G networks and beyond.

Resource Allocation Scheduling Algorithm Based on Incomplete Information Dynamic Game for Edge Computing

With the advent of the 5G era, the demands for features such as low latency and high concurrency are becoming increasingly significant. These sophisticated new network applications and services require huge gaps in network transmission bandwidth, network transmission latency, and user experience, making cloud computing face many technical challenges in terms of applicability. In response to cloud computing's shortcomings, edge computing has come into its own. However, many factors affect task offloading and resource allocation in the edge computing environment, such as the task offload latency, energy consumption, smart device mobility, end-user power, and other issues. This paper proposes a dynamic multi-winner game model based on incomplete information to solve multi-end users' task offloading and edge resource allocation. First, based on the history of end-users storage in edge data centers, a hidden Markov model can predict other end-users' bid prices at time t. Based on these predicted auction prices, the model determines their bids. A dynamic multi-winner game model is used to solve the offload strategy that minimizes latency, energy consumption, cost, and to maximizes end-user satisfaction at the edge data center. Finally, the authors designed a resource allocation algorithm based on different priorities and task types to implement resource allocation in edge data centers. To ensure the prediction model's accuracy, the authors also use the expectation-maximization algorithm to learn the model parameters. Comparative experimental results show that the proposed model can better results in time delay, energy consumption, and cost.

Massive IoT Access With NOMA in 5G Networks and Beyond Using Online Competitiveness and Learning

This paper studies the problem of online user grouping, scheduling and power allocation in beyond 5G cellular-based Internet of things networks. Due to the massive number of devices trying to be granted to the network, non-orthogonal multiple access method is adopted in order to accommodate multiple devices in the same radio resource block. Different from most previous works, the objective is to maximize the number of served devices while allocating their transmission powers such that their real-time requirements as well as their limited operating energy are respected. First, we formulate the general problem as a mixed integer non-linear program (MINLP) that can be transformed easily to MILP for some special cases. Second, we study its computational complexity by characterizing the NP-hardness of different special cases. Then, by dividing the problem into multiple NOMA grouping and scheduling subproblems, efficient online competitive algorithms are proposed. Further, we show how to use these online algorithms and combine their solutions in a reinforcement learning setting to obtain the power allocation and hence the global solution to the problem. Our analysis are supplemented by simulation results to illustrate the performance of the proposed algorithms with comparison to optimal and state-of-the-art methods.

A novel decision support system for optimizing aircraft maintenance check schedule and task allocation

Modern aircraft have thousands of parts, systems, and components that need to be recurrently inspected or replaced. To keep the fleet airworthy, maintenance planners have to schedule the maintenance checks for each aircraft and the associated tasks. In practice, these two complex problems are solved following the experience of planners, resulting in sub-efficient solutions. This paper presents the first decision support system (DSS) developed for optimizing both aircraft maintenance check schedule and task allocation. The DSS integrates aircraft maintenance check scheduling, task allocation to each maintenance check, and shift planning in the same framework. The practical relevance of the DSS is illustrated through three test cases. The results show that the DSS can be used not only to optimize maintenance plans but also to study future maintenance policies. The results reveal substantial improvements in all key performance indicators compared with the planning approach followed by a partner airline.

Tennis‐resource allocation mechanism in software‐defined data center network

With the rapid development of cloud computing, most network resources are usually stored at Data Center Network (DCN) for providing the remote storage services. Under such paradigm, there arises some issues, such as traffic scheduling, energy saving and resource allocation. At present, the resource allocation in DCN has attracted the extensive concern. In order to effectively manage and allocate these resources in DCN, Software‐Defined Networking can be employed to achieve such purpose, by leveraging its natural abilities for the global control and centralized management. Another point, the tennis is a very popular sport to enhance the physical fitness. However, the tennis‐resources are considerably limited as the building of venue is quite expensive. Therefore, it becomes very significant to investigate the tennis‐resource allocation in Software‐Defined DCN (SDCN). Consider that there are many tenants using the tennis‐resources in SDCN, this paper studies the multi‐tenant tennis‐resource allocation scenario. Meanwhile, the Global Superiority Equalization algorithm is devised. At first, the max‐min fairness is used to determine the allocated number of tennis‐resources for each round. Then, the allocation state is adjusted according to the current situation until the final allocation decision is determined. The experiment results show that the proposed tennis‐resource allocation mechanism can reduce the resource allocation cost and improve the resource utilization. Accordingly, SDCN can handle more tennis‐resource requests and increase the benefit of venue provider.

Deep Reinforcement Learning-Based Task Scheduling in IoT Edge Computing.

Edge computing (EC) has recently emerged as a promising paradigm that supports resource-hungry Internet of Things (IoT) applications with low latency services at the network edge. However, the limited capacity of computing resources at the edge server poses great challenges for scheduling application tasks. In this paper, a task scheduling problem is studied in the EC scenario, and multiple tasks are scheduled to virtual machines (VMs) configured at the edge server by maximizing the long-term task satisfaction degree (LTSD). The problem is formulated as a Markov decision process (MDP) for which the state, action, state transition, and reward are designed. We leverage deep reinforcement learning (DRL) to solve both time scheduling (i.e., the task execution order) and resource allocation (i.e., which VM the task is assigned to), considering the diversity of the tasks and the heterogeneity of available resources. A policy-based REINFORCE algorithm is proposed for the task scheduling problem, and a fully-connected neural network (FCN) is utilized to extract the features. Simulation results show that the proposed DRL-based task scheduling algorithm outperforms the existing methods in the literature in terms of the average task satisfaction degree and success ratio.

Resource pooling in vehicular fog computing

Vehicular fog computing (VFC) provisions computing services at the edge of networks by fully exploiting the idle resources of vehicle loaded computer systems. Task scheduling and resource allocation revolved around VFC have gained tremendous attention recently. Currently, most of these works in VFC have focused on response time optimization or energy reduction. Computing services are provisioned in a pay-as-you-go model and vehicles as resource contributors are stimulated by the benefits obtained by leasing these resources. How to maximize their own benefits is one of big concerns but few of current works have recognized its importance in VFC. We in this paper introduce the notion of resource pooling into VFC where the computing resources of vehicles are pooled together to jointly provision computational services in a community. A genetic algorithm based strategy is proposed to solve the optimization problem for the sake of benefit maximization. Extensive experiments have been carried out to evaluate the approach and the numeric results have demonstrated that our strategy outstands other approaches with regards to the optimization objective.

Allocation scheduling leads to optimum utilization of operation theater time

BackgroundCancellation of surgeries is a regular phenomenon in any hospital, and reasons may vary from clinical to managerial ones. The aim of the study is to suggest scheduling to address the problem of time over run related cancellations. This is an observational and descriptive study conducted in a tertiary care hospital with ophthalmology facilities. The sample size is calculated with 95% confidence interval using Epi Info 6 from the total surgeries performed in the last 5 years (n = 380). Simple random sampling technique was used. MethodsSurgical time for all types of ophthalmic surgeries (n = 582) was observed. Allocation of listed cases to the available operating rooms (ORs) was carried out using the observed time using LEKIN software. ResultsThe time over-run of 2 h and 6 h was noted for two units, whereas idle OR time was observed in other units. An average idle time of 19% was noted on each day. Reallocation of the cases to the ORs was carried out taking all the planned cases (of both the operating units of the day) as the number of jobs and all the available ORs as parallel machines using LEKIN software. All the planned cases could be accommodated; still, an average of 17% of the total available operation theater (OT) time was found idle on each day. ConclusionsPlanning of cases using procedure time and scheduling on a daily basis using allocation models with simple algorithms can provide optimal utilization of OTs and can address the time over-run and related cancellations.

Task scheduling on heterogeneous multiprocessor systems through coherent data allocation

AbstractEnergy consumption has become one of the main bottlenecks that limit the performance improvement of heterogeneous multiprocessor systems. In a heterogeneous distributed shared‐memory multiprocessor system (HDSMS), each processor can access all the memories, and each data can be stored in different memories. This article aims at addressing the problem of task scheduling and data allocation (TSDA) on HDSMS. To minimize the total energy consumption under a time constraint for TSDA, we propose two algorithms: the extended tree assignment for task scheduling incorporating data allocation (ETATS‐DA) and critical path task scheduling and data allocation (CPTSDA). The ETATS‐DA algorithm first utilizes the extended tree assignment to search the near optimal solution for task assignment, and then allocates data to memory based on the result of assignment. The CPTSDA algorithm considers TSDA jointly on a critical path simultaneously. Our proposed algorithms perform coherent data allocation under the consideration of best task scheduling by running two different heuristic strategies, respectively, and taking the best result as the final result. We conduct a large number of simulation experiments to test the performance of our algorithms, and the results validate the higher performance of our methods compared with the state‐of‐the‐art algorithms.

Min-Time TS: task resource mapping algorithm in cloud computing

Internet-established cloud environment provides enormous amount of ubiquitous, distributed resources such as processors, memory, and software. In recent years, task scheduling and resource allocation has become a significant issue in cloud computing. Task scheduling plays a crucial role in cloud system performance. For achieving elegant performance, arises a need of efficient scheduling technique. Numerous algorithms developed for scheduling. In this paper, we have proposed an efficient approach which schedules the tasks to the available cloud resources by forming a task pair. Min-Time TS algorithm forms a finite number of task sets; each set consists a pair of tasks one from Group G1 and another from Group G2 and are scheduled with minimum layover time.

Resource Allocation for Energy-Efficient NOMA System in Coordinated Multi-Point Networks

This paper studies user scheduling and power allocation problem to maximize the energy efficiency (EE) for non-orthogonal multiple access (NOMA) in downlink Coordinated Multi-Point networks. In the proposed framework, a more practical scenario the imperfect channel state information, imperfect successive interference cancellation and data outage are investigated. To address the considered problem, the optimization problem is formulated constrained by the total power and the outage probability requirements. However, the EE objective function is with a non-convex structure. Accordingly, we first convert the optimization problem to make the objective function concave and analytically tractable. Furthermore, we split the joint optimization problem to find a suboptimal solutions to the original problem. As a result, we first propose a suboptimal user-scheduling algorithm to improve the system's EE. Due to the non-convex function of the transmit power, we invoke a sequential successive convex approach to address the non-convex problem by its lower bound concave function. Besides, the fractional objective function is converted to its equivalent subtractive form. Finally, we derive a power control scheme to address the proposed framework. Simulation results endorse the effectiveness of the proposed algorithm and their performance gains in terms of EE compared to both NOMA and OFDMA variants.

Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge

We study federated learning (FL) at the wireless edge, where power-limited devices with local datasets collaboratively train a joint model with the help of a remote parameter server (PS). We assume that the devices are connected to the PS through a bandwidth-limited shared wireless channel. At each iteration of FL, a subset of the devices are scheduled to transmit their local model updates to the PS over orthogonal channel resources, while each participating device must compress its model update to accommodate to its link capacity. We design novel scheduling and resource allocation policies that decide on the subset of the devices to transmit at each round, and how the resources should be allocated among the participating devices, not only based on their channel conditions, but also on the significance of their local model updates. We then establish convergence of a wireless FL algorithm with device scheduling, where devices have limited capacity to convey their messages. The results of numerical experiments show that the proposed scheduling policy, based on both the channel conditions and the significance of the local model updates, provides a better long-term performance than scheduling policies based only on either of the two metrics individually. Furthermore, we observe that when the data is independent and identically distributed (i.i.d.) across devices, selecting a single device at each round provides the best performance, while when the data distribution is non-i.i.d., scheduling multiple devices at each round improves the performance. This observation is verified by the convergence result, which shows that the number of scheduled devices should increase for a less diverse and more biased data distribution.

MooFuzz: Many-Objective Optimization Seed Schedule for Fuzzer

Coverage-based Greybox Fuzzing (CGF) is a practical and effective solution for finding bugs and vulnerabilities in software. A key challenge of CGF is how to select conducive seeds and allocate accurate energy. To address this problem, we propose a novel many-objective optimization solution, MooFuzz, which can identify different states of the seed pool and continuously gather different information about seeds to guide seed schedule and energy allocation. First, MooFuzz conducts risk marking in dangerous positions of the source code. Second, it can automatically update the collected information, including the path risk, the path frequency, and the mutation information. Next, MooFuzz classifies seed pool into three states and adopts different objectives to select seeds. Finally, we design an energy recovery mechanism to monitor energy usage in the fuzzing process and reduce energy consumption. We implement our fuzzing framework and evaluate it on seven real-world programs. The experimental results show that MooFuzz outperforms other state-of-the-art fuzzers, including AFL, AFLFast, FairFuzz, and PerfFuzz, in terms of path discovery and bug detection.

Discrete quality factors aware channel scheduling in Cognitive Radio Ad-hoc Networks

The Recurrent Transmission Ratio (RTR), considered as confine for Cognitive Radio Ad-hoc Networks (CRAN), has attracted several researchers’ in the recent past. Among the manifold practices that are trying to lessen the RTR, the CRAN is one of the dimensions. The transmission scheduling channels and allocation of the suitable channel are the essential objectives for attaining CRAN concerning to reduce the ratio of data packet drop. Most of the scheduling methods represented in the existing literature targeted to attain optimal scheduling by choosing the channels have based on optimal idle time utilization. Few of the contributions try to choose channels by any quality metrics, and remarkably less amount of contributions endeavoured on the allocation of a channel for lessening the RTR under discrete quality factors. In respect to, here, this manuscript tried to attain optimal allocation of a channel under discrete quality factors (DQFACS)”. The simulation study conducted through the experiments exhibited that the suggested method DQFACS augmented the data transmission optimality by an allocation of the optimal channel under discrete quality factors compared to the contemporary methods.

Sub-Channel Scheduling, Task Assignment, and Power Allocation for OMA-Based and NOMA-Based MEC Systems

In this paper, sub-channel scheduling, task assignment and power allocation are investigated for orthogonal multiple access (OMA)-based and non-orthogonal multiple access (NOMA)-based mobile edge computing (MEC) systems. Based on different channel conditions and computational capacities, computational tasks are partially offloaded to the MEC server via OMA or NOMA protocols. In order to minimize the total energy consumption, an optimization problem under the task execution latency constraint is formulated and divided into two sub-problems. By utilizing matching theory, the formulated sub-channel allocation problem is solved by a proposed low-complexity algorithm, where the joint optimization of task assignment and power allocation is performed at each iteration. Based on the delay constraint, some insights are obtained, and the closed-form solutions of task assignment coefficients and transmit power are derived. Furthermore, the offloading strategy in both OMA and NOMA schemes is analyzed, which shows that the optimal task assignment coefficient is decided by the energy consumption efficiency (ECE). Simulation results indicate that: i) the proposed sub-channel allocation algorithm and derived closed-form solutions can significantly improve the MEC system in terms of the energy consumption; ii) the provided offloading strategy can be dynamically and efficiently employed with different channel conditions and computational capacities.

Joint Resource Allocation and UAV Scheduling With Ground Radio Station Sleeping

Applications of Unmanned aerial vehicles (UAVs) have advanced rapidly in recent years. The UAVs are used for a variety of applications, including surveillance, disaster management, precision agriculture, weather forecasting, etc. In near future, the growing number of UAV applications would necessitate densification of UAV infrastructure (ground radio station (GRS) and ground control station (GCS)) at the expense of increased energy consumption for UAV communications. Maximizing the energy efficiency of this UAV infrastructure is important. Motivated by this, we propose joint resource allocation and UAV scheduling with GRS sleeping. Further, we propose the use of coordinated multi-point (CoMP) with joint transmission (JT) and non-orthogonal multiple access (NOMA) along with GRS sleeping to increase the coverage and data rates, respectively. We then present exhaustive simulation results showcasing the trade-off between throughput and energy efficiency with varying UAV densities. We also compare the coverage, throughput, and energy efficiency for NOMA, CoMP with JT and the benchmark scenario.

Optimization of Business Process Execution in Services Architecture: A Systematic Literature Review

Web services have become a standard way to provide functions of information systems. The number of web services grows rapidly with the increasing popularity of microservices architecture. In consequence, many business processes are executed entirely through web services. Therefore, optimizing the performance of business process execution may bring many benefits. There are many optimization methods in this area. Our systematic literature review aims to introduce available methods to researchers interested in the optimization of business process execution. We queried four databases: ACM, IEEE Xplore, Science Direct, and Springer. Out of 12150 initially found papers, we have selected 128 for the review. We have grouped methods presented in those papers into three stages of business process optimization: Resource Allocation, Service Composition, and Service Scheduling. Service Composition attracts the largest group of researchers with a vast majority of 119 articles in it. Moreover, the most popular are genetic algorithms. In general, researchers mainly propose heuristic methods that optimize business processes during run-time. We see the potential for further exploration at both Resource Allocation and Service Scheduling stages.