Scheduling Allocation Research Articles

Efficient Scheduling of Jobs and Allocation of Resources in Cloud Computing

Due to the drastic utilization of clouds, a Proper and proficient allocation of resources in dynamically working environment of cloud systems turns into the challenging task. Different promising mechanisms have been created to work on the effectiveness of process of resource allocation. Yet at the same time there is some incompetency as far as resource allocation and job scheduling, when the systems become highly loaded. Hence, an effective algorithm for scheduling of jobs is needed to work on the proficiency of resource allocation activities. In this paper a advanced technique for scheduling of jobs is proposed for effective and unique process of allocation of resources in cloud computing. By making use of prediction-based techniques and mechanism of updating resource tables in dynamic manner, we achieve, better allocation of resources in the form of response time and completion of jobs. The experimental results demonstrate the effective outcomes compared to existing techniques, by achieving exactness in values for resource table updation.

Joint Scheduling and Resource Allocation for Hierarchical Federated Edge Learning

The concept of hierarchical federated edge learning (H-FEEL) has been recently proposed as an enhancement of federated learning model. Such a system generally consists of three entities, i.e., the server, helpers, and clients, in which each helper collects the trained gradients from clients nearby, aggregates them, and sends the result to the server for global model update. Due to limited communication resources, only a portion of helpers can be scheduled to upload their aggregated gradients in each round of the model training. And that necessitates a well-designed scheme for the joint helper scheduling and communication resources allocation. In this paper, we develop a training algorithm for the H-FEEL system which involves local gradient computing, weighted gradient uploading, and machine learning model updating phases. By characterizing these phases mathematically and analyzing one-round convergence bound of the training algorithm, we formulate an optimization problem to achieve the scheduling and resource allocation scheme. The problem simultaneously captures the uncertainty of the wireless channel and the importance of the weighted gradient. To solve the problem, we first transform it into an equivalent problem and then decompose the transformed problem into two subproblems: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bit and sub-channel allocation</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">helper scheduling</i> , which are mixed integer nonlinear programming and continuous nonlinear problems, respectively. For the first subproblem, we obtain an optimal solution of exponential complexity and a suboptimal solution that has polynomial complexity. For the second subproblem, we obtain a closed-form optimal solution in a special case and a suboptimal solution in the general case. The efficacy of our scheme is amply demonstrated via simulations and the analytical framework is shown to provide valuable design insights for the practical implementation of the H-FEEL system.

Dynamic Task Scheduling and Resource Allocation for Microservices in Cloud

With the emergence of new companies and the expansion of the information technology sector, the need for Cloud Computing becomes apparent. Currently, the enterprises are rapidly transitioning from monolithic architecture to microservice-driven architecture. This research study has discovered that all task scheduling algorithms were designed for a specific (set) number of virtual machines, which resulted in the bottleneck problem, where multiple tasks were assigned to the microservice scheduler and the execution time of processing the tasks was significantly increased. Therefore, to address this issue, a novel model was designed based on the number of tasks and accordingly the number of virtual machines were dynamically generated to send the tasks to the microservice scheduler one by one, and the difficulties with execution time were also addressed. The study also discovered that due to the multiple workloads on the microservices, resource allocation becomes extremely difficult. To address this issue, containerized microservices were discovered. Here, the microservices would be distributed in containers. To implement the dynamic work scheduling technique, a cloud microservice translator would be developed, where a user may upload a text file and quickly get it dynamically translated. The main aim of this research work is to improve the task scheduling and resource allocation in microservices.

Near Interference-Free Space-Time User Scheduling for mmWave Cellular Network

The highly directional beams applied in millimeter wave (mmWave) cellular networks make it possible to achieve near interference-free (NIF) transmission under judiciously designed space-time user scheduling, where the power of intra-/ inter-cell interference between any two users is below a predefined threshold. In this paper, we investigate two aspects of the NIF space-time user scheduling in a multi-cell mmWave network with multi-RF-chain base stations. Firstly, given that each user has a requirement on the number of space-time resource elements, we study the NIF user scheduling problem to minimize the unfulfilled user requirements, so that the space-time resources can be utilized most efficiently and meanwhile all strong interferences are avoided. A near-optimal scheduling algorithm is proposed with performance close to the lower bound of unfulfilled requirements. Furthermore, we study the joint NIF user scheduling and power allocation problem to minimize the total transmit power under the constraint of rate requirements. Based on our proposed NIF scheduling, an energy-efficient joint scheduling and power allocation scheme is designed with limited channel state information, which outperforms the existing independent set based schemes, and has near-optimal performance as well.

Fair Licensed Spectrum Sharing Between Two MNOs Using Resource Optimization in Multi-Cell Multi-User MIMO Networks

Licensed spectrum sharing has been a promised approach to provide mobile network operators (MNOs) with required spectrum at times of increased traffic, or to improve the mobile user data rate with limited spectral resources. In this paper, we investigate the use of multi-user multiple-input, multiple-output (MIMO) techniques to enable licensed spectrum sharing. Specifically, we present a fair spectrum sharing system between two MNOs in multi-cell multi-user MIMO networks. We impose fairness by ensuring that each MNO receives spectrum in proportion to the amount it contributes. We formulate a constrained optimization problem to determine resource allocation and user scheduling across two MNOs. Since the problem is non-convex, we develop an algorithm to provide an effective solution through fractional programming and block coordinate descent. Our numerical results illustrate that the proposed spectrum sharing scheme can achieve up to 60 % improvement in terms of the average user rate among the two operators while ensuring that neither MNO is exploited for participating in the sharing mechanism. This improvement is in relation to the baseline of each MNO using multi-user MIMO communications on its own. In addition, most users, especially cell-center users close to the BSs, take advantage of our proposed spectrum sharing framework.

Delay Aware Secure Offloading for NOMA-Assisted Mobile Edge Computing in Internet of Vehicles

In this paper, a multi-vehicle multi-task non-orthogonal multiple access (NOMA) assisted mobile edge computing (MEC) system with passive eavesdropping vehicles is investigated. To heighten the performances of edge vehicles, we propose a vehicle grouping pairing method, which utilizes vehicles near the MEC to assist edge vehicles as full-duplex relays. For promoting transmission security, we employ artificial noise to interrupt eavesdropping vehicles. Furthermore, we derive the approximate expression of the secrecy outage probability of the system. The combined optimization of vehicle task division, power allocation, and transmit beamforming is formulated to minimize the total delay of task completion for edge vehicles. Then, we design a power allocation and task scheduling algorithm based on the genetic algorithm to solve the mixed-integer non-linear programming problem. Numerical results demonstrate the superiority of our proposed scheme in terms of system security and transmission delay.

Deep-Reinforcement-Learning-Based Optimal Transmission Policies for Opportunistic UAV-Aided Wireless Sensor Network

When there are unmanned aerial vehicles (UAVs) performing their specifically assigned tasks in the air, some of them still have available resources to access different ground communication networks to improve their communication performance, especially for the wireless sensor network. Technically, when they execute their own given missions with predetermined trajectories, they can also provide opportunistic assistance for terrestrial networks at the same time. In this article, we solve an opportunistic UAV-assisted data transmission problem in a wireless sensor network from a novel perspective. In consideration of UAVs dynamic behaviors, varying transmission tasks, and real-time matching between UAVs and sensor clusters, we propose to jointly optimize UAV scheduling and power control aiming to obtain optimal policies to maximize the network data transmission in a long run under the opportunistic access mode. We reformulate this optimization problem as a Markov decision process (MDP) and take deep reinforcement learning (DRL) as our tool to obtain solutions. We develop a DQN-based and a deep deterministic policy gradient (DDPG)-based optimization approaches to adjust the power allocation of cluster heads, and the scheduling and bandwidth allocation of UAVs during their missions over the covered area to improve the whole network data transmission performance. Simulation results demonstrate the validity and superiority of our proposed approaches compared with other benchmark policies in different perspectives.

Availability-Aware Multiobjective Task Allocation Algorithm for Internet of Things Networks

Node failures are known to be among the generic problems in Internet of Things (IoT) networks. These failures can be caused by communication disturbances, battery depletion, or even hardware faults. The larger the IoT network and the larger the task to be executed in the network, the higher is the probability of a node failure in the relevant part of the network. This article studies the node failures and proposes a new task allocation algorithm based on multiobjective optimization to address this issue. This article proposes a specialized archive-selection mechanism to enhance diversity in the search space of the so-called multiobjective task allocation algorithm (MOTA). High diversity in the archive allows a reliable selection of alternative task assignments in the case of node failures in the IoT network. We evaluate the performance of the proposed approach regarding the network lifetime, its latency, and its availability, using a network simulation model and compare the results with the baseline MOTA and the dynamic task allocation scheduler (DTAS). The results show that the proposed approach provides significant performance improvements over the existing algorithms, especially in scenarios with high task-to-node ratios.

RoNS: Robust network function services in clouds

In multi-tenant clouds, the traffic of tenants (e.g., enterprises) needs to be processed by network functions (NFs), for security and business logic issues. Due to potential hardware failures and software errors, NFs may break down. When encountering NF failures, we should consider two critical requirements for maintaining cloud robustness: limited influence scope and fast failure recovery. Without considering these two requirements, prior works based on deploying backup NF instances may result in large influence scope and long recovery time when a failure occurs. To bridge the gap, this paper investigates how to build robust network function services (RoNS) in multi-tenant clouds. Specifically, RoNS limits the number of tenants that each NF instance will serve so as to control the influence scope of an NF failure, and schedule requests with the help of agents designed in the data plane to achieve fast failure recovery. This is however a difficult undertaking. To solve this problem, RoNS takes a two-phase approach: NF instance allocation and tenant request scheduling. For NF instance allocation, we propose an efficient algorithm with bounded approximation factors based on the randomized rounding method. For tenant request scheduling, we present a primal–dual-based algorithm with a superior competitiveness ratio to solve it. We implement RoNS on a real testbed for experimental studies and use simulations for large-scale investigation. Both experiment results and simulation results show the superior performance of the proposed algorithms compared with other alternatives. For example, RoNS can cut down the number of affected tenants by 60%, and reduce recovery delay from 1170 ms to 316 ms on average, compared with existing failure recovery mechanisms based on deploying backup instances.

Resource-controlled stochastic customer order scheduling via particle swarm optimization with bound information

PurposeCycle time reduction is important for order fulling process but often subject to resource constraints. This study considers an unrelated parallel machine environment where orders with random demands arrive dynamically. Processing speeds are controlled by resource allocation and subject to diminishing marginal returns. The objective is to minimize long-run expected order cycle time via order schedule and resource allocation decisions.Design/methodology/approachA stochastic optimization algorithm named CAP is proposed based on particle swarm optimization framework. It takes advantage of derived bound information to improve local search efficiency. Parameter impacts including demand variance, product type number, machine speed and resource coefficient are also analyzed through theoretic studies. The algorithm is evaluated and benchmarked with four well-known algorithms via extensive numerical experiments.FindingsFirst, cycle time can be significantly improved when demand randomness is reduced via better forecasting. Second, achieving processing balance should be of top priority when considering resource allocation. Third, given marginal returns on resource consumption, it is advisable to allocate more resources to resource-sensitive machines.Originality/valueA novel PSO-based optimization algorithm is proposed to jointly optimize order schedule and resource allocation decisions in a dynamic environment with random demands and stochastic arrivals. A general quadratic resource consumption function is adopted to better capture diminishing marginal returns.

Tasks and Resources Allocation Approach with Priority Constraints in Cloud Computing

Cloud computing is the most developing technology, which allow users to access data, software and IT services. Cloud systems are characterized by the uncertainty of the resources availability. For that reason, its performance is greatly affected by the applied scheduling and allocation algorithm used to map submitted tasks to resources. This paper introduces a heuristic approach that combine Ant Colony and priority-aware schema to achieve task scheduling and resource allocation in cloud computing environments. The algorithm provides three prioritized levels of quality of services to be employed by users per their demand. A level’s priorities dynamically affect the way tasks are distributed in the system. The resources are allocated using a modified version of Ant Colony Optimization. Results show that the proposed algorithm improves the performance of the system by minimizing makespan, decreasing the degree of imbalance between virtual machines, and enhancing the Cloud’s quality of service by achieving user-priority goals.

A baseline-reactive scheduling method for carrier-based aircraft maintenance tasks

Carrier-based aircraft maintenance tasks are conducted in time-critical, resource-constrained, and uncertain environments. Optimizing the scheduling allocation scheme of maintenance personnel and equipment, reasonably responding to uncertainty disturbances, and maintaining a high fleet availability are vital to the combat and training missions of carrier-based aircraft. The maintenance task scheduling problem for carrier-based aircraft is investigated in this study. First, a mathematical model for comprehensive carrier-based aircraft maintenance task scheduling that considers constraints such as maintenance personnel, equipment/shop, space, and parallel capacity is developed. Second, to generate the baseline scheduling scheme, an improved non-dominated sorting genetic algorithm II (I_NSGA-II) with local neighborhood search is proposed for the model optimization solution; I_NSGA-II uses the serial scheduling generation scheme mechanism to generate the time sequence scheduling scheme for maintenance personnel and equipment/workshop of different fleet sizes. Third, to cope with dynamic uncertainty disturbances, two reactive scheduling methods, i.e., complete rescheduling and partial rescheduling, are proposed to perform reactive scheduling corrections to the baseline schedule. Case simulation shows that the established mathematical model is reasonable and practical, and that the proposed I_NSGA-II is superior to the current mainstream algorithms. In addition, the decision maker can select between the two reactive scheduling methods flexibly based on the different forms and scales of disturbance.

Price-Aware Service Deployment in Hierarchical Mobile-Edge Computing

Mobile-edge computing (MEC) is considered as a promising solution to release pressure on the core network and reduce service response time. Edge nodes with storage and computation resources are able to cache various services and process tasks rather than offloading to remote clouds. However, it is difficult to make service deployment decisions appropriately since resources are limited in edge nodes and requirements of services are diverse. The hierarchical MEC structure in the 5G network causes extra complication, and the cost of service deployment aggravates the hardness, especially for service providers. In this article, we focus on the service deployment problem considering service caching, resource allocation, and task scheduling in the hierarchical MEC network, aiming at minimizing monetary cost. To address the heterogeneous limitations and requirements, we formulate the problem as a mixed-integer nonlinear programming problem and develop an iterative service deployment algorithm by exploiting Gibbs sampling. We make the service caching strategies of edge nodes iteratively. Furthermore, we transform the resource allocation and task scheduling optimization into the linear programming problem and employ a typical optimization function. Simulation results show that our algorithm always obtains minimal monetary cost for various number of services and task arrival rates, compared with benchmarks.

Intelligent Reflecting Surface (IRS) Allocation Scheduling Method Using Combinatorial Optimization by Quantum Computing

Intelligent Reflecting Surface (IRS) significantly improves the energy utilization efficiency in 6th generation cellular communication systems. Here, we consider a system with multiple IRS and users, with one user communicating via several IRSs. In such a system, the user to which an IRS is assigned for each unit time must be determined to realize efficient communication. The previous studies on the optimization of various parameters for IRS based wireless systems did not consider the optimization of such IRS allocation scheduling. Therefore, we propose an IRS allocation scheduling method that limits the number of users who allocate each IRS to one unit time and sets the reflection coefficients of the IRS specifically to the assigned user resulting in the maximum IRS array gain. Additionally, as the proposed method is a combinatorial optimization problem, we develop a quadratic unconstrained binary optimization formulation to solve this using quantum computing. This will lead to the optimization of the entire system at a high speed and low power consumption in the future. Using computer simulation, we clarified that the proposed method realizes a more efficient communication compared to the method where one IRS is simultaneously used by multiple users.

Post-Disaster Distribution System Restoration With Logistics Support and Geographical Characteristics

Repair scheduling and routing and logistics support are interdependent and critical for post-disaster distribution system restoration (PDSR), which is also influenced by the geographical characteristics of outage area. Hence, we develop a co-optimization model for the PDSR with logistics support and geographical characteristics. A hybrid improved bacterial colony chemotaxis algorithm is proposed to solve the model, in which A* algorithm is employed to route repair crews and material delivery in the transportation network considering geographical characteristics, and an improved bacterial colony chemotaxis algorithm is proposed to determine the repair scheduling and material allocation in the distribution system. Different scale of distribution system instances with different damage levels and different geographical characteristics are used to demonstrate the effectiveness of the proposed methodology.

Joint berth allocation and ship loader scheduling under the rotary loading mode in coal export terminals

Due to nonmatching between the efficiency of ship deballasting and terminal loading, operations in modern coal export terminals (CETs) are frequently interrupted and significantly restricted by ship deballasting delays. This paper addresses a novel integrated problem of berth allocation and ship loader scheduling arising in CETs with new features, including the symmetrical berth layout-based rotary loading mode (SBLRLM) and deballasting restrictions. A mixed-integer programming (MIP) model is proposed to make joint decisions to minimize the total tardiness of all ships. By exploring the problem's decomposable structure, an exact method that relies on logic-based Benders decomposition (LBBD) is proposed to obtain optimal solutions. According to the problem features, the monolithic MIP formulation is decomposed into a new MIP (master problem) and a constraint programming (subproblem). Combinatorial valid inequalities are developed to enhance the master problem model and obtain a tighter lower bound. A Benders cut strengthening heuristic is developed to generate feasibility and optimality cuts to ensure efficient algorithm convergence. To evaluate the proposed method, computational experiments are conducted on a set of test instances generated based on real data collected from a major CET in northern China. The results indicate that the LBBD algorithm can optimally solve large-scale problem instances within a reasonable amount of time. The plan generated by the LBBD algorithm achieves a 51.9% reduction in ship tardiness, on average, compared to that of practical scheduling methods. By using the proposed method, the superiority of the SBLRLM in reducing deballasting delays becomes more significant as the problem scale increases. In addition, considering different working intensities, the results could also help port operators make decisions about the number of berths in the SBLRLM.

Completion time minimization for multi-antenna UAV-enabled data collection in uncorrelated Rician fading

In 5G, the unmanned aerial vehicle (UAV) is regarded as an essential way to support energy-saving, reliable, and low-cost data collection in wireless sensor networks. However, UAV-based data collection inevitably suffers from more significant data collection delays due to the UAV movement time in large application scenarios. In this paper, we consider a UAV-enabled wireless sensor network under the uncorrelated Rician fading channel, where a multi-antenna UAV is dispatched to collect data from ground sensor nodes (SNs). The objective is to minimize the mission completion time, including UAV flight and data collection time, by jointly optimizing UAV trajectory, UAV-SN transmission scheduling, and trajectory-based time allocation under the constraints of UAV speed, outage probability, data size, and transmission power. To solve this nonconvex problem, we decompose it into two subproblems: 1) trajectory optimization and 2) UAV-SN transmission scheduling and trajectory-based time allocation optimization. In the trajectory optimization, we first rewrite the outage probability constraint into the communication distance constraint and propose a Spiral-based method to find the shortest trajectory covering all SNs. Then, we propose a scheduling initialization algorithm and employ alternating optimization to solve the UAV-SN transmission scheduling and trajectory-based time allocation optimization. Finally, numerical results show that the proposed algorithm outperforms the benchmark and typical algorithms in terms of the mission completion time through a large number of experiments.

Joint User Scheduling and Resource Allocation in Distributed MIMO Systems with Multi-Carriers

Compared with the traditional collocated multi-input multi-output system (C-MIMO), distributed MIMO (D-MIMO) systems have the advantage of higher throughput and coverage, making them strong candidates for next-generation communication architecture. As a practical implementation of a D-MIMO cooperative network, the multi-TRP (multiple transmission/reception point) system becomes a hotspot in the research of advanced 5G. Different from previous research on a cooperative D-MIMO network with single narrowband transmission, this paper proposes a joint optimization scheme to address the user scheduling problem along with carrier allocation to maximize the total spectral efficiency (SE) in the downlink of coherent multi-TRP systems with multi-carriers. We establish a joint optimization model of user scheduling and resource allocation to maximize the system spectral efficiency under the constraints of power consumption and the backhaul capacity limits at each RAU (remote antenna unit), as well as the QoS (quality of service) requirement at each user. Since the optimization model is both non-covex and non-smooth, a joint optimization algorithm is proposed to solve this non-convex combinatorial optimization problem. We first smooth the mixed-integer problem by employing penalty functions, and after decoupling the coupled variables by introducing auxiliary variables, the original problem is transformed into a series of tractable convex optimization problems by using successive convex approximation (SCA). Numerical results demonstrate that the proposed joint optimization algorithm for user scheduling and resource allocation can reliably converge and achieve a higher system SE than the general multi-TRP system without carrier allocation, and this advantage is more pronounced under a higher backhaul capacity or higher power consumption constraints.

An efficient radar-target assignment and power allocation strategy for low-angle tracking in the MIMO-multisite radar system

How to utilize limited resources effectively in a multiple-input multiple-output (MIMO) radar network plays a critical role in its tracking capability. In this paper, a joint radar target assignment and power allocation (JRTAPA) strategy is proposed for the MIMO-multisite radar system (MIMO-MSRS) tracking low-angle targets. The MIMO-MSRS integrates the distributed MIMO radar architecture and the digital beamforming (DBF) technique, and hence better tracking performance in low-angle tracking is achievable. The mechanism of JRTAPA strategy is to allocate the limited beam and power resources using the prior target information feedback from the tracking recursive cycle. The location posterior Cramér-Rao lower bound (PCRLB) under the multipath interference (MI) is derived and adopted as the constraint metric. Considering the specific tracking accuracy requirement of each target, a quality of service (QoS)-based optimization model is established to minimize the total power consumption. An efficient two-stage Karush–Kuhn–Tucker (KKT) condition-based solver is proposed for solving such a nonconvex optimization model. The simulation results confirm the superiority of the proposed JRTAPA strategy over the joint scheduling and power allocation (JSPA) strategy, the target assignment and resource optimization (JTARO) strategy, as well as the exhaustive search-based algorithm. The results also reveal that the target geometry and tracking accuracy requirements have important effects on resource allocation.

Proportional Fair Scheduling for Downlink mmWave Multi-User MISO-NOMA Systems

In this paper, we study non-orthogonal multiple access (NOMA) user scheduling and resource allocation problem for a generic downlink single-cell multiple input and single output (MISO) millimeter wave (mmWave) system. The larger number of packed antennas and the highly directional property of mmWave communications enable directional beamforming to achieve spatial diversity. Toward this end, we consider two different hybrid precoding schemes which are based on orthogonal matching pursuit (OMP). Users are assigned into different clusters and the base station (BS) transmits superposed signals that share the same precoding vector. Moreover, both fixed number of users per cluster and dynamic number of users per cluster are investigated. We aim to jointly optimize the user clustering, service scheduling, and power allocation strategy, in maximizing the proportional fairness (PF) among the users and exploring the multiuser diversity and multiplexing gain. Since the formulated joint user clustering, scheduling and power allocation problem is a mixed integer non-convex optimization problem, we propose a two-fold methodology. First, we apply a hybrid precoding and user clustering scheme, where the hybrid precoder is constructed by singular vector division (SVD) or minimum mean square error (MMSE). Then, with the obtained result, we approximate the proportional fairness power allocation problem by a sequence of Geometric Programming (GP) problems which are solved iteratively. The proposed scheme strikes a balance between the spectral efficiency and service fairness. Results show that the proposed MISO-NOMA scheme which is based on MMSE hybrid precoder and the proposed user scheduling and power allocation strategy under proportional fairness metric can outperform various conventional MISO schemes. Furthermore, our proposed dynamic number of users per cluster scheme outperforms the fixed scheme and can be considered as an upper bound in several aspects, including spectral efficiency and fairness.