Class Of Scheduling Algorithms Research Articles

Multitasking scheduling with shared processing

AbstractRecently, the problem of multitasking scheduling has raised a lot of interest in the service industries. Hall et al. (Discrete Applied Mathematics, 2016) proposed a shared processing multitasking scheduling model which allows a team to continue to work on the primary tasks while processing the routinely scheduled activities as they occur. With a team being modeled as a single machine, the processing sharing of the machine is achieved by allocating a fraction of the processing capacity to routine jobs and the remaining fraction, which we denote as sharing ratio, to the primary jobs. In this paper, we generalize this model to parallel machines and allow the fraction of the processing capacity assigned to routine jobs to vary from one to another. The objectives are minimizing makespan and minimizing the total completion time of primary jobs. We show that for both objectives, there is no polynomial time approximation algorithm unless P=NP if the sharing ratios are arbitrary for all machines. Then we consider the problems where the sharing ratios on some machines have a constant lower bound. For each objective, we analyze the performance of the classical scheduling algorithms and their variations and then develop a polynomial time approximation scheme when the number of machines is a constant.

Empowering communication networks with MMR scheduler: A novel approach to balancing user throughput and fairness

In the field of communication networks, the management of resource allocation is a crucial research area. To achieve efficient radio resource management, mobile telecommunications operators utilize opportunistic scheduling algorithms. This study presents a novel opportunistic scheduler, called MMR (Min–Max Rate), which aims to ensure equal treatment of users, good quality of service, and maximum overall system throughput simultaneously and improve network throughput and fairness by up to 70% compared to traditional and classical scheduling algorithms. To evaluate the performance of MMR, we compared it with other reference algorithms such as Round Robin, MaxSNR, and Proportional Fair, known for their scheduling efficiency and robustness. The simulation results obtained by the MATLAB software and analysis focused on fairness criteria and system throughput for different user groups, and we found that our proposed algorithm outperforms the existing algorithms.

Edge computing driven sustainable development: A case study on professional farmer cultivation mechanism

AbstractNew‐type professional farmers are new‐type rural talents with scientific and cultural literacy and professional knowledge of agriculture under the background of rural revitalization. The new type of professional farmers is of great significance for accelerating agricultural modernization and promoting the construction of a powerful modern socialist country. In the process of cultivating new types of professional farmers in rural areas, it is necessary to break through the previous informatization constraints of poor information flow, and give full play to the role of advanced information technology to promote the sustainable economic development. This paper proposes a new type of professional farmer cultivation platform based on edge computing architecture to improve the cultivation efficiency of new type of professional farmers, so as to better promote the development of rural informatization. Aiming at the challenges of energy consumption under the edge platform architecture, this paper proposes a dynamic scheduling method for semi‐online tasks for edge computing platforms. In order to evaluate the effectiveness of the algorithm, the method in this paper is compared with the classic scheduling algorithm, and simulated and verified on the CloudSim platform. Experimental results show that the proposed method outperforms other algorithms in the task completion time metric. With the expansion of task scale, more energy consumption can be saved by using the algorithm proposed in this paper.

A Task Planning Method for UAV Swarm Dynamic Reconstruction Based on a Fourth-Order Motif

The primary purpose of task allocation is to build each equipment platform and quickly complete integration planning at the actual combat speed to achieve efficient management of the entire task. In this process, higher requirements are put forward for dynamic, cooperative, and highly adaptive drone colony organization. In this paper, the scheduling problem of hybrid unmanned aerial vehicle (UAV) systems is studied under an uncertain environment. First, the system-capability-task organizational structure is defined and quantified, which lays a foundation for dynamic adjustment of the organizational structure in the future. Then, combined with the theory of flexible network and elastic network management, the model is calculated, and the linear transformation function and fuzzy theory are used to stratify and cluster the capability layers. On this basis, four motif structures are introduced for abnormal nodes in the process of dynamic adjustment, and a dynamic group reconstruction algorithm (DRA-M) is established. Finally, the time and communication load indexes are determined, and the alternative strategy is designed for the failure point. The performance of the classical scheduling algorithm is evaluated by benchmarking it under different conditions. The results show that the algorithm has a good dynamic adjustment ability in the event of a UAV swarm emergency, which is a bright light for the future study of highly adaptive UAV cluster organization.

Cloneable Jellyfish Search Optimizer Based Task Scheduling in Cloud Environments

For cloud environments, task scheduling focusing on the optimal completion time (makespan) is vital. Metaheuristic approaches can be used to produce efficient solutions that will provide important cost savings to both the cloud service provider and the clients. On the other hand, since there is a high probability of getting stuck in local minima in metaheuristic solutions due to the type of problem, it may not always be possible to quickly reach the optimal solution. This study, using a metaheuristic approach, proposes a solution based on the Cloneable Jellyfish Algorithm for optimal task distribution in cloud environments. The unique feature of the proposed algorithm is that it allows dynamic population growth to be carried out in a controlled manner in order not to get stuck in local minima during the exploration phase. In addition, this algorithm, which uses a different cloning mechanism so that similar candidates are not generated in the population growth, has made it possible to achieve the optimal solution in a shorter time. To observe the solution performance, cloud environment simulations created in the Cloudsim simulator have been used. In experiments, the success of the proposed solution compared to classical scheduling algorithms has been proven.

A Deep Reinforcement Learning Approach to the Optimization of Data Center Task Scheduling

With more businesses are running online, the scale of data centers is increasing dramatically. The task-scheduling operation with traditional heuristic algorithms is facing the challenges of uncertainty and complexity of the data center environment. It is urgent to use new technology to optimize the task scheduling to ensure the efficient task execution. This study aimed at building a new scheduling model with deep reinforcement learning algorithm, which integrated the task scheduling with resource-utilization optimization. The proposed scheduling model was trained, tested, and compared with classical scheduling algorithms on real data center datasets in experiments to show the effectiveness and efficiency. The experiment report showed that the proposed algorithm worked better than the compared classical algorithms in the key performance metrics: average delay time of tasks, task distribution in different delay time levels, and task congestion degree.

Allocating raw materials to competing projects

This paper considers the problem of material allocation to competing self-interested agents. A novel resource allocation model is presented and studied in a mechanism design setting without using money as incentive. The novelties and specialties of our contribution include that the materials are supplied at different dates, the jobs requiring them are related with precedence relations, and the utilities of the agents are based on the tardiness values of their jobs. We modify a classical scheduling algorithm for implementing the Serial Dictatorship Mechanism, which is then proven to be truthful and Pareto-optimal.

QL-HEFT: a novel machine learning scheduling scheme base on cloud computing environment

Cloud computing is a computing model that fully utilizes the resources on the Internet to maximize the utilization of resources. Due to a large number of users and tasks, it is important to achieve efficient scheduling of tasks submitted by users. Task scheduling is one of the crucial and challenging non-deterministic polynomial-hard problems in cloud computing. In task scheduling, obtaining shorter makespan is an important objective and is related to the pros and cons of the algorithm. Machine learning algorithms represent a new method for solving this type of problem. In this paper, we propose a novel task scheduling algorithm called QL-HEFT that combines Q-learning with the heterogeneous earliest finish time (HEFT) algorithm to reduce the makespan. The algorithm uses the upward rank (ranku) value of HEFT as the immediate reward in the Q-learning framework. The agent can obtain better learning results to update the Q-table through the self-learning process. The QL-HEFT algorithm is divided into two major phases: a task sorting phase based on Q-learning for obtaining an optimal order and a processor allocation phase using the earliest finish time strategy. Experiments show that QL-HEFT achieves a shorter makespan compared to three other classical scheduling algorithms as well as good performances in terms of the average response time.

Urgency-Based Fair Scheduling for LTE to Improve Packet Loss and Fairness: Design and Evaluation

Classic scheduling algorithms for the LTE uplink in the literature may cause severe packet loss, poor fairness, and inefficient resource allocation. To alleviate packet loss, delay budget rather than channel condition plays a key factor in our proposed algorithm that allows the user equipment (UE) with the least left delay budget to be scheduled first. Besides, the UEs with fewer allocated resources can be served earlier to improve fairness. For reaching efficient resource utilization, our proposed algorithm further poses a constraint in sequential resource allocation. Via extensive simulations, we successfully demonstrate that our proposed algorithm significantly outperforms the closely related algorithms in terms of packet loss ratio and fairness index at the cost of raised end-to-end delay but still within the delay bound. For our proposed algorithm, it also gains improvement in throughput due to its reduction in the packet loss ratio and bounded relaxation in the end-to-end delay.

Packet Aggregation Real-Time Scheduling for Large-Scale WIA-PA Industrial Wireless Sensor Networks

The IEC standard WIA-PA is a communication protocol for industrial wireless sensor networks. Its special features, including a hierarchical topology, hybrid centralized-distributed management and packet aggregation make it suitable for large-scale industrial wireless sensor networks. Industrial systems place large real-time requirements on wireless sensor networks. However, the WIA-PA standard does not specify the transmission methods, which are vital to the real-time performance of wireless networks, and little work has been done to address this problem. In this article, we propose a real-time aggregation scheduling method for WIA-PA networks. First, to satisfy the real-time constraints on dataflows, we propose a method that combines the real-time theory with the classical bin-packing method to aggregate original packets into the minimum number of aggregated packets. The simulation results indicate that our method outperforms the traditional bin-packing method, aggregating up to 35% fewer packets, and improves the real-time performance by up to 10%. Second, to make it possible to solve the scheduling problem of WIA-PA networks using the classical scheduling algorithms, we transform the ragged time slots of WIA-PA networks to a universal model. In the simulation, a large number of WIA-PA networks are randomly generated to evaluate the performances of several real-time scheduling algorithms. By comparing the results, we obtain that the earliest deadline first real-time scheduling algorithm is the preferred method for WIA-PA networks.

Delay-Based Maximum Power-Weight Scheduling With Heavy-Tailed Traffic

Heavy-tailed (HT) traffic (e.g., the Internet and multimedia traffic) fundamentally challenges the validity of classic scheduling algorithms, designed under conventional light-tailed (LT) assumptions. To address such a challenge, this paper investigates the impact of HT traffic on delay-based maximum weight scheduling (DMWS) algorithms, which have been proven to be throughput-optimal with enhanced delay performance under the LT traffic assumption. First, it is proven that the DMWS policy is not throughput-optimal anymore in the presence of hybrid LT and HT traffic by inducing unbounded queuing delay for LT traffic. Then, to solve the unbounded delay problem, a delay-based maximum power-weight scheduling (DMPWS) policy is proposed that makes scheduling decisions based on queuing delay raised to a certain power. It is shown by the fluid model analysis that DMPWS is throughput-optimal with respect to moment stability by admitting the largest set of traffic rates supportable by the network, while guaranteeing bounded queuing delay for LT traffic. Moreover, a variant of the DMPWS algorithm, namely the IU-DMPWS policy, is proposed, which operates with infrequent queue state updates. It is also shown that compared with DMPWS, the IU-DMPWS policy preserves the throughput optimality with much less signaling overhead, thus expediting its practical implementation.

Genetic optimization–based scheduling in maritime cyber physical systems

In this article, we attempt to solve the issue of optimal scheduling for vessels monitoring video data uploading in maritime cyber physical systems, during the period of sailing from the origin port to destination port. We consider the terrestrial infrastructure-based networking, delivering video packets assisted by the shoreside infostations to the authorities on land. Each video packet has respective elements (i.e. release time, deadline, processing time, and weight) to describe, in which deadline is utilized to demonstrate the time domain limitation before that to upload it successfully. In order to cope with the computation complexity of traditional scheduling algorithms in intermittent infostations scenario, time-capacity mapping method is exploited to transfer it to a continue scenario when classic scheduling algorithms could be utilized with lower time complexity. An ingenious mathematic job-machine scheduling formulation is indicated with the goal of minimizing the total penalties of tardiness of uploaded video packets, taking into account the tardiness and the weights of jobs simultaneously. A genetic based algorithm, as well as an improved genetic algorithm–based optimization scheme, is proposed to target this optimization formulation. Specially, the genetic based algorithm as well as the improved genetic based algorithm are described in detail, including a novel chromosome representation, a heuristic initialization procedure, as well as a modified crossover and mutation process. The effectiveness of the proposed schemes is verified by the simulation results.

Hybrid Uplink Traffic Scheduling Algorithms in FMC Networks: A Comparative Study of Performance

Todate, the integration between EPON and WiMAX to form a Hybrid ONU-BS (Optical Network Unit-Base Station) Architecture in a Fixed Mobile Convergence (FMC) network rapidly matures and represents one of the promising candidates for next generation network (NGN) broadband access networks. In this paper, first we propose a new QoS mapping policy between two types of networks to enable a dedicated transmission across wireless and optical networks; and propose a hybrid uplink scheduling algorithm which the intra-ONU-BS scheduler employs queue scheduling only on the packets that arrive before sending the REPORT message, and the inter-ONU-BS scheduler employs the transmission time slot assignment based on the REPORT message information. Lastly, the paper provides a comparative performance study to find a decent combination of hybrid uplink traffic scheduling in terms of classic well-known traffic scheduling algorithms. Simulation results show that the inter uplink scheduling has significant impacts on the overall packet delay, loss rate and jitter performance; on the other hand, the intra uplink scheduling has significant influences on the packet delay, loss rate, jitter and fairness performance. Both inter and intra uplink traffic scheduling have particular functions to enhance the QoS and Class of Service (CoS) of system performance, respectively.

Task Scheduling and Management of Phased Array Radar Based on Intelligent Environment Cognition

This paper presents a task scheduling model based on intelligent signal environment cognition is provided to analyze the phased array radar resource scheduling in complex combat environment. The traditional priority of scheduling methods can’t be satisfied to the needs in practice. According to the work mode and task of the phased array radar, some complex modulated signal waveform was established and WVD time-frequency transform was used to preprocess the features such as position of geometry center, polarimetric characteristics and et al which are provided to classifier and obtain the task priority. To overcome the weakness of classical scheduling algorithm, a novel task scheduling algorithm is provided which is based on Gaussian Process multitask classification. The method can effectively improve the performance of the radar scheduler and make the task scheduling more feasibly and practical.

A novel downlink scheduling strategy for traffic communication system based on TD-LTE technology.

There are many existing classical scheduling algorithms which can obtain better system throughput and user equality, however, they are not designed for traffic transportation environment, which cannot consider whether the transmission performance of various information flows could meet comprehensive requirements of traffic safety and delay tolerance. This paper proposes a novel downlink scheduling strategy for traffic communication system based on TD-LTE technology, which can perform two classification mappings for various information flows in the eNodeB: firstly, associate every information flow packet with traffic safety importance weight according to its relevance to the traffic safety; secondly, associate every traffic information flow with service type importance weight according to its quality of service (QoS) requirements. Once the connection is established, at every scheduling moment, scheduler would decide the scheduling order of all buffers’ head of line packets periodically according to the instant value of scheduling importance weight function, which calculated by the proposed algorithm. From different scenario simulations, it can be verified that the proposed algorithm can provide superior differentiated transmission service and reliable QoS guarantee to information flows with different traffic safety levels and service types, which is more suitable for traffic transportation environment compared with the existing popularity PF algorithm. With the limited wireless resource, information flow closed related to traffic safety will always obtain priority scheduling right timely, which can help the passengers’ journey more safe. Moreover, the proposed algorithm cannot only obtain good flow throughput and user fairness which are almost equal to those of the PF algorithm without significant differences, but also provide better realtime transmission guarantee to realtime information flow.

HSIP: A Novel Task Scheduling Algorithm for Heterogeneous Computing

High-performance heterogeneous computing systems are achieved by the use of efficient application scheduling algorithms. However, most of the current algorithms have low efficiency in scheduling. Aiming at solving this problem, we propose a novel task scheduling algorithm for heterogeneous computing named HSIP (heterogeneous scheduling algorithm with improved task priority) whose functionality relies on three pillars: (1) an improved task priority strategy based on standard deviation with improved magnitude as computation weight and communication cost weight to make scheduling priority more reasonable; (2) an entry task duplication selection policy to make the makespan shorter; and (3) an improved idle time slots (ITS) insertion-based optimizing policy to make the task scheduling more efficient. We evaluate our proposed algorithm on randomly generated DAGs, using some real application DAGs by comparison with some classical scheduling algorithms. According to the experimental results, our proposed algorithm appears to perform better than other algorithms in terms of schedule length ratio, efficiency, and frequency of best results.

Improving energy-efficiency of large-scale workflows in heterogeneous systems

With the rapid growth of grid computing, more and more data-intensive applications have been deployed in grid environments, which in turn increase the energy consumption in high-performance computing platforms. To address the issue of energy consumption optimisation when scheduling data-intensive workflows, a novel heuristic policy called 'minimal energy consumption path' is proposed. By using this heuristic, we devise two energy-aware algorithms which are deprived from two classical scheduling algorithms. Extensive experiments are conducted to investigate the performance of the proposed algorithms, and the results show that they can significantly reduce the data-accessing energy consumption. Also, the proposed algorithms show better adaptivity than conventional scheduling algorithms, especially when the system is in presence of large-scale workflows which involve highly intensive data-accessing operations.

Improving energy-efficiency of large-scale workflows in heterogeneous systems

With the rapid growth of grid computing, more and more data-intensive applications have been deployed in grid environments, which in turn increase the energy consumption in high-performance computing platforms. To address the issue of energy consumption optimisation when scheduling data-intensive workflows, a novel heuristic policy called 'minimal energy consumption path' is proposed. By using this heuristic, we devise two energy-aware algorithms which are deprived from two classical scheduling algorithms. Extensive experiments are conducted to investigate the performance of the proposed algorithms, and the results show that they can significantly reduce the data-accessing energy consumption. Also, the proposed algorithms show better adaptivity than conventional scheduling algorithms, especially when the system is in presence of large-scale workflows which involve highly intensive data-accessing operations.

Resource-aware hybrid scheduling algorithm in heterogeneous distributed computing

Today, almost everyone is connected to the Internet and uses different Cloud solutions to store, deliver and process data. Cloud computing assembles large networks of virtualized services such as hardware and software resources. The new era in which ICT penetrated almost all domains (healthcare, aged-care, social assistance, surveillance, education, etc.) creates the need of new multimedia content-driven applications. These applications generate huge amount of data, require gathering, processing and then aggregation in a fault-tolerant, reliable and secure heterogeneous distributed system created by a mixture of Cloud systems (public/private), mobile devices networks, desktop-based clusters, etc. In this context dynamic resource provisioning for Big Data application scheduling became a challenge in modern systems. We proposed a resource-aware hybrid scheduling algorithm for different types of application: batch jobs and workflows. The proposed algorithm considers hierarchical clustering of the available resources into groups in the allocation phase. Task execution is performed in two phases: in the first, tasks are assigned to groups of resources and in the second phase, a classical scheduling algorithm is used for each group of resources. The proposed algorithm is suitable for Heterogeneous Distributed Computing, especially for modern High-Performance Computing (HPC) systems in which applications are modeled with various requirements (both IO and computational intensive), with accent on data from multimedia applications. We evaluate their performance in a realistic setting of CloudSim tool with respect to load-balancing, cost savings, dependency assurance for workflows and computational efficiency, and investigate the computing methods of these performance metrics at runtime.

Worst-case performance guarantees of scheduling algorithms maximizing weighted throughput in energy-harvesting networks

Energy harvesting has recently emerged as a technique to enable longer operating time of sensor networks. However, due to harvesting energy's not-completely predictable stochastic nature, some packets may still fail to be transmitted due to insufficient energy supply. Also, packets in sensor networks are usually associated with sensitive time-critical information. Based on these observations, we theoretically study algorithms scheduling weighted packets with deadlines in energy-harvesting networks. In our model, packets arrive in an online manner, each packet has a value representing its priority and a value representing its deadline. Harvesting energy is gathered over time and transmitting one packet takes a unit of energy. The objective is to maximize the total value of the packets sent, subject to energy and deadline constraints. In this paper, we design both offline and online algorithms maximizing weighted throughput. We analyze these algorithms’ performance guarantees against their worst-case scenarios and empirically compare them with the conventional and classic scheduling algorithms. The simulation results show that our online algorithms have far better performance than conventional ones.