Dynamic Task Scheduling Algorithms Research Articles

Enhanced Wireless Communication Optimization with Neural Networks, Proximal Policy Optimization and Edge Computing for Latency and Energy Efficiency

This research proposes a novel approach for efficient resource allocation in wireless communication systems. It combines dynamic neural networks, Proximal Policy Optimization (PPO), and Edge Computing Orchestrator (ECO) for latency-aware and energy-efficient resource allocation. The proposed system integrates multiple components, including a dynamic neural network, PPO, ECO, and a Mobile Edge Computing (MEC) server. The experimental methodology involves utilizing the NS-3 simulation platform to assess latency and energy efficiency in resource allocation within a wireless communication network, incorporating an ECO, MEC server, and dynamic task scheduling algorithms. It demonstrates a holistic and adaptable approach to resource allocation in dynamic environments, showcasing a notable reduction in latency for devices and tasks. Latency values range from 5 to 20 milliseconds, with corresponding resource utilization percentages varying between 80% and 95%. Additionally, energy-efficient resource allocation demonstrates a commendable reduction in energy consumption, with measured values ranging from 10 to 30 watts, coupled with efficient resource usage percentages ranging from 70% to 85%. These outcomes validate the efficacy of achieving both latency-aware and energy-efficient resource allocation for enhanced wireless communication systems. The proposed system has broad applications in healthcare, smart cities, IoT, real-time analytics, autonomous vehicles, and augmented reality, offering a valuable solution to optimize energy consumption, reduce latency, and enhance system efficiency in these industries.

Flower pollination algorithm for dynamic task scheduling in edge-cloud continuum

Flower pollination algorithm for dynamic task scheduling in edge-cloud continuum

Dynamic Cloud Task Scheduling Based on a Two-Stage Strategy

To maximize task scheduling performance and minimize nonreasonable task allocation in clouds, this paper proposes a method based on a two-stage strategy. At the first stage, a job classifier motivated by a Bayes classifier’s design principle is utilized to classify tasks based on historical scheduling data. A certain number of virtual machines (VMs) of different types are accordingly created. This can save time of creating VMs during task scheduling. At the second stage, tasks are matched with concrete VMs dynamically. Dynamic task scheduling algorithms are accordingly proposed. Experimental results show that they can effectively improve the cloud’s scheduling performance and achieve the load balancing of cloud resources in comparison with existing methods. Note to Practitioners — Task scheduling is one of the challenging problems in cloud computing, especially when deadline and cost are considered. As an important actuator, virtual machines (VMs) play a vital role for cloud task scheduling. To meet task deadlines, one needs to save the time of creating VMs, task waiting time, and executing time. To minimize the task execution cost, one needs to schedule tasks onto their most suitable VMs for execution. We propose a cloud task scheduling framework based on a two-stage strategy to do so. It precreates VMs according to historical scheduling data, therefore saving time for tasks to wait for creating VMs. It matches tasks with their most suitable VMs dynamically, therefore saving their execution cost. Under the premise of meeting task deadlines, it minimizes the waiting time of VMs to schedule tasks, thus minimizing the cost to be paid by users who utilize VMs. The readily deployable algorithms are designed and illustrated to improve cloud task scheduling and execution results in comparison with those using traditional methods.

HYBRID CAT SWARM OPTIMIZATION AND SIMULATED ANNEALING FOR DYNAMIC TASK SCHEDULING ON CLOUD COMPUTING ENVIRONMENT

The unpredictable number of task arriving at cloud datacentre and the rescaling of virtual processing elements can affect the provisioning of better Quality of Service expectations during task scheduling in cloud computing. Existing researchers have contributed several task scheduling algorithms to provide better QoS expectations but are characterized with entrapment at the local search and high dimensional breakdown due to slow convergence speed and imbalance between global and local search, resulting from lack of scalability. Dynamic task scheduling algorithms that can adjust to long-time changes and continue facilitating the provisioning of better QoS are necessary for cloud computing environment. In this study, a Cloud Scalable Multi-Objective Cat Swarm Optimization-based Simulated Annealing algorithm is proposed. In the proposed method, the orthogonal Taguchi approach is applied to enhance the SA which is incorporated into the local search of the proposed CSMCSOSA algorithm for scalability performance. A multi-objective QoS model based on execution time and execution cost criteria is presented to evaluate the efficiency of the proposed algorithm on CloudSim tool with two different datasets. Quantitative analysis of the algorithm is carried out with metrics of execution time, execution cost, QoS and performance improvement rate percentage. Meanwhile, the scalability analysis of the proposed algorithm using Isospeed-efficiency scalability metric is also reported. The results of the experiment show that the proposed CSM-CSOSA has outperformed Multi-Objective Genetic Algorithm, Multi-Objective Ant Colony and Multi-Objective Particle Swarm Optimization by returning minimum execution time and execution cost as well as better scalability acceptance rate of 0.4811−0.8990 respectively. The proposed solution when implemented in real cloud computing environment could possibly meet customers QoS expectations as well as that of the service providers.

Static Task Scheduling Algorithm with Minimum Distance for Multiprocessor System (STMD)

Task scheduling in the multiprocessor environment is considered to be a NP-complete problem. It is also called a multiprocessor scheduling. Here, a parallel program is divided into a number of subtasks and it is mapped on the processors for concurrent execution. The objective of task scheduling is to reduce scheduling length of a given application program. Considering that task scheduling is represented by Directed Acyclic Grape (DAG). There are two types of task scheduling algorithms: static task scheduling and dynamic task scheduling algorithms. In this paper, we propose a new task scheduling algorithm based on minimum distance and an entry task duplicated on the processors. The minimum distance computes between an entry task and its successors. It includes, minimum distance of parent task, execution time and communication time Here , also considering a priority list (PL) attribute that contains tasks information of a given application program whose distance is minimum from an entry task. Finally, we will do comparative study of the proposed algorithm with Bounded Number of Processors (BNP) class of scheduling algorithms. It has done based on the following matrices: Scheduling Length, Speedup, Efficiency, Load Balancing and Normalized Scheduling Length (NSL). It showed proposed algorithm gives better result than BNP class of scheduling algorithms.

A novel algorithm for dynamic task scheduling

This paper deals with the problem of dynamic task scheduling in grid environment of multi-processors. First, this paper formulates task scheduling as an optimization problem and then optimizes with a novel hybrid optimization algorithm. The proposed algorithm combines the merits of Genetic Algorithm and Bacteria Foraging optimization. The simulation result proves the superior performance with the proposed algorithm.

Energy-efficient dynamic task scheduling algorithms for DVS systems

Dynamic voltage scaling (DVS) is a well-known low-power design technique that reduces the processor energy by slowing down the DVS processor and stretching the task execution time. However, in a DVS system consisting of a DVS processor and multiple devices, slowing down the processor increases the device energy consumption and thereby the system-level energy consumption. In this paper, we first use system-level energy consideration to derive the “optimal ” scaling factor by which a task should be scaled if there are no deadline constraints. Next, we develop dynamic task-scheduling algorithms that make use of dynamic processor utilization and optimal scaling factor to determine the speed setting of a task. We present algorithm duEDF , which reduces the CPU energy consumption and algorithm duSYS and its reduced preemption version, duSYS_PC , which reduce the system-level energy. Experimental results on the video-phone task set show that when the CPU power is dominant, algorithm duEDF results in up to 45% energy savings compared to the non-DVS case. When the CPU power and device power are comparable, algorithms duSYS and duSYS_PC achieve up to 25% energy saving compared to CPU energy-efficient algorithm duEDF , and up to 12% energy saving over the non-DVS scheduling algorithm. However, if the device power is large compared to the CPU power, then we show that a DVS scheme does not result in lowest energy. Finally, a comparison of the performance of algorithms duSYS and duSYS_PC show that preemption control has minimal effect on system-level energy reduction.

Dynamic task scheduling for irregular network topologies

In this study, a heterogeneous computing environment is employed as a computational platform. In order to increase the efficiency of the system, a dynamic task-scheduling algorithm is proposed, which balances the load among the nodes of the system. The technique is dynamic, nonpreemptive, adaptive, and it uses a mixed centralised and decentralised policies. Based on the divide and conquer principle, the algorithm models the system as hypergrids and then balances the load among them. Recursively, the hypergrids of dimension k are divided into grids of dimensions k − 1, until the dimension is 1. Then, all the nodes of the system are almost equally loaded. The optimum dimension of the hypergrid is chosen in order to achieve the best performance. The simulation results demonstrate the effectiveness of this technique. In addition, we determined the critical points representing lower bounds for which the algorithm should be effective and therefore should be triggered.

A heuristic algorithm for dynamic task scheduling in highly parallel computing systems

In this paper we have introduced the K1 heuristic algorithm for dynamic task scheduling with precedence constraints and communication delays. The execution of a task set repeats in cycles, while the execution and communication profile of a task set changes in time. During a task set execution, a new schedule is generated by tuning the previous schedule. The scheduling is distributed — performed on the processors of a highly parallel computer architecture. Only the tasks that can have an influence on dominant sequence reduction are considered for reordering/migration. The applied techniques are load balancing, task reordering, and data-wait reduction. We have analyzed the impact of the K1 scheduling cost on response time. The simulation results show that the periodic activation of the K1 scheduler significantly decreases the scheduling overhead and still generates much better response time than that of a fixed schedule.

“D 2 R”

The task-level dataflow language D 2 R ("Dynamic Dataflow Representation") is suggested (Version 1.0). D 2 R uses dynamic branching, loops with data-dependent numbers of iterations and alternatives. The dataflow information can be expressed either as a program text or as graph. Primarily the model has been developed for cooperation with dynamic task scheduling algorithms. Its suitability is demonstrated by examples. The representation supports environments allowing to simultaneously execute multiple user programs ("space sharing") as well as to restrict the assignments of task and data nodes to special processors (e.g. I/O nodes). The system has been implemented using the multi transputer system "DAMP" /Bauch-91/.