Rules class approach to scheduling algorithms

With the advent of increasingly higher numbers of processors on-chip, task scheduling has become an important concern in system design, and research in this area has produced substantial and diversified knowledge. As a result, the efficient management and taping of this knowledge has become a concern in itself. This paper addresses the issue of how to effectively extract performance information about a scheduling algorithm in the context of a set of applications, by learning the association rules between the applications' attributes and the algorithms' performance metrics. The new methodology that is presented serves to both increase the designer's knowledge about a particular scheduling algorithm and compare algorithms.

With the changes in the era of science and technology, the emergence of 5G technology makes a communication speed leap, and the delay is also getting shorter and shorter. At present, the computing power of computing equipment is also steadily improving. As one of its key technologies, mobile edge computing (MEC) is becoming more and more important. Here, we introduce MEC task scheduling and device collaboration algorithms based on deep learning. Aiming at the multi-task joint optimization scheduling strategy microtearing mode to minimize the total service cost of multiple tasks, we establish a theoretical optimization model of multi-task joint scheduling and use the model to design a multi-task cloud collaborative optimization scheduling algorithm based on Livepeer Token rules. We propose a deep-learning device collaboration algorithm based on alternating direction multiplier. The sub differential algorithm and the improved conjugate gradient method are designed to solve the sub problem respectively. This paper simulates the proposed deep-learning task scheduling algorithm and device collaboration algorithm. The simulation results show that, in the task scheduling algorithm, the proposed algorithm reduces the cost of completing the task, but in the total task time, this algorithm has a certain gap compared with other algorithms. In the equipment collaboration algorithm, with the increase of the n value, the algorithm converges in a certain iteration increment; in terms of algorithm convergence, the algorithm needs 142 iterations at most to achieve convergence, and the fastest speed only needs 17 iterations. In the experiment based on real data sets, the proposed algorithm is also better than the other two algorithms. From these results, we can see that the proposed algorithm has good convergence, stability, and scalability, and it has excellent performance in task scheduling and device collaboration of MEC.

Objectives: Current state-of-the-art task scheduling algorithms were mainly focused on deadline, load and energy factors in centralized cloud context. So, the proposed research objective focuses on dynamic and decentralized context. Methods/Statistical Analysis: Multi-objective task scheduling has become an important criterion for the dynamic and decentralized nature of cloud environment. Moreover, existing research works assumes that the resource load, energy and task execution time are known due its homogeneous nature. In order to improve the cloud consumer’s satisfaction, a novel Locality-Load-Prediction Aware Multi-objective Task Scheduling (LLPAMTS) algorithm is proposed to eventually distribute the tasks according to dynamic nature of cloud virtual machines. Findings: Proposed LLPAMTS algorithm will effectively schedule the tasks in an optimized manner by VM Scheduler component. This scheduling algorithm exploits the various monitoring parameters like locality, load and prediction parameters. It outperforms the existing deadline, load and energy aware scheduling algorithms in terms of task transfer time, task waiting time, task execution time, and task completion time. Applications/Improvements: The proposed LLPAMTS algorithm provides an average of 5 to 10% less task completion time compared to the existing deadline, load and energy aware scheduling algorithms. Keywords: Cloud Environment, Heterogeneous Cloud, Locality-Load-Prediction Aware Scheduling, Multi-Objective, Task Scheduling

Failure-resilient DAG task scheduling in edge computing

The scheduling of tasks with deadlines on clusters is a key issue for offering quality-of-service (QoS) assurance. A critical challenge in real-time task scheduling is to handle various types of applications. This paper investigates the scheduling problem for processing a set of tasks comprising both divisible and indivisible real-time tasks on cluster systems. Indivisible tasks are characterized by the property that they need to be processed on their entirety on a single processor while divisible tasks can be distributed across several processing nodes by exploiting the underlying data parallelism. We propose a dynamic (on-line) real-time scheduling algorithm referred to as Hybrid Loads Push-Pull Scheduling (HLPPS) algorithm for handling a set of tasks comprising both divisible and indivisible real-time tasks on cluster systems. HLPPS is shown to efficiently exploit the parallelism in divisible tasks without undermining the schedulability of indivisible tasks and thereby optimize the overall performance. We consider two distinct network platforms - tightly coupled and loosely coupled clusters in designing the strategy. We conduct extensive performance evaluation studies to quantify the performance of the proposed algorithm under a variety of scenarios.

Enhanced wireless communication improves the connectivity of vehicular networks in which vehicles are utilized as infrastructures for communication and computation. Thus, a new concept “Vehicular Edge Computing (VEC)” is formed. As VEC utilizes a collaborative multitude of near-user edge resources (i.e. vehicles) in the Internet of Vehicles, the capability of these joint resources becomes heterogeneous especially in their movements. Therefore, one critical problem is how to efficiently schedule each task under such mobile environments. For the reason, we propose a hybrid dynamic scheduling scheme (HDSS) that has the ability to optimize the task scheduling dynamically based on the changeable system environments. HDSS provides a decision function (DF) to select a better-performed scheduling algorithm from two provided candidates: the queue-based dynamic scheduling (QDS) algorithm and the time-based dynamic scheduling (TDS). QDS coincides with the Join-the-Shortest Queue scheme, which decides the scheduling by sorting out a server with the shortest queue-length; nevertheless, TDS is novel scheme that is designed to implement task allocation by estimating the waiting time of each server in order to select a server with the fastest response. Finally, this research generates formal models of each scheduling algorithm and the hybrid scheduling scheme to conduct performance evaluation with a fluid flow approximation technique. The analysis results in a superior performance of HDSS in the unstable VEC environments.

Optical Grid that provides a way to integrate the geographical distributed resources for large- scale grid applications. This paper mainly focuses on the task scheduling issue in Optical Grid and proposes two task scheduling algorithms to mini-mize the total time or scheduling length required for a given task. A simulated optical grid has been addressed to demon-strate the effectiveness of the schedulin g approach. The performance tests and anal ysis of the two algorithms are investi-gated to show the effect of the routing policy on the schedulin g length. A more flexible policy is able to further improve scheduling length. Keywords: Optical Grid, Task Scheduling Algorithm, Communication Contention I INTRODUCTION Distributed computing, also known as computational Grid, is a technology that makes it possible to utilize the geographi-cal distributed computational resources to solve the intensive computation of large-scale data. Most Grid systems were built over the IP packet switched networks in the past. In the situation when some data-intensive Grid applications re-quire moving Gigabytes or even larger of data, the low speed of the network becomes the bottleneck of Grid system, which could not allow the remote resources to be used efficiently. Nowadays the optical network with Synchronous Digital Hierarchy (SDH), Wavelength-D ivision Multiplexing (WDM), and Automa tic Switched Optical Network (ASON) infrastructure can provide a much larger amount of bandwidth than the electronic network, such as IP packet switched network. Therefore, it is significant to use the optical network as the network infrastructure of the computational Grid. We refer to this infrastructure as Optical Grid, which employs optical network to interconnect grid computational re-sources (e.g. supercomputers, computer clusters, storage and display devices) with dynamically provisioned lightpaths [1]. It has been realized that task scheduling is a fundamental issue in achieving high performance in grid environments [6]. Task scheduling aims to optimize the overall performance of a system, e.g., minimizing the average job response time and maximizing the number of jobs exec uted in certain period of time. Optical grid task scheduling is quite different from classic grid task scheduling model. In Optical Grid system, we can treat the optical network as a kind of resource, the scheduling algorithm in Optical Grid system should schedule the tasks to appropriate resources as we ll as scheduling the communications between tasks to appropriate optical links. The objec-tive of task scheduling is to minimize the completion time (we called it as scheduling length or make span ) of an appli-cation. Usually, this problem is modeled and solved under several assumptions. Unless the assumptions are simplified enough, the problem has been proven to be NP-complete [8]. Therefore, many heuristic algorithms have been proposed. But these heuristics either are based on the assumption that the processors are fully connected, or just don’t take into ac-count the optical network’s characters, es pecially the characters of WDM optical network. While actually, in the Optical Grid system, the computational resources are more likely not fully connected, and the communication contention is an important factor that affects the completion time of a Grid application. Thus, a new heuris tic algorithm that can schedule tasks and also the communications are needed. In this paper, we mainly focus on the static scheduling problem, which means the character of the Grid applica tion, including the task execution times, task dependencies, task communications and synchronization are known a priori.

Objective: To develop a task scheduling algorithm that efficiently approximates solutions for the multi-objective task scheduling problem in a cloud environment, optimizing resource utilization, execution time, cost, and overall system performance. Method: A Modified Sunflower Optimization Algorithm for Task Scheduling (MSOTS) is proposed to improve efficiency in cloud environments. The traditional sunflower optimization algorithm is enhanced with Levy flight, crossover, and mutation operations to achieve a better balance between exploration and exploitation while preventing entrapment in local minima. These enhancements help improve convergence speed and solution quality. CloudSim is utilized for comprehensive performance evaluation, comparing MSOTS with existing algorithms in terms of execution time, cost, and resource utilization. The randomly generated dataset (500 tasks and 50 VMs) is used for analyze the performance of the MSOTS. Findings: The results demonstrate that MSOA significantly reduces makespan and cost while enhancing resource utilization. Specifically, the proposed method reduces makespan by 22.43% compared to PSO and 14.95% compared to SOA. Additionally, cost is reduced by 14.47% and 10.38% compared to PSO and SOA, respectively, while resource utilization increases by 3.89% and 2.51% over these methods. These findings indicate that MSOA effectively improves task scheduling performance in terms of makespan, cost efficiency, and resource utilization, making it a promising approach for cloud computing environments. Novelty: The MSOA is integrated with single-point crossover, swap mutation, and Lévy flight to update the solution, which prevents entrapment in local minima and premature convergence and enhances task scheduling efficiency, optimizes resource allocation, minimizes execution time, and reduces overall costs in cloud computing environments. Keywords: Meta-heuristic, Sunflower optimization, Levy flight, Crossover, Mutation

In recent years, Artificial Neural Networks have evolved rapidly and are applied to various fields. Meanwhile, to enhance computation efficiency of neural network applications, more and more neural network accelerators have been developed. Though traditional task scheduling algorithms on heterogeneous systems have been intensively researched, they can’t be applied to neural network accelerators directly. Based on typical characteristics of neural network accelerators, we formalize the problem of tasks scheduling for neural networks, and transplant two listing heuristic scheduling algorithms, Heterogeneous-Earliest-Finish-Time (HEFT) and Critical-Path-on-a-Processor (CPOP). Inspired by the separable features of neural network operations, we propose two partition algorithms, the Iterative Partition Scheduling Algorithm (IPS) and the Partition Scheduling Combination Algorithm (PSC), which can be associated with scheduling algorithms. Further, we conduct experiments on some typical neural networks, and results show that compared to scheduling-only algorithms the partition associated algorithms achieve about 2x to 3x speedup.

SAAS parallel task scheduling based on cloud service flow load algorithm

For real time applications, time slacks of a preliminary task schedule may be exploited to conserve energy. This can be accomplished by leveraging the dynamic voltage scaling (DVS) technique to slow down clock frequency of certain cores as long as the deadline is met. In this chapter, the task of fine-tuning an existing task assignment and schedule and using DVS to lower the overall energy consumption is formulated as a graph-theoretic maximum weight clique (MWC) problem. An efficient heuristic algorithm is proposed to systematically solve this problem. A unique feature of our approach is concurrently applying DVS to slow down the execution of multiple tasks to achieve better energy savings. Extensive simulations are performed to compare this proposed algorithm against leading energy-aware task scheduling algorithm and DVS algorithm. Our algorithm exhibits 22% more energy savings than the Energy Aware Scheduling (EAS) algorithm. As for energy saving in DVS process, our MWC-based method provides a 97% saving improvement over the PV-DVS algorithm.

As on miniature devices to military applications, the cloud computing plays a vital role. Building efficient cloud management systems will lead to improve extraordinary features in the applications of cloud services. In cloud atmosphere, enormous tasks are performed simultaneously; an effectual task scheduling is very important role to get better performance of the cloud system. An assortment of cloud-based task scheduling algorithms is offered that schedule the user's task to resources for implementation. The innovation of cloud computing, conventional scheduling algorithms cannot gratify the cloud's requirements, the researchers are frustrating to modify conventional algorithms that can accomplish the cloud needs similar to rapid elasticity, resource pooling and on-demand self-service. Also, the priority becomes an important task when dealing with critical functionality systems. Real world invocations are needed to make an efficient selection in cloud services through a collection of functionally equivalent services. This research aims to detect a novel method to predict the system functionality without consuming more time and less expensive for implementation. Investigation on swarm intelligence-based task scheduling is presented. This will improve the power consumption by reducing overloads when more services opt for a single load. Experiments were carried out to test the effectiveness of the method.

Task scheduling is crucial for offering the users of an operating system the impression that the system’s response is direct and in real time. However, it is a complicated process that is characterized by vagueness and uncertainty. A solution to this problem is the usage of fuzzy logic. Therefore, in this paper a rule-based fuzzy scheduling method, which considers both the execution time and the waiting time for each task, is presented. Fuzzy sets are used to describe both criteria. The operation of the presented scheduling method is based on a rule-based reasoner that decides dynamically about the priority of the tasks that wait to be executed. This reasoner is triggered each time a change occurs (i.e. the execution of a task ends, a new task arrives etc.). It has been compared with Short Job First (SJF) and First Come First Served (FCFS) scheduling algorithms for a large number of different sets of tasks. The results showed that the presented fuzzy scheduling algorithm in all the cases has the same or almost the same average waiting time with SJF, which is considered as the scheduling algorithm with the best (minimum) average waiting time. However, the presented fuzzy scheduling algorithm ensures that priority is given not only to tasks with short execution time, but also to tasks that remain into the queue waiting to be executed for a long time. In this way, the starvation, which may be caused by SJF algorithm, is eliminated.

Task scheduling is a well-known NP-complete problem. It is one of the research areas in parallel computing. Task scheduling consists of an application program and a set of processors for allocating the various tasks. An application program is represented using a directed acyclic graph(DAG) and a set of processors that are homogenous and fully connected. In this paper, we study different task duplication-based (TDB) scheduling algorithms and their priority attributes and time complexity. Also, we will discuss different types of performance metrics in task scheduling algorithms. These performance metrics help to analyze different task scheduling algorithms.

In the field of cloud computing, efficient task scheduling plays a vital role. Cloud computing is an on-demand service where resources are shared virtually by the users to complete their tasks. The main concept of task scheduling is to execute the tasks in such an order that can enhance the performance with full resource utilization and minimum loss. Already a lot of scheduling algorithm exists targeting to various goals like reducing execution time, cost, makespan and increase resource utilization, load balancing, etc. In this work, we have mainly carried out the performance analysis and comparison among various well-known task scheduling algorithms. The algorithms are examined and implemented in a well-known cloud simulator (CloudSim) to observe their performances by varying different parameters. Finally, the performance metrics like makespan, waiting time, turnaround time, throughput, and load balancing are observed by plotting the results graphically. Along with the comparative analysis, this work also assists one to identify a better scheduling algorithm based on different scheduling constraints and criteria.