CLUSTER-BASED TASK SCHEDULING FOR THE LOGP MODEL

1999 ◽  
Vol 10 (04) ◽  
pp. 405-424 ◽  
Author(s):  
CRISTINA BOERES ◽  
ALINE NASCIMENTO ◽  
VINOD E. F. REBELLO
Keyword(s):  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Clustering Algorithms ◽  
Communication Overhead ◽  
Scheduling Problem ◽  
Delay Model ◽  
Logp Model ◽  
Realistic Communication ◽  
The Cost ◽  
Fully Connected

While the task scheduling problem under the delay model has been studied extensively, relatively little research exists for more realistic communication models such as the LogP model which considers, in addition to latency, the cost of sending and receiving messages, and the network or link capacity. The task scheduling problem is known to be NP-complete even under the delay model (a special case of the LogP model). This paper investigates the similarities and differences between task-clustering algorithms for the delay and LogP models, and describes task-scheduling algorithm for the allocation of arbitrary task graphs to fully connected networks of processors under the LogP model. The strategy exploits the replication and clustering of tasks to minimize the ill effects of communication overhead on the makespan. A number of restrictions are presented which are used to simplify the design of the new algorithm. The quality of the schedules produced by the algorithm compare favorably with two well-known delay model-based algorithms and a previously existing LogP strategy.

Task Scheduling in Heterogeneous Computing Systems Based on Machine Learning Approach

2020 ◽  
Vol 34 (12) ◽  
pp. 2051012
Author(s):  
Hui Xie ◽  
Li Wei ◽  
Dong Liu ◽  
Luda Wang
Keyword(s):  
Task Scheduling ◽  
Heterogeneous Computing ◽  
Scheduling Algorithm ◽  
Computing System ◽  
Scheduling Problem ◽  
Computing Systems ◽  
Task Scheduling Algorithm ◽  
Machine Learning Approach ◽  
Heterogeneous Computing Systems ◽  
Regression Theory

Task scheduling problem of heterogeneous computing system (HCS), which with increasing popularity, nowadays has become a research hotspot in this domain. The task scheduling problem of HCS, which can be described essentially as assigning tasks to the proper processor for executing, has been shown to be NP-complete. However, the existing scheduling algorithm suffers from an inherent limitation of lacking global view. Here, we reported a novel task scheduling algorithm based on Multi-Logistic Regression theory (called MLRS) in heterogeneous computing environment. First, we collected the best scheduling plans as the historical training set, and then a scheduling model was established by which we could predict the following schedule action. Through the analysis of experimental results, it is interpreted that the proposed algorithm has better optimization effect and robustness.

A Hybrid Differential Evolution Scheduling Algorithm to Heterogeneous Distributed System

2014 ◽  
Vol 631-632 ◽  
pp. 271-275
Author(s):  
Yan Kang ◽  
Zhong Min Wang ◽  
Ying Lin ◽  
Xiang Yun Guo
Keyword(s):  
Differential Evolution ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Solution Space ◽  
Greedy Heuristic ◽  
Particle Swarm Algorithm ◽  
Scheduling Problem ◽  
Heuristic Strategy

This paper presents a differential evolution algorithm with designed greedy heuristic strategy to solve the task scheduling problem. The static task scheduling problem is NP-complete and is a critic issue in parallel and distributed computing environment. A vector consists of a task permutation assigned to each individual in the target population by using DE mutation and crossover operators. A heuristic strategy is used to generate the feasible solutions as there a lot of infeasible solutions in the solution space as the size of the problem increase. And the strategies of the particle swarm algorithm are employed to modify the DE crossover operator for speeding up the search to optimal solution. And then, the individual is replaced with the corresponding target individual if it is global best or local best in terms of fitness. The performance of the algorithm is illustrated by comparing with the existing effectively scheduling algorithms. The performances of the proposed algorithms are tested on the benchmark and compared to the best-known solutions available. The computational results demonstrate that effectively and efficiency of the presented algorithm.

scholarly journals Deep Reinforcement Learning-Based Task Scheduling in IoT Edge Computing

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1666 ◽  
Author(s):  
Shuran Sheng ◽  
Peng Chen ◽  
Zhimin Chen ◽  
Lenan Wu ◽  
Yuxuan Yao
Keyword(s):  
Reinforcement Learning ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Edge Computing ◽  
Scheduling Problem ◽  
Satisfaction Degree ◽  
Markov Decision ◽  
Time Scheduling ◽  
Task Satisfaction

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.

scholarly journals Dynamic Task Scheduling Algorithm with Deadline Constraint in Heterogeneous Volunteer Computing Platforms

2019 ◽  
Vol 11 (6) ◽  
pp. 121 ◽  
Author(s):  
Ling Xu ◽  
Jianzhong Qiao ◽  
Shukuan Lin ◽  
Wanting Zhang
Keyword(s):  
Distributed Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Volunteer Computing ◽  
Scheduling Problem ◽  
Dynamic Task ◽  
Use Of Resources ◽  
Task Scheduling Algorithm ◽  
Deadline Constraint ◽  
Dynamic Task Scheduling

Volunteer computing (VC) is a distributed computing paradigm, which provides unlimited computing resources in the form of donated idle resources for many large-scale scientific computing applications. Task scheduling is one of the most challenging problems in VC. Although, dynamic scheduling problem with deadline constraint has been extensively studied in prior studies in the heterogeneous system, such as cloud computing and clusters, these algorithms can’t be fully applied to VC. This is because volunteer nodes can get offline whenever they want without taking any responsibility, which is different from other distributed computing. For this situation, this paper proposes a dynamic task scheduling algorithm for heterogeneous VC with deadline constraint, called deadline preference dispatch scheduling (DPDS). The DPDS algorithm selects tasks with the nearest deadline each time and assigns them to volunteer nodes (VN), which solves the dynamic task scheduling problem with deadline constraint. To make full use of resources and maximize the number of completed tasks before the deadline constraint, on the basis of the DPDS algorithm, improved dispatch constraint scheduling (IDCS) is further proposed. To verify our algorithms, we conducted experiments, and the results show that the proposed algorithms can effectively solve the dynamic task assignment problem with deadline constraint in VC.

Cost-enabled QoS aware task scheduling in the cloud management system

2021 ◽  
pp. 1-9
Author(s):  
Rajkumar Rajavel ◽  
Sathish Kumar Ravichandran ◽  
Partheeban Nagappan ◽  
Sivakumar Venu
Keyword(s):  
Task Scheduling ◽  
User Satisfaction ◽  
Service Providers ◽  
Scheduling Algorithm ◽  
Scheduling Algorithms ◽  
Cloud Service ◽  
Cloud Management ◽  
Qos Parameters ◽  
Cloud Providers ◽  
The Cost

Maintaining the quality of service (QoS) related parameters is an important issue in cloud management systems. The lack of such QoS parameters discourages cloud users from using the services of cloud service providers. The proposed task scheduling algorithms consider QoS parameters such as the latency, make-span, and load balancing to satisfy the user requirements. These parameters cannot sufficiently guarantee the desired user experience or that a task will be completed within a predetermined time. Therefore, this study considered the cost-enabled QoS-aware task (job) scheduling algorithm to enhance user satisfaction and maximize the profit of commercial cloud providers. The proposed scheduling algorithm estimates the cost-enabled QoS metrics of the virtual resources available from the unified resource layer in real-time. Moreover, the virtual machine (VM) manager frequently updates the current state-of-the art information about resources in the proposed scheduler to make appropriate decisions. Hence, the proposed approach guarantees profit for cloud providers in addition to providing QoS parameters such as make-span, cloud utilization, and cloud utility, as demonstrated through a comparison with existing time-and cost-based task scheduling algorithms.

Scheduling Problem of Biochemical Analyzer and Exploration of Neural Network-Greedy Algorithm

2020 ◽  
Vol 10 (8) ◽  
pp. 1912-1918
Author(s):  
Xiaohui Huang ◽  
Shuxia Zheng ◽  
Shilong Li ◽  
Jinxiang Wu ◽  
Graham Spence
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Greedy Algorithm ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Scheduling Problem ◽  
The Neural Network ◽  
Biochemical Analyzer ◽  
Analysis System ◽  
The Mathematical Model

The mathematical model of biochemical analysis system was established based on neural network-greedy algorithm. The optimal task scheduling sequence was solved by neural network algorithm. At the same time, the local optimization was obtained by combining greedy algorithm. In this way, the task scheduling problem in biochemical analyzer was transformed into a mathematical problem, and the mathematical model of scheduling algorithm was established. On the platform of MATLAB, eight groups of simulation tests were carried out on the same task scheduling problem using the neural network-greedy scheduling algorithm and the traditional fixedperiod scheduling algorithm. The task-time Gantt charts of the two algorithms were compared under different scheduling orders. The results showed that the average speed of the neural network-greedy algorithm was improved by 31% compared with that of the fixed-period scheduling algorithm. The mathematical model of biochemical analysis system on scheduling problem established by neural network-greedy scheduling algorithm has high efficiency compared with the traditional fixed-period scheduling algorithm.

A Novel Performance Enhancing Task Scheduling Algorithm for Cloud-Based E-Health Environment

2019 ◽  
Vol 10 (2) ◽  
pp. 102-117 ◽  
Author(s):  
Vijayakumar Pandi ◽  
Pandiaraja Perumal ◽  
Balamurugan Balusamy ◽  
Marimuthu Karuppiah
Keyword(s):  
Task Scheduling ◽  
Performance Optimization ◽  
Scheduling Algorithm ◽  
Rapid Development ◽  
Computation Time ◽  
Cloud Services ◽  
Network Bandwidth ◽  
Task Scheduling Algorithm ◽  
Efficient Resource ◽  
The Cost

The fast-growing internet services have led to the rapid development of storing, retrieving and processing health-related documents from a public cloud. In such a scenario, the performance of cloud services offered is not guaranteed, since it depends on efficient resource scheduling, network bandwidth, etc. The trade-off which lies between the cost and the QoS is that the cost should be variably low on achieving high QoS. This can be done by performance optimization. In order to optimize the performance, a novel task scheduling algorithm is proposed in this article. The main advantage of this proposed scheduling algorithm is to improve the QoS parameters which comprises of metrics such as response time, computation time, availability and cost. The proposed work is simulated in Aneka and shows better performance compared to existing paradigms.

Sign in / Sign up

Export Citation Format

Share Document