scholarly journals Smart DAG Tasks Scheduling between Trusted and Untrusted Entities Using the MCTS Method

2019 ◽  
Vol 11 (7) ◽  
pp. 1826 ◽  
Author(s):  
Yuxia Cheng ◽  
Zhiwei Wu ◽  
Kui Liu ◽  
Qing Wu ◽  
Yu Wang
Keyword(s):  
Reinforcement Learning ◽  
Heterogeneous Computing ◽  
Heuristic Algorithms ◽  
Scheduling Algorithm ◽  
Optimal Schedule ◽  
Scheduling Problem ◽  
Scheduling Policies ◽  
Monte Carlo Tree Search ◽  
Reward Function ◽  
Tasks Scheduling

Task scheduling is critical for improving system performance in the distributed heterogeneous computing environment. The Directed Acyclic Graph (DAG) tasks scheduling problem is NP-complete and it is hard to find an optimal schedule. Due to its key importance, the DAG tasks scheduling problem has been extensively studied in the literature. However, many previously proposed traditional heuristic algorithms are usually based on greedy methods and also lack the consideration of scheduling tasks between trusted and untrusted entities, which makes the problem more complicated, but there still exists a large optimization space to be explored. In this paper, we propose a trust-aware adaptive DAG tasks scheduling algorithm using the reinforcement learning and Monte Carlo Tree Search (MCTS) methods. The scheduling problem is defined using the reinforcement learning model. Efficient scheduling state space, action space and reward function are designed to train the policy gradient-based REINFORCE agent. The MCTS method is proposed to determine actual scheduling policies when DAG tasks are simultaneously executed in trusted and untrusted entities. Leveraging the algorithm’s capability of exploring long term reward, the proposed algorithm could achieve good scheduling policies while guaranteeing trusted tasks scheduled within trusted entities. Experimental results showed the effectiveness of the proposed algorithm compared with the classic HEFT/CPOP algorithms.

scholarly journals Generalized swarm intelligence algorithms with domain-specific heuristics

2021 ◽  
Vol 10 (1) ◽  
pp. 157
Author(s):  
P. Matrenin ◽  
V. Myasnichenko ◽  
N. Sdobnyakov ◽  
D. Sokolov ◽  
S. Fidanova ◽  
...  
Keyword(s):  
Swarm Intelligence ◽  
Minimum Distance ◽  
Heuristic Algorithms ◽  
Population Based ◽  
Scheduling Problem ◽  
Domain Specific ◽  
Hybrid Approaches ◽  
Self Organized ◽  
Swarm Algorithms ◽  
Tasks Scheduling

<span lang="EN-US">In recent years, hybrid approaches on population-based algorithms are more often applied in industrial settings. In this paper, we present the approach of a combination of universal, problem-free Swarm Intelligence (SI) algorithms with simple deterministic domain-specific heuristic algorithms. The approach focuses on improving efficiency by sharing the advantages of domain-specific heuristic and swarm algorithms. A heuristic algorithm helps take into account the specifics of the problem and effectively translate the positions of agents (particle, ant, bee) into the problem's solution. And a Swarm algorithm provides an increase in the adaptability and efficiency of the approach due to stochastic and self-organized properties. We demonstrate this approach on two non-trivial optimization tasks: scheduling problem and finding the minimum distance between 3D isomers.</span>

ONLINE SCHEDULING OF DYNAMIC TREES

1995 ◽  
Vol 05 (04) ◽  
pp. 635-646 ◽  
Author(s):  
MICHAEL A. PALIS ◽  
JING-CHIOU LIOU ◽  
SANGUTHEVAR RAJASEKARAN ◽  
SUNIL SHENDE ◽  
DAVID S.L. WEI
Keyword(s):  
Lower Bound ◽  
Competitive Ratio ◽  
Scheduling Algorithm ◽  
Optimal Schedule ◽  
Online Scheduling ◽  
The Other ◽  
Static Case ◽  
Scheduling Problem ◽  
Dynamic Trees ◽  
Dynamic Tree

The scheduling problem for dynamic tree-structured task graphs is studied and is shown to be inherently more difficult than the static case. It is shown that any online scheduling algorithm, deterministic or randomized, has competitive ratio Ω((1/g)/ log d(1/g)) for trees with granularity g and degree at most d. On the other hand, it is known that static trees with arbitrary granularity can be scheduled to within twice the optimal schedule. It is also shown that the lower bound is tight: there is a deterministic online tree scheduling algorithm that has competitive ratio O((1/g)/ log d(1/g)). Thus, randomization does not help.

Ground Antenna Scheduling Algorithm for Multi-Satellite Tracking

2011 ◽  
Author(s):  
Sang-Hyuk Yun ◽  
Hyo-Sung Ahn ◽  
Sun-Ju Park ◽  
Ok-Chul Jung ◽  
Dae-Won Chung
Keyword(s):  
Weight Function ◽  
Greedy Algorithm ◽  
Scheduling Algorithm ◽  
Optimal Schedule ◽  
Satellite Tracking ◽  
Scheduling Problem ◽  
Depth First Search ◽  
Ground Station ◽  
A Priority

In this paper, we address the optimal ground antenna scheduling problem for multiple satellites when multiple satellites have visibility conflicts at a ground station. Visibility conflict occurs when multiple satellites have either overlapping visibilities at a ground station or difference with time of loss of signal (LOS) of a satellite and time of acquisition of signal (AOS) of another satellite is less than reconfiguration time of ground station. Each satellite has a priority value that is a weight function with various factors. Multi-antenna scheduling (MAS) algorithm 1 and Multi-antenna scheduling (MAS) algorithm 2 are proposed to find the optimal schedule of multi-antenna at a ground station using pre-assigned priority values of satellites. We use the depth first search (DFS) method to search the optimal schedule in MAS algorithm 1 and MAS algorithm 2. Through the simulations, we confirm the efficiency of these algorithms by comparing with greedy algorithm.

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.

Expected Cost Based Greedy Scheduling Algorithm for Out-Tree Task Graphs

2014 ◽  
Vol 556-562 ◽  
pp. 3431-3437 ◽  
Author(s):  
Jian Jun Zhang ◽  
Tian Hong Wang ◽  
Yu Zhuo Wang
Keyword(s):  
High Performance ◽  
Heterogeneous Computing ◽  
Heuristic Algorithms ◽  
Scheduling Algorithm ◽  
Heterogeneous Environments ◽  
Task Graphs ◽  
Schedule Length ◽  
Current Task ◽  
Greedy Scheduling Algorithm ◽  
Greedy Scheduling

Effective task scheduling is crucial for achieving high performance in heterogeneous computing environments. Whiling scheduling Out-Tree task graphs, many previous heterogeneity based heuristic algorithms usually require high scheduling costs and may not deliver good quality schedules with lower costs. Aiming at the characteristics of Out-Tree task graphs and the features of heterogeneous environments and adopting the strategy based on expected costs and task duplications, this paper proposes a greedy scheduling algorithm, which, at each scheduling step, tries to guarantee not to increase the schedule length, schedules the current task onto the used processor which minimizes its execution finish time; meanwhile, takes load balances into account to economize the use of processors. The comparative experimental results show that the proposed algorithm has higher scheduling efficiency and robust performance, which could produce better schedule which has shorter schedule length and less number of used processors.

scholarly journals SLA-DQTS: SLA Constrained Adaptive Online Task Scheduling Based on DDQN in Cloud Computing

2021 ◽  
Vol 11 (20) ◽  
pp. 9360
Author(s):  
Kaibin Li ◽  
Zhiping Peng ◽  
Delong Cui ◽  
Qirui Li
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Performance Optimization ◽  
Heuristic Algorithms ◽  
Scheduling Algorithm ◽  
Computing Systems ◽  
Q Learning ◽  
Reward Function ◽  
Task Scheduling Algorithm ◽  
Comparison Algorithms

Task scheduling is key to performance optimization and resource management in cloud computing systems. Because of its complexity, it has been defined as an NP problem. We introduce an online scheme to solve the problem of task scheduling under a dynamic load in the cloud environment. After analyzing the process, we propose a server level agreement constraint adaptive online task scheduling algorithm based on double deep Q-learning (SLA-DQTS) to reduce the makespan, cost, and average overdue time under the constraints of virtual machine (VM) resources and deadlines. In the algorithm, we prevent the change of the model input dimension with the number of VMs by taking the Gaussian distribution of related parameters as a part of the state space. Through the design of the reward function, the model can be optimized for different goals and task loads. We evaluate the performance of the algorithm by comparing it with three heuristic algorithms (Min-Min, random, and round robin) under different loads. The results show that the algorithm in this paper can achieve similar or better results than the comparison algorithms at a lower cost.

Minimizing Total Idle Energy Consumption in the Permutation Flow Shop Scheduling Problem

2018 ◽  
Vol 35 (06) ◽  
pp. 1850041 ◽  
Author(s):  
Guo-Sheng Liu ◽  
Jin-Jin Li ◽  
Ying-Si Tang
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Heuristic Algorithms ◽  
Flow Shop ◽  
Power Level ◽  
Optimal Schedule ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop

In this paper, we investigate the well-known permutation flow shop (PFS) scheduling problem with a particular objective, the minimization of total idle energy consumption of the machines. The problem considers the energy waste induced by the machine idling, in which the idle energy consumption is evaluated by the multiplication of the idle time and power level of each machine. Since the problem considered is NP-hard, theoretical results are given for several basic cases. For the two-machine case, we prove that the optimal schedule can be found by employing a relaxed Johnson’s algorithm within O([Formula: see text]) time complexity. For the cases with multiple machines (not less than 3), we propose a novel NEH heuristic algorithm to obtain an approximate energy-saving schedule. The heuristic algorithms are validated by comparison with NEH on a typical PFS problem and a case study for tire manufacturing shows an energy consumption reduction of approximately [Formula: see text] by applying the energy-saving scheduling and the proposed algorithms.

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.

A NEW ALGORITHM FOR MULTI-SKILL RESOURCE CONSTRAINED PROJECT SCHEDULING PROBLEM BASED ON CUCKOO SEARCH STRATEGY

2020 ◽  
Vol 65 (6) ◽  
pp. 98-109
Author(s):  
Huu Dang Quoc ◽  
Loc Nguyen The ◽  
Cuong Nguyen Doan ◽  
Toan Phan Thanh
Keyword(s):  
Project Scheduling ◽  
Search Strategy ◽  
Scheduling Algorithm ◽  
Optimal Schedule ◽  
Cuckoo Search ◽  
Scheduling Problem ◽  
Resource Constrained ◽  
Start Time ◽  
Resource Constrained Project Scheduling ◽  
Project Scheduling Problem

The purpose of this paper is to consider the project scheduling problem under such limited constraint, called Multi-Skill Resource-Constrained Project Scheduling Problem or MS-RCPSP. The algorithm proposed in this paper is to find the optimal schedule, determine the start time for each task so that the execution time (also called makespan) taken is minimal. At the same time, our scheduling algorithm ensures that the given priority relationships and constraints are not violated. Our scheduling algorithm is built based on the Cuckoo Search strategy. In order to evaluate the proposed algorithm, experiments were conducted by using the iMOPSE dataset. The experimental results proved that the proposed algorithm found better solutions than the previous algorithm.

scholarly journals DYNAMIC JOB SCHEDULING IN GRID COMPUTING

2016 ◽  
pp. 186-189
Author(s):  
JANI KUNTESH KETAN ◽  
ARPITA SHAH
Keyword(s):  
Grid Computing ◽  
High Performance ◽  
Large Scale ◽  
Heterogeneous Computing ◽  
Ant Colony Algorithm ◽  
Job Scheduling ◽  
Heuristic Algorithms ◽  
Optimal Schedule ◽  
Heterogeneous Systems ◽  
Sequential Method

Grid computing is growing rapidly in the distributed heterogeneous systems for utilizing and sharing large-scale resources to solve complex scientific problems. Scheduling is the most recent topic used to achieve high performance in grid environments. It aims to find a suitable allocation of resources for each job. A typical problem which arises during this task is the decision of scheduling. It is about an effective utilization of processor to minimize tardiness time of a job, when it is being scheduled. Scheduling jobs to resources in grid computing is complicated due to the distributed and heterogeneous nature of the resources. The efficient scheduling of independent jobs in a heterogeneous computing environment is an important problem in domains such as grid computing. In general, finding optimal schedule for such an environment using the traditional sequential method is an NP-hard problem whereas heuristic approaches will provide near optimal solutions for complex problems. The Ant colony algorithm, which is one of the heuristic algorithms, suits well for the grid scheduling environment using stigmeric communication.

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