Problem-Independent Approach to Multiprocessor Dependent Task Scheduling

2012 ◽  
Vol 58 (4) ◽  
pp. 369-379 ◽  
Author(s):  
Dawid Król ◽  
Dawid Zydek ◽  
Leszek Koszałka
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Task Scheduling ◽  
Directed Acyclic Graph ◽  
Simulation Experiments ◽  
New Approach ◽  
Acyclic Graph ◽  
Graph Task

Abstract This paper concerns Directed Acyclic Graph task scheduling on parallel executors. The problem is solved using two new implementations of Tabu Search and genetic algorithm presented in the paper. A new approach to solution coding is also introduced and implemented in both metaheuristics algorithms. Results given by the algorithms are compared to those generated by greedy LPT and SS-FF algorithms; and HAR algorithm. The analysis of the obtained results of multistage simulation experiments confirms the conclusion that the proposed and implemented algorithms are characterized by very good performance and characteristics.

scholarly journals Uncertainty-aware workflow migration among edge nodes based on blockchain

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Zhanyang Xu ◽  
Qingfan Geng ◽  
Hao Cao ◽  
Chuanjian Wang ◽  
Xihua Liu
Keyword(s):  
Genetic Algorithm ◽  
Graph Theory ◽  
Energy Consumption ◽  
Directed Acyclic Graph ◽  
Processing Time ◽  
Resource Provisioning ◽  
Workflow Scheduling ◽  
Acyclic Graph ◽  
Time And Energy ◽  
Workflow Tasks

Abstract Workflow is one of the most typical applications in distributed computing, which makes a variety of complex computing work orderly. However, assigning workflow tasks to nodes in the process of multi-node collaboration is still a challenge, because there are some unpredictable emergencies, i.e., uncertainty, in the process of workflow scheduling. The paper proposes a blockchain-powered resource provisioning (BPRP) method to solve the above problems. Technically, we use the directed acyclic graph in the graph theory to represent the workflow task and optimize the workflow scheduling strategy in the presence of uncertainty. The processing time and energy consumption of workflow tasks are also optimized by using non-dominated sorting genetic algorithm III (NSGA-III). Finally, we carry out experimental simulations to verify the effectiveness of the proposed method.

scholarly journals A Network Simulation Tool for Task Scheduling

10.14311/1658 ◽  
2012 ◽  
Vol 52 (5) ◽  
Author(s):  
Ondřej Votava
Keyword(s):  
Distributed Computing ◽  
Task Scheduling ◽  
Directed Acyclic Graph ◽  
Computer Network ◽  
Simulation Tool ◽  
Scheduling Problem ◽  
Distributed Application ◽  
Data Dependencies ◽  
Acyclic Graph ◽  
Points Of View

Distributed computing may be looked at from many points of view. Task scheduling is the viewpoint, where a distributed application can be described as a Directed Acyclic Graph and every node of the graph is executed independently. There are, however, data dependencies and the nodes have to be executed in a specified order. Hence the parallelism of the execution is limited. The scheduling problem is difficult and therefore heuristics are used. However, many inaccuracies are caused by the model used for the system, in which the heuristics are being tested. In this paper we present a tool for simulating the execution of the distributed application on a “real” computer network, and try to tell how the executionis influenced compared to the model.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

scholarly journals IoT-enabled directed acyclic graph in spark cluster

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Jahwan Koo ◽  
Nawab Muhammad Faseeh Qureshi ◽  
Isma Farah Siddiqui ◽  
Asad Abbas ◽  
Ali Kashif Bashir
Keyword(s):  
Distributed Computing ◽  
Real Time ◽  
Directed Acyclic Graph ◽  
Random Access ◽  
Heterogeneous Data ◽  
The Body ◽  
Computing Environment ◽  
Time Data ◽  
Acyclic Graph ◽  
Sensory Data

Abstract Real-time data streaming fetches live sensory segments of the dataset in the heterogeneous distributed computing environment. This process assembles data chunks at a rapid encapsulation rate through a streaming technique that bundles sensor segments into multiple micro-batches and extracts into a repository, respectively. Recently, the acquisition process is enhanced with an additional feature of exchanging IoT devices’ dataset comprised of two components: (i) sensory data and (ii) metadata. The body of sensory data includes record information, and the metadata part consists of logs, heterogeneous events, and routing path tables to transmit micro-batch streams into the repository. Real-time acquisition procedure uses the Directed Acyclic Graph (DAG) to extract live query outcomes from in-place micro-batches through MapReduce stages and returns a result set. However, few bottlenecks affect the performance during the execution process, such as (i) homogeneous micro-batches formation only, (ii) complexity of dataset diversification, (iii) heterogeneous data tuples processing, and (iv) linear DAG workflow only. As a result, it produces huge processing latency and the additional cost of extracting event-enabled IoT datasets. Thus, the Spark cluster that processes Resilient Distributed Dataset (RDD) in a fast-pace using Random access memory (RAM) defies expected robustness in processing IoT streams in the distributed computing environment. This paper presents an IoT-enabled Directed Acyclic Graph (I-DAG) technique that labels micro-batches at the stage of building a stream event and arranges stream elements with event labels. In the next step, heterogeneous stream events are processed through the I-DAG workflow, which has non-linear DAG operation for extracting queries’ results in a Spark cluster. The performance evaluation shows that I-DAG resolves homogeneous IoT-enabled stream event issues and provides an effective stream event heterogeneous solution for IoT-enabled datasets in spark clusters.

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