An Adaptive Scheduler Framework for Complex Workflow Jobs on Grid Systems

2012 ◽  
Vol 3 (4) ◽  
pp. 63-79 ◽  
Author(s):  
G. M. Siddesh ◽  
K. G. Srinisas
Keyword(s):  
Large Scale ◽  
Job Scheduling ◽  
Scheduling Algorithm ◽  
Model Performance ◽  
Grid Resource ◽  
Distributed Resources ◽  
Allocation Model ◽  
Utilization Factor ◽  
First Come First Serve ◽  
Job Rank

Grid Computing provides sharing of geographically distributed resources among large scale complex applications. Due to dynamic nature of resources in grid, there is a need of highly efficient job scheduling and resource management policies in grid. A novel Grid Resource Scheduler (GRS) is proposed to effectively utilize the available resources in Grid. Proposed GRS contributes, an optimal job scheduling algorithm on Job Rank-Backfilling policy and a resource matching algorithm based on ranking of resources with best fit allocation model. Performance of GRS is measured by considering a web based BLAST algorithm – a bioinformatics application. GRS aims in reducing; Makespan of the job workflow, execution time of varied size jobs, response time of the submitted jobs and overhead of using the system. It also considers improving the utilization factor and throughput of the available heterogeneous resources in grid. The experimental results prove that proposed grid scheduler framework performs better when evaluated against widely used First Come First Serve (FCFS), Shortest Job First (SJF) and Minimum Time to Due Date (MTTD) scheduling strategies.

SCHEDULING WITH JOB CHECKPOINT IN COMPUTATIONAL GRID ENVIRONMENT

2011 ◽  
Vol 02 (03) ◽  
pp. 299-316
Author(s):  
MALARVIZHI NANDAGOPAL ◽  
S. GAJALAKSHMI ◽  
V. RHYMEND UTHARIARAJ
Keyword(s):  
Fault Tolerance ◽  
Large Scale ◽  
Job Scheduling ◽  
Fault Tolerant ◽  
Scheduling Algorithm ◽  
Computational Grids ◽  
Tolerance Mechanism ◽  
Grid Resource ◽  
Distributed Resources ◽  
Grid Environment

Computational grids have the potential for solving large-scale scientific applications using heterogeneous and geographically distributed resources. In addition to the challenges of managing and scheduling these applications, reliability challenges arise because of the unreliable nature of grid infrastructure. Two major problems that are critical to the effective utilization of computational resources are efficient scheduling of jobs and providing fault tolerance in a reliable manner. This paper addresses these problems by combining the checkpoint replication based fault tolerance mechanism with minimum total time to release (MTTR) job scheduling algorithm. TTR includes the service time of the job, waiting time in the queue, transfer of input and output data to and from the resource. The MTTR algorithm minimizes the response time by selecting a computational resource based on job requirements, job characteristics, and hardware features of the resources. The fault tolerance mechanism used here sets the job checkpoints based on the resource failure rate. If resource failure occurs, the job is restarted from its last successful state using a checkpoint file from another grid resource. Globus ToolKit is used as the grid middleware to set up a grid environment and evaluate the performance of the proposed approach. The monitoring tools Ganglia and Network Weather Service are used to gather hardware and network details, respectively. The experimental results demonstrate that, the proposed approach effectively schedule the grid jobs with fault-tolerant way thereby reduces TTR of the jobs submitted in the grid. Also, it increases the percentage of jobs completed within specified deadline and making the grid trustworthy.

Job Scheduling Algorithm for Cloud Computing Based on Particle Swarm Optimization

2013 ◽  
Vol 662 ◽  
pp. 957-960 ◽  
Author(s):  
Jing Liu ◽  
Xing Guo Luo ◽  
Xing Ming Zhang ◽  
Fan Zhang
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
High Performance ◽  
Large Scale ◽  
Job Scheduling ◽  
Scheduling Algorithm ◽  
Autonomous Systems ◽  
Particle Swarm ◽  
Cost Benefit ◽  
Swarm Optimization

Cloud computing is an emerging high performance computing environment with a large scale, heterogeneous collection of autonomous systems and flexible computational architecture. The performance of the scheduling system influences the cost benefit of this computing paradigm. To reduce the energy consumption and improve the profit, a job scheduling model based on the particle swarm optimization(PSO) algorithm is established for cloud computing. Based on open source cloud computing simulation platform CloudSim, compared to GA and random scheduling algorithms, the results show that the proposed algorithm can obtain a better solution concerning the energy cost and profit.

scholarly journals Exploration based Genetic Algorithm for Job Scheduling on Grid Computing

2016 ◽  
Vol 5 (3) ◽  
pp. 91-100
Author(s):  
Hanaa Abdelrahman ◽  
Mohammed Bakri Bashir ◽  
Adil Yousif
Keyword(s):  
Genetic Algorithm ◽  
Grid Computing ◽  
Large Scale ◽  
Job Scheduling ◽  
Optimization Problems ◽  
Scheduling Algorithm ◽  
Adaptive Method ◽  
Virtual Organizations ◽  
Good Effect ◽  
Exploration Process

Grid computing presents a new trend to distribute and Internet computing to coordinate large scale heterogeneous resources providing sharing and problem solving in dynamic, multi- institutional virtual organizations. Scheduling is one of the most important problems in computational grid to increase the performance. Genetic Algorithm is adaptive method that can be used to solve optimization problems, based on the genetic process of biological organisms. The objective of this research is to develop a job scheduling algorithm using genetic algorithm with high exploration processes. To evaluate the proposed scheduling algorithm this study conducted a simulation using GridSim Simulator and a number of different workload. The research found that genetic algorithm get best results when increasing the mutation and these result directly proportional with the increase in the number of job. The paper concluded that, the mutation and exploration process has a good effect on the final execution time when we have large number of jobs. However, in small number of job mutation has no effects.

Multi-Objective Scheduling for Parallel Jobs on Grid

2010 ◽  
Vol 439-440 ◽  
pp. 1281-1286 ◽  
Author(s):  
Peng Fei Liu ◽  
Shou Bin Dong
Keyword(s):  
Large Scale ◽  
Job Scheduling ◽  
Scheduling Algorithm ◽  
Turnaround Time ◽  
Experimental Result ◽  
Parallel Job Scheduling ◽  
Multi Objective ◽  
Parallel Jobs ◽  
Parallel Job ◽  
Average Turnaround Time

Focused on the complexity of the parallel job scheduling on heterogeneous Grid, the paper proposes a multi-objective optimization based scheduling algorithm. The algorithm first splits the parallel job up into a series of independent processes with constraints, and then adopts particles to represent the mapping of job-resource. Multi-objective PSO is employed to simultaneously optimize the scheduling objectives of throughput and average turnaround time. Experimental result indicates that the proposed approach is effective while dealing with large scale parallel jobs scheduling on heterogeneous Grid and outperforms other conventional algorithms.

scholarly journals Scheduling Parallel Jobs Using Migration and Consolidation in the Cloud

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Xiaocheng Liu ◽  
Bin Chen ◽  
Xiaogang Qiu ◽  
Ying Cai ◽  
Kedi Huang
Keyword(s):  
Quality Of Service ◽  
High Performance ◽  
Large Scale ◽  
Job Scheduling ◽  
Scheduling Algorithm ◽  
Parallel Applications ◽  
Parallel Job Scheduling ◽  
Parallel Job ◽  
Job Scheduling Problem

An increasing number of high performance computing parallel applications leverages the power of the cloud for parallel processing. How to schedule the parallel applications to improve the quality of service is the key to the successful host of parallel applications in the cloud. The large scale of the cloud makes the parallel job scheduling more complicated as even simple parallel job scheduling problem is NP-complete. In this paper, we propose a parallel job scheduling algorithm named MEASY. MEASY adopts migration and consolidation to enhance the most popular EASY scheduling algorithm. Our extensive experiments on well-known workloads show that our algorithm takes very good care of the quality of service. For two common parallel job scheduling objectives, our algorithm produces an up to 41.1% and an average of 23.1% improvement on the average response time; an up to 82.9% and an average of 69.3% improvement on the average slowdown. Our algorithm is robust even in terms that it allows inaccurate CPU usage estimation and high migration cost. Our approach involves trivial modification on EASY and requires no additional technique; it is practical and effective in the cloud environment.

scholarly journals Enhancing Ocean Biogeochemical Models With Phytoplankton Variable Composition

2021 ◽  
Vol 8 ◽  
Author(s):  
Prima Anugerahanti ◽  
Onur Kerimoglu ◽  
S. Lan Smith
Keyword(s):  
Large Scale ◽  
C:N Ratio ◽  
Model Performance ◽  
Variable Composition ◽  
State Variables ◽  
Allocation Model ◽  
Biogeochemical Models ◽  
Model Variant ◽  
Parameter Perturbations ◽  
Optimal Resource

Chlorophyll (Chl) is widely taken as a proxy for phytoplankton biomass, despite well-known variations in Chl:C:biomass ratios as an acclimative response to changing environmental conditions. For the sake of simplicity and computational efficiency, many large scale biogeochemical models ignore this flexibility, compromising their ability to capture phytoplankton dynamics. Here we evaluate modelling approaches of differing complexity for phytoplankton growth response: fixed stoichiometry, fixed stoichiometry with photoacclimation, classical variable-composition with photoacclimation, and Instantaneous Acclimation with optimal resource allocation. Model performance is evaluated against biogeochemical observations from time-series sites BATS and ALOHA, where phytoplankton composition varies substantially. We analyse the sensitivity of each model variant to the affinity parameters for light and nutrient, respectively. Models with fixed stoichiometry are more sensitive to parameter perturbations, but the inclusion of photoacclimation in the fixed-stoichiometry model generally captures Chl observations better than other variants when individually tuned for each site and when using similar parameter sets for both sites. Compared to the fixed stoichiometry model including photoacclimation, models with variable C:N ratio perform better in cross-validation experiments using model-specific parameter sets tuned for the other site; i.e., they are more portable. Compared to typical variable composition approaches, instantaneous acclimation, which requires fewer state variables, generally yields better performance but somewhat lower portability than the fully dynamic variant. Further assessments using objective optimisation and more contrasting stations are suggested.

Efficient Schedule of Energy-Constrained UAV Using Crowdsourced Buses in Last-Mile Parcel Delivery

2021 ◽  
Vol 5 (1) ◽  
pp. 1-23
Author(s):  
Yan Pan ◽  
Shining Li ◽  
Qianwu Chen ◽  
Nan Zhang ◽  
Tao Cheng ◽  
...  
Keyword(s):  
Traffic Congestion ◽  
Large Scale ◽  
Load Capacity ◽  
Scheduling Algorithm ◽  
Cost Effective ◽  
Promising Alternative ◽  
Logistics Industry ◽  
Last Mile ◽  
Delivery Scheduling ◽  
Schedule Approach

Stimulated by the dramatical service demand in the logistics industry, logistics trucks employed in last-mile parcel delivery bring critical public concerns, such as heavy cost burden, traffic congestion and air pollution. Unmanned Aerial Vehicles (UAVs) are a promising alternative tool in last-mile delivery, which is however limited by insufficient flight range and load capacity. This paper presents an innovative energy-limited logistics UAV schedule approach using crowdsourced buses. Specifically, when one UAV delivers a parcel, it first lands on a crowdsourced social bus to parcel destination, gets recharged by the wireless recharger deployed on the bus, and then flies from the bus to the parcel destination. This novel approach not only increases the delivery range and load capacity of battery-limited UAVs, but is also much more cost-effective and environment-friendly than traditional methods. New challenges therefore emerge as the buses with spatiotemporal mobility become the bottleneck during delivery. By landing on buses, an Energy-Neutral Flight Principle and a delivery scheduling algorithm are proposed for the UAVs. Using the Energy-Neutral Flight Principle, each UAV can plan a flying path without depleting energy given buses with uncertain velocities. Besides, the delivery scheduling algorithm optimizes the delivery time and number of delivered parcels given warehouse location, logistics UAVs, parcel locations and buses. Comprehensive evaluations using a large-scale bus dataset demonstrate the superiority of the innovative logistics UAV schedule approach.

scholarly journals On the Design of Efficient Hierarchic Architecture for Software Defined Vehicular Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1400
Author(s):  
Muhammad Adnan ◽  
Jawaid Iqbal ◽  
Abdul Waheed ◽  
Noor Ul Amin ◽  
Mahdi Zareei ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Traffic Management ◽  
Vehicular Networks ◽  
Scheduling Algorithm ◽  
Main Research ◽  
First Come First Serve ◽  
Computing Framework ◽  
Simulation Results

Modern vehicles are equipped with various sensors, onboard units, and devices such as Application Unit (AU) that support routing and communication. In VANETs, traffic management and Quality of Service (QoS) are the main research dimensions to be considered while designing VANETs architectures. To cope with the issues of QoS faced by the VANETs, we design an efficient SDN-based architecture where we focus on the QoS of VANETs. In this paper, QoS is achieved by a priority-based scheduling algorithm in which we prioritize traffic flow messages in the safety queue and non-safety queue. In the safety queue, the messages are prioritized based on deadline and size using the New Deadline and Size of data method (NDS) with constrained location and deadline. In contrast, the non-safety queue is prioritized based on First Come First Serve (FCFS) method. For the simulation of our proposed scheduling algorithm, we use a well-known cloud computing framework CloudSim toolkit. The simulation results of safety messages show better performance than non-safety messages in terms of execution time.

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