An integrated security-aware job scheduling strategy for large-scale computational grids

2010 ◽  
Vol 26 (2) ◽  
pp. 198-206 ◽  
Author(s):  
Chao-Chin Wu ◽  
Ren-Yi Sun
Keyword(s):  
Large Scale ◽  
Job Scheduling ◽  
Computational Grids ◽  
Scheduling Strategy

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.

scholarly journals USE OF GENETIC ALGORITHMS FOR SCHEDULING JOBS IN LARGE SCALE GRID APPLICATIONS

2006 ◽  
Vol 12 (1) ◽  
pp. 11-17 ◽  
Author(s):  
Javier Carretero ◽  
Fatos Xhafa
Keyword(s):  
Genetic Algorithms ◽  
Large Scale ◽  
Job Scheduling ◽  
Practical Interest ◽  
Fine Tuning ◽  
Computational Grids ◽  
Grid Applications ◽  
Large Scale Grid ◽  
Improved Performance ◽  
Scale Grid

In this paper we present the implementation of Genetic Algorithms (GA) for job scheduling on computational grids that optimizes the makespan and the total flowtime. Job scheduling on computational grids is a key problem in large scale grid‐based applications for solving complex problems. The aim is to obtain an efficient scheduler able to allocate a large number of jobs originated from large scale applications to grid resources. Several variations for GA operators are examined in order to identify which works best for the problem. To this end we have developed a grid simulator package to generate large and very large size instances of the problem and have used them to study the performance of GA implementation. Through extensive experimenting and fine tuning of parameters we have identified the configuration of operators and parameters that outperforms the existing implementations in the literature for static instances of the problem. The experimental results show the robustness of the implementation, improved performance of static instances compared to reported results in the literature and, finally, a fast reduction of the makespan making thus the scheduler of practical interest for grid environments.

scholarly journals A Data-Aware Scheduling Strategy for Executing Large-Scale Distributed Workflows

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 47354-47364
Author(s):  
Salvatore Giampa ◽  
Loris Belcastro ◽  
Fabrizio Marozzo ◽  
Domenico Talia ◽  
Paolo Trunfio
Keyword(s):  
Large Scale ◽  
Scheduling Strategy

Reliability Based Scheduling Model (RSM) for Computational Grids

2012 ◽  
pp. 37-54
Author(s):  
Zahid Raza ◽  
Deo P. Vidyarthi
Keyword(s):  
Data Exchange ◽  
Large Scale ◽  
A Priori ◽  
Dynamic Environment ◽  
Computational Grid ◽  
Computational Grids ◽  
Scheduling Model ◽  
Reliability Computation ◽  
Grid Resources ◽  
Maximum Reliability

Computational Grid attributed with distributed load sharing has evolved as a platform to large scale problem solving. Grid is a collection of heterogeneous resources, offering services of varying natures, in which jobs are submitted to any of the participating nodes. Scheduling these jobs in such a complex and dynamic environment has many challenges. Reliability analysis of the grid gains paramount importance because grid involves a large number of resources which may fail anytime, making it unreliable. These failures result in wastage of both computational power and money on the scarce grid resources. It is normally desired that the job should be scheduled in an environment that ensures maximum reliability to the job execution. This work presents a reliability based scheduling model for the jobs on the computational grid. The model considers the failure rate of both the software and hardware grid constituents like application demanding execution, nodes executing the job, and the network links supporting data exchange between the nodes. Job allocation using the proposed scheme becomes trusted as it schedules the job based on a priori reliability computation.

Scheduling Large-Scale DNA Sequencing Applications

2010 ◽  
pp. 841-857
Author(s):  
Sudha Gunturu ◽  
Xiaolin Li ◽  
Laurence Tianruo Yang
Keyword(s):  
Large Scale ◽  
Processing Time ◽  
Sequence Length ◽  
Communication Link ◽  
Load Scheduling ◽  
Scheduling Strategy ◽  
Optimal Processing ◽  
Dna Sequence Alignment ◽  
Alignment Algorithms ◽  
Large Scale Networks

This chapter studies a load scheduling strategy with near-optimal processing time that is designed to explore the computational characteristics of DNA sequence alignment algorithms, specifically, the Needleman-Wunsch Algorithm. Following the divisible load scheduling theory, an efficient load scheduling strategy is designed in large-scale networks so that the overall processing time of the sequencing tasks is minimized. In this study, the load distribution depends on the length of the sequence and number of processors in the network and, the total processing time is also affected by communication link speed. Several cases have been considered in the study by varying the sequences, communication and computation speeds, and number of processors. Through simulation and numerical analysis, this study demonstrates that for a constant sequence length as the numbers of processors increase in the network the processing time for the job decreases and minimum overall processing time is achieved.

scholarly journals Dynamic Job Scheduling Strategy Using Jobs Characteristics in Cloud Computing

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1638 ◽  
Author(s):  
Mohammed A. Alsaih ◽  
Rohaya Latip ◽  
Azizol Abdullah ◽  
Shamala K. Subramaniam ◽  
Kamal Ali Alezabi
Keyword(s):  
Execution Time ◽  
Job Scheduling ◽  
Scheduling Algorithm ◽  
Job Characteristics ◽  
Available Bandwidth ◽  
Scheduling Strategy ◽  
Cloud Users ◽  
Performance Variations ◽  
Available Resources

A crucial performance concern in distributed decentralized environments, like clouds, is how to guarantee that jobs complete their execution within the estimated completion times using the available resources’ bandwidth fairly and efficiently while considering the resource performance variations. Formerly, several models including reservation, migration, and replication heuristics have been implemented to solve this concern under a variety of scheduling techniques; however, they have some undetermined obstacles. This paper proposes a dynamic job scheduling model (DTSCA) that uses job characteristics to map them to resources with minimum execution time taking into account utilizing the available resources bandwidth fairly to satisfy the cloud users quality of service (QoS) requirements and utilize the providers’ resources efficiently. The scheduling algorithm makes use of job characteristics (length, expected execution time, expected bandwidth) with regards to available symmetrical and non-symmetrical resources characteristics (CPU, memory, and available bandwidth). This scheduling strategy is based on generating an expectation value for each job that is proportional to how these job’s characteristics are related to all other jobs in total. That should make their virtual machine choice closer to their expectation, thus fairer. It also builds a feedback method which deals with reallocation of failed jobs that do not meet the mapping criteria.

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