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A Learning Analytics Approach for Job Scheduling on Cloud Servers

  • Mohammad Samadi Gharajeh
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 94)

Abstract

Learning analytics improves the teaching and learning procedures by using the educational data. It uses analysis tools to carry out the statistical evaluation of rich data and the pattern recognition within data. This chapter, firstly, describes four learning analytics methods in educational institutions. Secondly, it proposes a learning analytics approach for job scheduling on cloud servers, called LAJOS. This approach applies a learning-based mechanism to prioritise users’ jobs on scheduling queues. It uses the three basic attributes “importance level”, “waiting time” and “deadline time” of various jobs on cloud servers. The cloud broker acts as a teacher and local schedulers of cloud sites act as students. The broker learns to local schedulers how to prioritise users’ jobs according to the values of their attributes. In the deployment phase, the effect of the above attributes on the system throughput is studied separately to select the best attribute. In the service phase, users’ jobs are prioritised by computer systems according to the selected attribute. Simulation results show that the LAJOS approach is more efficient compared to some of the job scheduling methods in terms of schedule length and system throughput.

Keywords

Learning analytics Teaching procedure Learning management Cloud computing Job scheduling 

Abbreviation

Symbol

Phrase

CM

Cyclomatic Complexity

CPU

Central Processing Unit

CSCL

Computer-Supported Collaborative Learning

HCI

Human Computer Interaction

HOU

Hellenic Open University

IaaS

Infrastructure-as-a-Service

ID

Identifier Number

IT

Information Technology

LAJOS

Learning Analytics approach for JOb Scheduling

QoS

Quality of Service

RAM

Random Access Memory

SL

Schedule Length

SMA

Services Management Agent

ST

System Throughput

VMs

Virtual Machines

WSA

Web Service Agent

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Young Researchers and Elite Club, Tabriz BranchIslamic Azad UniversityTabrizIran

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