scholarly journals Data Consolidation: A Task Scheduling and Data Migration Technique for Grid Networks

2008 ◽  
Author(s):  
P. Kokkinos ◽  
K. Christodoulopoulos ◽  
A. Kretsis ◽  
E. Varvarigos
Keyword(s):  
Task Scheduling ◽  
Data Migration ◽  
Grid Networks ◽  
Data Consolidation ◽  
Migration Technique

scholarly journals Data-Intensive Task Scheduling for Heterogeneous Big Data Analytics in IoT System

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4508
Author(s):  
Xin Li ◽  
Liangyuan Wang ◽  
Jemal H. Abawajy ◽  
Xiaolin Qin ◽  
Giovanni Pau ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Task Scheduling ◽  
Execution Time ◽  
Data Centers ◽  
Big Data Analysis ◽  
Data Locality ◽  
Data Migration ◽  
Task Execution ◽  
Task Execution Time

Efficient big data analysis is critical to support applications or services in Internet of Things (IoT) system, especially for the time-intensive services. Hence, the data center may host heterogeneous big data analysis tasks for multiple IoT systems. It is a challenging problem since the data centers usually need to schedule a large number of periodic or online tasks in a short time. In this paper, we investigate the heterogeneous task scheduling problem to reduce the global task execution time, which is also an efficient method to reduce energy consumption for data centers. We establish the task execution for heterogeneous tasks respectively based on the data locality feature, which also indicate the relationship among the tasks, data blocks and servers. We propose a heterogeneous task scheduling algorithm with data migration. The core idea of the algorithm is to maximize the efficiency by comparing the cost between remote task execution and data migration, which could improve the data locality and reduce task execution time. We conduct extensive simulations and the experimental results show that our algorithm has better performance than the traditional methods, and data migration actually works to reduce th overall task execution time. The algorithm also shows acceptable fairness for the heterogeneous tasks.

scholarly journals Data migration: relational RDBMS to non-relational NoSQL

2021 ◽  
Author(s):  
Feroz Alam
Keyword(s):  
Relational Databases ◽  
Data Migration ◽  
Business Decision ◽  
Nosql Databases ◽  
Dynamic Provisioning ◽  
Object Relational ◽  
Significant Performance ◽  
Query Structure ◽  
Day By Day ◽  
Migration Technique

As a part of achieving specific targets, business decision making involves processing and analyzing large volumes of data that leads to growing enterprise databases day by day. Considering the size and complexity of the databases used in today’s enterprises, it is a major challenge for enterprises to re-engineering their applications that can handle large amounts of data. Compared to traditional relational databases, non-relational NoSQL databases are better suited for dynamic provisioning, horizontal scaling, significant performance, distributed architecture and developer agility benefits. Based on the concept of Object Relational Mapping (ORM) and traditional ETL data migration technique this thesis proposes a methodology for migrating data from RDBMS to NoSQL. The performance of the proposed solution is evaluated through a comparative analysis of RDBMS and NoSQL implementations based on query performance evaluation, query structure and developmental agility.

Efficient data consolidation in grid networks and performance analysis

2011 ◽  
Vol 27 (2) ◽  
pp. 182-194 ◽  
Author(s):  
P. Kokkinos ◽  
K. Christodoulopoulos ◽  
E. Varvarigos
Keyword(s):  
Performance Analysis ◽  
Grid Networks ◽  
Efficient Data ◽  
Data Consolidation ◽  
And Performance

scholarly journals Data Migration Technique Options on Reforming Jiwasraya's Existing Database

2021 ◽  
Vol 3 (3) ◽  
pp. 137-142
Author(s):  
Vellyne Tjiam ◽  
William Chrisandy ◽  
Hanna Nadia Savira ◽  
Karel Alexander
Keyword(s):  
High Risk ◽  
Data Migration ◽  
Insurance Policy ◽  
Worst Case ◽  
Bottom Up ◽  
Applied Technology ◽  
Migration Technique

PT Asuransi Jiwasraya has been facing a crisis since 2020, which later probably demand them to reconstruct their insurance policy. Nothing is decided aside from the reconstruction idea. This is a hard task to deal with as there is a high risk to be borne along. In the worst case, bankruptcy awaits. As technology has taken over most industries, including insurance, it is only normal for the company to take advantage of the applied technology. However, it is still unknown whether the database used could help fulfill the mission. Considering loads of data might be higher by year, it will be more efficient to use the integrated database to transfer the whole data into a new-adapted database rather than creating a new one and manually adapt then add the data. This could be done by doing a Bottom-up approach that occurs in two big steps. This is the safest choice now which is handy and possible.

scholarly journals Data migration: relational RDBMS to non-relational NoSQL

2021 ◽  
Author(s):  
Feroz Alam
Keyword(s):  
Relational Databases ◽  
Data Migration ◽  
Business Decision ◽  
Nosql Databases ◽  
Dynamic Provisioning ◽  
Object Relational ◽  
Significant Performance ◽  
Query Structure ◽  
Day By Day ◽  
Migration Technique

As a part of achieving specific targets, business decision making involves processing and analyzing large volumes of data that leads to growing enterprise databases day by day. Considering the size and complexity of the databases used in today’s enterprises, it is a major challenge for enterprises to re-engineering their applications that can handle large amounts of data. Compared to traditional relational databases, non-relational NoSQL databases are better suited for dynamic provisioning, horizontal scaling, significant performance, distributed architecture and developer agility benefits. Based on the concept of Object Relational Mapping (ORM) and traditional ETL data migration technique this thesis proposes a methodology for migrating data from RDBMS to NoSQL. The performance of the proposed solution is evaluated through a comparative analysis of RDBMS and NoSQL implementations based on query performance evaluation, query structure and developmental agility.

How does manipulation of secondary task scheduling affect human performance?

2006 ◽  
Author(s):  
Patrice D. Tremoulet ◽  
Kathleen M. Stibler ◽  
Patrick Craven ◽  
Joyce Barton ◽  
Adam Gifford ◽  
...  
Keyword(s):  
Task Scheduling ◽  
Secondary Task ◽  
Human Performance

Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

2018 ◽  
pp. 96-101
Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Waiting Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Task Scheduling Algorithm ◽  
Machine Utilization ◽  
Independent Tasks ◽  
Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

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