Resource allocation on computational grids using a utility model and the knapsack problem

2009 ◽  
Vol 25 (1) ◽  
pp. 35-50 ◽  
Author(s):  
Daniel C. Vanderster ◽  
Nikitas J. Dimopoulos ◽  
Rafael Parra-Hernandez ◽  
Randall J. Sobie
Keyword(s):  
Resource Allocation ◽  
Knapsack Problem ◽  
Computational Grids ◽  
Utility Model

Security, energy, and performance-aware resource allocation mechanisms for computational grids

2014 ◽  
Vol 31 ◽  
pp. 77-92 ◽  
Author(s):  
Joanna Kołodziej ◽  
Samee Ullah Khan ◽  
Lizhe Wang ◽  
Marek Kisiel-Dorohinicki ◽  
Sajjad A. Madani ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Computational Grids ◽  
And Performance

A Fuzzy Rule-Based Optimisation Model for Efficient Resource Utilisation in a Grid Environment Using Proximity Awareness and Semantic Technology

2018 ◽  
Vol 17 (02) ◽  
pp. 1850023 ◽  
Author(s):  
Abdul Khalique Shaikh ◽  
Saadat M. Alhashmi ◽  
Rajendran Parthiban ◽  
Amril Nazir
Keyword(s):  
Resource Allocation ◽  
Fuzzy Rule ◽  
Resource Discovery ◽  
Computational Grids ◽  
Resource Utilisation ◽  
Grid Environment ◽  
Rule Based ◽  
Efficient Resource ◽  
First Come First Serve ◽  
Fuzzy Techniques

The performance of computational grids mainly depends on the resource allocation service of a resource management system. Efficient resource allocation is essential for better resource utilisation which could be for both providers and grid users. Resource allocation includes the scheduling of gridlets to the available resources. However, the biggest challenges for grid users are to select the best resources from the available grid resources and to allocate these resources for scheduling of the gridlets. To address these issues and enhance the resource utilisation process, we propose a semantic and proximity-aware fuzzy rule-based model that improves the resource utilisation in a grid environment. The model uses fuzzy techniques with four parameters such as semantic similarity, proximity, number of total machines and number of total processors of each machine. The experimental results provide promising results. Overall, the proposed semantic and proximity-aware fuzzy rule-based decentralised resource discovery model improves the resource utilisation by 23% as compared to non-fuzzy first come first serve (FCFS) technique in a computational grid environment.

CAPM Indexed Hybrid E-Negotiation for Resource Allocation in Grid Computing

2013 ◽  
Vol 5 (2) ◽  
pp. 72-91 ◽  
Author(s):  
Ashiqur Md. Rahman ◽  
Rashedur M Rahman
Keyword(s):  
Resource Allocation ◽  
Grid Computing ◽  
Dynamic Pricing ◽  
Large Scale ◽  
Service Providers ◽  
Computational Grids ◽  
Grid Environment ◽  
Expected Return ◽  
Grid Systems ◽  
Equilibrium Relationship

Computational Grids are a promising platform for executing large-scale resource intensive applications. This paper identifies challenges in managing resources in a Grid computing environment and proposes computational economy as a metaphor for effective management of resources and application scheduling. It identifies distributed resource management challenges and requirements of economy-based Grid systems, and proposes an economy based negotiation system protocol for cooperative and competitive trading of resources. Dynamic pricing for services and good level of Pareto optimality make auctions more attractive for resource allocation over other economic models. In a complex Grid environment, the communication demand can become a bottleneck; that is, a number of messages need to be exchanged for matching suitable service providers and consumers. The Fuzzy Trust integrated hybrid Capital Asset Pricing Model (CAPM) shows the higher user centric satisfaction and provides the equilibrium relationship between the expected return and risk on investments. This paper also presents an analysis on the communication requirements and the necessity of the CAPMAuction in Grid environment.

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