Name
Abstract | ||
|---|---|---|
Educational institutions have become highly dependent on information technology to support the delivery of personalised material, digital content, interactive classes, and others. These institutions are progressively transitioning into Cloud Computing technology to shift costs from locally-hosted services to a "renting model" often with higher availability, elasticity, and resilience. However, in order to properly explore the cost benefits of the pay-as-you-go business model, there is a need for processes for resource allocation, monitoring, and self-adjustment that take advantage of characteristics of the application domain. In this paper we perform a numerical analysis of three resource allocation methods that work by (i) pre-allocating resource capacity to handle peak demands; (ii) reactively allocating resource capacity based on current demand; and (iii) proactively allocating and releasing resources prior to load increases or decreases by exploring characteristics of the educational domain and more precise information about expected demand. The results show that there is an opportunity for both educational institutions and Cloud providers to collaborate in order to enhance the quality of services and reduce costs. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-35194-5_14 | GECON |
Keywords | Field | DocType |
application domain,resource allocation,reactively allocating resource capacity,current demand,educational domain,cost analysis,cloud computing,cloud computing technology,cloud provider,resource allocation method,educational institution,pre-allocating resource capacity,education systems,quality of service | Psychological resilience,Computer science,Information technology,Quality of service,Resource allocation,Application domain,Business model,Digital content,Environmental economics,Marketing,Distributed computing,Cloud computing | Conference |
Citations | PageRank | References |
9 | 0.51 | 20 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fernando Koch | 1 | 64 | 5.56 |
| Marcos D. Assuncao | 2 | 55 | 3.89 |
| Marco A. S. Netto | 3 | 284 | 13.16 |