Cloud providers collaboration for a higher service level in cloud computing
Cloud computing is increasingly becoming a desirable and foundational element in international enterprise computing. There are many companies which design, develop, and offer cloud technologies. However, cloud providers are still like lone islands. While current cloud computing models have provided...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , |
| التنسيق: | conferenceObject |
| منشور في: |
2013
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/10725/15387 https://doi.org/10.1109/ICCITechnology.2013.6579532 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://ieeexplore.ieee.org/abstract/document/6579532 |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| الملخص: | Cloud computing is increasingly becoming a desirable and foundational element in international enterprise computing. There are many companies which design, develop, and offer cloud technologies. However, cloud providers are still like lone islands. While current cloud computing models have provided significant benefits of maximizing the use of resources within a cloud, the current solutions still face many challenges including the lack of cross-leverage of available resources across clouds, the need to move data between clouds in some cases, and the lack of a global efficient cooperation between clouds. In this paper, we address these challenges by providing an approach that enables various cloud providers to cooperate in order to execute, together, common requests. Several enhancements are provided by integrating hardware acceleration with the computation services. We extend the Hadoop framework by adding provisions for hardware acceleration with Field Programmable Gate Arrays (FPGAs) within the cloud, for multi-cloud interaction, and for global cloud management. Hardware acceleration is used to offload computations when needed or as a service within the clouds. It can provide additional sources of revenues, reduced operating costs, and increased resource utilization. We used a k-means clustering application as a case study to demonstrate the effectiveness of hardware acceleration. |
|---|