Centralized cloud infrastructures have become the de-facto platform for data-intensive computing today. However, they suffer from inefficient data mobility due to the centralization of cloud resources, and hence, are highly unsuited for dispersed-data-intensive applications, where the data may be spread at multiple geographical locations. In this paper, we present Nebula: a dispersed cloud infrastructure that uses voluntary edge resources for both computation and data storage. We describe the lightweight Nebula architecture that enables distributed data-intensive computing through a number of optimizations including location-aware data and computation placement, replication, and recovery. We evaluate Nebula's performance on an emulated volunteer platform that spans over 50 PlanetLab nodes distributed across Europe, and show how a common data-intensive computing framework, MapReduce, can be easily deployed and run on Nebula. We show Nebula MapReduce is robust to a wide array of failures and substantially outperforms other wide-area versions based on a BOINC like model.
|Original language||English (US)|
|Title of host publication||Proceedings - 2014 IEEE International Conference on Cloud Engineering, IC2E 2014|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||10|
|State||Published - Sep 18 2014|
|Event||2nd IEEE International Conference on Cloud Engineering, IC2E 2014 - Boston, United States|
Duration: Mar 10 2014 → Mar 14 2014
|Name||Proceedings - 2014 IEEE International Conference on Cloud Engineering, IC2E 2014|
|Other||2nd IEEE International Conference on Cloud Engineering, IC2E 2014|
|Period||3/10/14 → 3/14/14|
Bibliographical notePublisher Copyright:
© 2014 IEEE.
- Cloud programming models and tools
- Data Intensive