The past few years have seen a growing number of mobile and sensor applications that rely on Cloud support. The role of the Cloud is to allow these resource-limited devices to offload and execute some of their compute-intensive tasks in the Cloud for energy saving and/or faster processing. However, such offloading to the Cloud may result in high network overhead which is not suitable for many mobile/sensor applications that require low latency. So, people have looked at an alternative Cloud design whose resources are located at the edge of the Internet, called Edge Cloud. Although the use of Edge Cloud can mitigate the offloading overhead, the computational power and network bandwidth of Edge Cloud’s resources are typically much more limited compared to the centralized Cloud and hence are more sensitive to workload variation (e.g., due to CPU or I/O contention). In this paper, we propose a locality-aware load sharing technique that allows edge resources to share their workload in order to maintain the low latency requirement of Mobile-Cloud applications. Specifically, we study how to determine which edge nodes should be used to share the workload with and how much of the workload should be shared to each node. Our experiments show that our locality-aware load sharing technique is able to maintain low average end-to-end latency of mobile applications with low latency variation, while achieving good utilization of resources in the presence of a dynamic workload.
|Original language||English (US)|
|Title of host publication||UCC 2017 - Proceedings of the10th International Conference on Utility and Cloud Computing|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||10|
|State||Published - Dec 5 2017|
|Event||10th IEEE/ACM International Conference on Utility and Cloud Computing, UCC 2017 - Austin, United States|
Duration: Dec 5 2017 → Dec 8 2017
|Name||UCC 2017 - Proceedings of the10th International Conference on Utility and Cloud Computing|
|Other||10th IEEE/ACM International Conference on Utility and Cloud Computing, UCC 2017|
|Period||12/5/17 → 12/8/17|
Bibliographical noteFunding Information:
The authors would like to acknowledge grant NSF CSR-1162405 and CNS-1619254 that supported this research.
- Edge Cloud
- Load Sharing
- Mobile Cloud Computing