Render farm is a bunch of networked servers dedicated to render image in a distributed or parallel fashion. The capacity of in-house render farm has an upper bound but a rendering workload in real world tends to highly fluctuate. Thus execution of overloaded jobs on a remote cloud in a seamless way is very attractive to build cost-effective rendering infrastructures. However, extending a render farm from a cluster to a public cloud is not straightforward. The most critical obstacle is data synchronization between render farms. A simple approach to overcome this issue is that rendering data are uploaded to or downloaded from public cloud. This kind of configuration is used by most cloud-based rendering services. Despite its simplicity, data synchronization by an explicit data transferring imposes a heavy burden on a user and incurs unnecessary data transmission as well as startup latency. A more elaborated data synchronization technique armed with an on-demand fetch and cooperative caching can ease the burden and improve a rendering throughput by exploiting rendering workload characteristics. Our proposed revision-aware caching is that rendering data identifiable with additional revisions are cached on cloud and are fetched from on-premises revision-aware repository or a cache pool of peer cloud node. We proved the performance of our scheme via experiments based on synthetic workload and real workload.
|Title of host publication
|CCIOT 2019 - 2019 4th International Conference on Cloud Computing and Internet of Things
|Number of pages
|Published - 20 Sep 2019
|4th International Conference on Cloud Computing and Internet of Things, CCIOT 2019 - Tokyo, Japan
Duration: 20 Sep 2019 → 22 Sep 2019
|PervasiveHealth: Pervasive Computing Technologies for Healthcare
|4th International Conference on Cloud Computing and Internet of Things, CCIOT 2019
|20/09/19 → 22/09/19
Bibliographical noteFunding Information:
This work was partly supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2016R1A6A3A11930295) and Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No. IITP 2019-0-00074)
© 2019 Copyright is held by the owner/author(s). Publication rights licensed to ACM.
- Hybrid Cloud
- Render Farm
- Revision-aware Caching