A Benchmark to Evaluate Mobile Video Upload to Cloud Infrastructures

Abstract. The number of mobile devices (e.g., smartphones, tablets, wearable devices) is rapidly growing. In line with this trend, a massive amount of mobile videos with metadata (e.g., geospatial properties), which are captured using the sensors available on these devices, are being collected. Clearly, a computing in-frastructure is needed to store and manage this ever-growing large-scale video dataset with its structured data. Meanwhile, cloud computing service providers such as Amazon, Google and Microsoft allow users to lease computing re-sources with varying combinations of computing resources such as disk, net-work and CPU capacities. To effectively use these emerging cloud platforms in support of mobile video applications, the application workflow and resources required at each stage must be clearly defined. In this paper, we deploy a mo-bile video application (dubbed MediaQ), which manages a large amount of us-er-generated mobile videos, to Amazon EC2. We define a typical video upload workflow consisting of three phases: 1) video transmission and archival, 2) metadata insertion to database, and 3) video transcoding. While this workflow has a heterogeneous load profile, we introduce a single metric, frames-per-second, for video upload benchmarking and evaluation purposes on various cloud server types. This single metric enables us to quantitatively compare main system resources (disk, CPU, and network) with each other towards selecting the right server types on cloud infrastructure for this workflow