Cloud platforms have emerged as the primary data warehouse for a variety of applications, such as DropBox, iCloud, Google Music, etc. These applications allow users to store data in the cloud and access it from anywhere in the world. Commercial clouds are also well suited for providing high-end servers for rent to execute applications that require computation resources sporadically. Cloud users only pay for the time they actually use the hardware and the amount of data that is transmitted to and from the cloud, which has the potential to be more cost effective than purchasing, hosting, and maintaining dedicated hardware. In this dissertation, we look into the efficiency of the cloud Infrastructure-as-a-Service (IaaS) model for two real time high bandwidth applications: A scientific application of short-term weather forecasting and Video on Demand services. We show that, cloud services are efficient in both network and computation for real time scientific application of weather forecasting. We present a related list reordering approach, which reduces the network traffic of serving videos from VoD services and improve the efficiency of caches deployed to serve them. Also, we present transcoding policies to reduce the transcoding workload and present prediction models to maintain performance of providing ABR streaming of VoD services at the client with online transcoding in the cloud
