Migrating applications to clouds has been a trend over the past decade or so. From a cloud operator's perspective, existing works have focused on how resources should be provisioned to users with a guaranteed level of quality-of-service (QoS). However, most of these works are for generic applications, where the distinct features of specific applications are not considered.

In this thesis, we take advantage of applications' distinct features in the resource provisioning procedure to improve the total revenue of the operator and provide better QoS to users. We first focus on two types of applications that are widely deployed in clouds, namely, applications with bursty workloads and stream data analytics. In the fiirst technical chapter, we investigate how burstable instances, an i...[ Read more ]

Migrating applications to clouds has been a trend over the past decade or so. From a cloud operator's perspective, existing works have focused on how resources should be provisioned to users with a guaranteed level of quality-of-service (QoS). However, most of these works are for generic applications, where the distinct features of specific applications are not considered.

In this thesis, we take advantage of applications' distinct features in the resource provisioning procedure to improve the total revenue of the operator and provide better QoS to users. We first focus on two types of applications that are widely deployed in clouds, namely, applications with bursty workloads and stream data analytics. In the fiirst technical chapter, we investigate how burstable instances, an instance type that was recently introduced by leading cloud operators, can match the time-varying workloads of applications. We present the first unified framework to model, analyze, and optimize the operation of burstable instances.

In the second technical chapter, we consider a heterogeneous cloud-based cluster for stream data analytics, which is shared by multiple analytics jobs. An efficient resource allocation scheme is proposed to achieve max-min fairness in the utilities of the throughput for the jobs.

Finally, we move to Internet of Things (IoT) applications in the third technical chapter. In view of the stringent communication delay requirements between the computation facilities and end users from many IoT applications, fog computing has recently been proposed, where computation tasks can be offloaded extensively along the cloud-to-things continuum. In this chapter, we derive an offloading scheme for a heterogeneous fog computing network shared by multiple tasks that have heterogeneous delay requirements, where lexicographic max-min fairness is enforced in the offloading procedure.