Subscriber-Driven Cloud Interference Mitigation for Network Services

Abstract

Network services, e.g., video streaming services, are increasingly being deployed on public cloud platforms. Such services often employ horizontal scaling where a group of resource instances, e.g., virtual machines (VMs), handle the incoming workload. The response time of such services is often affected by interference, i.e., contention among resource instances belonging to multiple cloud subscribers for shared cloud resources. Most commercial cloud platforms do not support built-in mechanisms to detect interference and mitigate its impact. This paper outlines a solution called PRIMA that subscribers of such platforms, i.e., network service operators, can deploy to ensure a specified end user response time target is met even in the face of fluctuations in workload and interference. PRIMA uses automated and controlled performance tests to build models that capture the joint impact of workload and interference on the response time of each resource instance employed by a service. PRIMA adapts the system to changing workload and interference conditions by using these models at runtime to control the number of instances in the system and the distribution of load among these instances. Unlike existing subscriber-oriented interference mitigation techniques in literature, PRIMA provides an explicit mechanism to guarantee that the specified response time threshold is met at every resource instance assigned to a service. Furthermore, in contrast to these approaches PRIMA can help an operator avoid using more instances than necessary for handling the observed workload and interference.