As more and more applications need to analyze unbounded data streams in a real-time manner, data stream processing platforms, such as Storm, have drawn the attention of many researchers, especially the scheduling problem. However, there are still many challenges unnoticed or unsolved. In this paper, we propose and implement an adaptive online scheme to solve three important challenges of scheduling. First, how to make a scaling decision in a real-time manner to handle the fluctuant load without congestion? Second, how to minimize the number of affected workers during rescheduling while satisfying the resource demand of each instance? We also point out that the stateful instances should not be placed on the same worker with stateless instances. Third, currently, the application performance cannot be guaranteed because of resource contention even if the computation platform implements an optimal scheduling algorithm. In this paper, we realize resource isolation using Cgroup, and then the performance interference caused by resource contention is mitigated. We implement our scheduling scheme and plug it into Storm, and our experiments demonstrate in some respects our scheme achieves better performance than the state-of-the-art solutions.

Anomalies of multitier services running in cloud platform can be caused by components of the same tenant or performance interference from other tenants. If the performance of a multitier service degrades, we need to find out the root causes precisely to recover the service as soon as possible. In this paper, we argue that cloud providers are in a better position than tenants to solve this problem, and the solution should be non-intrusive to tenants' services or applications. Based on these two considerations, we propose a solution for cloud providers to help tenants to localize root causes of any anomaly. We design a non-intrusive method to capture the dependency relationships of components, which improves the feasibility of root cause localization system. Our solution can find out root causes no matter they are in the same tenant as the anomaly or from other tenants. Our proposed two-step localization algorithm exploits measurement data of both application layer and underlay infrastructure and a random walk procedure to improve its accuracy. Our realworld experiments of a three-tier web application running in a small-scale cloud platform show a 38.9% improvement in mean average precision compared to current methods.