Saeed Ghanbari

Adaptive Learning of Metric Correlations for Temperature-Aware Database Provisioning

This paper introduces a transparent self-configuring architecture for automatic scaling with temperature awareness in the database tier of a dynamic content Web server. We use a unified approach to achieving the joint objectives of performance, efficient resource usage and avoiding temperature hot-spots in a replicated database cluster. The key novelty in our approach is a lightweight on-line learning method for fast adaptations to bottleneck situations. Our approach is based on deriving a lightweight performance model of the replicated database cluster on the fly. The system trains its own model based on perceived correlations between various system and application metrics and the query latency for the application. The model adjusts itself dynamically to changes in the application workload mix. We use our performance model for query latency pre diction and determining the number of database replicas necessary to meet the incoming load. We adapt by adding the necessary replicas, pro-actively in anticipation of a bottleneck situation and we remove them automatically in underload. Finally, the system adjusts its query scheduling algorithm dynamically in order to avoid temperature hot- spots within the replicated database cluster. We investigate our transparent database provisioning mechanism in the database tier using the TPC-W industry- standard e-commerce benchmark. We demonstrate that our technique provides quality of service in terms of both performance and avoiding hot-spot machines under different load scenarios. We further show that our method is robust to dynamic changes in the workload mix of the application.

Semantic-Driven Model Composition for Accurate Anomaly Diagnosis

In this paper, we introduce a semantic-driven approach to system modeling for improving the accuracy of anomaly diagnosis. Our framework composes heterogeneous families of models, including generic statistical models, and resource-specific models into a belief network, i.e., Bayesian network. Given a set of models which sense the behavior of various system components, the key idea is to incorporate expert knowledge about the system structure and dependencies within this structure, as meta-correlations across components and models. Our approach is flexible, easily extensible and does not put undue burden on the system administrator. Expert beliefs about the system hierarchy, relationships and known problems can guide learning, but do not need to be fully specified. The system dynamically evolves its beliefs about anomalies over time. We evaluate our prototype implementation on a dynamic content site running the TPC-W industry-standard e- commerce benchmark. We sketch a system structure and train our belief network using automatic fault injection. We demonstrate that our technique provides accurate problem diagnosis in cases of single and multiple faults. We also show that our semantic-driven modeling approach effectively finds the component containing the root cause of injected anomalies, and avoids false alarms for normal changes in environment or workload.

Model ensemble tools for self-management in data centers

We introduce Ensemble, a runtime framework and associated tools for building query latency models on-the-fly. These dynamic performance models can be used to support complex, highly dimensional resource allocation, and/or what-if performance inquiry in modern database environments, such as data centers and Clouds. Ensemble combines simple, partially specified, lower-dimensionality models to provide good initial approximations for higher dimensionality, end-to-end query latency models. We perform an experimental evaluation on industry-standard applications running on a multi-tier dynamic content server. We show that the Ensemble on-the-fly modeling framework provides accurate, fast and flexible performance modelling by using partial, lower dimensionality models to approximate end-to-end query latency models.

Ensemble: A Tool for Performance Modeling of Applications in Cloud Data Centers

We introduce Ensemble, a runtime framework and associated tools for building application performance models on-the-fly. These dynamic performance models can be used to support complex, highly dimensional resource allocation, and/or what-if performance inquiry in modern heterogeneous environments, such as data centers and Clouds. Ensemble combines simple, partially specified, and lower-dimensionality models to provide good initial approximations for higher dimensionality application performance models. We evaluated Ensemble on industry-standard and scientific applications. The results show that Ensemble provides accurate, fast, and flexible performance models even in the presence of significant environment variability.

Stage-aware anomaly detection through tracking log points

We introduce Stage-aware Anomaly Detection (SAAD), a low-overhead real-time solution for detecting runtime anomalies in storage systems. Modern storage server architectures are multi-threaded and structured as a set of modules, which we call stages. SAAD leverages this to collect stage-level log summaries at runtime and to perform statistical analysis across stage instances. Stages that generate rare execution flows and/or register unusually high duration for regular flows at run-time indicate anomalies. SAAD makes two key contributions: i) limits the search space for root causes, by pinpointing specific anomalous code stages, and ii) reduces compute and storage requirements for log analysis, while preserving accuracy, through a novel technique based on log summarization. We evaluate SAAD on three distributed storage systems: HBase, Hadoop Distributed File System (HDFS), and Cassandra. We show that, with practically zero overhead, we uncover various anomalies in real-time.