Fan Jing Meng

A Generic Framework for Application Configuration Discovery with Pluggable Knowledge

Discovering application configurations and dependencies in the existing runtime environment is a critical prerequisite to the success of cloud migration, which attracts many attentions from both researchers and commercial vendors. However, the high complexity and diversity of enterprise applications as well as their runtime environment challenge the existing approaches which generally depend on the pre-built domain specific knowledge. In this paper, we propose a generic framework for application configuration discovery which can be applied even when the domain knowledge is missing or incomplete. We design a generic approach to significantly narrow down the configuration discovery scale based on the iterative comparison and enable users to manually identify configurations from reasonable scaled file sets with semantic tags. To maximize automation, we further design an easy extensible and pluggable knowledge base to assist configuration discovery. Through extensive case study, the capability and efficiency of our framework have been demonstrated.

A Novel Service Composition Approach for Application Migration to Cloud

Migrating business applications to cloud can be costly, labor-intensive, and error-prone due to the complexity of business applications, the constraints of the clouds, and the limitations of existing migration techniques provided by migration service vendors. However, the emerging software-as-a-service offering model of migration services makes it possible to combine multiple migration services for a single migration task. In this paper, we propose a novel migration service composition approach to achieve a cost-effective migration solution. In particular, we first formalize the migration service composition problem into an optimization model. Then, we present an algorithm to determine the optimal composition solution for a given migration task. Finally, using synthetic trace driven simulations, we validate the effectiveness and efficiency of the proposed optimization model and algorithm.

DriftInsight: Detecting Anomalous Behaviors in Large-Scale Cloud Platform

Detecting anomalous behaviors of cloud platforms is one of critical tasks for cloud providers. Every anomalous behavior potentially causes incidents, especially some unaware and/or unknown issues, which severely harm their SLA (Service Level Agreement). Existing solutions generally monitor cloud platform at different layers and then detect anomalies based on rules or learning algorithms on monitoring metrics. However, complexity of nowadays cloud platforms, high dynamics of cloud workloads and thousands of various types of metrics make anomalous behavior detection more challenging to be applied in production, especially in large scale cloud production environments. In this paper, we present a practical cloud anomalous behavior detection system called DriftInsight. It firstly analyzes multi-denominational metrics of each component and identifies a set of representative steady components based on the convergences of their states. Then it generates a state model and a state transit model for each steady cloud component. Finally, it detects behavior anomalies of these steady components in near real-time and meanwhile evolve behavior models on the fly. The evaluation results of this approach in a commercial large-scale PaaS (Platform-as-a-Service) cloud are demonstrated its capability and efficiency.

Facilitating Business-Oriented Cloud Transformation Decision with Cloud Transformation Advisor

To move applications to the cloud is not only a technical decision but also a business-oriented decision, in which both business and technical factors (e.g. transformation effort, multi-tenancy and auto-scaling enablement, scalability and extensibility) should be considered. However, existing approaches and tools do not support a consumable business oriented cloud transformation decision to select more suitable transformation solution with the right cloud delivery model, services type, affordable transformation effort and etc. In this paper, we introduce a practical three-step approach and a tool, CTA (Cloud Transformation Advisor) to enable decision makers to identify the most suitable cloud transformation solution to satisfy their business goals based on a well-structured cloud transformation knowledge base.

Learning from Metadata: A Fuzzy Token Matching Based Configuration File Discovery Approach

Discovery of configuration files is one of the prerequisite activities for a successful workload migration to the cloud. The complicated and super-sized file systems, the considerable variance of configuration files, and the multiple-presence of configuration items make configuration file discovery very difficult. Traditional approaches usually highly rely on experts to compose software specific scripts or rules to discover configuration files, which is very expensive and labor-intensive. In this paper, we propose a novel learning based approach named MetaConf to convert configuration file discovery to a supervised file classification task using the file metadata as learning features such that it can be conducted automatically, efficiently, and independently of domain expertise. We report our evaluation with extensive and real-world case studies, and the experimental results validate that our approach is effective and it outperforms our baseline method.