Provisioning legacy simulation applications for Product Lifecycle Management (PLM) is very challenging. There are two key issues: (i) Hosting applications for simulation job executions. These legacy applications are developed in various platforms, usually monolithic, and may heavily consume system resources in the runtime. (ii) Input/output data storage. During the lifecycle of a project, huge amount of design data is generated by various design tools. When a design is completed, it needs to be validated by numerous simulation applications, which consequently create substantial amount of result data. Therefore, among others, the scalability in both system runtime resources and data storage is one of the biggest challenges. A generic cloud solution, which promises elastic scalability in both computation resources and data storage but lack of coordination between runtime resource allocation and data placement, is insufficient for supporting these legacy applications efficiently. In this paper, we share our experience on implementing a cloud solution that incorporates both scalable application hosting and usage-aware data placement into an integrated mechanism. With such a mechanism, our system can not only scale out according to workload, but also improve performance by increasing the possibility of local data access.

With advancement in science and technology, computing systems are becoming increasingly more complex with a growing number of heterogeneous software and hardware components. They are thus becoming more difficult to monitor, manage, and maintain. Traditional approaches to system management have been largely based on domain experts through a knowledge acquisition solution that translates domain knowledge into operating rules and policies. This process has been well known as cumbersome, labor intensive, and error prone. In addition, traditional approaches for system management are difficult to keep up with the rapidly changing environments. There is a pressing need for automatic and efficient approaches to monitor and manage complex computing systems. In this paper, we propose an integrated data-driven framework for computing system management by acquiring the needed knowledge automatically from a large amount of historical log data. Specifically, we apply text mining techniques to automatically categorize the log messages into a set of canonical categories, incorporate temporal information to improve categorization performance, develop temporal mining techniques to discover the relationships between different events, and take a novel approach called event summarization to provide a concise interpretation of the temporal patterns.

In system management applications, an overwhelming amount of data are generated and collected in the form of temporal events. While mining temporal event data to discover interesting and frequent patterns has obtained rapidly increasing research efforts, users of the applications are overwhelmed by the mining results. The extracted patterns are generally of large volume and hard to interpret, they may be of no emphasis, intricate and meaningless to non-experts, even to domain experts. While traditional research efforts focus on finding interesting patterns, in this paper, we take a novel approach called event summarization towards the understanding of the seemingly chaotic temporal data. Event summarization aims at providing a concise interpretation of the seemingly chaotic data, so that domain experts may take actions upon the summarized models. Event summarization decomposes the temporal information into many independent subsets and finds well fitted models to describe each subset.