Harald Burose

Run-time performance optimization and job management in a data protection solution

The amount of stored data in enterprise Data Centers quadruples every 18 months. This trend presents a serious challenge for backup management and sets new requirements for performance efficiency of traditional backup and archival tools. In this work, we discuss potential performance shortcomings of the existing backup solutions. During a backup session a predefined set of objects (client filesystems) should be backed up. Traditionally, no information on the expected duration and throughput requirements of different backup jobs is provided. This may lead to an inefficient job schedule and the increased backup session time. We analyze historic data on backup processing from eight backup servers in HP Labs, and introduce two additional metrics associated with each backup job, called job duration and job throughput. Our goal is to use this additional information for automated design of a backup schedule that minimizes the overall completion time for a given set of backup jobs. This problem can be formulated as a resource constrained scheduling problem which is known to be NP-complete. Instead, we propose an efficient heuristics for building an optimized job schedule, called FlexLBF. The new job schedule provides a significant reduction in the backup time (up to 50%) and reduced resource usage (up to 2–3 times). Moreover, we design a simulation-based tool that aims to automate parameter tuning for avoiding manual configuration by system administrators while helping them to achieve nearly optimal performance.

Enhancing and optimizing a data protection solution

Analyzing and managing large amounts of unstructured information is a high priority task for many companies. For implementing content management solutions, companies need a comprehensive view of their unstructured data. In order to provide a new level of intelligence and control over data resident within the enterprise, one needs to build a chain of tools and automated processes that enable the evaluation, analysis, and visibility into information assets and their dynamics during the information life-cycle. We propose a novel framework to utilize the existing backup infrastructure by integrating additional content analysis routines and extracting already available filesystem metadata over time. This is used to perform data analysis and trending to add performance optimization and self-management capabilities to backup and information management tasks. Backup management faces serious challenges on its own: processing ever increasing amount of data while meeting the timing constraints of backup windows could require adaptive changes in backup scheduling routines. We revisit a traditional backup job scheduling and demonstrate that random job scheduling may lead to inefficient backup processing and an increased backup time. In this work, we use a historic information about the object backup processing time and suggest an additional job scheduling, and automated parameter tuning which may significantly optimize the overall backup time. Under this scheduling, called LBF, the longest backups (the objects with longest backup time) are scheduled first. We evaluate the performance benefits of the introduced scheduling using a realistic workload collected from the seven backup servers at HP Labs. Significant reduction of the backup time (up to 30%) and improved quality of service can be achieved under the proposed job assignment policy.