Computing and communication have continued to impact on the way we run business, the way we learn, and the way we live. The rapid technology evolution of computing has also expedited the growth of digital data, the workload of services, and the complexity of applications. Today, the cost of managing storage hardware ranges from two to ten times the acquisition cost of the storage hardware. We see an increasing demand on technologies for transferring management burden from humans to software. Data migration and application migration are one of popular technologies that enable computing and data storage management to be autonomic and self-managing.
In this dissertation, we examine important issues in designing and developing scalable architectures and techniques for efficient and effective data migration and application migration. The first contribution we have made is to investigate the opportunity of automated data migration across multi-tier storage systems. The significant IO improvement in Solid State Disks (SSD) over traditional rotational hard disks (HDD) motivates the integration of SSD into existing storage hierarchy for enhanced performance. We developed adaptive look-ahead data migration approach to effectively integrate SSD into the multi-tiered storage architecture. When using the fast and expensive SSD tier to store the high temperature data (hot data) while placing the relatively low temperature data (low data) in the HDD tier, one of the important functionality is to manage the migration of data as their access patterns are changed from hot to cold and vice versa. For example, workloads during day time in typical banking applications can be dramatically different from those during night time. We designed and implemented an adaptive lookahead data migration model. A unique feature of our automated migration approach is its ability to dynamically adapt the data migration schedule to achieve the optimal migration effectiveness by taking into account of application specific characteristics and I/O profiles as well as workload deadlines. Our experiments running over the real system trace show that the basic look-ahead data migration model is effective in improving system resource utilization and the adaptive look-ahead migration model is more efficient for continuously improving and tuning of the performance and scalability of multi-tier storage systems. The second main contribution we have made in this dissertation research is to address the challenge of ensuring reliability and balancing loads across a network of computing nodes, managed in a decentralized service computing system. Considering providing location based services for geographically distributed mobile users, the continuous and massive service request workloads pose significant technical challenges for the system to guarantee scalable and reliable service provision. We design and develop a decentralized service computing architecture, called Reliable GeoGrid, with two unique features. First, we develop a distributed workload migration scheme with controlled replication, which utilizes a shortcut-based optimization to increase the resilience of the system against various node failures and network partition failures. Second, we devise a dynamic load balancing technique to scale the system in anticipation of unexpected workload changes. Our experimental results show that the Reliable GeoGrid architecture is highly scalable under changing service workloads with moving hotspots and highly reliable in the presence of massive node failures. The third research thrust in this dissertation research is focused on study the process of migrating applications from local physical data centers to Cloud. We design migration experiments and study the error types and further build the error model. Based on the analysis and observations in migration experiments, we propose the CloudMig system which provides both configuration validation and installation automation which effectively reduces the configuration errors and installation complexity. In this dissertation, I will provide an in-depth discussion of the principles of migration and its applications in improving data storage performance, balancing service workloads and adapting to cloud platform.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/41078 |
| Date | 19 May 2011 |
| Creators | Zhang, Gong |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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