ABSTRACT

Cloud computing has become a dominant scalable computing platform for both online services and conventional data-intensive computing (examples include Amazon's EC2, Microsoft's Azure, IBM's SmartCloud, etc.). Cloud computing data centers share computing resources among a large set of users, providing a cost effective means to allow users access to computational power and data storage not practical for an individual. A data center often has to over-commit its resources to meet Quality of Service contracts. The data center software needs to effectively manage its resources to meet the demands of users submitting a variety of applications, without any prior knowledge of these applications.

This work is focused on the issue of management of memory resources in a data center. Recent progress in transfer learning methods inspires this work in the creation of dynamic models to predict the cache and memory requirements of an application. The project has four main tasks: (i) an investigation into how recent advancements in transfer learning can help solve data center resource management problems, (ii) development of a dynamic cache predictor using on-the-fly virtual machine measurements, (iii) creation of a dynamic memory predictor using runtime characteristics of a virtual machine, and (iv) development of a unified resource management scheme creating a set of heuristics that dynamically adjust cache and memory allocation to fulfill Quality of Service goals. In tasks (i)-(iii), transfer learning methods are employed and explored to facilitate the transfer of knowledge and models to new system environments and applications based on extensive training on existing systems and benchmark applications. The prediction models and management scheme will be evaluated on common benchmarks including SPEC WEB and CloudSuite 2.0. The results of this research will have broad impact on the design and implementation of cloud computing data centers. The results will help improve resource utilization, boost system throughput, and improve predication performance in a cloud computing virtualization system. Additionally, the methods designed and knowledge they impart will advance understanding in both systems research and machine learning.

Cloud computing has become a dominant for online services. To guarantee Quality of Service (QoS), A cloud center usually commits resources than necessary for time of high demand. This project focused on the effective management of machine resources in order to allow a cloud center to utilize fewer machines and achieve the same QoS.

Specifically, this research developed automatic techniques to 

	1) Predict the memory resources needed by an application from the source code using machine learning, and

	2) Use that prediction to manage the resources used by the application at run-time to achieve QoS

This research has advanced our understanding of both systems and machine learning as they apply to source code. The methods developed in this research will help improve resource utilization, boost system throughput, and provide predictable performance. Our research may thus benefit a broad range of end users

of cloud computing. 

This project helped fund the education of one Ph.D. student. The nature of this interdisciplinary research provided the student a deep insight

into two fundamental areas in computer science: computer systems and machine learning. In addition, an undergraduate student, and future graduate student in computer science, developed and understanding of the same areas as part of a Research Experience for Undergraduates supplemental award.

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