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On the design and optimization of heterogeneous distributed storage systems

Research Area: Distributed storage
Status: Finished Degree: Phd
Directors: Students:
Proposed start date: 2006-09-19 Proposed end date: 2011-07-19
Digital version

Over the last decade, users’ storage demands have been growing exponentially year over year. Besides demanding more storage capacity and more data reliability, today users also demand the possibility to access their data from any location and from any device. These new needs encourage users to move their personal data (e.g., E-mails, documents, pictures, etc.) to online storage services such as Gmail, Facebook, Flickr or Dropbox. Unfortunately, these online storage services are built upon expensive large datacenters that only a few big enterprises can afford.


To reduce the costs of these large datacenters, a new wave of online storage services has recently emerged integrating storage resources from different small datacenters, or even integrating user storage resources into the provider’s storage infrastructure. However, the storage resources that compose these new storage infrastructures are highly heterogeneous, which poses a challenging problem to storage systems designers: How to design reliable and efficient distributed storage systems over heterogeneous storage infrastructures?

This thesis provides an analysis of the main problems that arise when one aims to answer this question. Besides that, this thesis provides different tools to optimize the design of heterogeneous distributed storage systems. The contribution of this thesis is threefold:

First, we provide a novel framework to analyze the effects that data redundancy has on the storage and communication costs of distributed storage systems. Given a generic redundancy scheme, the presented framework can predict the average storage costs and the average communication costs of a storage system deployed over a specific storage infrastructure.

Second, we analyze the impacts that data redundancy has on data availability and retrieval times. For a given redundancy and a heterogeneous storage infrastructure, we provide a set of algorithms that allow to determine the expected data availability and expected retrieval times.

Third, we design different data assignment policies for different storage scenarios. We differentiate between scenarios where the entire storage infrastructure is managed by the same organization, and scenarios where different parties contribute their storage resources. The aims of our assignment policies are: (i) to minimize the required redundancy, (ii) to guarantee fairness among all parties, and (iii) to encourage different parties to contribute their local storage resources to the system.


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