Date of Award


Document Type

Campus Access Dissertation

Degree Name

Doctor of Philosophy (PhD)


Computer Science

First Advisor

Bo Sheng

Second Advisor

Dan Simovici

Third Advisor

Marc Pomplun


Disaggregated storage systems represent a promising approach for large-scale data center storage frameworks. This architecture involves physically separating computing devices and storage hosts and connecting them via high-speed networks. This design enables more flexible resource management, simplified upgrade and maintenance, and other desirable features for enterprise storage systems.

However, one of the significant challenges of such systems is the network connection between computing devices and storage hosts. Similar to traditional data center networks, disaggregated storage systems require ultra-low latency to manage storage I/O requests. Consequently, network congestion becomes a significant concern in such a system. Accurately evaluating network congestion control solutions without an evaluation platform designed explicitly for disaggregated storage systems poses a challenge. A comprehensive evaluation platform of disaggregated storage systems is essential for researchers to construct dependable and fast experiments.

The dissertation majorly contains three contributions. The initial contribution presents a prototype of a storage-network iterative simulator to model and assess the performance of disaggregated storage systems. This task poses a challenge for two primary reasons. Firstly, the performance of such systems is contingent on network protocols and storage solutions, both of which interact with each other, resulting in unpredictability at runtime. Consequently, modeling storage and network latency independently and aggregating them may produce inaccurate results. Secondly, the available trace datasets used for generating the workload are insufficient as they do not consider network delay and storage processing time integration. To address these challenges, we develop a storage-network iterative simulator based on the existing storage simulator (MQSim) and network simulator (NS3). This simulator provides an accurate evaluation of network congestion control solutions for large-scale disaggregated storage systems by considering the above-mentioned challenges.

The dissertation's second contribution concentrates on creating a network congestion control method for disaggregated storage systems. The study reveals that existing congestion control algorithms for data center networks are not suitable for our target system due to the unique characteristics of the network topology and storage I/O workload. As a response to the identified issues, we develop a new approach that dynamically determines the initial sending rate for each flow. Our solution particularly assists in enhancing the effectiveness of congestion control protocols under heavy I/O traffic by setting an appropriate initial rate for a flow that reduces congestion from the outset without degrading network performance.

The third contribution enhances the network congestion control method developed in the second contribution by borrowing sending rate information from long network traffic to make our method more adaptive for different types of network workloads.


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