Cluster-based Storage Systems Research Articles

Traffic Throttling to Improve TCP Incast Throughput

Cluster-based storage systems are widely used by many large data centers because of the manageability, low cost and other advantages. However, typical cluster-based storage systems rely on the standard TCP/IP Ethernet for clients to access data. Clients would experience the TCP effective throughput (called goodput) collapse which is termed as Incast problem. The main reason of Incast is TCP retransmit timeout which is caused by unfairness between the competing flows. In this paper, we analyze the TCP Incast dynamics from the fairness aspect and propose a method to make the competing flows share the network resource fairly by throttling the traffic. The simulation results show that our method is able to improve the TCP Incast goodput by 10% comparing to the high-timer-resolution

Ironmodel

Traditional performance models are too brittle to be relied on for continuous capacity planning and performance debugging in many computer systems. Simply put, a brittle model is often inaccurate and incorrect. We find two types of reasons why a model's prediction might diverge from the reality: (1) the underlying system might be misconfigured or buggy or (2) the model's assumptions might be incorrect. The extra effort of manually finding and fixing the source of these discrepancies, continuously, in both the system and model, is one reason why many system designers and administrators avoid using mathematical models altogether. Instead, they opt for simple, but often inaccurate, "rules-of-thumb". This paper describes IRONModel, a robust performance modeling architecture. Through studying performance anomalies encountered in an experimental cluster-based storage system, we analyze why and how models and actual system implementations get out-of-sync. Lessons learned from that study are incorporated into IRONModel. IRONModel leverages the redundancy of high-level system specifications described through models and low-level system implementation to localize many types of system-model inconsistencies. IRONModel can guide designers to the potential source of the discrepancy, and, if appropriate, can semi-automatically evolve the models to handle unanticipated inputs.

The reliability design and availability analysis of a distributed storage system

Current distributed parallel file systems and database systems can not satisfy the demands of data-intensive applications, such as storage capacity, access performance, reliability, scalability, and so on. Cluster-based storage systems have some shortcomings, too. To solve this kind of problems, a novel PC storage cluster solution is proposed, a distributed storage system based on 3-tiered agent architecture is designed, the system reliability model based on the master-slave backup mode is built, and the system availability is analyzed with the Markov model. According to the system availability formula and the values of the system parameters, the novel system can provide higher reliability and availability to satisfy users' requirements.