Consider a linear [n,k,d]qcodeC. We say that theith coordinate ofChas localityr, if the value at this coordinate can be recovered from accessing some otherrcoordinates ofC. Data storage applications require codes with small redundancy, low locality for information coordinates, large distance, and low locality for parity coordinates. In this paper, we carry out an in-depth study of the relations between these parameters. We establish a tight bound for the redundancyn-kin terms of the message length, the distance, and the locality of information coordinates. We refer to codes attaining the bound as optimal. We prove some structure theorems about optimal codes, which are particularly strong for small distances. This gives a fairly complete picture of the tradeoffs between codewords length, worst case distance, and locality of information symbols. We then consider the locality of parity check symbols and erasure correction beyond worst case distance for optimal codes. Using our structure theorem, we obtain a tight bound for the locality of parity symbols possible in such codes for a broad class of parameter settings. We prove that there is a tradeoff between having good locality and the ability to correct erasures beyond the minimum distance.

Windows Azure Storage (WAS) is a cloud storage system that provides customers the ability to store seemingly limitless amounts of data for any duration of time. WAS customers have access to their data from anywhere at any time and only pay for what they use and store. In WAS, data is stored durably using both local and geographic replication to facilitate disaster recovery. Currently, WAS storage comes in the form of Blobs (files), Tables (structured storage), and Queues (message delivery). In this paper, we describe the WAS architecture, global namespace, and data model, as well as its resource provisioning, load balancing, and replication systems.

This work presents an analytical queuing network model for bulk data transfers over long distances. Large scale scientific applications like climate modeling and high energy physics generate huge amounts of data that needs to be replicated across long distances for collaboration purposes. In addition, businesses need to copy critical data to remote sites for disaster recovery. The performance of these kinds of applications depends on the latency of the link due to the physical distance between the sites. The model presented in this paper uses the most prominent characteristics of remote data transfer applications and simplifies the performance prediction. This study shows that the overall throughput is mostly determined by the latency of the link, bandwidth of the link, number of round trips required for each I/O operation, and the number of simultaneously active I/O operations (asynchronous window size). Through laboratory and field experiments, we show that the model is capable of approximating the performance of remote data transfers within a very small error range.

With increasing development of applications for heterogeneous, distributed computing grids, the focus of performance analysis has shifted from a posteriori optimization on homogeneous parallel systems to application tuning for heterogeneous resources with time varying availability. This shift has profound implications for performance instrumentation and analysis techniques. Autopilot is a new infrastructure for dynamic performance tuning of heterogeneous computational grids based on closed loop control. The paper describes the Autopilot model of distributed sensors, actuators, and decision procedures, reports preliminary performance benchmarks, and presents a case study in which the Autopilot library is utilized in the development of an adaptive parallel input/output system.