The ability to recover from disasters is an important requirement for many enterprises. With enterprise-class workloads increasingly hosted on the cloud, cloud customers have come to expect disaster recovery (DR) as a necessary feature from cloud platforms. This paper identifies key challenges in providing DR as a service on enterprise cloud platforms, and portrays DR solutions for a managed cloud platform. In particular, we present the reference architecture for DR solutions, and describe our practical experiences in providing a portfolio of DR solutions for the cloud platform. The solutions cover diverse target recovery sites, such as an equivalent cloud site, a dedicated recovery site, and a customer-owned site. From the experiences, we provide insights into and lessons on implementing DR for enterprise-class clouds.

While IT management services represent a mature subject in the IT business arena, the emerging cloud generation of management services require critical enhancements to the current processes and technologies in order to deliver IT management remotely with rapid on-boarding and minimal labor involvement from experts, to be affordable and scale up to the promise of the cloud. Traditional Remote Infrastructure Management (RIM) service providers use their own Network Operations Centers (NOC) to remotely monitor and manage customerspsila IT infrastructure. The primary business value for RIM services is that it helps global enterprises to small and medium businesses (SMB) to outsource the burden of managing their IT infrastructure. Although the IT management service itself delivered this way is more affordable, the RIM customer on-boarding process particularly is not, taking between one to two months of expensive labor. This paper describes what and how IT management processes, technologies and skills can be improved to provide remote customer on-boarding at an appropriate speed for delivery from the cloud. Our contributions consist of major enhancements in a key on-boarding area, namely IT discovery. Experimental results show that our approach aligns the RIM on-boarding methods to the cloud expectations both from a time as well as quality perspective.

A persistent flaw in the evaluation of page segmentation algorithms is examined.

In this paper we describe the current status of the trainable text-to-speech system at IBM. Recent algorithmic and database changes to the system have led to significant gains in the output quality. On the algorithms side, we have introduced statistical models for predicting pitch and duration targets which replace the rule-based target generation previously employed. Additionally, we have changed the cost function and the search strategy, introduced a post-search pitch smoothing algorithm, and improved our method of preselection. Through the combined data and algorithmic contributions, we have been able to significantly improve (p < 0.0001) the mean opinion score (MOS) of our female voice, from 3.68 to 4.85 when heard over loudspeakers and to 5.42 when heard over the telephone (seven point scale).

There has been a recent upsurge in the deployment of emerging technologies such as speech and speaker recognition which are reaching maturity. We discuss the details of the components required to build a system for audio indexing and retrieval for spoken documents using content and speaker based information facilitated by speech and speaker recognition. The real power of spoken document analysis is in using both content and speaker information together in retrieval by combining the results. The experiments described here are in the broadcast news domain, but the underlying techniques can easily be extended to other speech-centric applications and transactions.

A method for extracting alternating horizontal and vertical projection profiles are from nested sub-blocks of scanned page images of technical documents is discussed. The thresholded profile strings are parsed using the compiler utilities Lex and Yacc. The significant document components are demarcated and identified by the recursive application of block grammars. Backtracking for error recovery and branch and bound for maximum-area labeling are implemented with Unix Shell programs. Results of the segmentation and labeling process are stored in a labeled x-y tree. It is shown that families of technical documents that share the same layout conventions can be readily analyzed. Results from experiments in which more than 20 types of document entities were identified in sample pages from two journals are presented.>

This paper discusses the design of a matrix quantizer to be used to encode 1) homomorphically processed images, and 2) mean subtracted images. An image obtained by optical means is modeled as a product of the illumination and reflectance components. The illumination part is lowpass and the reflectance part which reveals the edge and texture details, is highpass. The homomorpphic system as applied to image processing consists of a logarithmic point transformation followed by linear filtering. Homomorphic processing achieves a simultaneous dynamic range reduction and contrast enhancement. A matrix quantizer operating on such processed images is shown to achieve bit rates as low as 0.16 bpp with acceptable quality.