International Business Machines Corporation

Yexi Jiang

Chang-Shing Perng

Tao Tao

Anca Sailer

Thomas E Chefalas

Frank Andre Schaffa

Jim Alain Laredo

Alla Segal

Rahul P Akolkar

Ignacio Silva-Lepe

Yang Zhou

Hidayatullah Habeebullah Shaikh

Rong Nickle Chang

Edward Cholchin So

Meir M Laker

Mihwa Choi

Offline functionality-based co-ranking and clustering is carried out on a knowledge base that characterizes a heterogeneous information technology services network including a plurality of services, a plurality of providers, and a plurality of attributes. Results of the functionality-based co-ranking and clustering are stored as annotations of the services and the providers in the knowledge base, to obtain an annotated knowledge base. A service requirement is obtained from a customer requiring information technology services. The annotated knowledge base is queried, based on the service requirement; and an ordered list of at least given ones of the services, based on the querying, is returned to the customer.

Complex service network ranking and clustering

A natural language specification of at least one high level information technology services requirement is obtained from a user, via a conversational interface; the same is parsed into first pre-defined semi-structured data, using a conversation parser. Based on the first pre-defined semi-structured data, a subset of candidate information technology services is identified, with a dialog engine, from a plurality of candidate information technology services provided by a plurality of vendors, the dialog engine is used to formulate a response including second pre-defined semi-structured data. The response is reverse-parsed into a natural language response, using the conversation parser. The natural language response includes a question for the user to assist in further refining the subset of candidate information technology services; the natural language response is presented to the user via the conversational interface.

Interactive acquisition of remote services

High level characteristics are extracted from a virtual machine provisioning request stream for a cloud computing environment. A plurality of prediction models are generated for a plurality of different types of virtual machines. A regression-correlation ensemble is applied to appropriate ones of the prediction models to predict future virtual machine demand for the cloud computing environment.

Cloud provisioning accelerator

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Miami, FL, United States of America

Yexi Jiang