Dagan Gilat

Autonomic self-optimization according to business objectives

A central challenge in the runtime management of computing environments is the necessity to keep these environments continuously optimized. In this paper we introduce a new paradigm, which focuses on self-optimization according to high-level business objectives such as maximizing revenues. It replaces the more traditional optimizations that are based upon IT measures such as resource availability. A general, autonomous process is defined to enable such optimizations, and a set of technologies and methodologies is introduced to support the implementation of such a process. The paper concludes with two types of validation tests carried out on an eCommerce site, that demonstrate the value and applicability of this approach.

Creating operational shift schedules for third-level IT support: challenges, models and case study

IT support can be divided into first-level support, second-level support and third-level support. Although there is a large body of existing work regarding demand forecasting and shift schedule creation for various domains such as call centres, very little work exists for second- and third-level IT support. Moreover, there is a significant difference between such support and other types of services. As a result, current best practices for scheduling such work are not based on demand, but rather on primitive rules of thumb. Due to the increasing number of people providing such support, theory and practice is sorely needed for scheduling second- and third-level support shifts according to actual demand. In this work, we present an end-to-end methodology for forecasting and scheduling this type of work. We also present a case study in which this methodology demonstrated significant potential savings in terms of manpower resources.

Inference of reactive rules from dependency models

Reactive rules are rules that specify reactions to events. In some cases it is easier and more intuitive for users to define a dependency model representing an ontology. In this paper, we introduce the ADI model and its inference capabilities to run-time rule execution. We introduce a case study on eTrade and define the model building blocks exemplified by this case study. Then we show the specific rule language that is being used as the execution infrastructure. We explain the inference mechanism and its dynamic nature. The paper concludes with related work and a discussion about its utilization.

Modeling and monitoring dynamic dependency environments

Enterprise modeling using data dependencies is common in monitoring and business performance management systems. The modern enterprise is a dynamic creature, constantly adapting itself to the changing environment. This adaptation may result in changes in enterprise components and data dependencies between them. An enterprise model must be able to express this dynamism, and business performance management services must be able to react accordingly. In this paper, we briefly introduce ADI (active dependency integration technology), a language for modeling data dependencies between entities. We discuss developments related to support in modeling dynamic environments, where elements may be added or deleted. Dynamism-related developments include the support of automatic dependency instantiation from an abstract dependency. The abstract dependency expresses a general pattern in the ontology, functioning as a template for dependency instances. Another aspect of dynamism is support for changes in existing dependencies rather than only creating new dependencies; for example, adding a new entity to a dependency. Changes in topology do not imply system redeployment. ADI also supports the influence of dynamism on data items and subsequent propagation of this influence through the model.

A Top-Down Approach from Service Centers to Business Processes

Business service centers (BSC) design a framework for defining non-overlapping, independent, reusable, cost-effective business centers that provide business services. Business services define the way service centers interact and exchange information and assets to achieve business goals. BSC modeling offers many advantages over traditional techniques, such as reuse, simplicity, implementation autonomy, and responsiveness. On the other hand, defining and modeling business processes (BP) is a critical factor in improving business performance. We propose a top-down approach from the business level to the operational level, taking advantages of both BSC and BP approaches. Using the guidelines described in this framework, business designers can use both BSC and BP techniques to provide an end-to-end view of business operations. In a climate of constant and unpredictable change, synergy between process thinking and service center thinking becomes essential to the success of enterprises.

Calculating the Business Importance of Entities in a Service-Oriented Enterprise

Component business modeling (CBM) serves as a powerful analytical framework for reasoning about the business as a set of business components that collaborate through the provision and consumption of business services. This paper proposes and illustrates a method to calculate the relative importance of the entities that make up a componentized enterprise architecture. The proposed method includes a formal definition of the importance of each entity in the business architecture calculated from the high level business values.

A methodological framework for business-oriented modeling of IT infrastructure

The creation of IT simulation models for uses such as capacity planning and optimization is becoming more and more widespread. Traditionally, the creation of such models required deep modeling and/or programming expertise, thus severely limiting their extensive use. Moreover, many modern intelligent tools now require simulation models in order to carry out their function. For these tools to be widely deployable, the derivation of simulation models must be made possible without requiring excessive technical knowledge. Hence we introduce a general methodology that enables an almost automatic deployment of IT simulation models, based on three fundamental principles: Modeling only at the required level of detail; modeling standard components using pre-prepared models; and automatically deriving the application-specific model details. The technical details underlying this approach are presented. In addition, a case study, showing the application of this methodology to an eCommerce site, demonstrates the applicability of this approach.

A top-down approach to aligning business service centres with business processes

Business Service Centres (BSCs) design a framework for defining non-overlapping, independent, reusable, cost-effective business centres that provide business services. Business services define the way service centres interact and exchange information and assets to achieve business goals. BSC modelling offers many advantages over traditional techniques such as reuse, simplicity, implementation autonomy and responsiveness. On the other hand, defining and modelling Business Processes (BPs) is a critical factor in improving business performance. We propose a top-down approach from the business level to the operational level, taking advantages of both BSC and BP approaches. Using the guidelines described in this framework, business designers can use both BSC and BP techniques to provide an end-to-end view of business operations. In a climate of constant and unpredictable change, synergy between process thinking and service centre thinking becomes essential to the success of enterprises.

Reactive rules-based dependency resolution for monitoring dynamic environments

Monitoring systems commonly use data dependencies and are very often required to have real-time, or near real-time, capabilities. Resolution of dependencies using a reactive rule engine is an evident choice, since it provides inherent real-time characteristics. We introduce the novel approach taken by Active Dependency Integration (ADI) technology in using reactive rules for dependency resolution, i.e., for the purpose of calculating an updated value using the value elements on which it depends. The salient property of this approach is that it demonstrates autonomic behavior. The set of reactive rules used for dependency resolution does not depend on the model for which it provides dependency resolution. The same rules handle every dependency model and support dynamic models, where elements may be added or deleted, without having to change any code or rule definitions, or stop the monitoring for manual system reconfiguration and redeployment. The rules are implemented in AMIT, an event-driven rule engine.