Maher Rahmouni

Activity-Based Scheduling of IT Changes

Change management is a disciplined process for introducing required changes onto the IT environment, with the underlying objective of minimizing disruptions to the business services as a result of performing IT changes. Currently, one of the most pressing problems in change management is the scheduling and planning of changes. Building on an earlier mathematical formulation of the change scheduling problem, in this paper we take the formulation of the problem one step further by breaking down the changes into the activities that compose them. We illustrate the theoretical viability of the approach, discuss the limit of its applicability to real life scenarios, describe heuristic techniques that promise to bridge the scalability gap and provide experimental validation for them.

Casebook: A Cloud-Based System of Engagement for Case Management

Casebook embraces social and collaboration technology, analytics, and intelligence to advance the state of the art in case management from systems of record to a system of engagement for knowledge workers. It addresses complex, inefficient work practices, information loss during hand offs between teams, and failure to learn from previous case experience. Intelligent agents help people adapt to changing work practices by tracking process evolution and providing updates and recommendations. Social collaboration surrounding cases integrates communication with information and supports collaborative roadmapping to enable people to work as they collaborate, thus accelerating how quickly and accurately they handle cases.

Learning from past experiences to enhance decision support in IT change management

the number of changes that IT departments have to deal with is growing at a fast pace in response to changing business needs of enterprises. As changes are getting executed and deployed, knowledge is being created and stored. It is of paramount importance to the success of the business to re-use that knowledge for future changes. In fact, those who do not learn from past experiences are doomed to repeat the same mistakes as well as not bear the fruit of the ones that were successful. This paper addresses this concern by providing for every change being worked out the most similar past changes. Our solution combines data mining and optimization paradigms to model the problem of finding past similar changes by designing and learning similarity functions. Our approach enhances the efficiency and effectiveness of dealing with changes, by reducing the risk and shortening the time of introducing new changes.

Similarity metric for risk assessment in IT change plans

The proper management of IT infrastructures is essential for organizations that aim to deliver high quality services. Given the dynamics of these infrastructures, changes become imminent. In some cases, these changes might raise failures, causing disruption to provided services and consequently affecting the business continuity. Therefore, it is strongly recommended to evaluate the risks associated with changes before their actual execution. Learning from information of past deployed changes it is possible to estimate the risks for recently planned ones. Thereby, in this paper, we propose a solution to weigh the information available from past executed plans by the similarity calculated in relation with the analyzed change plan. A prototype system has been developed in order to evaluate the effectiveness of the solution over an emulated IT infrastructure. The results obtained show that the solution is capable of capturing similarity among activities in change plans, improving the accuracy of risk assessment for IT change planning.

LearnIT: enhanced search and visualization of IT projects

Year over year, the majority of IT projects fail to deliver the required features on time and budget, costing billions of dollars. The task of portfolio managers is to make a selection of prospective projects, given only rough cost-benefit estimates for them. In this paper, we present LearnIT, a tool to aid portfolio managers in their portfolio selection job. LearnIT identifies past or current IT projects within a large database on the basis of similarity to a given IT project or proposal. Furthermore, LearnIT is capable of being trained with expert user feedback, via example. LearnIT also provides a means for visualizing and making evident any relationships between IT projects in order to help IT managers design templates for future projects thus increasing their success rate.

Trust path: a distributed model of search paths of trust in a peer-to-peer system

Trust management is not an easy task in a peer-to-peer system because a peer is not able to know all the other peers in the system, and therefore, it is not obvious to exchange documents safely. Accordingly, an efficient model of trust management is necessary to manage the trust between peers in order to, quickly and efficiently, make the difference between potentially malicious and benevolent peers before downloading a document. Several approaches have been developed to calculate the indirect trust value. These approaches are based on sequential search of trust paths. In this paper and in order to optimize the research response time of trust paths, we propose a new distributed model called Trust_Path to search all trust paths between two peers that have no direct knowledge. This search is performed by propagation, in a distributed manner, to all known peers. The experiments realized on a network of 16 servers, on which we have set up virtual machines, show that the response time of search of trust paths with the parallel execution using Trust_Path model is much faster than sequential execution and that its acceleration increases according to the number of nodes. The simulation results show that our model can be applied to a large-scale environment.Copyright

A rule-based approach to prioritization of IT work requests maximizing net benefit to the business

With the growth of IT outsourcing opportunities, providers of managed IT services, such as HP, are compelled to exploit any possible economy of scale in dealing with emerging requirements of their customers. In this paper we present a methodology and a set of tools for enhanced executive decision support capability for the best allocation of scarce development resources through timelier and more accurate delivery of forecast indicators relative to the net benefit. Examples of such indicators are total forecast revenue for a bundle of work requests and total forecast cost reductions for a bundle of work requests. The tools deliver on reduced development cost and shorter time to value through the identification of synergies, duplication and commonality in work requests. For example, our approach will be able to identify in a reliable manner work that falls into these discrete categories pertaining to potential duplication thereby highlighting areas of potential cost reduction. Moreover they guarantee a reduced turn around time of delivery to trade customers through prioritization driven by net benefit and optimized release.

Modelling cloud computing infrastructure

We present a modelling approach for an adaptive cloud infrastructure consisting of secure pools of virtualized resources, in order to facilitate automated management tasks and interaction with the system by a human administrator, or programmatically by a higher level service. The topology of such a system is rapidly changing as, for example, it has the abilities to create, modify or destroy pools of virtual resources according to customer demand, as well as dynamically modify the mapping of virtual to physical resources. It is also highly distributed and management data needs to be compiled from disparate sources. Our modelling approach, based on the semantic web, allows us to represent complex topologies, model incomplete or erroneous systems and perform operations such as query, while still allowing validation of the models against system invariants and policies. It also supports distributed modelling, allowing sub-models to be combined, data merging, and shared vocabularies.