Anca Sailer

Problem Determination Using Dependency Graphs and Run-Time Behavior Models

Key challenges in managing an I/T environment for e-business lie in the area of root cause analysis, proactive problem prediction, and automated problem remediation. Our approach as reported in this paper, utilizes two important concepts: dependency graphs and dynamic runtime performance characteristics of resources that comprise an I/T environment to design algorithms for rapid root cause identification in case of problems. In the event of a reported problem, our approach uses the dependency information and the behavior models to narrow down the root cause to a small set of resources that can be individually tested, thus facilitating quick remediation and thus leading to reduced administrative costs.

Ranking Services by Service Network Structure and Service Attributes

Service network analysis is an essential aspect of web service discovery, search, mining and recommendation. Many popular web service networks are content-rich in terms of heterogeneous types of entities, attributes and links. A main challenge for ranking services is how to incorporate multiple complex and heterogeneous factors, such as service attributes, relationships between services, relationships between services and service providers or service consumers, into the design of service ranking functions. In this paper, we model services, attributes, and the associated entities, such as providers, consumers, by a heterogeneous service network. We propose a unified neighborhood random walk distance measure, which integrates various types of links and vertex attributes by a local optimal weight assignment. Based on this unified distance measure, a reinforcement algorithm, ServiceRank, is provided to tightly integrate ranking and clustering by mutually and simultaneously enhancing each other such that the performance of both can be improved. An additional clustering matching strategy is proposed to efficiently align clusters from different types of objects. Our extensive evaluation on both synthetic and real service networks demonstrates the effectiveness of ServiceRank in terms of the quality of both clustering and ranking among multiple types of entity, link and attribute similarities in a service network.

Mining activity data for dynamic dependency discovery in e-business systems

The growing popularity of e-business has stimulated web sites to evolve from static content servers to complex multi-tier systems built from heterogeneous server platforms. E-businesses now spend a large fraction of their IT budgets maintaining, troubleshooting, and optimizing these web sites. It has been shown that such system management activities may be simplified or automated to various extents if a dynamic dependency graph of the system were available. Currently, all known solutions to the dynamic dependency graph extraction problem are intrusive in nature, i.e. require modifications at application or middleware level. In this paper, we describe non-intrusive techniques based on data mining, which process existing monitoring data generated by server platforms to automatically extract the system component dependency graphs in multi-tier e-business platforms, without any additional application or system modification.

Taking IT Management Services to a Cloud

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 customers’ 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.

Graph-Based Cloud Service Placement

The emergent IT clouds as the future of datacenters enable considerable opportunities for the services creation, deployment, management and usability. Users all over the world, from individuals to businesses have been taking advantage of the new cloud services automation and scalability benefits. However, the services creation and business support are still dominated by intensive manual labor. Offerings with similar infrastructure requirements and dependencies are mainly built from scratch as separated entities, making the service development inefficient and error prone. We propose a graph based solution for semi-automated service creation, which expresses the mapping between a business support system and an operations support system. We first identify and expose, at the leaf level of our graph, the meaningful IT operations in the form of basic services. Then, we extend our graph by representing existing services offerings in terms of these operation level service definitions as well as simpler services offerings. At service creation time, an offering manager can re-combine existing building blocks to define new services, besides implementing new blocks down to the operations support system. Our solution takes into consideration the constraints and costs of the service offering sub-components as far as their mapping down to datacenter resources for optimizing the service placement into data-centers. We present a study of the Desktop Service use case.

Threshold management for problem determination in transaction based e-commerce systems

Managing the service level objectives (SLO) in environments that implement e-commerce systems is a challenging task. It typically involves a clear understanding of how user transactions are supported by the I/T resources that comprise the e-commerce system. This paper investigates a subset of this important management problem. Using transaction to resource dependencies, the authors show how one can experimentally calculate the extent to which supporting resources for a transaction contribute to the end-to-end SLOs for that transaction. An important aspect of this process is the classification of user transactions, based on the profile of their resource usage, enabling one to set appropriate thresholds for different classes. This approach is then used to aid the detection and remediation of application performance bottlenecks.

The Future of Service Marketplaces in the Cloud

For as long as there have been services there has been a desire to have a convenient medium to expose and discover service offerings. Since early on, various efforts have attempted various approaches at the exchange of computational services, prompting the question of whether there is a market for Web services. We believe that a services marketplace should fulfill the promise of an electronic emporium where third party service providers are able to offer their services in a ubiquitous ecosystem, and where service consumers are able to acquire service solutions that are tailored to their requirements. This paper explores the landscape of cloud services marketplaces, where we are, what enablers are needed to realize the vision, and it presents a prospective architecture to that end.

Evolution of the IBM cloud: enabling an enterprise cloud services ecosystem

Cloud computing is a new paradigm that is transforming the information technology (IT) industry and reshaping the way enterprise services are developed, deployed, sold, delivered, and consumed. Instead of managing complex IT systems, customers can focus on the core competence of their enterprise while obtaining all required IT functions as a service. From the perspective of a cloud provider, remaining competitive and realizing full potential of economies of scale that the cloud paradigm promises require extreme levels of standardization, automation, and optimization. This paper describes the evolution of the Common Cloud Management Platform (CCMP), a management system providing business and operations support for cloud services. We cover its initial implementation and applications, discuss the latest challenges faced when adapting enterprise solutions to the cloud, and introduce the exploratory research topics to which this work led. We address the business services aspects, including framework-based integration of the catalog, and customer and revenue management, as well as the operational aspects, including novel approaches for scalable virtual machine provisioning and adaptive workload placement optimization. We discuss architecture, design, and implementation details of key CCMP components and highlight the challenging aspects of providing such architecture while promoting scalability, modularity, and reuse.

Automatic Structuring of IT Problem Ticket Data for Enhanced Problem Resolution

In this paper we propose a novel technique to automatically structure problem tickets consisting of free form, heterogeneous textual data, so that IT problem isolation and resolution can be performed rapidly. The originality of our technique consists in applying the conditional random fields (CRFs) supervised learning process to automatically identify individual units of information in the raw data. The CRFs have been shown to be effective on real-world tasks in various fields. We apply our technique to identify structural patterns specific to the problem ticket data used in call centers to enhance the problem resolution system used by remote technical assistance personnel. Most of the existing ticketing data is not explicitly structured, is highly noisy, and very heterogeneous in content, making it hard to effectively apply common data mining techniques to analyze and search the raw data. An example of such an analysis is the detection of the units of information containing the steps taken by the technical people to resolve a particular customer issue. We present a study of the accuracy of our results.

Hierarchical Online Problem Classification for IT Support Services

The overwhelming amount of various monitoring and log data generated in multitier IT systems makes problem determination one of the most expensive and labor-intensive tasks in IT Services arena. Particularly the initial step of problem classification is complicated by error propagation making secondary problems surfacing on multiple dependent resources. In this paper, we propose to automate the process of problem classification by leveraging machine learning. The main focus is to categorize the problem a user experiences by recognizing the real root cause specificity leveraging available training data such as monitoring and logs across the systems. We transform the structure of the problem into a hierarchy using an existing taxonomy. We then propose an efficient hierarchical incremental learning algorithm which is capable of adjusting its internal local classifier parameters in realtime. Comparing to the traditional batch learning algorithms, this online solution decreases the computational complexity of the training process by learning from new instances on an incremental fashion. Our approach significantly reduces the memory required to store the training instances. We demonstrate the efficiency of our approach by learning hierarchical problem patterns for several issues occurring in distributed web applications. Experimental results show that our approach substantially outperforms previous methods.