Manish Sethi

Determining configuration parameter dependencies via analysis of configuration data from multi-tiered enterprise applications

Multi-tiered enterprise applications, which comprise of many (heterogeneous and possibly loosely coupled) components at various layers of the IT stack, typically have configuration dependencies between these different components. A change in the value of a configuration parameter of a component may require changing one or more parameters of other components to the same value. Keeping track of these dependencies is not a mean task as they are typically cross product and requires in-depth product/system knowledge. Our aim in this paper is to introduce techniques that do not require a system administrator to have in-depth knowledge about a multi-tiered system in order to be aware of the possible configuration dependencies in a system. Our focus in this paper does not include, for example, the performance-related dependencies such as the one that exists between the maximum number of threads and session timeout in an Apache server. In this paper we provide a method for analyzing existing deployments (that are functioning properly) to infer the configuration dependencies in a probabilistic sense. Firstly, we analyze the values of the configuration parameters of the various components to determine a candidate list of dependencies which includes both true and false ones. We then provide a heuristic that makes use of the statistics on the parameter name and value strings, computed from the configuration data and also the information on the web, to compute a rank for a dependency to be true. We provide a rank-ordered list of dependencies so that administrators can consult it to quickly and systematically identify the true dependencies. To complement our proposed approach we have performed an extensive study and experimentation to show the efficacy of our technique on data from two real-world solutions.

An open framework for federating integrated management model of distributed it environment

Heterogeneity has been the curse of the IT industry and systems management is no exception. As organizations are expanding their global business, and new technology and platforms are being introduced, the complexity of systems management is increasing rapidly due to heterogeneity and getting unified view of IT environments is becoming more difficult. To simplify and expedite information integration, this paper brings best practices of model-driven system and information integration to the problems of information integration in systems management. Specifically, we present an open framework for describing meta-model of a new component in isolation using UML and relating it with meta-models of other components through semantic relationships. The resulting integrated meta-model together with querying mechanisms for various sources can federate automatically and on-demand, integrated model of IT environment without relying on a central database repository.

SASH: Enabling continuous incremental analytic workflows on Hadoop

There is an emerging class of enterprise applications in areas such as log data analysis, information discovery, and social media marketing that involve analytics over large volumes of unstructured and semi-structured data. These applications are leveraging new analytics platforms based on the MapReduce framework and its open source Hadoop implementation. While this trend has engendered work on high-level data analysis languages, NoSQL data stores, workflow engines etc., there has been very little attention to the challenges of deploying analytic workflows into production for continuous operation. In this paper, we argue that an essential platform component for enabling continuous production analytic workflows is an analytics store. We highlight five key requirements that impact the design of such a store: (i) efficient incremental operations, (ii) flexible storage model for hierarchical data, (iii) snapshot support (iv) object-level incremental updates, and (v) support for handling change sets. We describe the design of SASH, a scalable analytics store that we have developed on top of HBase to address these requirements. Using the workload from a production workflow that powers search within IBMs intranet and extranet, we demonstrate orders of magnitude improvement in IO performance using SASH.

A framework for migrating production snapshots of composite applications to virtualized environments

Migrating production applications from physical datacenters to a virtualized environment is becoming essential to reduce operational costs. In order to avoid direct access to production systems, migration from disk snapshots is preferable. Model based migration approaches are not suitable for this purpose since they need to perform configuration discovery on production systems. Recent approaches that rely upon an isolated network require special setup and are limited to handling network configurations only. In this paper we present a framework for instantiating an application from disk snapshots in a virtualized environment. Our approach comprises of formally specifying the knowledge about product configurations and compiling the knowledge into a plan. The plan is capable of discovering application configurations in an inconsistent configuration setup. A key feature of our framework is that knowledge is specified once per product and re-used across applications that use the product. We have implemented a prototype of our approach and evaluated it on a real world application. The evaluation demonstrates the feasibility of our approach for migration using disk snapshots.

ERMIS: designing, developing, and delivering a remote managed infrastructure services solution

Remote management of IT (information technology) infrastructures, as a service, has received significant attention due to the numerous benefits it offers. In this paper, we discuss our work on taking a previously India-only remote IT infrastructure management service offering to a global audience. We first provide an overview of this IBM offering, called Express™ Remote Managed Infrastructure Services (ERMIS), and then focus on two specific aspects, service catalog and problem determination and resolution (PDR). In service catalog-based remote management, customers browse the catalog and place orders as they would using an online store. Our service catalog entries contain workflows to automate the deployment of requested applications or the configuration change of customer resources. High-level customer requirements are translated into capacity sizing and configuration parameters, which are then used to provision the final solution by making use of ITIL® (Information Technology Infrastructure Library) release and configuration management processes. PDR involves detecting, locating, and fixing anomalies. Probe-based techniques are used for targeting the problem location, while knowledge management assists with problem analysis, diagnosis, and classification. ERMIS architectural decisions have been driven by emerging-business requirements related to small and mediumsized businesses.

On concurrency improvements in enterprise SOA middleware

SOA is becoming prevalent in enterprise IT environments. As a result, component middleware are adding support for composite web services. Till recent past, most of these middleware were employed in tiered architectures. However, nature of call graphs differs in SOA. In this paper we revisit concurrency and deadlock issues that appear due to hosting SOA applications on middleware with prevalent thread allocation schemes. We propose a novel thread allocation scheme which performs an off-line analysis of web services call graphs and equips containers with essential information. At run-time, containers use this information and do not consult any central point or each other for thread allocation decisions. This makes our approach practical and scalable. We demonstrate, through simulations, that our approach can provide significant throughput improvements when compared with traditional thread allocation schemes.