Conversational IT services are expected to reduce user wait times and improve overall customer satisfaction. Cloud-based solutions are readily available for enterprise subject matter experts (SMEs) to train user-question classifiers and build conversational services with little effort. However, methodologies that the SMEs can use to improve the response accuracy and conversation quality are merely stated and evaluated. In complex service scenarios such as software support, the scope of topics is typically large and the training samples are often limited. Thus, training the classifier based on labeled samples of plain user utterances is not effective in most cases. In this paper, we identify several methods for improving classification quality and evaluate them in concrete training set scenarios. Particularly, a process-based methodology is described that builds and refines on top of service domain knowledge in order to develop a scalable solution for training accurate conversation services. Enterprises and service providers are continuously seeking new ways to improve customer experience on working with IT systems, where user wait times and service resolution quality are critical business metrics. One of the latest trends is the use of conversational IT services. Customers can interact with a conversational service to express their questions in natural language and the system can automatically return relevant answers or execute back-end processes for automated actions. Various text classification techniques have been developed and applied to understand the user questions and trigger the correct responses. For instance, in the context of IT software support, customers can use conversational systems to get answers about software product errors, licenses, or upgrade processes. While the potential benefits of building conversational services are huge, it is often difficult to effectively train classification models that cover well the scope of realistically complex services. In this paper, we propose a training methodology that addresses the limitations in both the scope of topics and the scarcity of the training set. We further evaluate the proposed methodology in a real service support scenario and share the lessons learned.

Regulations such as FFIEC [5] and HIPAA [6] require activities of system administration to be captured and reviewed regularly. In IT service delivery environment, system maintenance activities are usually performed by the service provider whose system administrators access customer environment based on problem and change ticket being assigned.

In recent years, IT service management (ITSM) has become one of the most researched areas of IT. Incident and problem management are two of the service operation processes in the IT infrastructure library (ITIL). These two processes aim to recognize, log, isolate and correct errors which occur in the environment and disrupt the delivery of services. Incident management and problem management form the basis of the tooling provided by an incident ticket systems (ITS). In an ITS system, seemingly unrelated tickets created by end users and monitoring systems can coexist and have the same root cause. The connection between failed resource and malfunctioning services is not realized automatically, but often established manually by means of human intervention. This need for human involvement reduces productivity. The introduction of automation would increase productivity and therefore reduce the cost of incident resolution. In this paper, we propose a model to correlate incident tickets based on three criteria. First, we employ a category-based correlation that relies on matching service identifiers with associated resource identifiers, using similarity rules. Secondly, we correlate the configuration items which are critical to the failed service with the earlier identified resource tickets in order to optimize the topological comparison. Finally, we augment scheduled resource data collection with constraint adaptive probing to minimize the correlation interval for temporally correlated tickets. We present experimental data in support of our proposed correlation model.

Current techniques employed to aurally render HTML tables often result in output that is very difficult for sight-impaired users to understand. This paper proposes TTPML, an XML-compliant markup language, which facilitates the generation of prose descriptions of tabular information. The markup language enables content creators to specify contextual reinforcement of, and linear navigation through, tabular information. The markup language may be applied to pre-existing Web content and is reusable across multiple tables. TTPML may be interpreted by origin servers, proxy servers, or browsers. We believe that our approach benefits sight-impaired users by improving accessibility to tabular information.

This study is the first to evaluate the performance benefits of using the recently proposed TCP Splice kernel service in Web proxy servers. Previous studies show that splicing client and server TCP connections in the IP layer improves the throughput of proxy servers like firewalls and content routers by reducing the data transfer overheads. In a Web proxy server, data transfer overheads represent a relatively large fraction of the request processing overheads, in particular when content is not cacheable or the proxy cache is memory-based. The study is conducted with a socket-level implementation of TCP Splice. Compared to IP-level implementations, socket-level implementations make possible the splicing of connections with different TCP characteristics, and improve response times by reducing recovery delay after a packet loss. The experimental evaluation is focused on HTTP request types for which the proxy can fully exploit the TCP Splice service, which are the requests for non-cacheabl.content and SSL tunneling. The experimental testbed includes an emulated WAN environment and benchmark applications for HTTP/1.0 Web client, Web server, and Web proxy running on AIX RS/6000 machines. Our experiments demonstrate that TCP Splice enables reductions in CPU utilization of 10-43% of the CPU, depending on file sizes and request rates. Larger relative reductions are observed when tunneling SSL connections, in particular for small file transfers. Response times are also reduced by up to 1.8sec.

The perturbations induced by adaptation and resource allocation decisions on the adapted applications may have the undesirable side effect of causing timing constraint failures. In order to benefit from available adaptation capabilities yet avoid critical timing failures, the dynamic resource allocation mechanism should be aware of the perturbation induced by its decisions. Therefore, the impact of adaptation on short-term performance should be considered a first-class decision criterion, along with traditional criteria such as long-term performance and application criticality. Towards this end, we propose the FARACost, an adaptation cost model that captures the impact of application-specific adaptation procedures and uses this information to evaluate adaptation choices. Experimental evaluations with two applications demonstrate that the use of models like FARACost reduces or prevents pending timing constraint failures, while leading to long-term performance improvements. The experiments are conducted in a cluster environment with a fully implemented infrastructure for adaptation and resource allocation based on the FARA framework.

Resource allocation for high-performance real-time applications is challenging due to the applications' data-dependent nature, dynamic changes in their external environment, and limited resource availability in their target embedded system platforms. These challenges may be met by use of adaptive resource allocation (ARA) mechanisms that can promptly adjust resource allocation to changes in an application's resource needs, whenever there is a risk of failing to satisfy its timing constraints. By taking advantage of an application's adaptation capabilities, ARA eliminates the need for 'over-sizing' real-time systems to meet worst-case application needs. This paper proposes a model for describing an application's adaptation capabilities and the runtime variation of its resource needs. The paper also proposes a satisfiability-driven set of performance metrics for capturing the impact of ARA mechanisms on the performance of adaptable real-time applications. The relevance of the proposed set of metrics is demonstrated experimentally, using a synthetic application designed to represent time-critical applications in C31 systems.

Workstations and personal computers are increasingly being used for applications with real-time characteristics such as speech understanding and synthesis, media computations and I/O. and animation, often concurrently executed with traditional nonreal-time workloads.This paper presents a system that can schedule multiple independent activities so that: activities can obta& minimum guaranteed execution rates with application-specified reservation granularities via CPU Reservations, CPU Reservations, which are of the form "reserve X units of time out of every Y units", provide not just an average case execution rate of X/Y over long periods of time, but the stronger guarantee that from any instant of time, by Y time units later, the activity will have executedfor at least X time units, applications can use Time Constraints to schedule tasks by deadlines, with on-time completion guaranteed for tasks with accepted constraints, and both CPU Reservations and Time Constraints are implemented very efficiently.In particular, CPU scheduling overhead is bounded by a constant and is not a function of the number of schedulable tasks.Other key scheduler properties are: l activities cannot violate other activities' guarantees, l time constraints and CPU reservations may be used together, separately, or not at all (which gives a round-robin schedule), with well-defined interactions between all combinations, and l spare CPU time is fairly shared among all activities.The Rialto operating system, developed at Microsoft Research, achieves these goals by using a precomputed schedule, which is the fundamental basis of this work.

Parallel and distributed computer architectures are increasingly being considered for application in a wide variety of computationally intensive embedded systems. Many such applications impose highly dynamic demands for resources (processors, memory, and communication network), because their computations are data-dependent, or because the applications must constantly interact with a rapidly changing physical environment, or because the applications themselves are adaptive. This paper presents a set of dynamic resource allocation techniques aimed at maintaining high levels of application performance in the presence of varying resource demands. It focuses on a class of applications structured as multiple pipelines of data-parallel stages, as this structure is common to many sensor-based applications. We discuss the issues involved in resource management for such applications, and present preliminary results from our implementations on Intel Paragon. Our approach uses feedback control-a real-time monitoring system is used to detect significant performance shortfalls, and resources are reallocated among the application components in an attempt to improve performance. The main contribution of this work is that it combines real-time monitoring of an application's performance with dynamic resource allocation, and focuses on practical implementations rather than simulation and analysis.

A problem frequently faced by complex distributed applications is to control the interaction of their communication and computational activities such that they jointly adhere to desired performance and timing requirements. This research concerns communication infrastructures able to cope with the varying processing and QoS/sup 1/ requirements imposed on them by application programs. Specifically, we describe and evaluate COMM/sup adapt/, a communication infrastructure enabling the on-line adaptation of a protocol's resource usage to currently available resources and application requirements. The key feature of COMM/sup adapt/ is its dynamic (auto-)configurability, which is its support of on-line configuration transparent to application programs. Such configuration is performed by a heuristic that accommodates changes in a connection's resource requirements by reallocating resources based on its knowledge of actual resource usage by other active connections. The heuristic's design and implementation are based on extensive investigations of the manner in which alternative assignments of protocol tasks to underlying processing resources can influence program-level latency and throughput requirements.