Automatic configuration of internet services

Abstract. This contribution describes the development of a plugin for the geographic information system QGIS to interface the openModeller software package. The aim is to use openModeller to generate species’ potential distribution models for various archaeological applications (site catchment analysis, for example). Since the usage of openModeller’s command-line interface and configuration files can be a bit inconvenient, an extension of the QGIS user interface to handle these tasks, in combination with the management of the geographic data, was required. The implementation was realized in Python using PyQGIS and PyQT. The plugin, in combination with QGIS, handles the tasks of managing geographical data, data conversion, generation of configuration files required by openModeller and compilation of a project folder. The plugin proved to be very helpful with the task of compiling project datasets and configuration files for multiple instances of species occurrence datasets and the overall handling of openModeller. In addition, the plugin is easily extensible to take potential new requirements into account in the future.

In an information system, applications often make use of services that they access using the parameters described in their configuration files. Various applications then use different codes to denote the same parameters. When access parameters of a service are modified, it is necessary to update them in every configuration file using them. These changes are necessary, for example because of security policies involving regular changes of passwords, or departure of some system administrators. The database password could be changed for example. When system administrators can not immediately identify all services affected by a change or when they feel they don’t have the skills to edit these files, these parameters remain unchanged, creating critical security flaws. This was observed in more than 80% of the organizations we studied. It then becomes necessary to ensure automatic synchronization of all affected files when changing certain settings. Conventional synchronization solutions are difficult to apply when the relevant applications have already been developed by third parties. In this paper, we propose and implement a solution to automatically update all configuration files affected by a change, respecting their structure and codification. It combines a parameters database, a mapping between the configuration files parameters codes and those of the database, and templates for the generation of files. It achieves the objective for all non-encrypted configuration files.

This paper presents a stochastic model to evaluate the reliability and availability of a network service configuration consisting of a centralized database server and two distributed database server sets for a local/metropolitan area network. The centralized server monitors and controls the distributed database servers, which can be remotely separated from each other and from the center by almost any distance depending on the means of information transmission media between the center and the distributed servers. Using exponential time to failure and time to repair distributions for each of the database servers, exact numerical solutions are obtained for the steady state service reliability and availability measures. The results are then compared to approximate steady state numerical solutions for the general Weibull distribution case under different initial conditions. Finally, the model is extended to the case of three distributed database server sets. The reliability and availability study of such configurations has many applications in widely used internet services, mission-critical business fault tolerant systems, and automated manufacturing and service industries. A decision tool for the computer center manager to assess an upgrade to a three distributed database system from a two database system is also presented.

The impact of perceived ease of use on Internet service adoption: The moderating effects of temporal distance and perceived risk

Because of increasing configuration file, programmer need deal with the more work than ever. This paper put forward an idea that we can apply Class diagram of UML to the process of automatic building SCA configuration files framework, and complete a plugin through which this work can be carried out. Compared with the one written by manual work, the configuration produced by the plugin has many advantages, such as: clear structure, little errors, less work of development and short time of development.

Revisiting Mackay Online

A big data-centric architecture metamodel for Industry 4.0

Search engines are nowadays widely applied to store and analyze logs generated by large-scale distributed systems. To adapt to various workload scenarios, log search engines such as Elasticsearch usually expose a large number of performance-related configuration parameters. As manual configuring is time consuming and labor intensive, automatically tuning configuration parameters to optimize performance has been an urgent need. However, it is challenging because: 1) Due to the complex implementation, the relationship between performance and configuration parameters is difficult to model and thus the objective function is actually a black box; 2) In addition to application parameters, JVM and kernel parameters are also closely related to the performance and together they construct a high dimensional configuration space; 3) To iteratively search for the best configuration, a tool is necessary to automatically deploy the newly generated configuration and launch tests to measure the corresponding performance. To address these challenges, this paper designs and implements HDConfigor, an automatic holistic configuration parameter tuning tool for log search engines. In order to solve the high dimensional optimization problem, we propose a modified Random EMbedding Bayesian Optimization algorithm (mREMBO) in HDConfigor which is a black-box approach. Instead of directly using a black-box optimization algorithm such as Bayesian optimization (BO), mREMBO first generates a lower dimensional embedded space through introducing a random embedding matrix and then performs BO in this embedded space. Therefore, HDConfigor is able to find a competitive configuration automatically and quickly. We evaluate HDConfigor in an Elasticsearch cluster with different workload scenarios. Experimental results show that compared with the default configuration, the best relative median indexing results achieved by mREMBO can reach $2.07\times $ . In addition, under the same number of trials, mREMBO is able to find a configuration with at least a further 10.31% improvement in throughput compared to Random search, Simulated Annealing and BO.

This paper provides three contributions to the study of quality of service (QoS) configuration in component-based DRE systems. First, we describe the challenges associated with mapping the platform-independent QoS policies of an application into platform-dependent values of QoS parameters used to configure the behavior of QoS-enabled component middleware. Second, we describe a novel approach that uses model-transformation to map these QoS policies onto component middleware QoS configuration parameters. Third, we demonstrate the use of model-checking to verify the properties of the transformation and automate the synthesis of configuration parameters required to tune the QoS-enabled component middleware. Our results indicate that model-transformation and model-checking provide significant benefits with respect to automation, reusability, verifiability, and scalability of the QoS mapping process compared with conventional middleware configuration techniques

The National Association of Social Workers' (NASW's) Code of Ethics urges social workers to engage in political action. However, little recent research has been conducted to examine whether social workers support this admonition and the extent to which they actually engage in politics. The authors gathered data from a survey of social workers in Austin, Texas, to address three questions. First, because keeping informed about government and political news is an important basis for action, the authors asked what sources of knowledge social workers use. Second, they asked what the respondents believe are appropriate political behaviors for other social workers and NASW. Third, they asked for self-reports regarding respondents' own political behaviors. Results indicate that social workers use the Internet and traditional media services to stay informed; expect other social workers and NASW to be active; and are, overall, more active than the general public in many types of political activities. The comparisons made between expectations for others and their own behaviors are interesting in their complex outcomes. Social workers should strive for higher levels of adherence to the code's urgings on political activity. Implications for future work are discussed.

Motivated by the pressing need for increased dependability in corporate and Internet services and by the perspective that effective recovery can improve dependability as much or more than avoiding failures, we introduce a novel recovery mechanism that gives human system operators the power of system-wide undo. System-wide undo allows operators to roll back erroneous changes to a service's state without losing end-user data or updates, to make retroactive repairs in the historical timeline of the service system, and thereby to quickly recover from catastrophic state corruption, operator error, failed upgrades, and external attacks, even when the root cause of the catastrophe is unknown. We explore system-wide undo via a framework based on the novel concept of spheres of undo, bubbles of state and time that provide scope to the state recoverable by undo and serve as a structuring tool for implementing undo on standalone services, hierarchically-composed systems, and distributed interacting services. Crucially, spheres of undo allow us to define the concept of paradoxes, inconsistencies that occur when an undo process retroactively alters state that has been exposed outside of its containing sphere of undo. Managing paradoxes is the grand challenge of system-wide undo, and to tackle it we introduce a framework that automatically detects and compensates for paradoxes; our approach exploits the relaxed consistency semantics already present in existing services that interact with human end-users. We describe an implementation of our system-wide undo framework for standalone services with human end-users. We explore its applicability by assembling and evaluating a prototype undoable e-mail store service, by analyzing what would be necessary to construct an undoable online auction service, and by developing a set of guidelines to help service designers retrofit their services with undo. We find that system-wide undo functionality imposes non-negligible but tolerable overhead in terms of both time and space. Using a novel methodology we develop to benchmark human-assisted recovery processes, we also find that undo-based recovery has a net positive effect on dependability, providing significant improvements in correctness while only slightly degrading availability.

It is not uncommon that meta-heuristic algorithms contain some intrinsic parameters, the optimal configuration of which is crucial for achieving their peak performance. However, evaluating the effectiveness of a configuration is expensive, as it involves many costly runs of the target algorithm. Perhaps surprisingly, it is possible to build a cheap-to-evaluate surrogate that models the algorithm's empirical performance as a function of its parameters. Such surrogates constitute an important building block for understanding algorithm performance, algorithm portfolio/selection, and the automatic algorithm configuration. In principle, many off-the-shelf machine learning techniques can be used to build surrogates. In this paper, we take the differential evolution (DE) as the baseline algorithm for proof-of-concept study. Regression models are trained to model the DE's empirical performance given a parameter configuration. In particular, we evaluate and compare four popular regression algorithms both in terms of how well they predict the empirical performance with respect to a particular parameter configuration, and also how well they approximate the parameter versus the empirical performance landscapes.

Soft material robotic systems provide increased adaptability and flexibility compared to conventional rigid metal robots. The soft systems benefit from their inherent compliance, which enables them to be used in applications that require safe interaction between humans and robots or manipulation in cluttered environment. Despite advancements in recent years research on soft material robots still needs to make progress in terms of modeling for model based control or path planning. The high nonlinearity of soft material robots makes efficient and accurate modeling difficult. In this work we introduce a kinematic modeling approach based on cubic hermite splines. The method is applied to a soft pneumatic actuator and evaluated against the widely used constant curvature approach. The hermite spline offers the possibility of accurate shape reconstruction from simulated or measured deformation data. Both the shape of a robot's segment and its orientation can be approximated this way. In this paper a machine learning approach is used to train the kinematic relation between actuating pressure and configuration parameters.

Nowadays, the widely used NoSQL databases play a fundamental role for big data storage, processing and analysis. However, NoSQL databases usually consume a large amount of computing resources and always run in a low performance state, which seriously impacts the end-to-end quality of service. Therefore, tuning the performance of NoSQL databases via benchmarking tools becomes a critical issue. Unfortunately, it is non-trivial to solve this problem because there exists a considerable number of performance-related configuration parameters from both the databases (application-specific) and the operating system kernel (kernel-specific), and manually tuning methods only consider a small subset of the whole candidate configuration parameters space, leading to a sub-optimal performance. To address this challenge, we design ConfAdvisor, an automatic configuration tuning framework for NoSQL database benchmarking. Specifically, ConfAdvisor treats the database performance as a black-box function of configuration parameters and leverage an online learning method to search the best configurations. Experimental results based on several popular NoSQL databases show that: 1) Through only tuning the kernel-specific parameters can improve database performance by up to 30%, which is often overlooked in previous studies. 2) ConfAdvisor is able to achieve well-tuned configurations with a very few trials, even in a high-dimension configuration space. And compared with default configurations, the well-tuned parameters can improve database performance by up to 88%.

Distributed systems usually have many configurable parameters such as those included in common configuration files. Performance of distributed systems is partially dependent on these system configurations. While operators may choose default settings or manually tune parameters based on their experience and intuition, the resulted settings may not be the optimal one for specific services running on the distributed system. In this paper, we formulate the problem of autotuning configurations as a black-box optimization problem. This problem becomes quite challenging since the joint parameter search space is huge and also no explicit relationship between performance and configurations exists. We propose to use a well known evolutionary algorithm called covariance matrix adaptation (CMA) to automatically tune system parameters. We compare CMA algorithm to another existing techniques called smart hill climbing (SHC) and demonstrate that CMA algorithm outperforms SHC algorithm both on synthetic data and in a real system.