Today, root cause analysis of failures in data centers is mostly done through manual inspection. More often than not, cus- tomers blame the network as the culprit. However, other components of the system might have caused these failures. To troubleshoot, huge volumes of data are collected over the entire data center. Correlating such large volumes of diverse data collected from different vantage points is a daunting task even for the most skilled technicians. In this paper, we revisit the question: how much can you infer about a failure in the data center using TCP statistics collected at one of the endpoints? Using an agent that cap- tures TCP statistics we devised a classification algorithm that identifies the root cause of failure using this information at a single endpoint. Using insights derived from this classi- fication algorithm we identify dominant TCP metrics that indicate where/why problems occur in the network. We val- idate and test these methods using data that we collect over a period of six months in a production data center.

In a standard workflow for the validation of a control system, the control system is implemented as an extension to a simulator. Such simulators are complex software systems, and engineers may unknowingly violate constraints a simulator places on extensions. As such, errors may be introduced in the implementation of either the control system or the simulator leading to invalid simulation results. This paper presents a novel runtime verification approach for verifying control system implementations within simulators. The major contribution of the approach is the two-tier specification process. In the first tier, engineers model constraints using a domain-specific language tailored to modeling a controller's response to changes in its input. The language is high-level and effectively hides the implementation details of the simulator, allowing engineers to specify design-level constraints independent of low-level simulator interfaces. In the second tier, simulator developers provide mapping rules for mapping design-level constraints to the implementation of the simulator. Using the rules, an automated tool transforms the design-level specifications into simulator-specific runtime verification specifications and generates monitoring code which is injected into the implementation of the simulator. During simulation, these monitors observe the input and output variables of the control system and report changes to the verifier. The verifier checks whether these changes follow the constraints of the control system. We describe application of this approach to the verification of the constraints of an HVAC control system implemented with the power grid simulator Grid LAB-D.

In power grid operations, security is an essential component for any middleware platform. Security protects data against unwanted access as well as cyber attacks. GridOpticsTM Software System (GOSS) is an open source power grid analytics platform that facilitates ease of access between applications and data sources and promotes development of advanced analytical applications. GOSS contains an API that abstracts many of the difficulties in connecting to various heterogeneous data sources. A number of applications and data sources have already been implemented to demonstrate functionality and ease of use. A security framework has been implemented which leverages widely accepted, robust Java TM security tools in a way such that they can be interchanged as needed. This framework supports the complex fine-grained, access control rules identified for the diverse data sources already in GOSS. Performance and reliability are also important considerations in any power grid architecture. An evaluation is done to determine the overhead cost caused by security within GOSS and ensure minimal impact to performance.

The ongoing modernization of power grids consists of integrating them with communication networks in order to achieve robust and resilient control of grid operations. To understand the operation of the new smart grid, one approach is to use simulation software. Unfortunately, current power grid simulators at best utilize inadequate approximations to simulate communication networks, if at all. Cooperative simulation of specialized power grid and communication network simulators promises to more accurately reproduce the interactions of real smart grid deployments. However, co-simulation is a challenging problem. A co-simulation must manage the exchange of information, including the synchronization of simulator clocks, between all simulators while maintaining adequate computational performance. This paper describes two new conservative algorithms for reducing the overhead of time synchronization, namely Active Set Conservative and Reactive Conservative. We provide a detailed analysis of their performance characteristics with respect to the current state of the art including both conservative and optimistic synchronization algorithms. In addition, we provide guidelines for selecting the appropriate synchronization algorithm based on the requirements of the co-simulation. The newly proposed algorithms are shown to achieve as much as 14% and 63% improvement in performance, respectively, over the existing conservative algorithm.

In practice, inconsistencies between architectural documentation and the code might arise due to improper implementation of the architecture or the separate, uncontrolled evolution of the code. Several approaches have been proposed to detect inconsistencies between the architecture and the code but these tend to be limited for capturing inconsistencies that might occur at runtime. We present a runtime verification approach for detecting inconsistencies between the dynamic behavior of the documented architecture and the actual runtime behavior of the system. The approach is supported by a set of tools that implement the architecture and the code patterns in Prolog, and automatically generate runtime monitors for detecting inconsistencies. We illustrate the approach and the toolset for a Crisis Management System case study.

This paper presents NetSim-Steer, a runtime steering framework for network simulators. With this framework, users can specify execution constraints for the network protocols and network models. In addition to this, users can implement steering rules to be executed when a constraint is violated. These rules allow users to alter the parameters of the protocols/models during the simulation so that they stay within the boundaries of the constraints.

In this paper, we describe a framework for the computer-aided evolution of the designs of object-oriented software systems. Evolution mechanisms are software structures that prepare software for certain type of evolutions. The framework uses a database which holds the evolution mechanisms, modeled as template graph transformations, with the supported evolution types. To evolve the software, the designer enters the type of evolution and provides the names of the software entities that are going to be evolved. The framework fetches the evolution mechanisms, converts the design to a graph model and applies the transformations. As an application of the framework, we implemented a tool for computer-aided evolution that uses object-oriented evolution mechanisms.