Overview of the Lambda-* Performance Reasoning Frameworks

The Predictable Assembly from Certifiable Code (PACC) Initiative at the Carnegie Mellon Software Engineering Institute is developing methods and technologies to enable the production of software with predictable behavior by making the application of analytic methods accessible to software engineering practitioners. The use of reasoning frameworks is a means to achieving this goal. A reasoning framework is a packaging of an analysis theory along with other important elements that are needed for its application, such as methods for creating analysis models and evaluating them. Lambda-* is a suite of performance reasoning frameworks founded on the principles of Generalized Rate Monotonic Analysis (GRMA) for predicting the average and worst-case latency of periodic and stochastic tasks in real-time systems. Lambda-* can be applied to many different, uniprocessor, real-time systems having a mix of tasks with hard and soft deadlines with periodic and stochastic event interarrivals. Some examples include embedded control systems (e.g., avionic, automotive, robotic) and multimedia systems (e.g., audio mixing). This report provides an overview of the Lambda-* performance reasoning frameworks, their current capabilities, and ongoing research. The Lambda-* reasoning frameworks have been implemented as a part of the PACC Starter Kit (PSK), a development environment that integrates a collection of technologies to enable the development of software with predictable runtime behavior

The Amaranth Framework: Probabilistic, Utility-Based Quality of Service Management for High-Assurance Computing

System resource management for high-assurance applications such as the command and control of a battle group is a complex problem. These applications often require guaranteed computing services that must satisfy both hard and soft deadlines. In addition, their resource demands can vary significantly over time with bursts of high activity amidst periods of inactivity. A traditional solution has been to dedicate resources to critical application tasks and to share resources among noncritical tasks. With the increasing complexity of high-assurance applications and the need to reduce system costs, dedicating resources is not a satisfactory solution. The Amaranth Project at Carnegie Mellon is researching and developing a framework for allocating shared resources to support multiple quality of service (QoS) dimensions and to provide probabilistic assurances of service. This paper is an overview of the Amaranth framework, the current results from applying the framework, and the future research directions for the Amaranth project.</p

Resource Allocation in Dynamic Environments

<p>This technical report examines two challenges related to resource allocation that can negatively affect system operation in a dynamic environment, where warfighter needs for resources, resource availability, environmental effects, and mission conditions can change from moment to moment. The first challenge occurs when warfighters overstate their individual needs of a shared resource, leading to inefficient allocation. Overstatement may bring local optimization; however, it can cause global inefficiencies that result in a detriment to overall mission success. This challenge is addressed by using computational mechanism design, more specifically, the dynamic Vickrey-Clark-Groves allocation mechanism. The second challenge involves resource availability that may change frequently. Such is the case in a wireless mesh network where routes and bandwidth may vary over even small intervals of time. In such a case, an adaptive quality of service (AQoS) approach is used, and the available resource is allocated using the Dynamic QoS-based Resource Allocation Model (D-Q-RAM). Computational mechanism design is used to allocate sensors, and the AQoS approach allocates the available network bandwidth in a way consistent with the sensor allocation, providing an approach for dealing with resource allocation and adaptation in a dynamic environment. Initial experimental results of applying the approach are reported.</p

Results of SEI Independent Research and Development Projects

The Software Engineering Institute (SEI) annually undertakes several independent research and development (IRAD) projects. These projects serve to (1) support feasibility studies investigating whether further work by the SEI would be of potential benefit and (2) support further exploratory work to determine whether there is sufficient value in eventually funding the feasibility study work as an SEI initiative. Projects are chosen based on their potential to mature and/or transition software engineering practices, develop information that will help in deciding whether further work is worth funding, and set new directions for SEI work. This report describes the IRAD projects that were conducted during fiscal year 2009 (October 2008 through September 2009)

Results of SEI Independent Research and Development Projects (FY 2010)

The Software Engineering Institute (SEI) annually undertakes several independent research and development (IRAD) projects. These projects serve to (1) support feasibility studies investigating whether further work by the SEI would be of potential benefit and (2) support further exploratory work to determine whether there is sufficient value in eventually funding the feasibility study work as an SEI initiative. Projects are chosen based on their potential to mature and/or transition software engineering practices, develop information that will help in deciding whether further work is worth funding, and set new directions for SEI work. This report describes the IRAD projects that were conducted during fiscal year 2010 (October 2009 through September 2010).</p