Charles Cavanaugh

Heterogeneous resource management for dynamic real-time systems

Dynamic real-time systems face many resource management problems. This paper addresses the following problems: (1) dynamic resource allocation to provide QoS objectives, (2) heterogeneous resources, and (3) non-intrusive accurate monitoring of QoS, resource availability and resource needs. This paper describes the techniques of a resource manager handling the above problems to support the QoS of dynamic distributed real-time systems. The contributions of this paper to solve these problems are as follows: unification of dynamic resource requirements among heterogeneous hosts, control of resources in heterogeneous environments, feasibility analysis and dynamic load balancing/sharing. Our heuristic allocation scheme not only allows higher workloads than the random, round-robin and least-load schemes by 257%, 142% and 36.4%, respectively, but it also improves the QoS compared to those schemes by 38.6%, 28.5% and 31.6%, respectively.

DynBench: A Dynamic Benchmark Suite for Distributed Real-Time Systems

In this paper we present the architecture and framework for a benchmark suite that has been developed as part of the DeSiDeRaTa project. The proposed benchmark suite is representative of the emerging generation of distributed, mission-critical, real-time control systems that operate in dynamic environments. Systems that operate in such environments may have unknown worst-case scenarios, may have large variances in the sizes of the data and event sets that they process (and thus, have large variances in execution latencies and resource requirements), and may be very difficult to characterize statically, even by time-invariant statistical distributions. The proposed benchmark suite (called DynBench) is useful for evaluation of the Quality of Service (QoS) management and/or Resource Management (RM) services in distributed real-time systems. As such, DynBench includes a set of performance metrics for the evaluation of the QoS and RM technologies in dynamic distributed real-time systems. The paper demonstrates the successful application of DynBench in evaluation of the DeSiDeRaTa QoS management middle-ware.

A resource management model for dynamic, scalable, dependable, real-time systems

Dynamic real-time systems function in unpredictable environments and have requirements that span many domains such as time, survivability, and scalability. The system requirements are typically determined as a function of the environment, further exacerbating the unpredictability of the problem. Existing solutions, for the most part, have focussed on problems for which the attributes are static, and there exists a rich set of solutions for such problems. Our problem domain has attributes that are inherently dynamic rather than static, requiring a new approach.

Accommodating QoS Prediction in an Adaptive Resource Management Framework

Resource management for dynamic, distributed real-time systems requires handling of unknown arrival rates for data and events; additional desiderata include: accommodation of heterogeneous resources, high resource utilization, and guarantees of real-time quality-of-service (QoS). This paper describes the techniques employed by a resource manager that addresses these issues. The specific contributions of this paper are: QoS monitoring and resource usage profiling; prediction of real-time QoS (via interpolation and extrapolation of execution times) for heterogeneous resource platforms and dynamic real-time environments; and resource contention analysis.

Load balancing for dynamic real-time systems

Some classes of real-time systems function in environments, which cannot be modeled with static approaches. In such environments, the arrival rates of events which drive transient computations may be unknown. Also, periodic computations may be required to process varying numbers of data elements per period, but the number of data elements to be processed in an arbitrary period cannot be known at the time of system engineering, nor can an upper bound be determined for the number of data items; thus, a worst case execution time cannot be obtained for such periodics. This paper presents middleware services that support such dynamic real-time systems through load balancing. The middleware services have been implemented and employed for (1) the DynBench dynamic real-time benchmark suite and (2) an experimental Navy system. Experimental results show the effectiveness of our load balancing techniques for consistently delivering real-time quality-of-service, even in highly dynamic environments.

Important considerations for execution time analysis of dynamic, periodic processes

Some classes of real-time systems operate in environments that cannot be modeled with static approaches. In such an environment, we neither have a priori knowledge about the system workload, nor is it possible to have a priori knowledge about worst-case execution time (WCET). There is no guarantee that all the deadlines of the periodic tasks will be met by using the rate monotonic analysis (RMA) approach. This paper presents an empirical and space-efficient way to predict the execution time. Experimental results have shown that for the dynamic real-time system, the best approach is a combination of static system profiling and dynamic prediction.

Network Load Monitoring in Distributed Systems

Monitoring the performance of a network by which a real-time distributed system is connected is very important. If the system is adaptive or dynamic, the resource manager can use this information to create or use new processes. We may be interested to determine how much load a host is placing on the network, or what the network load index is. In this paper, a simple technique for evaluating the current load of network is proposed. If a computer is connected to several networks, then we can get the load index of that host for each network. We can also measure the load index of the network applied by all the hosts. The dynamic resource manager of DeSiDeRaTa should use this technique to achieve its requirements. We have verified the technique with two benchmarks - LoadSim and DynBench.

A robust QoS forecasting technique for a dynamic, distributed real-time testbed

Dynamic, distributed, real-time control systems must control changing environments in a timely manner despite the fact that the systems load and timing vary in a way that is not characterizable by time-invariant statistical distributions. A quality of service (QoS) manager has been implemented that forecasts timing constraint violations in such systems and corrects them before they occur. The majority of forecasting techniques rely on moving averaging to extrapolate the future values, therefore the existence of outliers frequently impose disastrous effects on the accuracy of prediction. Most existing forecasting methods in literature use thresholding steps to empirically eliminate outliers, whose success heavily depends on the prior knowledge in choosing the initial fit and threshold values. In this paper, we propose a robust algorithm to automatically reject outliers and thus achieve accurate forecasting of host load and path latency. Our algorithm involves minimizing the integral of the squared error (ISE or L2E) between a Gaussian model of the residual and its true density function. The residual here refers to the difference between the path latencies and the trend line. We present the implementation results using L2E as well as other two widely used forecasting methods: least-squares linear regression and Box-Jenkins AR(2) forecasting, with DynBench dynamic, distributed real-time benchmark being employed as the testbed. We experimentally show that our L2 E-based scheme yields higher forecasting accuracy over the other two approaches