Results 1 
8 of
8
Combined approach to system level performance analysis of embedded systems
 In CODES
, 2007
"... Compositional approaches to systemlevel performance analysis have shown great flexibility and scalability in the design of heterogeneous systems. These approaches often assume certain system architectures and application domains, and are thus tailored to give tight analysis results for specific sys ..."
Abstract

Cited by 5 (0 self)
 Add to MetaCart
Compositional approaches to systemlevel performance analysis have shown great flexibility and scalability in the design of heterogeneous systems. These approaches often assume certain system architectures and application domains, and are thus tailored to give tight analysis results for specific systems. We consider two different compositional analysis methods. Both methods have their particular strengths for different architectures and applications. In this paper, we aim to enhance the analysis capabilities for these techniques. A method for event model conversion allows us a seamless integration of the two methods. Finally, we present a detailed case study to show the applicability and benefits of the enhanced performance analysis technique.
Performance Analysis of Reconfiguration in Adaptive RealTime Streaming Applications
"... We propose a design optimization framework for adaptive realtime streaming applications. The main contribution is a hybrid approach for performance analysis combining formal analysis and simulation using a twophase framework. We formulate the scheduling problem of adaptive streaming applications w ..."
Abstract

Cited by 3 (3 self)
 Add to MetaCart
We propose a design optimization framework for adaptive realtime streaming applications. The main contribution is a hybrid approach for performance analysis combining formal analysis and simulation using a twophase framework. We formulate the scheduling problem of adaptive streaming applications with ILP analysis, and use the simulation based on the synchronous model of computation to ensure throughput guarantees. We finally illustrate the capabilities of our methodology by experiments. 1.
Lightweight Modeling of Complex State Dependencies in Stream Processing Systems
"... Over the last few years, RealTime Calculus has been used extensively to model and analyze embedded systems processing continuous data/event streams. Towards this, bounds on the arrival process of streams and bounds on the processing capacity of resources serve as inputs to the model, which are used ..."
Abstract
 Add to MetaCart
Over the last few years, RealTime Calculus has been used extensively to model and analyze embedded systems processing continuous data/event streams. Towards this, bounds on the arrival process of streams and bounds on the processing capacity of resources serve as inputs to the model, which are used to calculate endtoend delays suffered by streams, maximum backlog, utilization of resources, etc. This “functional ” model, although amenable to computationally inexpensive analysis methods, has limited modeling capability. In particular, “statebased ” processing, e.g. blocking write – where the processing depends on the “state ” or filllevel of the buffer – cannot be modeled in a straightforward manner. This has led to a number of recent proposals on using automatatheoretic models for stream processing systems (e.g. Event Count Automata [RTSS 2005]). Although such models offer better modeling flexibility, they suffer from the usual statespace explosion problem. In this paper we show that a number of complex statedependencies can be modeled in a lightweight manner, using a feedback control technique. This avoids explicit state modeling, and hence the statespace explosion problem. Our proposed modeling and analysis therefore extend the original RealTime Calculusbased functional modeling in a very useful way, and cover much larger problem domain compared to what was previously possible without explicit statemodeling. We illustrate its utility through two case studies and also compare our analysis results with those obtained from detailed system simulations (which are significantly more time consuming). 1
A MultiMode RealTime Calculus
"... The RealTime Calculus (RTC) framework proposed in [Chakraborty et al., DATE 2003] and subsequently extended in [Wandeler et al., RealTime Systems 29(23), 2005] and a number of other papers is geared towards the analysis of realtime systems that process various types of streaming data. The main s ..."
Abstract
 Add to MetaCart
The RealTime Calculus (RTC) framework proposed in [Chakraborty et al., DATE 2003] and subsequently extended in [Wandeler et al., RealTime Systems 29(23), 2005] and a number of other papers is geared towards the analysis of realtime systems that process various types of streaming data. The main strength of RTC is a countbased abstraction, where arrival patterns of event streams are specified as constraints on the number of events that may arrive over any specified time interval. In this framework, algebraic techniques can be used to compute system properties in a compositional way. However, the main drawback of RTC is that it cannot model state information in a natural way. For example, when a scheduling policy depends on the filllevel of a certain buffer or there is a shift from one type of data stream into another. In this paper, we extend RTC in a manner that enables state information to be easily captured while limiting the statespace explosion caused by fine grained statebased models such as timed automata. Our model, called multimode RTC, specifies event streams as finite automata whose states are annotated with functions that specify constraints on the arrival patterns of event streams or the service available to process them. Our new framework combines the expressiveness of statebased models with the algebraic and compositional features of the RTC formalism. In particular, system properties within a single mode can be analyzed using the RTCbased algebraic techniques and statespace exploration can be used to piece together the results obtained algebraically for the individual modes. We show how to determine typical system properties with the focus on efficient approximate techniques and illustrate the advantages of multimode RTC using two case studies. 1
in Automotive Architectures
"... Automotive architectures consist of multiple electronic control units (ECUs) which run distributed control applications. Such ECUs are connected to sensors and actuators and communicate via shared buses. Resource arbitration at the ECUs and also in the communication medium, coupled with variabilitie ..."
Abstract
 Add to MetaCart
Automotive architectures consist of multiple electronic control units (ECUs) which run distributed control applications. Such ECUs are connected to sensors and actuators and communicate via shared buses. Resource arbitration at the ECUs and also in the communication medium, coupled with variabilities in execution requirements of tasks results in jitter in the signal/data streams existing in the system. As a result, buffers are required at the ECUs and bus controllers. However, these buffers often implement different semantics – FIFO queuing, which is the most straightforward buffering scheme, and data refreshing, where stale data is overwritten by freshly sampled data. Traditional timing and schedulability analysis that are used to compute, e.g., endtoend delays, in such automotive architectures can only model FIFO buffering. As a result, they return pessimistic delay and resource estimates because in reality
Lightweight Modeling of Complex State Dependencies in Stream Processing Systems
, 2009
"... Over the last few years, RealTime Calculus has been used extensively to model and analyze embedded systems processing continuous data/event streams. Towards this, bounds on the arrival process of streams and bounds on the processing capacity of resources serve as inputs to the model, which are used ..."
Abstract
 Add to MetaCart
Over the last few years, RealTime Calculus has been used extensively to model and analyze embedded systems processing continuous data/event streams. Towards this, bounds on the arrival process of streams and bounds on the processing capacity of resources serve as inputs to the model, which are used to calculate endtoend delays suffered by streams, maximum backlog, utilization of resources, etc. This “functional ” model, although amenable to computationally inexpensive analysis methods, has limited modeling capability. In particular, “statebased ” processing, e.g. blocking write – where the processing depends on the “state ” or filllevel of the buffer – cannot be modeled in a straightforward manner. This has led to a number of recent proposals on using automatatheoretic models for stream processing systems (e.g. Event Count Automata [RTSS 2005]). Although such models offer better modeling flexibility, they suffer from the usual statespace explosion problem. In this paper we show that a number of complex statedependencies can be modeled in a lightweight manner, using a feedback control technique. This avoids explicit state modeling, and hence the statespace explosion problem. Our proposed modeling and analysis therefore extend the original RealTime Calculusbased functional modeling in a very useful way, and cover much larger problem domain compared to what was previously possible without explicit statemodeling. We illustrate its utility through two case studies and also compare our analysis results with those obtained from detailed system simulations (which are significantly more time consuming). 1
Formal Modelling and Analysis of Stream Processing Systems
"... Stream processing systems (SPS) encompass classes of prevalent systems such as media and graphics processing, network packet processing, and control systems. Diverse in range and features, these systems operate on a large amount of data in a streamlike fashion, thus availing themselves to modelling ..."
Abstract
 Add to MetaCart
Stream processing systems (SPS) encompass classes of prevalent systems such as media and graphics processing, network packet processing, and control systems. Diverse in range and features, these systems operate on a large amount of data in a streamlike fashion, thus availing themselves to modelling using stream abstraction. Unlike traditional systems, SPS exhibit both the complexity of dataoriented and controloriented applications. The event streams are highly bursty in nature and usually comprising multiple types, each with a different execution demand. The architecture resources are also heterogeneous, and they often vary in scheduling policies. Further, the processing of the events frequently depends on the state of the systems. For instance, the processor may provide different amount of resource to different streams depending on the filllevel of the buffers in
Modeling Buffers with Data Refresh Semantics in Automotive Architectures
"... Automotive architectures consist of multiple electronic control units (ECUs) which run distributed control applications. Such ECUs are connected to sensors and actuators and communicate via shared buses. Resource arbitration at the ECUs and also in the communication medium, coupled with variabilitie ..."
Abstract
 Add to MetaCart
Automotive architectures consist of multiple electronic control units (ECUs) which run distributed control applications. Such ECUs are connected to sensors and actuators and communicate via shared buses. Resource arbitration at the ECUs and also in the communication medium, coupled with variabilities in execution requirements of tasks results in jitter in the signal/data streams existing in the system. As a result, buffers are required at the ECUs and bus controllers. However, these buffers often implement different semantics – FIFO queuing, which is the most straightforward buffering scheme, and data refreshing, where stale data is overwritten by freshly sampled data. Traditional timing and schedulability analysis that are used to compute, e.g., endtoend delays, in such automotive architectures can only model FIFO buffering. As a result, they return pessimistic delay and resource estimates because in reality overwritten data items do not get processed by the system. In this paper we propose an analytical framework for accurately modeling such data refresh semantics. Our model exploits a novel feedback control mechanism and is purely functional in nature. As a result, it is scalable and does not involve any explicit state modeling. Using this model we can estimate various timing and performance metrics for automotive ECU networks consisting of buffers implementing different data handling semantics. We illustrate the utility of this model through three case studies from the automotive electronics domain.