Email Address. Sign In. Execution-Efficient Response Time Analysis on Global Multiprocessor Platforms Abstract: Response time analysis RTA is an important and fundamental tool for analyzing the schedulability of real-time tasks on multiprocessor platforms, and many promising techniques have been developed during the past few years.
However, most of the existing researches focus on improving the analysis precision, while less has been done on enhancing the execution efficiency.
In this paper, we take, to our best knowledge, the first effort towards improving the efficiency of the state-of-the-art RTA methods for sporadic tasks under both global fixed-priority G-FP and global earliest deadline first G-EDF scheduling. By addressing these three limitations, we propose two efficient RTA methods for G-FP and G-EDF scheduling, respectively, which achieve better run-time performance but without sacrificing any analysis precision.
Experimental evaluations with randomly generated task sets show that the proposed methods exhibit remarkable performance improvements and can save on average 60 and 61 percent run time, as compared to the state-of-the-art technologies under G-FP and G-EDF scheduling, respectively. Article :. Date of Publication: 04 June DOI: Need Help?Gerardine Immaculate Mary, Z. This paper reviews the research work done on the response time analysis of messages in controller area network CAN from the time CAN specification was submitted for standardization and became a standard up to the present Such research includes the worst-case response time analysis which is deterministic and probabilistic response time analysis which is stochastic.
A detailed view on both types of analyses is presented here. In addition to these analyses, there has been research on statistical analysis of controller area network message response times.
The arbitration mechanism employed by CAN means that messages are sent as if all the nodes on the network share a single global priority-based queue. In effect, messages are sent on the bus according to fixed priority nonpreemptive scheduling [ 1 ]. In the early s, a common misconception was that although the protocol was very good at transmitting the highest priority messages with low latency, it was not possible to guarantee that the less urgent signals carried in lower priority messages would meet their deadlines [ 1 ].
InTindell et al. This analysis provided a method of calculating the worst-case response times of all CAN messages. Using this analysis it became possible to engineer CAN-based systems for timing correctness, providing guarantees that all messages and the signals that they carry would meet their deadlines.
InDavis et al. Real-time researchers have extended schedulability analysis to a mature technique which for non-trivial systems can be used to determine whether a set of tasks executing on a single CPU or in a distributed system will meet their deadlines or not [ 1245 ].
The essence of this analysis is to investigate if deadlines are met in a worst-case scenario. Whether this worst case actually will occur during execution, or if it is likely to occur, is not normally considered [ 6 ]. In contrast with schedulability analysis, reliability modelling involves the study of fault models, the characterization of distribution functions of faults, and the development of methods and tools for composing these distributions and models in estimating an overall reliability figure for the system [ 6 ].
This is because the deterministic schedulability analysis is quite pessimistic, since it assumes that a missed deadline in the worst case is equivalent to always missing the deadline, whereas the stochastic analysis extends the knowledge of the system by computing how often a deadline is violated [ 7 ].
There are many other sources of pessimism in the analysis, including considering worst-case execution times and worst-case phasings of executions, as well as the usage of pessimistic fault models. In a related work [ 8 ], a model for calculating worst-case latencies of controller area network CAN frames messages under error assumptions is proposed.
This model is pessimistic, in the sense that there are systems that the analysis determines to be unschedulable, even though deadlines will be missed only in extremely rare situations with pathological combinations of errors. In [ 910 ] the level of pessimism is reduced by introducing a better fault model, and in [ 9 ] variable phasings between message queuing are also considered, in order to make the model more realistic.
In [ 11 ] the pessimism introduced by the worst-case analysis of CAN message response times is reduced by using bit-stuffing distributions in the place of the traditional worst-case frame sizes which are referred to in [ 67 ]. In both sections, the method of bit stuffing is reviewed. In automotive applications, the messages sent on CAN are used to communicate state information, referred to as signalsbetween different ECUs.
Examples of signals include wheel speeds, oil and water temperature, engine rpm, gear selection, accelerator position, dashboard switch positions, climate control settings, window switch positions, fault codes, and diagnostic information. In a high-end vehicle there can be more than distinct signals, each effectively replacing what would have been a separate wire in a traditional point-to-point wiring loom.
Many of these signals have real-time constraints associated with them. For example, an ECU reads the position of a switch attached to the brake pedal. This ECU must send a signal, carrying information that the brakes have been applied, over the CAN network so that the ECU responsible for the rear light clusters can recognise the change in the value of the signal and switch the brake lights on.In this chapter, let us discuss the time response of the first order system.
Consider the following block diagram of the closed loop control system. We know that the transfer function of the closed loop control system has unity negative feedback as. The power of s is one in the denominator term. Hence, the above transfer function is of the first order and the system is said to be the first order system.
In the previous chapter, we have seen the standard test signals like impulse, step, ramp and parabolic. Let us now find out the responses of the first order system for each input, one by one. The name of the response is given as per the name of the input signal. For example, the response of the system for an impulse input is called as impulse response.
On both the sides, the denominator term is the same. So, they will get cancelled by each other. Hence, equate the numerator terms. It is gradually increasing from zero value and finally reaches to one in steady state. So, the steady state value depends on the magnitude of the input. The unit ramp responsec t follows the unit ramp input signal for all positive values of t. But, there is a deviation of T units from the input signal. Substitute these values in the above partial fraction expansion of C s.
No document with DOI "10.1.1.174.3635"
From these responses, we can conclude that the first order control systems are not stable with the ramp and parabolic inputs because these responses go on increasing even at infinite amount of time. The first order control systems are stable with impulse and step inputs because these responses have bounded output. So, the step signal is widely used in the time domain for analyzing the control systems from their responses.
Response of the First Order System Advertisements. Previous Page. Next Page. Previous Page Print Page. Dashboard Logout.Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
Moreover, the calculated evaluation criteria are limited and comprise many over-pessimistic estimations, such as the worst-case response time WCRT.
To tackle these problems, this paper proposes an RTA method for CANs with randomly occurring messages, which replaces the constant transmission period with the average number of transmission instances per unit time.
This approach finds the approximate probability distribution of the response time to any message in a CAN, providing a more general measure than the WCRT. The model accurately emulates the behavior of nonperiodic CAN messages and enables RTA with minimal pessimistic estimations. When tested against the benchmarks, the proposed method accurately obtained response time distributions within several seconds to several tens of seconds.
Article :. Date of Publication: 28 January DOI: Need Help?Before starting this section make sure you understand how to create a transfer function representation of a system.
Zero input and zero state solutions of a system can be found if the transfer function is known, though the transfer function is more commonly used for the zero state response.
We will first develop the transfer function for a system, and then solve for the zero state and zero input solutions. The system shown is a simplified model of a part of a suspension system of a wheel on a car or motorcyle.
The mass, m, represents the weight of the vehicle supported by the wheel, and the spring and dashpot represent the suspension system. The standard form has the coefficient of the highest order of "s" in the denominator equal to one. This might represent a curb 10 cm high. We can find the inverse Laplace Transform by performing a partial fraction expansion to get the solution into forms that are in the table. In the Laplace domain we use the Transfer Function to find the zero state response by simply multiplying the Laplace Transform of the input function by the Transfer Function.
Since multiplication in the Laplace domain is equivalent to convolution in the time domain, this means that we can find the zero state response by convolving the input function by the inverse Laplace Transform of the Transfer Function.
In other words, if. A discussion of the evaluation of the convolution is elsewhere. In other words, the impulse response of the system is simply the inverse Laplace Transform of the Transfer Function.
This means that if we can find the impulse response of the system, we immediately know the transfer function and the system differential equation Knowing the impulse response gives us complete information about the system.
Note: we could have skipped the first steps and immediately write the differential equation using the coefficients of the denominator polynomial as the coefficients of the differential equation with order of polynomial equal to order of differentiation.
To demonstrate this we start from the transfer function, and then write the Laplace Domain form of the differential equation.
Set the input to zero, and then take the inverse Laplace Transform to find the original zero input form of the differential.
The first equation below shows the zero-input differential equation in the Laplace domain, the second equation shows the zero-input differential equation in the time domain. In the time domain equation the n-i term above the y denotes a derivative of order n-i. Take the Laplace Transform, but this time include the initial conditions using the differentiation property of the Laplace Transform.
The zero input response is found by first finding the system differential equation with the input equal to zeroand then applying initial conditions. What is the output? We have already determined the zero input response to the initial condition, and the zero state response to the input, so the complete response is simply the sum of the zero input and zero stat response. If either the initial condition or the input is changed, we would need to recalculate either the zero input or zero state part of the solution.
The complete response would then be found by adding the new zero input and zero state solutions. Note that the complete response converges to the zero state response at long times as the zero input response decays to zero.
To find the complete response of a system from its transfer function:. Aside: A general solution for finding the zero state response from the transfer function In general if we have a transfer function of the form then the zero input solution is given by To demonstrate this we start from the transfer function, and then write the Laplace Domain form of the differential equation Set the input to zero, and then take the inverse Laplace Transform to find the original zero input form of the differential.Response time analysis is a more effective approach to improving database performance.
Also referred to as wait time analysis, it allows IT teams to align their efforts with service level delivery for IT customers. Each SQL query request passes through the database instance.
By measuring the time at each step, the total Response Time can be analyzed. Rather than watching server health statistics and making guesses about their performance impact, wait and response time methods measure the time taken to complete a desired operation. The best implementations break down the time into discrete and individually measurable steps, and identify exactly which steps in which operations cause application delays. Since the database primary mission is to respond with a result, response time is the most important criteria in making database performance decisions.
Response time is defined as the sum of actual processing time and the time as session spends waiting on availability of resources such as a lock, log file or hundreds of other Wait Events or Wait Types. When multiple sessions compete for the same processing resources, the wait time becomes the most significant component of the actual Response Time. To accurately measure the Response Time for a database, it is necessary to discretely identify the steps accumulating time.
While the specifics are unique for each vendor, the general idea is the same. Typical database performance monitoring tools focus on server health measures and execution ratios. Even with a sophisticated presentation these statistics do not reflect the end-user experience or reveal where the problem originated.
Knowing an operation took place millions of times does not inform whether it was actually the cause of an application delay. The Response Time approach to performance monitoring is only practical if it can be implemented efficiently in a performance sensitive production environment.
With increased focus on service levels as the most important measure of IT productivity, response time analysis has emerged as the preferred monitoring technique for those customer-focused organizations. Response time analysis tells IT organizations the exact origin of the problem, what impact that problem is having on the end user, and which organization can best fix it.
A dose and time response analysis of the treatment of Hodgkin's disease with MOPP chemotherapy
Response time analysis assesses database performance by analyzing time spent at each wait […]. Solving the root cause of these problems can be challenging, because the issues may not even be in our software but the way the environment is […].
The focus must shift from resource metrics, logs and health to time. Explore best practice methodology for uncovering performance issues using response time analytics to find the true bottlenecks in your database s.
Once you understand the power of this approach, […]. Save my name, email, and website in this browser for the next time I comment. Toggle navigation. What is Response Time Analysis? Wait Events and Wait Types To accurately measure the Response Time for a database, it is necessary to discretely identify the steps accumulating time.
Back to the Basics: Analyzer Response Time
Usually, this is measured in terms of going from black to white to black again, in terms of milliseconds. A typical LCD response time is under ten milliseconds 10 mswith some being as fast as one millisecond.
They sound similar, but the refresh rate is the number of times a screen displays a new image every second, expressed in Hertz.
Most monitors use a 60 Hertz refresh rate, though some go higher—and higher is better. In contrast, for response time lower is better. The exception is gaming. For gamers, every single millisecond counts—the difference between winning and losing a fighting match, landing a long-range sniper shot, or even getting that perfect line in a racing game can indeed be a single millisecond. So for gamers who are looking for every possible competitive edge, a low refresh rate between 1 and 5 milliseconds is worth the expense of a more pricey, gaming-focused monitor.
If you want a monitor that can keep up with even the fastest of games, get one with a TN or VA screen panel. To cut down on response time, gaming monitors often forego more complex image processing that gets in between the signal from the computer.
This includes color-correcting portions of the monitor itself, boosted brightness, eyestrain-reducing blue light filters, and similar features. Is it worth it? For a lot of games, not really. The Best Tech Newsletter Anywhere. Joinsubscribers and get a daily digest of news, comics, trivia, reviews, and more. Windows Mac iPhone Android. Smarthome Office Security Linux. The Best Tech Newsletter Anywhere Joinsubscribers and get a daily digest of news, geek trivia, and our feature articles.
Skip to content. Note the difference between refresh rate and response time. How-To Geek is where you turn when you want experts to explain technology. Since we launched inour articles have been read more than 1 billion times. Want to know more?