16111193
Bounding Cache-Related Preemption Delay for Real-Time Systems
Cache memory is used in almost all computer systems today to bridge the ever increasing speed gap between the processor and main memory. However, its use in multitasking computer systems introduces additional preemption delay due to the reloading of memory blocks that are replaced during preemption. This cache-related preemption delay poses a serious problem in realtime computing systems where predictability is of utmost importance. We propose an enhanced technique for analyzing and thus bounding the cache-related preemption delay in fixed-priority preemptive scheduling focusing on instruction caching. The proposed technique improves upon previous techniques in two important ways. First, the technique takes into account the relationship between a preempted task and the set of tasks that execute during the preemption when calculating the cache-related preemption delay. Second, the technique considers the phasing of tasks to eliminate many infeasible task interactions. These two features are expressed as constraints of a linear programming problem whose solution gives a guaranteed upper bound on the cache-related preemption delay. This paper also compares the proposed technique with previous techniques using randomly generated task sets. The results show that the improvement on the worst-case response time prediction by the proposed technique over previous techniques ranges between 5 percent and 18 percent depending on the cache refill time when the task set utilization is 0.6. The results also show that as the cache refill time increases, the improvement increases, which indicates that accurate prediction of cache-related preemption delay by the proposed technique becomes increasingly important if the current trend of widening speed gap between the processor and main memory continues.
8167367
Online QoS Optimization Using Service Classes in Surveillance Radar Systems
Many application level qualities are functions of available computation resources. Recent studies have handled the computation resource allocation problem to maximize the overall application quality. However, such QoS problems are fundamentally multi-dimensional optimization problems that require extensive computation. Therefore, online usage of optimization procedures may significantly reduce the computation resource available for applications. This raises the question of how to best use the optimization procedures for dynamic real-time task sets. In dynamic real-time systems, it is important to improve the performance by re-allocating the resources adapting to dynamic situations. However, the overhead of changing task parameters (i.e., algorithms and frequencies) for resource re-allocation is non-negligible in many applications. Thus, too frequent change of resource allocation may not be desirable. This paper proposes a method called service classes configuration to address the QoS problem with dynamic arrival and departure of tasks. The method avoids online usage of optimization procedures by offline designing templates (called service classes) of resource allocation, which will be adaptively used depending on online situations. The service classes are designed by best trading-off the accuracy of dynamic adaptation against the overhead of resource re-allocation. A simplified radar application is used as an illustrative example.
12313444
Real time guarantee of aperiodic packets in single hop ad hoc wireless networks
Building real-time applications on 802.11 wireless networks is challenging because the medium access protocol is distributed and nodes contend for the channel nondeterministically. So far all attempts at providing real-time services in 802.11 require restrictive traffic assumptions such as periodicity. In this paper we describe distributed admission control, a modification to 802.11 that, when used with distributed prioritization, provides real-time guarantees for aperiodic packets. We show that barring external errors such as interference on the channel or incorrect priority scheduling this protocol guarantees deadlines will be met.
16132542
Stochastic analysis of periodic real-time systems
This paper describes a stochastic analysis method for general periodic real-time systems. The proposed method accurately computes the response time distribution of each task in the system, thus making it possible to determine the deadline miss probability of individual tasks, even for systems with maximum utilization factor greater than one. The method uniformly covers both fixed-priority scheduling (such as rate monotonic) as well as dynamic-priority scheduling (such as earliest deadline first) and can handle arbitrary relative deadlines and execution time distributions. The accuracy of the method is proven by comparing the results from the analysis with those obtained from simulations, as well as other methodologies in the literature.
42435746
Efficiently supporting hard/soft deadline transactions in real-time database systems
As in other application areas, there is an increasing need for managing a large amount of data in the real-time area. This need gave birth to a system called a real-time database system (RTDBS) that provides database operations with timing constraints. One typical timing constraint in an RTDBS is temporal consistency that states that a transaction must read temporally valid data objects. This requires the data objects to be updated repeatedly. This paper proposes three novel schemes that aim to minimize the number of updates of data objects needed for guaranteeing the temporal consistency requirements of transactions with hard-deadlines. The three guarantee schemes differ from each other in the amount of updates and the implementation complexity. This paper also gives a framework for integrating transactions with soft-deadlines into the three guarantee schemes.
6076905
Finite-horizon scheduling of radar dwells with online template construction
Timing constraints for radar tasks are usually specified in terms of the minimum and maximum temporal distance between successive radar dwells. We utilize the idea of feasible intervals for dealing with the temporal distance constraints. In order to increase the freedom that the scheduler can offer a high-level resource manager, we introduce a technique for nesting and interleaving dwells online while accounting for the energy constraint that radar systems need to satisfy. Further, in radar systems, the task set changes frequently and we advocate the use of finite horizon scheduling in order to avoid the pessimism inherent in schedulers that assume a task will execute forever. The combination of feasible intervals and online dwell packing allows modular schedule updates whereby portions of a schedule can be altered without affecting the entire schedule, hence reducing the complexity of the scheduler. Through extensive simulations we validate our claims of providing greater scheduling flexibility without compromising on performance when compared with earlier work based on templates constructed offline. We also evaluate the impact of two parameters in our scheduling approach: the template length (or the extent of dwell nesting and interleaving) and the length of the finite horizon.
33610569
Compression-based broadcast strategies in wireless information systems
Data delivery is through broadcast in wireless information systems due to the asymmetric communication property. However, broadcasting data easily wastes large access time because the large amount of data pages needs to be broadcast. In this paper we propose three compression-based strategies considering access order to reduce access time. Then, the expected access time of three proposed strategies is also derived by using the probability model. Our analytic and experimental results show that the compression-based broadcast strategies can dramatically reduce the access time for mobile users (MU).
8596907
Multiple multicast tree allocation in IP network
The multicasting is defined as the distribution of the same information stream from one to many nodes concurrently. There has been an intensive research effort to design protocols and construct multicast routing graphs for a single multicast group. In this paper, the multiple multicast tree allocation problem is discussed and algorithms are proposed to solve the congestion problem in the IP network. As the congestion measure the minimum residual capacity is considered. Two phase algorithm MMTA is investigated for multiple multicast tree allocation both for identical and different bandwidth requirement by the multicast groups. The central and distributed implementation of the multiple multicast tree is discussed for the deployment in the real IP network. The performance of the proposed MMTA is compared with other procedures. Computational results show that the two-phase MMTA outperforms other procedures. Approximately 3-7% improvement in the residual capacity is obtained by the MMTA. The solution gap from the upper bound by the well-known branch and bound is within 2-11% depending on the problem size.
7053531
Attention meter: a vision-based input toolkit for interaction designers
This paper shows how a software toolkit can allow graphic designers to make camera-based interactive environments in a short period of time without experience in user interface design or machine vision. The Attention Meter, a vision-based input toolkit, gives users an analysis of faces found in a given image stream, including facial expression, body motion, and attentive activities. This data is fed to a text file that can be easily understood by humans and programs alike. A four day workshop demonstrated that some Flash-savvy architecture students could construct interactive spaces (e.g. TaiKer-KTV and ScreamMarket) based on body and head motions.
3194921
Product distribution theory for control of multi-agent systems
Product Distribution (PD) theory is a new framework for controlling Multi-Agent Systems (MASýs). First we review one motivation of PD theory, as the information-theoretic extension of conventional full-rationality game theory to the case of bounded rational agents. In this extension the equilibrium of the game is the optimizer of a Lagrangian of the (probability distribution of) the joint state of the agents. Accordingly we can consider a team game havbing a shared utility which is a performance measure of the behavior of the MAS. For such a scenario the game is at equilibrium ¿ the Lagrangian is optimized ¿ when the joint distribution of the agents optimizes the systemýs expected performance. One common way to find that equilibrium is to have each agent run a reinforcement learning algorithm. Here we investigate the alternative of exploiting PD theory to run gradient descent on the Lagrangian. We present computer experiments validating some of the predictions of PD theory for how best to do that gradient descent. We also demonstrate how PD theory can improve performance even when we are not allowed to rerun the MAS from different initial conditions, a requirement implicit in some previous work.
962689
Supporting Benefit-Oriented Retrieval for Data on Air
Battery power is always a limitation for using mobile devices to access information in a wireless environment. One of the ways to alleviate the problem is to let the device go into a doze (no-reaction or disconnection) mode periodically so as to let the battery energy last longer. Broadcast disk technology is becoming a popular method for data dissemination in a wireless information system. In this paper, we study from a mobile host’s viewpoint how to achieve the highest “benefit” if it listens to the broadcast discontinuously. We propose three fast and near optimal algorithms for the MH to retrieve the broadcast data.