11605665
Implementing a general real-time scheduling framework in the RED-Linux real-time kernel
Many scheduling paradigms have been studied for real-time applications and real-time communication network. Among them, the most commonly used paradigms include priority-driven, time-driven and share-driven paradigms. In this paper, we present a general scheduling framework which is designed to integrate these paradigms in one framework. The framework is implemented in our real-time extension of the Linux kernel, RED-Linux. Two scheduler components are used in the framework: Allocator and Dispatcher. For each job, the framework identifies four scheduling attributes: priority, start time, finish time and budget. We show that the framework can be used to efficiently implement many well-known scheduling algorithms. We also measure and analyze the performance of the framework implemented in RED-Linux.
13997680
Distributed real-time system design using CBS-based end-to-end scheduling
Distributed real-time applications share a group of processors connected by some local area network. A rigorous and sound methodology to design real-time systems from independently designed distributed real-time applications is needed. In this paper, we study a distributed real-time system design scheme using CBS-based end-to-end scheduling. The scheduling scheme utilizes CBS to allocate both CPU shares and network bandwidth to a distributed real-time application when it arrives at the system. Our proposed solution uses the same scheduling paradigm for both resources. In this way, we believe the system can have a more consistent scheduling objective and may achieve a tighter schedulability condition.
14104203
SM: real-time multicast protocols for simultaneous message delivery
Traditionally, a multicast message is sent once by a sender. All receivers will receive the same message but may receive it at different times. This may result in unfair advantage for some receivers. Simultaneous Multicast (SM) applications require that all receivers receive the same message at the same time. SM protocols therefore need to meet a strict deadline to ensure that each receiver will receive a message at the same time, regardless of the network conditions, heterogeneity of the environment and differences between the local clocks in the receivers. Such a real time SM protocol is proposed so that the coordinated deadline can be met by controlling the reserved bandwidth and without the overhead of clock synchronization. A sensing protocol is also proposed to continuously search for better levels of quality and to avoid the overhead of an external feedback protocol. The protocols are bandwidth conscious, enforce fairness and provide scalability.
27737931
Designing multimedia applications on real-time systems with SMP architecture
Large real-time multimedia systems such as flight simulators have adopted multiprocessor architecture to handle the large amount of events and computations. Existing realtime scheduling algorithms designed for single-processor systems such as Rate Monotonic (RM) and Earliest Deadline First Algorithm (EDF) have very poor performance on multiprocessor systems. On the other hand, most online scheduling algorithms for multiprocessor systems have poor schedulability conditions for real-time applications. In this paper, we study different ways to design real-time applications on SMP machines.
22001959
A Pinwheel Scheduler for Three Distinct Numbers with a Tight Schedulability Bound
Abstract. Given a multiset of positive integers 
 $A=\{a_1,a_2,\ldots,a_n\}$ , the pinwheel problem is to find an infinite sequence over 
 $\{1,2,\ldots,n\}$ such that there is at least one symbol i within any subsequence of length ai. The density of A is defined as 
 $\rho(A)=\sum^n_{i=1} (1/a_i)$ . In this paper we limit ourselves to instances composed of three distinct integers. The best scheduler [5] published previously can schedule all instances with a density of less than 0.77. A new and fast scheduling scheme based on spectrum partitioning is presented in this paper which improves the 0.77 result to a new 
 $\frac{5}{6}\approx 0.83$ density threshold. This scheduler has achieved the tight schedulability bound of this problem.
15072976
The design and implementation of real-time schedulers in RED-linux
Researchers in the real-time system community have designed and studied many advanced scheduling algorithms. However, most of these algorithms have not been implemented since it is very difficult to support new scheduling algorithms on most operating systems. To address this problem, we enhance the scheduling mechanism in Linux to provide a flexible scheduling framework. In the real-time and embedded Linux (RED-Linux) project, we implement a general scheduling framework which divides the system scheduler into two components: dispatcher and allocator. The dispatcher provides the mechanism of system scheduling and resides in the kernel space. The allocator is used to define the scheduling policy and implemented as a user space function. This framework allows users to implement application-specific schedulers in the user space which is easy to program and to debug. The framework also relieves the deficiency from the stock Linux scheduler which is not designed for real-time applications. To further enhance its power, a hierarchical scheduling mechanism has been provided in RED-Linux to allow a system designer to integrate different real-time applications together. Using scheduling groups, real-time jobs can be managed and scheduled in a hierarchical manner. In this paper, we discuss how the group mechanism is implemented in RED-Linux.
16978334
Evaluating a class of distance-mapping algorithms for data mining and clustering
A distance-mapping algorithm takes a set of objects and a distance metric and then maps those objects to a Euclidean or pseudoEuclidean space in such a way that the distances among objects are approximately preserved. Distance mapping algorithms are a useful tool for clustering and visualization in data intensive applications, because they replace expensive distance calculations by sum-of-square calculations. This can make clustering in large databases with expensive distance metrics practical. In this paper we present five distance-mapping algorithms and conduct experiments to compare their performance in data clustering applications. These include two algorithms called FastMap and MetricMap, and three hybrid heuristics that combine the two algorithms in different ways. Experimental results on both synthetic and RNA data show the superiority of the hybrid algorithms. The results imply that FastMap and MetricMap capture complementary information about distance metrics and therefore can be used together to great benefit. The net effect is that multi-day computations may be done in minutes.
15481025
Rule Discovery from Time Series
We consider the problem of finding rules relating patterns in a time series to other patterns in that series, or patterns in one series to patterns in another series. A simple example is a rule such as "a period of low telephone call activity is usually followed by a sharp rise in call volume". Examples of rules relating two or more time series are "if the Microsoft stock price goes up and Intel falls, then IBM goes up the next day," and "if Microsoft goes up strongly for one day, then declines strongly on the next day, and on the same days Intel stays about level, then IBM stays about level." Our emphasis is in the discovery of local patterns in multivariate time series, in contrast to traditional time series analysis which largely focuses on global models. Thus, we search for rules whose conditions refer to patterns in time series. However, we do not want to define beforehand which patterns are to be used; rather, we want the patterns to be formed from the data in the context of rule discovery. We describe adaptive methods for finding rules of the above type from time-series data. The methods are based on discretizing the sequence by methods resembling vector quantization. We first form subsequences by sliding a window through the time series, and then cluster these subsequences by using a suitable measure of time-series similarity. The discretized version of the time series is obtained by taking the cluster identifiers corresponding to the subsequence. Once the time-series is discretized, we use simple rule finding methods to obtain rules from the sequence. We present empirical results on the behavior of the method.
11135944
Fuzzy Clustering of Web Logs for User Classification
1387511
FastMap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets
A very promising idea for fast searching in traditional and multimedia databases is to map objects into points in k-d space, using k feature-extraction functions, provided by a domain expert [25]. Thus, we can subsequently use highly fine-tuned spatial access methods (SAMs), to answer several types of queries, including the 'Query By Example' type (which translates to a range query); the 'all pairs' query (which translates to a spatial join [8]); the nearest-neighbor or best-match query, etc.However, designing feature extraction functions can be hard. It is relatively easier for a domain expert to assess the similarity/distance of two objects. Given only the distance information though, it is not obvious how to map objects into points.This is exactly the topic of this paper. We describe a fast algorithm to map objects into points in some k-dimensional space (k is user-defined), such that the dis-similarities are preserved. There are two benefits from this mapping: (a) efficient retrieval, in conjunction with a SAM, as discussed before and (b) visualization and data-mining: the objects can now be plotted as points in 2-d or 3-d space, revealing potential clusters, correlations among attributes and other regularities that data-mining is looking for.We introduce an older method from pattern recognition, namely, Multi-Dimensional Scaling (MDS) [51]; although unsuitable for indexing, we use it as yardstick for our method. Then, we propose a much faster algorithm to solve the problem in hand, while in addition it allows for indexing. Experiments on real and synthetic data indeed show that the proposed algorithm is significantly faster than MDS, (being linear, as opposed to quadratic, on the database size N), while it manages to preserve distances and the overall structure of the data-set.
1161117
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
We introduce a new model of similarity of time sequences that captures the intuitive notion that two sequences should be considered similar if they have enough non-overlapping time-ordered pairs of subsequences thar are similar. The model allows the amplitude of one of the two sequences to be scaled by any suitable amount and its offset adjusted appropriately. Two subsequences are considered similar if one can be enclosed within an envelope of a specified width drawn around the other. The model also allows non-matching gaps in the matching subsequences. The matching subsequences need not be aligned along the time axis. Given this model of similarity, we present fast search techniques for discovering all similar sequences in a set of sequences. These techniques can also be used to find all (sub)sequences similar to a given sequence. We applied this matching system to the U.S. mutual funds data and discovered interesting matches.