14548816
Enhancing the real-time capability of the Linux kernel
Linux has become one of the most popular UNIX operating systems. Commercial support for Linux and adoption of Linux in real world applications have now started to emerge. Some of the applications have strict real-time requirements. We discuss how to make Linux a real-time operating system. The real-time capability is achieved by three new kernel mechanisms. We port a microtimer into the kernel. We adopt the time-driven scheduling paradigm and implement a time-driven scheduler in the Linux kernel. Finally, we insert preemption points in the kernel to make it more preemptable so that real-time jobs may experience shorter blockings. Our approach is general and may be used to enhance the real-time capability of other non-real-time operating systems as well.
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.
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.
15828117
Enhancing external consistency in real-time transactions
Real-time computing brings two new requirements to database management. The first is the deadline constraint which requires better scheduling algorithms to meet transaction deadfines. The second requirement is the temporal validity, or external consistency[Lin89], requirement of data: a real-time database must prevent data from being corrupted not only by the executions of concurrent transactions but also by the delay from sharing computing resources and real-time scheduler decisions. In this paper, we study the external consistency requirement in real-time databases. We propose a semantic-based concurrency control scheme that prefers the external consistency to the traditional serializability. Two new concepts are introduced in our work. An update of a physical object value is considered to be compatible to those read operations that need to access the most recent data. Secondly, if a transaction depends strongly on the validity of the value of an object, a new update may cause the transaction to be aborted since continuing the transaction may be harmful to the appfication. A real-Life example is the stock market database. A trading program may be executed for a long time and its read operations should not block any new updates. Moreover, if there is a big sudden change in a particular stock price, it may be better to abort an on-going transaction rather than letting it make some invalid decisions.
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.
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.
18594508
An index structure for efficient reverse nearest neighbor queries
The Reverse Nearest Neighbor (RNN) problem is to find all points in a given data set whose nearest neighbor is a given query point. Just like the Nearest Neighbor (NN) queries, the RNN queries appear in many practical situations such as marketing and resource management. Thus, efficient methods for the RNN queries in databases are required. The paper introduces a new index structure, the Rdnn-tree, that answers both RNN and NN queries efficiently. A single index structure is employed for a dynamic database, in contrast to the use of multiple indexes in previous work. This leads to significant savings in dynamically maintaining the index structure. The Rdnn-tree outperforms existing methods in various aspects. Experiments on both synthetic and real world data show that our index structure outperforms previous methods by a significant margin (more than 90% in terms of number of leaf nodes accessed) in RNN queries. It also shows improvement in NN queries over standard techniques. Furthermore, performance in insertion and deletion is significantly enhanced by the ability to combine multiple queries (NN and RNN) in one traversal of the tree. These facts make our index structure extremely preferable in both static and dynamic cases.
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.
2416912
Adaptive Generalized Estimation Equation with Bayes Classifier for the Job Assignment Problem
We propose combining advanced statistical approaches with data mining techniques to build classifiers to enhance decision-making models for the job assignment problem. Adaptive Generalized Estimation Equation (AGEE) approaches with Gibbs sampling under Bayesian framework and adaptive Bayes classifiers based on the estimations of AGEE models which uses modified Naive Bayes algorithm are proposed. The proposed classifiers have several important features. Firstly, it accounts for the correlation among the outputs and the indeterministic subjective noise into the estimation of parameters. Secondly, it reduces the number of attributes used to predict the class. Moreover, it drops the assumption of independence made by the Naive Bayes classifier. We apply our techniques to the problem of assigning jobs to Navy officers, with the goal of enhancing happiness for both the Navy and the officers. The classification results were compared with nearest neighbor, Multi-Layer Perceptron and Support Vector Machine approaches.
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.
12237012
Discovering the Biomedical Deep Web
The rapid growth of biomedical information in the Deep Web has produced unprecedented challenges for traditional search engines. This paper describes a new Deep web resource discovery system for biomedical information. We designed two hypertext mining applications: a Focused Crawler that selectively seeks out relevant pages using a classifier that evaluates the relevance of the document with respect to biomedical information, and a Query Interface Extractor that extracts information from the page to detect the presence of a Deep Web database. Our anecdotes suggest that combining focused crawling with query interface extraction is very effective for building high-quality collections of Deep Web resources on biomedical topics.