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.
5475349
Scheduling Real-Time Systems with End-to-End Timing Constraints Using the Distributed Pinwheel Model
Real-time distributed applications have timing constraints on tasks running on several processors. To design real-time systems with end-to-end performance requirements, we need to have algorithms to schedule and to coordinate tasks on different processor nodes. An end-to-end scheduling approach based on the pinwheel scheduling model is presented for distributed real-time systems, We show how tasks on different nodes may be transformed to have periods consisting of only harmonic numbers. With harmonic periods, we can use a polynomial-time algorithm to find the start and finish times of each task on each node. Phase alignment algorithms are then applied to adjust the phases between schedules of neighboring nodes so that the overall end-to-end delay is minimized. Using the distributed pinwheel model, schedules on different nodes are synchronized to lessen the delays. For many real-time systems, this approach provides a predictable performance and a short end-to-end delay.
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.
41113698
Scheduling Jobs with Temporal Distance Constraints
The job scheduling problems for real-time jobs with temporal distance constraints (JSD) are presented. In JSD, the start times of two related jobs must be within a given distance. The general JSD problem is NP-hard. We define the multilevel unit-time JSD (MUJSD) problem for systems with $m$ chains of unit-time jobs in which neighboring jobs in each chain must be scheduled within $c$ time units. We present an $O(n^2)$-time algorithm, where $n$ is the total number of jobs in the system, and also an $O(m^2 c^2)$-time algorithm. Some other variations of the JSD problems are also investigated.
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.
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.
13951248
A survey on evaluating and realizing IS/IT benefits in Taiwanese b2bec companies
A number of issues have emerged from the analysis of the data collected via a survey conducted in Taiwanese B2BEC companies. The results show relatively high usage of IS/IT investment evaluation and benefits realization methodologies, and yet, these methodologies were generally not used effectively within the responding organizations. Most of these organizations were not yet mature in terms of their IT. However, there was a clear association between level of IT maturity and both wide and effective use of methodologies for IS/IT investment evaluation and benefits management.
18266651
A similarity-based soft clustering algorithm for documents
Document clustering is an important tool for applications such as Web search engines. Clustering documents enables the user to have a good overall view of the information contained in the documents that he has. However, existing algorithms suffer from various aspects, hard clustering algorithms (where each document belongs to exactly one cluster) cannot detect the multiple themes of a document, while soft clustering algorithms (where each document can belong to multiple clusters) are usually inefficient. We propose SISC (similarity-based soft clustering), an efficient soft clustering algorithm based on a given similarity measure. SISC requires only a similarity measure for clustering and uses randomization to help make the clustering efficient. Comparison with existing hard clustering algorithms like K-means and its variants shows that SISC is both effective and efficient.
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.
5900836
The TV-tree: An index structure for high-dimensional data
We propose a file structure to index high-dimensionality data, which are typically points in some feature space. The idea is to use only a few of the features, using additional features only when the additional discriminatory power is absolutely necessary. We present in detail the design of our tree structure and the associated algorithms that handle such “varying length” feature vectors. Finally, we report simulation results, comparing the proposed structure with theR*-tree, which is one of the most successful methods for low-dimensionality spaces.The results illustrate the superiority of our method, which saves up to 80% in disk accesses.