Dynamic Voltage/Frequency Scaling (DVFS) allows designers to improve energy efficiency through adjusting supply voltage at runtime in order to meet the workload demand. Previous works solving real-time DVFS problems often refer to the canonical schedules with the exponential length. Other solutions for online scheduling depend on empirical or stochastic heuristics, which potentially result in frequent fluctuations of voltage/speed scaling. This paper aims at increasing the schedule predictability using period transformation in the pinwheel task model and improves the control on power-awareness by decreasing the speeds of as many tasks as possible to the same level. Experimental results show the maximum energy savings of 6% over the recent Dynamic Power Management (DPM) method and 12% over other slack reclamation algorithms.

This paper presents a new interconnection network called bi-rotator graph. It is originated from the rotator graph. The rotator graph has many unidirectional edges and the bi-rotator graph is constructed by making edges of the rotator graph bidirectional. The bidirectional edges can help to reduce the average routing distance and increase the flexibility of applications. Therefore, we propose the bi-rotator graph as an alternative to the rotator graph. In this paper, we will first illustrate how to construct the bi-rotator graph and present the node-to-node routing algorithm. Next, we will propose the algorithm for building Hamiltonian cycle, which demonstrates that the bi-rotator graph is Hamiltonian. Finally, we provide a dilation-one algorithm for embedding arbitrary size of cycle into the bi-rotator graph and show that the bi-rotator graph is Hamiltonian-connected.

The supertask approach was proposed by Moir and Ramamthy as a means of supporting non-migratory tasks in Pfair-scheduled systems. In this approach, tasks bound to the same processor are combined into a single server task, called a supertask, which is scheduled as an ordinary Pfair task. When a supertask is scheduled, one of its component tasks is selected for execution. In previous work, Holman et al. showed that component-task deadlines can be guaranteed by inflating each supertask's utilization. In addition, their experimental results showed that the required inflation factors should be small in practice. Consequently, the average inflation produced by their rules is much greater than that actually required by the supertasks. In this paper, we first propose a notion of Transient Behavior Prediction for supertasks, which predicts the latest possible finish time of subtasks that belong to supertasks. On the basis of the notion, we present an efficient schedulability algorithm for Pfair supertasks in which the deadlines of all component tasks can be guaranteed. In addition, we propose a task merging process which combines the unschedulable supertasks with some Pfair tasks; hence, a newly supertask can be scheduled in the system. Finally, we propose the new reweighting functions that can be used when the previous two methods fail. Our reweighting functions produce smaller inflation factor than the previous work does. To demonstrate the efficacy of the supertasking approach, we present the experimental evaluations of our algorithm, which decreases substantially a number of reweights and the size of inflation when there are many supertasks in the Pfair-scheduled systems.

A hard real-time system is one whose correctness depends not only on the logical result, but also when the results are produced. While many techniques have been proposed for single processor real-time systems, multiprocessor systems have not been studied so extensively. In this paper, we mainly propose two variant (DCTS) by using the Early-Release-Fair (ERfair) and Proportionate-fair (Pfair) model with integral assumptions for identical multi-processor real-time systems. ERfair is a scheduling model for real-time tasks on a multiprocessor system. On the different definitions of distance constraint, we propose two efficient scheduling algorithms designed to probe whether the distance constraints of all ER-fair tasks can be guaranteed. If the distance constraints cannot be guaranteed, then the proposed algorithms gather the unfeasible tasks and inflate them with a reweighting function. The proposed algorithms are linear-time and most suitable for dynamic systems. The experimental results reveal that the proposed algorithms increase significantly the ratio of schedulable task sets.

Data clustering is a technique for grouping similar data items together for convenient understanding. Conventional data clustering methods, including agglomerative hierarchical clustering and partitional clustering algorithms, frequently perform unsatisfactorily for large text collections, since the computation complexities of the conventional data clustering methods increase very quickly with the number of data items. Poor clustering results degrade intelligent applications such as event tracking and information extraction. This paper presents an unsupervised document clustering method which identifies topic keyword clusters of the text corpus. The proposed method adopts a multi-stage process. First, an aggressive data cleaning approach is employed to reduce the noise in the free text and further identify the topic keywords in the documents. All extracted keywords are then grouped into topic keyword clusters using the k-nearest neighbor approach and the keyword clustering technique. Finally, all documents in the corpus are clustered based on the topic keyword clusters. The proposed method is assessed against conventional data clustering methods on a web news corpus. The experimental results show that the proposed method is an efficient and effective clustering approach.