Time Constraint Applications Research Articles

FPGA Implementation of DFT Processor using Vedic Multiplier

Many a times Mathematical computations are easier in frequency domain than time domain. Discrete Fourier transform (DFT) is a tool to convert signals from time domain to frequency domain which is widely used mainly in Digital Signal and Image Processing applications. Fast Fourier Transform (FFT) is a method to find DFT in time constraint applications which is affected by number of complex multiplier. In this paper 16-bit DFT using Vedic Multiplier, a high- speed multiplier is proposed with an objective to replace conventional complex multiplier and to decrease computation time. The proposed system is implemented in Virtex-4 FPGA and the results show that FFT using Urdhva Tiryakbhyam algorithm of Vedic multiplier is faster than other methods of DFT

A Fairness-Aware topology independent TDMA MAC policy in time constrained wireless ad hoc networks

As current network technologies evolve towards 5G supporting increased throughput and low time delays, a need emerges for supporting time constraint applications at the wireless end user level. The network formed due to the dynamicity of the Internet of Things (IoT), can be seen as a wireless ad hoc environment where certain time constraints need to be satisfied motivating the revisit of topology independent TDMA policies in this work. It has been shown that such policies, even though suitable to satisfy certain time constraints, suffer either from low throughput or from fairness issues. In this paper a new fairness-aware policy for medium access control in wireless ad hoc networks is proposed that exploits the topology independent properties of the existing policies in the literature and in addition, it takes into account fairness by accessing the medium according to the individual envy metric that is easily calculated by each node. In particular, under the proposed policy, nodes that are treated “unfairly” increase their medium access opportunities in subsequent frames and vice versa. The proposed policy is evaluated against the existing topology independent policies demonstrating its effectiveness of throughput increment while at the same time nodes are treated “fairly” (given certain fairness metrics). It is shown here that the best case regarding throughput corresponds to the policy of the worst case regarding fairness while those policies that treat nodes “fairly”, result in small throughput. Under the proposed policy, fairness remains close to the best fairness case but at the same time throughput is improved compared to the same case. Furthermore, it is also shown here that individual nodes that are treated “unfairly” tend to experience a larger number of consecutive re-transmissions and consequently time delays, thus emphasizing the need to consider fairness in these emerging environments.

Elastic Spatial Query Processing in OpenStack Cloud Computing Environment for Time-Constraint Data Analysis

Geospatial big data analysis (GBDA) is extremely significant for time-constraint applications such as disaster response. However, the time-constraint analysis is not yet a trivial task in the cloud computing environment. Spatial query processing (SQP) is typical computation-intensive and indispensable for GBDA, and the spatial range query, join query, and the nearest neighbor query algorithms are not scalable without using MapReduce-liked frameworks. Parallel SQP algorithms (PSQPAs) are trapped in screw-processing, which is a known issue in Geoscience. To satisfy time-constrained GBDA, we propose an elastic SQP approach in this paper. First, Spark is used to implement PSQPAs. Second, Kubernetes-managed Core Operation System (CoreOS) clusters provide self-healing Docker containers for running Spark clusters in the cloud. Spark-based PSQPAs are submitted to Docker containers, where Spark master instances reside. Finally, the horizontal pod auto-scaler (HPA) would scale-out and scale-in Docker containers for supporting on-demand computing resources. Combined with an auto-scaling group of virtual instances, HPA helps to find each of the five nearest neighbors for 46,139,532 query objects from 834,158 spatial data objects in less than 300 s. The experiments conducted on an OpenStack cloud demonstrate that auto-scaling containers can satisfy time-constraint GBDA in clouds.

모바일 애드혹 네트워크에서 캐쉬 접근 시간 향상을 위한 시간보증 기반의 캐쉬무효화 기법

모바일 장치의 증가와 무선 네트워크 기술의 발전으로 인해 새롭게 모바일 애드혹 네트워크에 대한 관심이 증가하고 있다. 최근까지의 연구는 데이터 접근성(Accessibility) 향상을 위해 데이터 복사본 활용이 효율적임을 증명하였다. 그러나, 데이터 복사본이 모바일 애드혹 네트워크에서 활용되기 위해서는 원본과 복사본 사이에 일관성(Consistency)이 확보되어야만 한다. 기존의 연구에서 사용되고 있는 일관성 확보 기법은 원본을 가진 노드에게 복사본 사용 노드들이 매번 일관성을 가지는지 확인하는 기법이 활용되었다. 그러나, 시간제약을 가지는 응용의 경우 원본을 가진 노드에게 확인하는 기법은 효율적이지 못하거나 때로는 심각한 문제를 일으킬 수도 있다. 본 논문에서는 시간제약응용을 위한 캐쉬 무효화 기법을 제안하고 있다. 제안하는 기법을 통해 기존의 일관성 확인 기법에서 일어나는 ‘롤백(Rollback)’ 문제를 해결할 수 있었으며, 접근시간(Access time) 측면에서 성능향상을 얻을 수 있었다. 또한 본 논문에서는 임계값 기반의 보완 기법을 제안한다. 우리는 실험 결과를 통해 제안하는 기법이 약간의 현재성(Currency) 손해는 있지만, 접근시간 측면에서 효율적임을 증명하여 준다. Due to the popularity of mobile devices and advances in wireless communication technologies, a mobile ad hoc network has received a lot of attention. In the existing data replication management research, the use of a replica has been shown to be an efficient technique for improving data accessibility. However, to use a replica in ad hoc networks, the data consistency between the original data and the replica should be guaranteed. In the traditional research, a mobile node should check an original data whether the data is updated or not. However, It may be costly or sometimes impossible to check the original data. In the case of the time constraint applications, the checking cost can cause more serious problem. In this paper, we propose the time-guarantee based cache invalidation schemes for time constraint applications and the threshold based compensation method to enhance the time-guarantee based scheme. The proposed schemes can remove the "rollback" problem. Simulation results show that our schemes outperform the previous ones in terms of access time with little loss of data currency.

An on-line frame scheduling algorithm for the internet video conterencing

A window based on-line scheduling algorithm to efficiently distribute processor service time to tasks for processing image frames is proposed for the Internet video conferencing system. The algorithm lets the tasks share a limited service time in a manner that the overall quality of the conferencing system is maximized. A task for processing an image frame is modeled as an IRIS (increasing reward with increasing services) task and its reward is mapped into a strictly concave function. Based on the modeling and an observation that the tasks have a strong dependency in their execution order, the scheduler schedules only tasks in a small window at each scheduling instant. And the small window in which a few tasks are contained lets the algorithm have a very low computational complexity, which is a very important consideration for time constraint applications like the Internet conferencing system. Even with low complexity, the scheduler nearly maximizes the total accrued reward as obtained by an on-line optimal algorithm and enhances smooth display, which may be another important quality measure of the conferencing system. To evaluate the quality, three metrics are suggested and the performance of the proposed algorithm is measured in aspects of the metrics.