Improvement and performance analysis on statistical selection algorithms

Over the years, the distributed database has been developed so fast that there's a need to develop an effective selection algorithm for it. Loo and Choi has proposed a statistical selection algorithm with the same objective and run in multicast / broadcast environment that has been proved that it is the best among others in terms of the number of messages needed to complete the searching process. However, this algorithm has a high probability of failure. A few improvements have been done to this original algorithm. This new algorithm is developed based on the simulation of the real multicast environment. Three modifications have been added in the new algorithm to solve the problem. Two performance measures have been conducted for the purpose of performance analysis between original and new algorithm

Statistical selection algorithm for peer-to-peer system

Over the years, the distributed database has been developed so fast that there's a need to develop an effective selection algorithm for it. Loo et. al. (2002) has proposed a statistical selection algorithm with the same objective and run in multicast / broadcast environment that has been proved that it is the best among others in terms of the number of messages needed to complete the searching process. However, this algorithm has a high probability of failure. A few improvements have been done to this original algorithm. This improved algorithm is developed based on the simulation of the real multicast environment. Modifications have been added in the improved algorithm to ensure that the unique pivot that never been used before is selected every time, and to solve problem that involve rank for certain key value that occur in more than one participant. Four performance measures have been conducted for the purpose of performance analysis between original and improved algorithm. These measures include probability of failure, number of messages needed, number of rounds needed and execution time. As a result, the probability of failure for the newly improved algorithm is 3.2% while the original algorithm is 19.2% without much overhead in increasing the number of messages and number of rounds needed

Revisit of statistical selection algorithm for peer-to-peer system

Over the years, the distributed computing environment has been developed so fast that there’s a need to develop an effective selection algorithm for it. Loo [9] has proposed a statistical selection algorithm with the same objective and run in the multicast / broadcast environment that has been proved that it is the best among others algorithm in the same platform in terms of the number of messages and the number of rounds needed to complete the searching process. This work revisited the work by Loo [9] through simulation. The result obtained from the simulation is almost the same with [9] in terms of the number of messages needed and the number of rounds needed

Static range multiple selection algorithm for peer-to-peer system

In this research, a new multiple selection algorithm, which is known as "static range statistical multiple selection algorithm" is proposed. This algorithm is developed based on the statistical knowledge about the uniform distribution nature of the data which has been arranged according to certain order in the file. A global file with n keys is distributed evenly among n peers in the peer-to-peer network. The selection algorithm can performs multiple selections concurrently to find multiple target keys with different predefined target ranks. The algorithm uses a fixed filter approach in which the algorithm is able to make sure that the target key is within certain filter range in each local file. The range is made smaller and smaller as the selection process iterates until all target keys are found. The algorithm is able to reduce the number of messages needed and increases the success rate of all multiple selections in the selection process compared to the previous multiple selection algorithms proposed by Loo in 2005

A novel mining system for criminal issues from a video file within cloud computing environment

This paper presents a description of a novel mining system which mines the different occurrences (instances) of the same object from a video file. The framework of the system consists of four steps: segmenting the video file into stable tracks, extracting objects and their features from the tracks, grouping these tracks into clusters based on their residing objects, and finally mining the instances of each object in the shared pool of configurable computing resources within cloud environment for more security. The paper also presents a critique and feedback for the system and proposes an idea to improve its performance

Statistical fixed range multiple selection algorithm for peer-to-peer system

In this research, a new multiple selection algorithm, which is known as "statistical fixed range multiple selection algorithm" is proposed. This algorithm is developed based on the statistical knowledge about the uniform distribution nature of the data which has been arranged in ascending order in the local file. A global file with n keys is distributed evenly among p peers in the peer-to-peer network. The selection algorithm can performs multiple selections concurrently to find multiple target keys with different predefined target ranks. The algorithm uses a fixed filter range approach that has been defined before the process begin, in which the algorithm is able to make sure that the target key is within the specified filter range in each local file. The range is made smaller and smaller as the selection process iterates until all target keys are found. The algorithm is able to reduce the number of rounds needed and increase the success rate of all multiple selections in the selection process compared to the previous multiple selection algorithms proposed by Loo in 2005

QoS class-based proportional resource allocation for LTE downlink

In LTE multi-service communication system, a trade-off between QoS assurance and fairness is a challenging issue, since the QoS provisioning at the cost of starving users in low service demand classes is not favorable for the operator. In this paper, we adopt the time-domain Knapsack algorithm and fine tune it to provide fair resource allocation while support QoS requirements in LTE downlink scheduling system when the bearers are from different classes of service, having different QoS characteristics. We demonstrate that more efficient performance can be achieved in two aspects of fairness and QoS provisioning in terms of normalized throughput, and packet loss and delay rate, which are evaluated using simulation results

Downlink scheduling for heterogeneous traffic with Gaussian weights in LTE-A

In Long Term Evolution-Advanced (LTE-A) networks, different aspects of radio resource scheduling such as fairness and Quality of Service (QoS) assurance must be provided for heterogeneous traffic, having different characteristics. However, the ever-growing number of mobile devices sharing the limited radio resources leads to the high cost and difficulty for information acquisition and computations in resource scheduling process. In the present paper, we propose a proportional knapsack scheduling approach for fairness- and QoS-aware downlink transmission of all different service classes in LTE-A networks. Moreover, we assess the computational cost created by the uncertainty and lack of information on user operations, and propose a Gaussian-based approach for ranking the bearers in presence of limited information and computational capabilities. This approach is particularly suitable for emerging next generation wireless networks to support a wide range of applications with huge number of users. The results indicate that significant advantages are achieved both in terms of QoS and fairness and that it is a scalable solution for overload states of the network

Overload-state downlink resource scheduling and its challenges towards 5G networks

The growing variety and consumption of the mobile services throughout the cellular networks lead to various challenging issues in radio resource scheduling. To have an apparent perspective over the resource scheduling in real implementation of the next generation cellular networks, it is essential to consider sequences of alternating overload and normal states of the traffic, occurring much in the system. In this paper, we do a performance study of three overload-state schedulers by implementing such a network environment and exploiting the advantages and drawbacks of the compared algorithms. This performance study through the simulation results reveals that the existing overload-state resource scheduling schemes do not satisfy the fifth generation (5G) mobile network's requirements to be more optimized in hard real time fashion. Then, open challenges and potential research directions for resource management in future 5G mobile networks are presented at the end