• Journal of Communications and Networks
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• v.6 no.4
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• pp.343-351
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• 2004

Scalability is one of the toughest challenges in ad hoc networking. Recent work outlines theoretical bounds on how well routing protocols could scale in this environment. However, none of the popular routing solutions really scales to large networks, by coming close enough to these bounds. In this paper, we study the case of link state routing and OLSR, one of the strongest candidates for standardization. We analyze how these bounds are not reached in this case, and we study how much the scalability is enhanced with the use of Fish eye techniques in addition to the link state routing framework. We show that with this enhancement, the theoretical scalability bounds are reached.

• JSTS:Journal of Semiconductor Technology and Science
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• v.17 no.2
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• pp.199-203
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• 2017

Mutually-actuated-nano-electromechanical (MA-NEM) memory switches are proposed for scalability improvement. While conventional NEM memory switches have fixed electrode lines, the proposed MA-NEM memory switches have mutually-actuated cantilever-like electrode lines. Thus, MA-NEM memory switches show smaller deformations of beams in switching. This unique feature of MA-NEM memory switches allows aggressive reduction of the beam length while maintaining nonvolatile property. Also, the scalability of MA-NEM memory switches is confirmed by using finite-element (FE) simulations. MA-NEM memory switches can be promising solutions for reconfigurable logic (RL) circuits.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.3
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• pp.762-777
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• 2014

With the increasing usage of cloud applications such as MapReduce and social networking, the amount of data traffic in data center networks continues to grow. Moreover, these appli-cations follow the incast traffic pattern, where a large burst of traffic sent by a number of senders, accumulates simultaneously at the shallow-buffered data center switches. This causes severe packet losses. The currently deployed TCP is custom-tailored for the wide-area Internet. This causes cloud applications to suffer long completion times towing to the packet losses, and hence, results in a poor quality of service. An Explicit Congestion Notification (ECN)-based approach is an attractive solution that conservatively adjusts to the network congestion in advance. This legacy approach, however, lacks scalability in terms of the number of flows. In this paper, we reveal the primary cause of the scalability issue through analysis, and propose a new congestion-control algorithm called FaST. FaST employs a novel, virtual congestion window to conduct fine-grained congestion control that results in improved scalability. Fur-thermore, FaST is easy to deploy since it requires only a few software modifications at the server-side. Through ns-3 simulations, we show that FaST improves the scalability of data center networks compared with the existing approaches.

• 전자공학회논문지 IE
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• v.47 no.2
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• pp.21-26
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• 2010

This paper proposed an efficient algorithm to reduce the amount of calculation for Scalability Extension which takes a great deal of the operational time in H.264/AVC. This algorithm decides a search range according to the direction of predicted motion vector, and then performs an adaptive spiral search for the candidates with JM(Joint Model) FME(Fast Motion Estimation) which employs the rate-distortion optimization(RDO) method. Experimental results by applying the proposed method to various video sequences showed that the process time was decreased up to 80% comparing to the previous prediction methods. The degradation of video Quality was only from 0.05dB to 0.19dB and the compression ratio decreased as small as 0.58% in average. Therefore, we are sure that the proposed method is an efficient method for the fast inter prediction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.331-333
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• 2015

Data is increasing explosively with the spread of networks and mobile devices and there are problems in effectively processing the rapidly increasing data using existing recommendation techniques. Therefore, researches are being conducted on how to solve the scalability problem of the collaborative filtering technique. In this paper applies MapReduce, which is a distributed parallel process framework, to the collaborative filtering technique to reduce the scalability problem and heighten accuracy. The proposed technique applies MapReduce and the index technique to a user-based collaborative filtering technique and as a method which improves neighbor numbers which are used in similarity calculations and neighbor suitability, scalability and accuracy improvement effects can be expected.

• Journal of KIISE:Software and Applications
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• v.37 no.7
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• pp.503-517
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• 2010

Service-oriented computing, the effective paradigm for developing service applications by using reusable services, becomes popular. In service-oriented computing, service consumer has no responsibility for managing services, just invokes services what service providers are producing. On the other hand, service providers should manage any resources and data for service consumers can use the service anytime and anywhere. However, it is hard service providers manage the quality of the services because an unspecified number of service consumers. Therefore, service scalability for providing services with higher quality of services specified in a service level agreement becomes a potential problem in service-oriented computing. There have been many researches for scalability in network, database, and distributed computing area. But a research about a definition of service scalability and metrics of measuring service scalability is still not mature in service engineering area. In this paper, we construct a service network which connects multiple service nodes, and integrate all the resources to manage it. And we also present a service scalability framework for managing service scalability by using a mechanism of service migration or replication. In section 3, we, firstly, present the structure of the scalability management framework and basic functionalities. In section 4, we propose scalability enhancement mechanism which is needed to release functionality of the framework. In section 5, we design and implement the framework by using proposed mechanism. In section 6, we demonstrate the result of our case study which dynamically manages services in multi-nodes environment by applying our framework. Through the case study, we show the applicability of our scalability management framework and mechanism.

• The KIPS Transactions:PartA
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• v.14A no.7
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• pp.443-450
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• 2007

Performance scalability and storage scalability become important in a large scale cluster of wireless internet proxy cache servers. Performance scalability means that the whole performance of the cluster increases linearly according as servers are added. Storage scalability means that the total size of cache storage in the cluster is constant, regardless of the number of cache servers used, if the whole cache data are partitioned and each partition is stored in each server, respectively. The Round-Robin based load balancing method generally used in a large scale server cluster shows the performance scalability but no storage scalability because all the requested URL data need to be stored in each server. The hashing based load balancing method shows storage scalability because all the requested URL data are partitioned and each partition is stored in each server, respectively. but, it shows no performance scalability in case of uneven pattern of client requests or Hot-Spot. In this paper, we propose a novel dynamic hashing method with performance and storage scalability. In a time interval, the proposed scheme keeps to find some of requested URLs allocated to overloaded servers and dynamically reallocate them to other less-loaded servers. We performed experiments using 16 PCs and experimental results show that the proposed method has the performance and storage scalability as different from the existing hashing method.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2002.11a
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• pp.195-199
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• 2002

Requirements of a well-designed VPNs(Virtual Private Networks) are scalability, performance, reliability, ease of management, interoperability and security. Tunneling is a important technology to support these. This paper researches VPNs tunneling technologies used currently and proposes VPN service models for the scalability that is a problem in VPNs and for the resource limit of Mobile Station in Mobile VPNs environment.

• Journal of Computing Science and Engineering
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• v.6 no.4
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• pp.294-309
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• 2012

In the age of multi-core and specialized accelerators in high performance computing (HPC) systems, it is critical to understand application characteristics and apply suitable optimizations in order to fully utilize advanced computing system. Often time, the process involves multiple stages of application performance diagnosis and a trial-and-error type of approach for optimization. In this study, a general guideline of performance optimization has been demonstrated with two class-representing applications. The main focuses are on node-level optimization and inter-node scalability improvement. While the number of optimization case studies is somewhat limited in this paper, the result provides insights into the systematic approach in HPC applications performance engineering.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.86-87
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• 2018

As blockchain technology has the potential to revolutionize trust models and business processes across industries, applications are expected to be endless. However, this technology is still in the early stage, and the scalability caused by the accumulation of transaction data due to the increase of blocks is emerging as a serious problem. In this paper, we propose various alternatives to solve the scalability problem.