• Journal of Korea Society of Industrial Information Systems
• /
• v.29 no.5
• /
• pp.11-19
• /
• 2024

As task offloading from user devices transitions from the cloud to the edge, the demand for efficient resource management techniques has emerged. While numerous studies have employed reinforcement learning to address this challenge, many fail to adequately consider the overhead associated with real-world offloading tasks. This paper proposes a reinforcement learning-based distributed offloading policy generation method that incorporates task overhead. A simulation environment is constructed to validate the proposed approach. Experimental results demonstrate that the proposed method reduces edge queueing time, achieving up to 46.3% performance improvement over existing approaches.

• Information Systems Review
• /
• v.15 no.3
• /
• pp.129-139
• /
• 2013

The Internet of Things (or IoT for short) which refers to uniquely identifiable objects and their virtual representations in an Internet-like structure is to be reality today. The amount of data on IoT is expected to increase abruptly and there are several key issues like usefulness interoperability between multiple distributes systems, services and databases. In this paper a methodology is proposed to realize a recently developed cloud service model, Contents as a Service (CaaS), which is contents delivery model referred to as 'on-demand contents'. In the proposed method, the global object identification, ePosition, comprising the structured form of two sorts of text strings with a separation symbol like # is applied to identify a specific content and registered with the content at the same server. It is easy-to-realize and effective to solve the interoperability problem systematically and logically. Some APIs for the proposed CaaS service are to be converged to provide some upgraded cloud service model such as 'CaaS supported SaaS' and 'CaaS supported PaaS'.

• Journal of KIISE:Software and Applications
• /
• v.37 no.5
• /
• pp.341-353
• /
• 2010

Mobile Internet Device (MID) allows users to flexibly use various forms of wireless internet such as Wi-Fi, GSM, CDMA, and 3G. Using such Internet, MID users can utilize application services. MID usage is expected to grow due to the benefits of portability, Internet accessibility, and other convenience. However, it has resource constraints such as limited CPU power, small memory size, limited battery life, and small screen size. Consequently, MIDs are not capable to hold large-sized complex applications and to process a large amount of data in memory. An effective solution to remedy these limitations is to develop cloud services for the required application functionality, to deploy them on the server side, and to let MID users access the services through internet. A major concern on running cloud services for MIDs is the potential problems with low Quality of Service (QoS) due to the characteristics of MIDs. Even measuring the QoS of such services is more technically challenging than conventional quality measurements. In this paper, we first identify the characteristics of MIDs and cloud services for MIDs. Based on these observations, we derive a number of quality attributes and their metrics for measuring QoS of mobile services. A case study of applying the proposed quality model is presented to show its effectiveness and applicability.

• KIPS Transactions on Software and Data Engineering
• /
• v.6 no.6
• /
• pp.309-314
• /
• 2017

In general, there are many difficulties in collecting, storing, and analyzing SNS (social network service) data, since those data have big data characteristics, which occurs very fast with the mixture form of structured and unstructured data. In this paper, we propose a new data visualization framework that works on Apache Storm, and it can be useful for real-time and dynamic analysis of SNS data. Apache Storm is a representative big data software platform that processes and analyzes real-time streaming data in the distributed environment. Using Storm, in this paper we collect and aggregate the real-time Twitter data and dynamically visualize the aggregated results through the tag cloud. In addition to Storm-based collection and aggregation functionalities, we also design and implement a Web interface that a user gives his/her interesting keywords and confirms the visualization result of tag cloud related to the given keywords. We finally empirically show that this study makes users be able to intuitively figure out the change of the interested subject on SNS data and the visualized results be applied to many other services such as thematic trend analysis, product recommendation, and customer needs identification.

• Journal of Digital Convergence
• /
• v.19 no.4
• /
• pp.151-157
• /
• 2021

Recently, IoT technology has been applied to various cloud environments, requiring accurate verification of various information generated by IoT devices. However, due to the convergence of IoT technologies and 5G technologies, accurate analysis is required as IoT information processing is rapidly processed. This paper proposes a blockchain-based multi-IoT verification model for overlay cloud environments. The proposed model multi-processes IoT information by further classifying IoT information two layers (layer and layer) into bits' blockchain to minimize the bottleneck of overlay networks while ensuring the integrity of information sent and received from embedded IoT devices within local IoT groups. Furthermore, the proposed model allows the layer to contain the weight information, allowing IoT information to be easily processed by the server. In particular, transmission and reception information between IoT devices facilitates server access by distributing IoT information from bits into blockchain to minimize bottlenecks in overlay networks and then weighting IoT information.

• KIISE Transactions on Computing Practices
• /
• v.22 no.11
• /
• pp.601-613
• /
• 2016

With the increasing need for big data processing, building a shared big data processing platform is important to minimize time and monetary costs. In shared big data processing, multitenancy is a major requirement that needs to be addressed, in order to provide a single isolated personal big data platform for each user, but to share the underlying hardware is shared among users to increase hardware utilization. In this paper, we explore two well-known shared big data processing platform models. One is to use a native Hadoop cluster, and the other is to build a virtual Hadoop cluster for each user. For each model we verified whether it is sufficient to support multi-tenancy. We also present a method to complement unsupported multi-tenancy features in a native Hadoop cluster model. Lastly we built prototype platforms and compared the performance of both models.

• Journal of Advanced Navigation Technology
• /
• v.15 no.4
• /
• pp.632-639
• /
• 2011

Searching distributed resource efficiently is very important in distributed computing, cloud computing environment. Distributed resource searching may have system overheads and take much time in proportion to the searching number, because distributed resource searching has to circuit many peers for searching information. The open-source community project JXTA defines an open set of standard protocols for ad hoc, pervasive, peer-to-peer computing as a common platform for developing a wide variety of decentralized network applications. In this paper, we proposed peer search algorithm based on JXTA-Sim. original JXTA peer searching algorithm selected a loosely-consistent DHT. Our Lookup algorithm decreases message number of WALK_LOOKUP and reduce the network system overload, and we make a result of same performance both original algorithm and our proposed algorithm.

• Journal of Korea Multimedia Society
• /
• v.15 no.8
• /
• pp.1048-1058
• /
• 2012

This paper proposes the DDMPF(Distributed Data Management Protocol using FAT) which prevents data loss and keeps the security of self-organized storages by comprising a client, a storage server, and a verification server in clouding environment. The DDMPF builds a self-organized storage server, solves data loss by decentralizing the partitioned data in it in contrast to the centralized problem and the data loss caused by the storage server problems of existing clouding storages, and improves the efficiency of distributed data management with FAT(File Allocation Table). And, the DDMPF improves the reliability of data by a verification server's verifying the data integrity of a storage server, and strengthens the security in double encryption with a client's private key and the system's master key using EC-DH algorithm. Additionally, the DDMPF limits the number of verification servers and detects the flooding attack by setting the TS(Time Stamp) for a verification request message and the replay attack by using the nonce value generated newly, whenever the verification is requested.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.9
• /
• pp.1655-1665
• /
• 2017

Recently data centers are being constructed actively by many cloud service providers, enterprises, research institutes, etc. Generally, they are built on tree topology using ECMP data forwarding scheme for load balancing. In this paper, we examine data center network topologies like tree topology and fat-tree topology, and load balancing technologies like MLAG and ECMP. Then, we propose a scheduling algorithm to efficiently transmit particular files stored on the hosts in the data center to the destination node outside the data center, where fat-tree topology and OpenFlow protocol between infrastructure layer and control layer are used. We run performance analysis by numerical method, and compare the analysis results with those of ECMP. Through the performance comparison, we show the outperformance of the proposed algorithm in terms of throughput and file transfer completion time.

• The Journal of Korean Institute of Next Generation Computing
• /
• v.14 no.5
• /
• pp.60-70
• /
• 2018

A software defined storage (SDS) is being deployed in cloud environment to allow multiple users to virtualize physical servers, but a solution for optimizing space efficiency with limited physical resources is needed. In the conventional data deduplication system, it is difficult to deduplicate redundant data uploaded to distributed storages. In this paper, we propose a distributed deduplication method using similarity-based clustering and multi-layer bloom filter. Rabin hash is applied to determine the degree of similarity between virtual machine servers and cluster similar virtual machines. Therefore, it improves the performance compared to deduplication efficiency for individual storage nodes. In addition, a multi-layer bloom filter incorporated into the deduplication process to shorten processing time by reducing the number of the false positives. Experimental results show that the proposed method improves the deduplication ratio by 9% compared to deduplication method using IP address based clusters without any difference in processing time.