• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4345-4363
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• 2021

Deep Learning as a Service (DLaaS), utilizing the cloud-based deep neural network models to provide customer prediction services, has been widely deployed on mobile cloud computing (MCC). Such services raise privacy concerns since customers need to send private data to untrusted service providers. In this paper, we devote ourselves to building an efficient protocol to classify users' images using the convolutional neural network (CNN) model trained and held by the server, while keeping both parties' data secure. Most previous solutions commonly employ homomorphic encryption schemes based on Ring Learning with Errors (RLWE) hardness or two-party secure computation protocols to achieve it. However, they have limitations on large communication overheads and costs in MCC. To address this issue, we present LeHE4SCNN, a scalable privacy-preserving and communication-efficient framework for CNN-based DLaaS. Firstly, we design a novel low-expansion rate homomorphic encryption scheme with packing and unpacking methods (LeHE). It supports fast homomorphic operations such as vector-matrix multiplication and addition. Then we propose a secure prediction framework for CNN. It employs the LeHE scheme to compute linear layers while exploiting the data shuffling technique to perform non-linear operations. Finally, we implement and evaluate LeHE4SCNN with various CNN models on a real-world dataset. Experimental results demonstrate the effectiveness and superiority of the LeHE4SCNN framework in terms of response time, usage cost, and communication overhead compared to the state-of-the-art methods in the mobile cloud computing environment.

• Korean Journal of Health Education and Promotion
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• v.32 no.2
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• pp.1-13
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• 2015

Objectives: This study was conducted to investigate derived factors associated with a public service announcement on handwashing to evaluate the effect of this announcement. Methods: In early september, 1,000 persons over 19 years old was investigated by Computer Aided Telephone Interview, at last 993 persons for analysis. Outcomes were the exposure to the public service announcements on handwashing, the comprehension of this announcements, the intention of behavioral change and the practice of handwashing. Results: In particular, the proportion of exposure is more women and highly educated. The comprehension of the announcement was no statistically significant difference. The intention of behavior change showed higher in workers and understanding of the announcements. The practice of handwashing is higher in women and lower in student/unemployed. There was no difference in practice in accordance with the exposure, the comprehension and the intention of behavioral change. Conclusions: About the comprehension of the announcements and the intention of behavioral change, the effects of the public service announcements on handwashing can be very positive. But it does not affect the actual practice of hand washing. The handwashing promotion programs will have to devise a strategy and structure, operation method considering realistic and scalable platform.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.5
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• pp.401-415
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• 2005

Owing to the improved wireless communication technologies, it is possible to provide streaming service of multimedia with PDAs and mobile phones in addition to desktop PCs. Since mobile client devices have low computing power and low network bandwidth due to wireless network, the transcoding technology to adapt media for mobile client devices considering their characteristics is necessary. Transcoding servers transcode the source media to the target media within corresponding grades and provide QoS in real-time. In particular, an effective load balancing policy for transcoding servers is inevitable to support QoS for large scale mobile users. In this paper, the resource weighted load distribution policy is proposed for a fair load balance and a more scalable performance in cluster-based transcoding servers. Our proposed policy is based on the resource weighted table and number of maximum supported users, which are pre-computed for each pre-defined grade. We implement the proposed policy on cluster-based transcoding servers and evaluate its fair load distribution and scalable performance with the number of transcoding servers.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.3
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• pp.137-148
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• 2012

Cloud service users request dedicated virtual computing resource from the cloud service provider to process jobs in independent environment from other users. To optimize this process with automated method, in this paper we proposed a framework for workflow scheduling in the cloud environment, in which the core component is the middleware called broker mediating the interaction between users and cloud service providers. To process jobs in on-demand and virtualized resources from cloud service providers, many papers propose scheduling algorithms that allocate jobs to virtual machines which are dedicated to one machine one job. With this method, the isolation of being processed jobs is guaranteed, but we can't use each resource to its fullest computing capacity with high efficiency in resource utilization. This paper therefore proposed a cost-efficient job scheduling algorithm which maximizes the utilization of managed resources with increasing the degree of multiprogramming to reduce the number of needed virtual machines; consequently we can save the cost for processing requests. We also consider the performance degradation in proposed scheme with thrashing and context switching. By evaluating the experimental results, we have shown that the proposed scheme has better cost-performance feature compared to an existing scheme.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.1
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• pp.1-10
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• 2012

Mobility and quality of service (QoS) are becoming the more important issues in wireless communications. The traditional Internet service is expanding into new access media and applications. Since wireless communication services are accompanied by frequent handovers at remote sites, scalable and fast handover has become a prerequisite for ubiquitous communication. In this paper, the differentiated service (Diffserv) model is deployed in heterogeneous proxy mobile IPv6 (PMIPv6) networks to satisfy the QoS guaranteed service and fast handover requirements. The operational procedures for QoS guaranteed global roaming are presented. In addition, QoS management and handover cost evaluation schemes based on a mobile host's movement scope are proposed. This paper analyzes the reduction in handover delay in a network-based localized mobility management framework. We propose and analyze a PMIPv6 optimized with a global mobile access gateway (G-MAG), which is a network-based entity, to further improve the handover performance in terms of handover delay while maintaining minimal signaling overhead in the air interface among converged heterogeneous wireless networks. The handover signaling procedures with host-based MIPv6 are compared with network-based proxy MIPv6 (PMIPv6) and fast PMIPv6 assisted by G-MAG to show how much handover delay reduction can be achieved. Analytical results show that the handover delay is significantly reduced.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.345-353
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• 2021

Cloud computing represent a new era of computing that's forms through the combination of service-oriented architecture (SOA), Internet and grid computing with virtualization technology. Virtualization is a concept through which every cloud is enable to provide on-demand services to the users. Most IT service provider adopt cloud based services for their users to meet the high demand of computation, as it is most flexible, reliable and scalable technology. Energy based performance tradeoff become the main challenge in cloud computing, as its acceptance and popularity increases day by day. Cloud data centers required a huge amount of power supply to the virtualization of servers for maintain on- demand high computing. High power demand increase the energy cost of service providers as well as it also harm the environment through the emission of CO₂. An optimization of cloud computing based on energy-performance tradeoff is required to obtain the balance between energy saving and QoS (quality of services) policies of cloud. A study about power usage of resources in cloud data centers based on workload assign to them, says that an idle server consume near about 50% of its peak utilization power [1]. Therefore, more number of underutilized servers in any cloud data center is responsible to reduce the energy performance tradeoff. To handle this issue, a lots of research proposed as energy efficient algorithms for minimize the consumption of energy and also maintain the SLA (service level agreement) at a satisfactory level. VM (virtual machine) consolidation is one such technique that ensured about the balance of energy based SLA. In the scope of this paper, we explore reinforcement with fuzzy logic (RFL) for VM consolidation to achieve energy based SLA. In this proposed RFL based active VM consolidation, the primary objective is to manage physical server (PS) nodes in order to avoid over-utilized and under-utilized, and to optimize the placement of VMs. A dynamic threshold (based on RFL) is proposed for over-utilized PS detection. For over-utilized PS, a VM selection policy based on fuzzy logic is proposed, which selects VM for migration to maintain the balance of SLA. Additionally, it incorporate VM placement policy through categorization of non-overutilized servers as- balanced, under-utilized and critical. CloudSim toolkit is used to simulate the proposed work on real-world work load traces of CoMon Project define by PlanetLab. Simulation results shows that the proposed policies is most energy efficient compared to others in terms of reduction in both electricity usage and SLA violation.

• Journal of Broadcast Engineering
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• v.20 no.2
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• pp.310-339
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• 2015

In this paper, the possibility of a terrestrial fixed 4K UHD (Ultra High Definition) and mobile HD (High Definition) convergence broadcasting service through a single channel employing the FEF (Future Extension Frame) multiplexing technique in DVB (Digital Video Broadcasting)-T2 (Second Generation Terrestrial) systems is examined. The performance of such a service is also investigated. FEF multiplexing technology can be used to adjust the FFT (fast Fourier transform) and CP (cyclic prefix) size for each layer, whereas M-PLP (Multiple-Physical Layer Pipe) multiplexing technology in DVB-T2 systems cannot. The convergence broadcasting service scenario, which can provide fixed 4K UHD and mobile HD broadcasting through a single terrestrial channel, is described, and transmission requirements of the SHVC (Scalable High Efficiency Video Coding) technique are predicted. A convergence broadcasting transmission system structure is described by employing FEF and transmission technologies in DVB-T2 systems. Optimized transmission parameters are drawn to transmit 4K UHD and HD convergence broadcasting by employing a convergence broadcasting transmission structure, and the reception performance of the optimized transmission parameters under AWGN (additive white Gaussian noise), static Brazil-D, and time-varying TU (Typical Urban)-6 channels is examined using computer simulations to find the TOV (threshold of visibility). From the results, for the 6 and 8 MHz bandwidths, reliable reception of both fixed 4K UHD and mobile HD layer data can be achieved under a static fixed and very fast fading multipath channel.

• Journal of Broadcast Engineering
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• v.25 no.2
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• pp.265-274
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• 2020

Free-viewpoint video service is a technology that allows users to watch at any angle, location and distance through interaction. In this paper, the free-viewpoint video services are defined in four viewing modes: Inward view, outward view, 3D object view and first person view. And we developed and implemented a new integrated program that plays all the suggested views. In the contents of girl band performances and basketball games, multi-view cameras suitable for each viewing mode are installed to acquire media, and data stored on the server is streamed over the network, making it available for viewing. Users can freely choose four viewing modes, space location, angle and so on, and the media data such as images and sounds are provided to them by rendering appropriately for the selected the viewpoint. Our system is expected to be a scalable free-viewpoint video service player as well as provide users with immersion and presence by combining various viewing modes.

• Journal of KIISE
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• v.41 no.9
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• pp.674-685
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• 2014

The Knowledge service system needs to infer a new knowledge from indicated knowledge to provide its effective service. Most of the Knowledge service system is expressed in terms of ontology. The volume of knowledge information in a real world is getting massive, so effective technique for massive data of ontology is drawing attention. This paper is to provide the method to infer massive data-ontology to the extent of RDFS, based on cloud computing environment, and evaluate its capability. RDFS inference suggested in this paper is focused on both the method applying MapReduce based on RDFS meta table, and the method of single use of cloud computing memory without using MapReduce under distributed file computing environment. Therefore, this paper explains basically the inference system structure of each technique, the meta table set-up according to RDFS inference rule, and the algorithm of inference strategy. In order to evaluate suggested method in this paper, we perform experiment with LUBM set which is formal data to evaluate ontology inference and search speed. In case LUBM6000, the RDFS inference technique based on meta table had required 13.75 minutes(inferring 1,042 triples per second) to conduct total inference, whereas the method applying the cloud computing memory had needed 7.24 minutes(inferring 1,979 triples per second) showing its speed twice faster.

• Journal of KIISE:Computing Practices and Letters
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• v.6 no.2
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• pp.120-134
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• 2000

In this paper we present a Distributed Object oriented Virtual computing Environment, called DOVE which consists of autonomous distributed objects interacting with one another via method invocations based on a distributed object model. DOVE appears to a user logically as a single virtual computer for a set of heterogeneous hosts connected by a network as if objects in remote site reside in one virtual computer. By supporting efficient parallelism, heterogeneity, group communication, single global name service and fault-tolerance, it provides a transparent and easy-to-use programming environment for parallel applications. Efficient parallelism is supported by diverse remote method invocation, multiple method invocation for object group, multi-threaded architecture and synchronization schemes. Heterogeneity is achieved by automatic data arshalling and unmarshalling, and an easy-to-use and transparent programming environment is provided by stub and skeleton objects generated by DOVE IDL compiler, object life control and naming service of object manager. Autonomy of distributed objects, multi-layered architecture and decentralized approaches in hierarchical naming service and object management make DOVE more extensible and scalable. Also,fault tolerance is provided by fault detection in object using a timeout mechanism, and fault notification using asynchronous exception handling methods