• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.1-17
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• 2017

Mobile Cloud Computing has become a promising computing platform. It moves users' data to the centralized large data centers for users' mobile devices to conveniently access. Since the data storage service may not be fully trusted, many public verification algorithms are proposed to check the data integrity. However, these algorithms hardly consider the huge computational burden for the verifiers with resource-constrained mobile devices to execute the verification tasks. We propose an energy-efficient algorithm for assigning verification tasks (EEAVT) to optimize the energy consumption and assign the verification tasks by elastic and customizable ways. The algorithm prioritizes verification tasks according to the expected finish time of the verification, and assigns the number of checked blocks referring to devices' residual energy and available operation time. Theoretical analysis and experiment evaluation show that our algorithm not only shortens the verification finish time, but also decreases energy consumption, thus improving the efficiency and reliability of the verification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2405-2423
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• 2012

In a traditional Single Sign-On (SSO) scheme, the user and the Service Providers (SPs) have given their trust to the Identity Provider (IdP) or Authentication Service Provider (ASP) for the authentication and correct assertion. However, we still need a better solution for the local/native true SSO to gain user confidence, whereby the trusted entity must play the role of the ASP between distinct SPs. This technical gap has been filled by Trusted Computing (TC), where the remote attestation approach introduced by the Trusted Computing Group (TCG) is to attest whether the remote platform integrity is indeed trusted or not. In this paper, we demonstrate a Trustworthy Mutual Attestation (TMutualA) protocol as a proof of concept implementation for a local true SSO using the Integrity Measurement Architecture (IMA) with the Trusted Platform Module (TPM). In our proposed protocol, firstly, the user and SP platform integrity are checked (i.e., hardware and software integrity state verification) before allowing access to a protected resource sited at the SP and releasing a user authentication token to the SP. We evaluated the performance of the proposed TMutualA protocol, in particular, the client and server attestation time and the round trip of the mutual attestation time.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.300-305
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• 2005

Context-aware intelligent services are essential in ubiquitous computing and intelligent robots environments, which make decisions on which services to start with the consideration of surrounding contexts. In the ubiquitous and intelligent environments, context-aware service engines should be light-weighted due to the resource restrictions on the devices. This paper presents a context-aware service engine which is designed for light-weighted devices. The context-aware service engine has been designed with special attention to improve the execution speed and to minimize the memory requirement.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.137-141
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• 2015

Mobile cloud computing uses cloud service to solve the resource constraint problem of mobile devices. Offloading means that a task executed on the mobile device commits to cloud and many studies related to the energy consumption have been researched. In this paper, we designed a response time model considering sensing data synchronization to estimate the efficiency of the offloading scheme in terms of the response time. The proposed model considers synchronization of required sensing data to improve the accuracy of response time estimation when cloud processes the task requested from a mobile device. We found that the response time is effected by new sensing data generation rate and synchronization period through simulation results.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.10
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• pp.27-35
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• 2010

In wireless packet networks, energy and wireless resource efficiency is critical issue to addressed for wide deployment. To achieve the both goals of saving the mobile station's energy and increasing the wireless capacity, transmission power control is introduced to wireless packet networks. In the transmission power controled networks, it is not deeply studied on unfairness among transmissions with different power levels that reaches starvation. Through the performance analysis, this paper explains the throughput unfairness of high power transmission with the unfair media access probability owing to the contending node number difference and proposes a simple PHY-MAC cross layer approach.

• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.89-98
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• 2007

In these days, the use of context in application running on mobile devices such as PDAs and smart Phone has become a crucial requirement for several research areas, including ubiquitous computing. mobile computing. Previous middlewares which support context provisioning uses single strategy. But, this paper proposed middleware integrated multiple strategies for context provisioning, namely internal sensors-based, external infrastructure-based, and distributed provisioning in ad hoc networks. Applications can query needed context items using SQL like context query language and require context information to use different provisioning mechanisms depending on resource availability and presence of external infrastructures.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.132-144
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• 2020

Cloud computing is an emerging technology based on the concept of enabling data access from anywhere, at any time, from any platform. The exponential growth of cloud users has resulted in the emergence of multiple issues, such as the workload imbalance between the virtual machines (VMs) of data centers in a cloud environment greatly impacting its overall performance. Our axis of research is the load balancing of a data center's VMs. It aims at reducing the degree of a load's imbalance between those VMs so that a better resource utilization will be provided, thus ensuring a greater quality of service. Our article focuses on two phases to balance the workload between the VMs. The first step will be the determination of the threshold of each VM before it can be considered overloaded. The second step will be a task allocation to the VMs by relying on an improved and faster version of the meta-heuristic "simulated annealing (SA)". We mainly focused on the acceptance probability of the SA, as, by modifying the content of the acceptance probability, we could ensure that the SA was able to offer a smart task distribution between the VMs in fewer loops than a classical usage of the SA.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.7
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• pp.1314-1320
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• 2008

Recently, LBS(Location Based Service) which provides useful service based on the location of objects or human has drawn the attention of the research community. To provide LBS, many researchers have proposed many localization systems such as Cricket or Ubisense, however, these systems have the limit that it is very hard to perform the complicated computation on these systems because these systems consist of sensor nodes which have very limited computing power. In the paper, we propose a new localization system with the gateway node which has very high computing power and resource which is suitable for the complicated computation needed for localization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2824-2837
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• 2019

Container technologies are widely used in infrastructures to deploy and manage applications in cloud computing environment. As containers are light-weight software, the cluster of cloud applications can easily scale up or down to provide Internet-based services. Container-based applications can well deal with fluctuate workloads by dynamically adjusting physical resources. Current works of scheduling applications often construct applications' performance models with collected historical training data, but these works with static models cannot self-adjust physical resources to meet the dynamic requirements of cloud computing. Thus, we propose a self-adaptive automatic container scheduling framework AutoScale for cloud applications, which uses a feedback-based approach to adjust physical resources by extending, contracting and migrating containers. First, a queue-based performance model for cloud applications is proposed to correlate performance and workloads. Second, a fuzzy Kalman filter is used to adjust the performance model's parameters to accurately predict applications' response time. Third, extension, contraction and migration strategies based on predicted response time are designed to schedule containers at runtime. Furthermore, we have implemented a framework AutoScale with container scheduling strategies. By comparing with current approaches in an experiment environment deployed with typical applications, we observe that AutoScale has advantages in predicting response time, and scheduling containers to guarantee that response time keeps stable in fluctuant workloads.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.86-109
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• 2019

Cloud computing systems' performance is still a central focus of research for determining optimal resource utilization. Running several existing benchmarks simultaneously serves to acquire performance information from specific cloud system resources. However, the complexity of monitoring the existing performance of computing systems is a challenge requiring an efficient and interactive user directing performance-monitoring system. In this paper, we propose an effective multivariate control framework for monitoring cloud systems performance. The proposed framework utilizes the hardware cloud systems performance metrics, collects and displays the performance measurements in terms of meaningful graphics, stores the graphical information in a database, and provides the data on-demand without requiring a third party software. We present performance metrics in terms of CPU usage, RAM availability, number of cloud active machines, and number of running processes on the selected machines that can be monitored at a high control level by either using a cloud service customer or a cloud service provider. The experimental results show that the proposed framework is reliable, scalable, precise, and thus outperforming its counterparts in the field of monitoring cloud performance.