• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.35-57
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• 2021

Fog computing aims to provide the solution of bandwidth, network latency and energy consumption problems of cloud computing. Likewise, management of data generated by healthcare IoT devices is one of the significant applications of fog computing. Huge amount of data is being generated by healthcare IoT devices and such types of data is required to be managed efficiently, with low latency, without failure, and with minimum energy consumption and low cost. Failures of task or node can cause more latency, maximum energy consumption and high cost. Thus, a failure free, cost efficient, and energy aware management and scheduling scheme for data generated by healthcare IoT devices not only improves the performance of the system but also saves the precious lives of patients because of due to minimum latency and provision of fault tolerance. Therefore, to address all such challenges with regard to data management and fault tolerance, we have presented a Fault Tolerant Data management (FTDM) scheme for healthcare IoT in fog computing. In FTDM, the data generated by healthcare IoT devices is efficiently organized and managed through well-defined components and steps. A two way fault-tolerant mechanism i.e., task-based fault-tolerance and node-based fault-tolerance, is provided in FTDM through which failure of tasks and nodes are managed. The paper considers energy consumption, execution cost, network usage, latency, and execution time as performance evaluation parameters. The simulation results show significantly improvements which are performed using iFogSim. Further, the simulation results show that the proposed FTDM strategy reduces energy consumption 3.97%, execution cost 5.09%, network usage 25.88%, latency 44.15% and execution time 48.89% as compared with existing Greedy Knapsack Scheduling (GKS) strategy. Moreover, it is worthwhile to mention that sometimes the patients are required to be treated remotely due to non-availability of facilities or due to some infectious diseases such as COVID-19. Thus, in such circumstances, the proposed strategy is significantly efficient.

• International Journal of Contents
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• v.7 no.2
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• pp.1-5
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• 2011

Allocation of computing resources is a crucial issue when dealing with a huge number of tasks to be completed according to a given deadline and cost constraints. The task scheduling to several resources (e.g. grid, cloud or a supercomputer) with different characteristics is not trivial, especially if a trade-off in terms of time and cost is considered. We propose an allocation approach able to fulfill the given requirements about time and cost through the use of optimizing techniques and an adaptive behavior. Simulated productions of tasks have been run in order to evaluate the characteristics of the proposed approach.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.153-155
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• 2015

다양한 과학 응용들은 데이터들을 처리하기 위해 높은 효율성을 제공하는 클라우드 인프라를 활용한다. 이 때 클라우드 컴퓨팅 환경에서 작업을 효율적으로 스케쥴링하는 것은 작업 처리 성능, 자원 활용을 및 작업 처리 시스템의 처리량에 큰 영향을 미친다. 본 논문에서는 클라우드 인프라에서 제공된 자원이 갖는 성능의 변동을 고려하여 사용자의 작업 완료 시간에 대한 품질 제약을 만족시키기 위한 작업 스케쥴링 기법을 제시하였다. 성능 평가를 통해 작업 지연이 발생한 상황에서 본 논문에서 제안한 작업 복제를 이용한 워크플로우 스케쥴링 기법을 활용했을 때, 작업 복제를 이용하지 않았을 때에 비해 효과적으로 요청된 워크플로우 종료 시간 내에 처리를 수행하는 것을 확인할 수 있었다.

• Smart Media Journal
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• v.5 no.4
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• pp.18-25
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• 2016

Virtualized hardware resources provides efficiency in use and easy of management. Based on the benefits, virtualization techniques are used to build large server clusters and cloud systems. The performance of a virtualized system is significantly affected by the virtual machine scheduler. However, the existing virtual machine scheduler have a problem in that the I/O response is reduced in accordance with the scheduling delay becomes longer. In this paper, we introduce the Loan/Redemption mechanism of a virtual machine scheduler in order to improve the responsiveness to I/O events. The proposed scheme gives additional credits for to virtual machines and classifies the task characteristics of each virtual machine by analyzing the credit consumption pattern. When an I/O event arrives, the scheduling priority of a virtual machine is temporally increased based on the analysis. The evaluation based on the implementation shows that the proposed scheme improves the I/O response 60% and bandwidth of virtual machines 62% compared to those of the existing virtual machine scheduler.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1749-1773
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• 2024

Recent advances in deep neural networks (DNNs) have greatly improved the accuracy and universality of various intelligent applications, at the expense of increasing model size and computational demand. Since the resources of end devices are often too limited to deploy a complete DNN model, offloading DNN inference tasks to cloud servers is a common approach to meet this gap. However, due to the limited bandwidth of WAN and the long distance between end devices and cloud servers, this approach may lead to significant data transmission latency. Therefore, device-edge collaborative inference has emerged as a promising paradigm to accelerate the execution of DNN inference tasks where DNN models are partitioned to be sequentially executed in both end devices and edge servers. Nevertheless, collaborative inference in heterogeneous edge environments with multiple edge servers, end devices and DNN tasks has been overlooked in previous research. To fill this gap, we investigate the optimization problem of collaborative inference in a heterogeneous system and propose a scheme CIS, i.e., collaborative inference scheme, which jointly combines DNN partition, task offloading and scheduling to reduce the average weighted inference latency. CIS decomposes the problem into three parts to achieve the optimal average weighted inference latency. In addition, we build a prototype that implements CIS and conducts extensive experiments to demonstrate the scheme's effectiveness and efficiency. Experiments show that CIS reduces 29% to 71% on the average weighted inference latency compared to the other four existing schemes.