• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권11호
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• pp.5269-5286
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• 2018

Task scheduling is one of the key issues in improving system performance and optimizing resource management in cloud computing environment. In order to provide appropriate services for heterogeneous users, we propose a novel task scheduling strategy with service differentiation, in which the delay sensitive tasks are assigned to the rapid cloud with high-speed processing, whereas the fault sensitive tasks are assigned to the reliable cloud with service restoration. Considering that a user can receive service from either local SaaS (Software as a Service) servers or public IaaS (Infrastructure as a Service) cloud, we establish a hybrid queueing network based system model. With the assumption of Poisson arriving process, we analyze the system model in steady state. Moreover, we derive the performance measures in terms of average response time of the delay sensitive tasks and utilization of VMs (Virtual Machines) in reliable cloud. We provide experimental results to validate the proposed strategy and the system model. Furthermore, we investigate the Nash equilibrium behavior and the social optimization behavior of the delay sensitive tasks. Finally, we carry out an improved intelligent searching algorithm to obtain the optimal arrival rate of total tasks and present a pricing policy for the delay sensitive tasks.

• 한국인터넷방송통신학회논문지
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• 제10권5호
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• pp.293-298
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• 2010

유비쿼터스 센서 네트워크(Ubiquitous Sensor Network) 기술의 핵심은 저전력 무선 통신기술과 효율적 라우팅을 위한 적절한 자원할당 기술이다. 센서 네트워크에서 효율적인 자원할당을 위해서는 서비스에 따른 차별화된 자원할당 방식이 필요하다. 이를 위하여, 본 논문에서는 유선망에 사용되는 PQ와 WRR의 단점을 보완하여 USN에 적용이 가능한 스케줄러 알고리즘을 제안한다. 제안된 알고리즘은 센서 네트워크에서 각 클래스의 큐 상태를 체크하여 퍼지 이론을 적용한 제어 정책에 따라 WRR 스케쥴러의 가중치를 동적으로 할당하였다. 시뮬레이션 결과 제안된 알고리즘은 EF 클래스의 패킷 손실률에서 WRR 스케쥴러 방식보다 평균 6.5% 향상되었으며, AF4 클래스에서는 PQ 방식보다 평균 45% 향상된 결과를 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권9호
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• pp.4103-4121
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• 2018

Quality of Service (QoS) awareness is recognized as a key point for the success of Internet of Things (IOT).Realizing the full potential of the Internet of Things requires, a real-time task scheduling algorithm must be designed to meet the QoS need. In order to schedule tasks with diverse QoS requirements in cloud environment efficiently, we propose a task scheduling strategy based on dynamic priority and load balancing (DPLB) in this paper. The dynamic priority consisted of task value density and the urgency of the task execution, the priority is increased over time to insure that each task can be implemented in time. The scheduling decision variable is composed of time attractiveness considered earliest completion time (ECT) and load brightness considered load status information which by obtain from each virtual machine by topic-based publish/subscribe mechanism. Then sorting tasks by priority and first schedule the task with highest priority to the virtual machine in feasible VMs group which satisfy the QoS requirements of task with maximal. Finally, after this patch tasks are scheduled over, the task migration manager will start work to reduce the load balancing degree.The experimental results show that, compared with the Min-Min, Max-Min, WRR, GAs, and HBB-LB algorithm, the DPLB is more effective, it reduces the Makespan, balances the load of VMs, augments the success completed ratio of tasks before deadline and raises the profit of cloud service per second.

• 대한산업공학회지
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• 제26권2호
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• pp.183-192
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• 2000

This paper considers the problem of scheduling a set of n jobs on m parallel machines to minimize total weighted tardiness. For the problem a genetic algorithm is proposed, in which solutions are encoded using the random key method suggested by Bean and new crossover operators are employed to increase performance of the algorithm. The algorithm is compared with the Modified Due-Date (MDD) algorithm after series of tests to find appropriate values for genetic parameters. Results of computational tests on randomly generated test problems show that the suggested algorithm performs better than the MDD algorithm and gives good solutions in a reasonable amount of computation time.

• 전기전자학회논문지
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• 제24권2호
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• pp.468-474
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• 2020

IoT, 무선 센서 네트워크와 같이 제한된 자원을 갖는 응용분야의 보안에 적합하도록 개발된 경량 블록 암호 알고리듬 SPECK의 하드웨어 구현에 관해 기술한다. 블록 암호 SPECK 크립토 코어는 8가지의 블록/키 크기를 지원하며, 회로 경량화를 위해 내부 데이터 패스는 16-비트로 설계되었다. 키 초기화 과정을 통해 복호화에 사용될 최종 라운드 키가 미리 생성되어 초기 키와 함께 저장되며, 이를 통해 연속 블록에 대한 암호화/복호화 처리가 가능하도록 하였다. 또한 처리율을 높이기 위해 라운드 연산과 키 스케줄링이 독립적으로 연산되도록 설계하였다. 설계된 SPECK 크립토 코어를 FPGA 검증을 통해 하드웨어 동작을 확인하였으며, Virtex-5 FPGA 디바이스에서 1,503 슬라이스로 구현되었고, 최대 동작 주파수는 98 MHz로 추정되었다. 180 nm 공정으로 합성하는 경우, 최대 동작 주파수는 163 MHz로 추정되었으며, 블록/키 크기에 따라 154 Mbps ~ 238 Mbps의 처리량을 갖는다.

• 한국통신학회논문지
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• 제31권6B호
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• pp.495-507
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• 2006

무선 LAN에서 스케줄링 알고리즘은 실시간 멀티미디어 서비스가 요구하는 엄격한 QoS(Quality of Service) 요구사항을 보장하는데 있어 가장 중요한 요소 중 하나이다. 본 논문에서는 MPEG 스트림과 같은 멀티미디어 트래픽의 지연을 보장하기 위한 다중폴링 기반의 동적 스케줄링 알고리즘을 제안한다. 제안된 알고리즘은 MPEG 스트림의 특성을 이용한다. 채널할당 요청 정보를 갖고 있는 동적 파라미터를 AP(Access Point)에서 동작하는 PC(Point Coordinator)로 전달하기 위해 미니 프레임이라는 피드백(Feedback) 제어 프레임을 이용한다. 하나의 수퍼프레임 동안에 각 단말에 할당될 채널시간은 MPEG 프레임 종류와 트래픽 양, MPEG 프레임의 지연한도(Delay bound) 등에 따라 동적으로 변한다. 시뮬레이션을 통하여 제안된 방법의 성능을 다른 방법과 비교 분석한다. 비교 분석결과, 제안된 방법이 지연한도를 보장하면서 높은 성능을 보임을 확인하였다.

• 한국정보과학회논문지:시스템및이론
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• 제27권2호
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• pp.227-233
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• 2000

이 논문은 회로 설계의 상위 단계에서 저 전력 회로 합성을 위한 효율적인 알고리즘을 제시한다. 풀고자하는 문제는, 상위 단계 회로 합성의 두 가지 주요 작업인 스케줄링과 하드웨어 할당 과정에서 최소의 전력 소모를 가지는 데이타 경로를 합성해 내는 것이다 이 문제의 해결 방안으로， 우리는 과거 연구 결과에서 도출된 전력 소모를 줄이기 위한 기존의 요소들을 기초로 하여， 상위 단계 회로 합성에서 최적(optimal)의 전력 소모를 가지는 데이타 경로를 얻기 위한 IP (integer programming) 표현을 유도하였다. 그 다음， 유도된 IP 식을 이용하여 최소 전력 소모의 회로 합성을 위한 스케줄링과 하드웨어 할당 작업을 빠른 시간에 수행하기 위한 단계적 근사치 계산 방법을 고안하였다 실제， 우리는 실험을 통해 제안된 알고리즘이 매우 빠르며, 상위 단계에서의 데이터-경로 합성에서 전력 소모량을 줄이는데 매우 효과적임을 보여준다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권9호
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• pp.4342-4366
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• 2016

Congestion control in Cluster Wireless Sensor Networks (CWSNs) has drawn widespread attention and research interests. The increasing number of nodes and scale of networks cause more complex congestion control and management. Active Queue Management (AQM) is one of the major congestion control approaches in CWSNs, and Random Early Detection (RED) algorithm is commonly used to achieve high utilization in AQM. However, traditional RED algorithm depends exclusively on source-side control, which is insufficient to maintain efficiency and state stability. Specifically, when congestion occurs, deficiency of feedback will hinder the instability of the system. In this paper, we adopt the Additive-Increase Multiplicative-Decrease (AIMD) adjustment scheme and propose an improved RED algorithm by using neighbor feedback and scheduling scheme. The congestion control model is presented, which is a linear system with a non-linear feedback, and modeled by Lur'e type system. In the context of delayed Lur'e dynamical network, we adopt the concept of cluster synchronization and show that the congestion controlled system is able to achieve cluster synchronization. Sufficient conditions are derived by applying Lyapunov-Krasovskii functionals. Numerical examples are investigated to validate the effectiveness of the congestion control algorithm and the stability of the network.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.374-388
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• 2022

Cloud computing has been one of the most critical technology in the last few decades. It has been invented for several purposes as an example meeting the user requirements and is to satisfy the needs of the user in simple ways. Since cloud computing has been invented, it had followed the traditional approaches in elasticity, which is the key characteristic of cloud computing. Elasticity is that feature in cloud computing which is seeking to meet the needs of the user's with no interruption at run time. There are traditional approaches to do elasticity which have been conducted for several years and have been done with different modelling of mathematical. Even though mathematical modellings have done a forward step in meeting the user's needs, there is still a lack in the optimisation of elasticity. To optimise the elasticity in the cloud, it could be better to benefit of Machine Learning algorithms to predict upcoming workloads and assign them to the scheduling algorithm which would achieve an excellent provision of the cloud services and would improve the Quality of Service (QoS) and save power consumption. Therefore, this paper aims to investigate the use of machine learning techniques in order to predict the workload of Physical Hosts (PH) on the cloud and their energy consumption. The environment of the cloud will be the school of computing cloud testbed (SoC) which will host the experiments. The experiments will take on real applications with different behaviours, by changing workloads over time. The results of the experiments demonstrate that our machine learning techniques used in scheduling algorithm is able to predict the workload of physical hosts (CPU utilisation) and that would contribute to reducing power consumption by scheduling the upcoming virtual machines to the lowest CPU utilisation in the environment of physical hosts. Additionally, there are a number of tools, which are used and explored in this paper, such as the WEKA tool to train the real data to explore Machine learning algorithms and the Zabbix tool to monitor the power consumption before and after scheduling the virtual machines to physical hosts. Moreover, the methodology of the paper is the agile approach that helps us in achieving our solution and managing our paper effectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권4호
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• pp.1282-1301
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• 2015

Traditional infrastructure has been superseded by cloud computing, due to its cost-effective and ubiquitous computing model. Cloud computing not only brings multitude of opportunities, but it also bears some challenges. One of the key challenges it faces is recovery of computing nodes, when an Information Technology (IT) failure occurs. Since cloud computing mainly depends upon its nodes, physical servers, that makes it very crucial to recover a failed node in time and seamlessly, so that the customer gets an expected level of service. Work has already been done in this regard, but it has still proved to be trivial. In this study, we present a Cost-Time aware Genetic scheduling algorithm, referred to as CTaG, not only to globally optimize the performance of the cloud system, but also perform recovery of failed nodes efficiently. While modeling our work, we have particularly taken into account the factors of network bandwidth and customer's monetary cost. We have implemented our algorithm and justify it through extensive simulations and comparison with similar existing studies. The results show performance gain of our work over the others, in some particular scenarios.