• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권11호
• /
• pp.3030-3049
• /
• 2023

With the development and wide application of hybrid network computing modes like cloud computing, edge computing and fog computing, the customer service requests and the collaborative optimization of various computing resources face huge challenges. Considering the characteristics of network environment resources, the optimized deployment of service resources is a feasible solution. So, in this paper, the optimal goals for deploying service resources are customer experience and service cost. The focus is on the system impact of deploying services on load, fault tolerance, service cost, and quality of service (QoS). Therefore, the alternate node filtering algorithm (ANF) and the adjustment factor of cost matrix are proposed in this paper to enhance the system service performance without changing the minimum total service cost, and corresponding theoretical proof has been provided. In addition, for improving the fault tolerance of system, the alternate node preference factor and algorithm (ANP) are presented, which can effectively reduce the probability of data copy loss, based on which an improved cost-efficient replica deployment strategy named ICERD is given. Finally, by simulating the random occurrence of cloud node failures in the experiments and comparing the ICERD strategy with representative strategies, it has been validated that the ICERD strategy proposed in this paper not only effectively reduces customer access latency, meets customers' QoS requests, and improves system service quality, but also maintains the load balancing of the entire system, reduces service cost, enhances system fault tolerance, which further confirm the effectiveness and reliability of the ICERD strategy.

• 정보처리학회논문지A
• /
• 제14A권2호
• /
• pp.99-106
• /
• 2007

지역적으로 분산되어 있는 이기종의 시스템들을 하나로 묶어 사영하는 그리드 컴퓨팅이 차세대 병렬.분산 연산을 위한 새로운 패러다임으로 관심을 끌고 있다. 고속 네트워크로 연결된 다수의 컴퓨터 시스템이 사용자에게 통합된 가상의 컴퓨팅 서비스를 제공하는 그리드 시스템은 통신비용에 대한 중요성이 매우 크다. 따라서 그리드 환경에서 스케줄링 알고리즘은 작업의 실행시간을 단축하기 위하여 자원들의 연산능력과 함께 통신에 대한 비용을 고려하여야 한다. 그러나 현재까지 발표된 대부분의 스케줄링 알고리듬들은 작업이 한 클러스터에서 처리되는 것을 가정함으로써 통신비용을 무시하였으며, 작업이 다수의 클러스터에 분산되어 처리되는 경우에도 통신비용에 관한 오버헤드를 고려하지 않았다. 본 논문에서는 그리드 시스템에 적합한 기존 스케줄링 알고리즘들의 성능을 분석하였으며, 작업이 다수의 클러스터에 분산되어 수행되는 co-allocation 환경에서 통신비용을 고려하여 알고리즘들의 성능을 비교하고 분석하였다.

• 한국IT서비스학회지
• /
• 제11권3호
• /
• pp.129-159
• /
• 2012

Cloud-computing is in the limelight with expectation for cost reduction, because it alleviates the burden of initial investment and maintenance cost and based on pay-as-you-use billing policy. However, many suppliers of Cloud-computing service are suggesting diverse and complicated billing policies without consideration for setting reasonable service billing policy and definite criteria of properties to determine service billing system. So companies willing to use Cloud-computing service are hard to understand the billing system and often spend more expensive cost than necessary. Therefore, this study invested billing system properties of four representative suppliers. Based on these properties of billing system, this study found priorities using AHP survey which conducted to experts who are able to make decisions for adopting Cloud-computing in the company using or willing to use Cloud-computing service. We expect that this study can suggest basic guideline for comparing and analyzing properties of Cloud-computing service with standardized and objective method.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
• /
• 제2권1호
• /
• pp.27-34
• /
• 2013

클라우드 컴퓨팅 서비스에 대한 관심이 높아지고 있다. 하지만 많은 관심에도 불구하고 클라우드 컴퓨팅 서비스에 대한 기존 연구는 한계점이 있다. 선행연구들은 이 서비스가 제공하는 많은 혜택에 대한 연구와 이 서비스를 선택할 경우 발생할 수 있는 문제점에 대해 독립적으로 연구하고 있다. 하지만 실제 생활에서 소비자는 클라우드 컴퓨팅 서비스를 선택할 경우 혜택과 비용에 대해 동시에 고려한다. 따라서 본 연구에서는 클라우드 컴퓨팅 서비스 선택 시 혜택과 비용에 대한 상호작용 효과를 탐색적으로 분석하였다. 분석 결과 독립변수로써 유용성, 사회적영향, 그리고 혁신성은 인지된 가치에 모두 정(+)의 유의한 영향을 주었다. 하지만 혜택과 비용과의 상호작용 효과 분석 결과 유용성과 혁신성만이 비용과의 인지된 가치에 대해 통계학적으로 유의한 것으로 나타났다. 유용성은 비용과의 인지된 가치에 대해 상호작용 효과에 대해 음(-)의 결과가 나타났으며, 혁신성은 비용과의 인지된 가치에 대해 상호작용 효과 검정 결과 정(+)의 상호작용 효과가 있는 것으로 나타났다. 결국 본 연구는 클라우드 컴퓨팅 서비스를 선택할 경우 소비자가 느끼는 혜택의 경우 비용을 고려할 때 인지된 가치에 서로 다른 영향을 줄 수 있음을 실증했다는 점에서 의의가 있다고 하겠다.

• Journal of Information Processing Systems
• /
• 제17권3호
• /
• pp.615-629
• /
• 2021

With the development of mobile edge computing, how to utilize the computing power of edge computing to effectively and efficiently offload data and to compute offloading is of great research value. This paper studies the computation offloading problem of multi-user and multi-server in mobile edge computing. Firstly, in order to minimize system energy consumption, the problem is modeled by considering the joint optimization of the offloading strategy and the wireless and computing resource allocation in a multi-user and multi-server scenario. Additionally, this paper explores the computation offloading scheme to optimize the overall cost. As the centralized optimization method is an NP problem, the game method is used to achieve effective computation offloading in a distributed manner. The decision problem of distributed computation offloading between the mobile equipment is modeled as a multi-user computation offloading game. There is a Nash equilibrium in this game, and it can be achieved by a limited number of iterations. Then, we propose a distributed computation offloading algorithm, which first calculates offloading weights, and then distributedly iterates by the time slot to update the computation offloading decision. Finally, the algorithm is verified by simulation experiments. Simulation results show that our proposed algorithm can achieve the balance by a limited number of iterations. At the same time, the algorithm outperforms several other advanced computation offloading algorithms in terms of the number of users and overall overheads for beneficial decision-making.

• Journal of Information Processing Systems
• /
• 제17권3호
• /
• pp.489-504
• /
• 2021

By distributing computing tasks among devices at the edge of networks, edge computing uses virtualization, distributed computing and parallel computing technologies to enable users dynamically obtain computing power, storage space and other services as needed. Applying edge computing architectures to Internet of Vehicles can effectively alleviate the contradiction among the large amount of computing, low delayed vehicle applications, and the limited and uneven resource distribution of vehicles. In this paper, a predictive offloading strategy based on the MEC load state is proposed, which not only considers reducing the delay of calculation results by the RSU multi-hop backhaul, but also reduces the queuing time of tasks at MEC servers. Firstly, the delay factor and the energy consumption factor are introduced according to the characteristics of tasks, and the cost of local execution and offloading to MEC servers for execution are defined. Then, from the perspective of vehicles, the delay preference factor and the energy consumption preference factor are introduced to define the cost of executing a computing task for another computing task. Furthermore, a mathematical optimization model for minimizing the power overhead is constructed with the constraints of time delay and power consumption. Additionally, the simulated annealing algorithm is utilized to solve the optimization model. The simulation results show that this strategy can effectively reduce the system power consumption by shortening the task execution delay. Finally, we can choose whether to offload computing tasks to MEC server for execution according to the size of two costs. This strategy not only meets the requirements of time delay and energy consumption, but also ensures the lowest cost.

• International Journal of Contents
• /
• 제8권4호
• /
• pp.7-11
• /
• 2012

Cloud Computing can be viewed as a dynamically-scalable pool of resources. Virtualization is one of the key technologies enabling Cloud Computing functionalities. Virtual machines (VMs) scheduling and allocation is essential in Cloud Computing environment. In this paper, two dynamic VMs scheduling and allocating schemes are presented and compared. One dynamically on-demand allocates VMs while the other deploys optimal threshold to control the scheduling and allocating of VMs. The aim is to dynamically allocate the virtual resources among the Cloud Computing applications based on their load changes to improve resource utilization and reduce the user usage cost. The schemes are implemented by using SimPy, and the simulation results show that the proposed adaptive scheme with one threshold can be effectively applied in a Cloud Computing environment both performance-wise and cost-wise.

• 한국콘텐츠학회논문지
• /
• 제16권7호
• /
• pp.667-680
• /
• 2016

하둡 맵리듀스와 같은 분산 컴퓨팅 플랫폼이 개발됨에 따라, 기존 단일 컴퓨터 상에서 수행되는 질의 처리 기법을 분산 컴퓨팅 환경에서 효율적으로 수행하는 것이 필요하다. 특히, 주어진 두 데이터 집합에서 유사도가 높은 모든 데이터 쌍을 탐색하는 유사 조인 질의를 분산 컴퓨팅 환경에서 수행하려는 연구가 있어 왔다. 그러나 분산 병렬 환경에서의 기존 유사 조인 질의처리 기법은 데이터 전송 비용만을 고려하기 때문에 클러스터 간에 비균등 연산 부하 분산의 문제점이 존재한다. 본 논문에서는 분산 컴퓨팅 환경에서 효율적인 유사 조인 처리를 위한 행렬 기반 부하 분산 알고리즘을 제안한다. 제안하는 알고리즘은 클러스터의 균등 부하 분산을 위해 행렬을 이용하여 예상되는 연산 부하를 측정하고 이에 따라 파티션을 생성한다. 아울러, 클러스터에서 질의 처리에 사용되지 않는 데이터를 필터링함으로서 연산 부하를 감소시킨다. 마지막으로 성능 평가를 통해 제안하는 알고리즘이 기존 기법에 비해 질의 처리 성능 측면에서 우수함을 보인다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권1호
• /
• pp.35-57
• /
• 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.

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