• 한국시뮬레이션학회논문지
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• 제22권1호
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• pp.77-86
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• 2013

클라우드 환경은 여러 개의 컴퓨팅 자원들을 이용하는 분산 컴퓨팅 환경의 일종으로 가상머신을 이용 하여 작업을 처리한다. 클라우드 환경은 작업 요청에 따르는 부하분산과 빠른 작업 처리를 위한 프로비저닝 기술을 이용하여 가상머신의 상태에 따라 작업을 할당 한다. 하지만, 클라우드 환경의 작업 스케줄링을 위해서는 가상머신의 성능에 따르는 애매모호한 상태에 대한 가용성의 정의가 필요하다. 본 논문에서는 클라우드 환경의 프로비저닝 스케줄링을 위해 퍼지 로직 기반의 자원평가를 이용한 가상머신 프로비저닝 스케줄링(FVPRE: Fuzzy logic driven Virtual machine Provisioning scheduling using Resource Evaluation)을 제안한다. FVPRE는 각 가상머신의 정의하기 어려운 성능의 상태를 분석하여 자원 가용성에 대한 값을 구체화하여 정확한 자원의 가용성 평가를 통해 효율적인 프로비저닝 스케줄링이 가능하다. FVPRE는 클라우드 환경의 작업 처리에 대해 높은 처리율과 활용율을 보인다.

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

• 한국정보과학회논문지:시스템및이론
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• 제37권4호
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• pp.246-255
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• 2010

CPU 자원이 다수의 가상머신에 의해 공유되는 Xen 가상화 환경에서는, CPU가 하나의 가상머신의 요청을 처리하는 동안 다른 가상머신은 CPU를 기다려야 하는 가상머신 스케줄링 지연이 존재한다. 가상화 환경에서 응용프로그램의 QoS 요구사항을 만족시키기 위하여 자원을 관리하는 대부분의 시스템은 가상머신의 자원 사용률과 가상머신에서 운영하는 응용프로그램의 성능을 모니터링하고 분석하여 자원을 재할당한다. 이 때 응용프로그램의 성능 분석을 위해 큐잉 모델 등과 같은 수학적인 모델링 기법이 사용되지만 비가상화 환경에서 사용되던 모델은 가상머신 스케줄링 지연을 고려하지 않으므로, 가상화 환경에서는 정확한 분석과 예측이 어렵고, 따라서 이를 기반으로 자원을 관리하는 시스템은 요구되는 응용프로그램의 성능을 제공하지 못할 수 있다. 따라서 본 논문에서는 Xen 가상화 환경에서 가상머신 스케줄링 지연을 반영하여 응용프로그램의 성능을 측정하고, 모든 가상머신이 일으킬 수 있는 스케줄링 지연을 최소화하는 방향으로 CPU 사용 우선순위를 설정하는 기법을 제안하고, 제안한 기법이 스케줄링을 고려하지 않은 방법에 비해 응용 프로그램의 성능을 향상시킴을 보인다.

• International journal of advanced smart convergence
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• 제12권4호
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• pp.126-133
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• 2023

Recent advancements in cloud service virtualization technologies have witnessed a shift from a Virtual Machine-centric approach to a container-centric paradigm, offering advantages such as faster deployment and enhanced portability. Container orchestration has emerged as a key technology for efficient management and scheduling of these containers. However, with the increasing complexity and diversity of heterogeneous workloads and service types, resource scheduling has become a challenging task. Various research endeavors are underway to address the challenges posed by diverse workloads and services. Yet, a systematic approach to container orchestration for effective cloud management has not been clearly defined. This paper proposes the DRA-Engine (Dynamic Resource Allocation Engine) for resource scheduling in container orchestration. The proposed engine comprises the Request Load Procedure, Required Resource Measurement Procedure, and Resource Provision Decision Procedure. Through these components, the DRA-Engine dynamically allocates resources according to the application's requirements, presenting a solution to the challenges of resource scheduling in container orchestration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권4호
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• pp.1302-1320
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• 2015

In cloud, everything can be provided as a service wherein a large number of users submit their jobs and wait for their services. hus, scheduling plays major role for providing the resources efficiently to the submitted jobs. The brainwave of the proposed ork is to improve user satisfaction, to balance the load efficiently and to bolster the resource utilization. Hence, this paper roposes an Adaptive Multilevel Scheduling System (AMSS) which will process the jobs in a multileveled fashion. The first level ontains Preprocessing Jobs with Multi-Criteria (PJMC) which will preprocess the jobs to elevate the user satisfaction and to itigate the jobs violation. In the second level, a Deadline Based Dynamic Priority Scheduler (DBDPS) is proposed which will ynamically prioritize the jobs for evading starvation. At the third level, Contest Mapping Jobs with Virtual Machine (CMJVM) is roposed that will map the job to suitable Virtual Machine (VM). In the last level, VM Scheduler is introduced in the two-tier VM rchitecture that will efficiently schedule the jobs and increase the resource utilization. These contributions will mitigate job iolations, avoid starvation, increase throughput and maximize resource utilization. Experimental results show that the performance f AMSS is better than other algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제5권9호
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• pp.1544-1572
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• 2011

Task placement and scheduling are traditionally studied in following aspects: resource utilization, application throughput, application execution latency and starvation, and recently, the studies are more on application scalability and application performance. A methodology for task placement and scheduling centered on tasks based on virtual machines is studied in this paper to improve the performances of systems and dynamic adaptability in applications development and deployment oriented parallel computing. For parallel applications with no real-time constraints, we describe a thought of feature model and make a formal description for four layers of task placement and scheduling. To place the tasks to different layers of virtual computing systems, we take the performances of four layers as the goal function in the model of task placement and scheduling. Furthermore, we take the personal preference, the application scalability for a designer in his (her) development and deployment, as the constraint of this model. The workflow of task placement and scheduling based on virtual machines has been discussed. Then, an algorithm TPVM is designed to work out the optimal scheme of the model, and an algorithm TEVM completes the execution of tasks in four layers. The experiments have been performed to validate the effectiveness of time estimated method and the feasibility and rationality of algorithms. It is seen from the experiments that our algorithms are better than other four algorithms in performance. The results show that the methodology presented in this paper has guiding significance to improve the efficiency of virtual computing systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권3호
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• pp.1164-1183
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• 2019

The rapid development of cloud computing and high requirements of operators requires strong support from the underlying Data Center Networks. Therefore, the effectiveness of using resources in the data center networks becomes a point of concern for operators and material for research. In this paper, we discuss the online virtual-cluster provision problem for multiple tenants with an aim to decide when and where the virtual cluster should be placed in a data center network. Our objective is maximizing the total revenue for the data center networks under the constraints. In order to solve this problem, this paper divides it into two parts: online multi-tenancy scheduling and virtual cluster placement. The first part aims to determine the scheduling orders for the multiple tenants, and the second part aims to determine the locations of virtual machines. We first approach the problem by using the variational inequality model and discuss the existence of the optimal solution. After that, we prove that provisioning virtual clusters for a multi-tenant data center network that maximizes revenue is NP-hard. Due to the complexity of this problem, an efficient heuristic algorithm OMS (Online Multi-tenancy Scheduling) is proposed to solve the online multi-tenancy scheduling problem. We further explore the virtual cluster placement problem based on the OMS and propose a novel algorithm during the virtual machine placement. We evaluate our algorithms through a series of simulations, and the simulations results demonstrate that OMS can significantly increase the efficiency and total revenue for the data centers.

• ETRI Journal
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• 제36권2호
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• pp.197-205
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• 2014

This paper proposes a novel framework for virtual content delivery networks (CDNs) based on cloud computing. The proposed framework aims to provide multimedia content delivery services customized for content providers by sharing virtual machines (VMs) in the Infrastructure-as-a-Service cloud, while fulfilling the service level agreement. Furthermore, it supports elastic virtual CDN services, which enables the capabilities of VMs to be scaled to encompass the dynamically changing resource demand of the aggregated virtual CDN services. For this, we provide the system architecture and relevant operations for the virtual CDNs and evaluate the performance based on a simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권12호
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• pp.5780-5802
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• 2017

Cloud computing system consists of distributed resources in a dynamic and decentralized environment. Therefore, using cloud computing resources efficiently and getting the maximum profits are still challenging problems to the cloud service providers and cloud service users. It is important to provide the efficient scheduling. To schedule cloud resources, numerous heuristic algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Cuckoo Search (CS) algorithms have been adopted. The paper proposes a Modified Particle Swarm Optimization (MPSO) algorithm to solve the above mentioned issues. We first formulate an optimization problem and propose a Modified PSO optimization technique. The performance of MPSO was evaluated against PSO, and GA. Our experimental results show that the proposed MPSO minimizes the task execution time, and maximizes the resource utilization rate.

• Journal of information and communication convergence engineering
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• 제16권4호
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• pp.264-270
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• 2018

In our research study, we aim at optimizing multiple load in cloud, effective resource allocation and lesser response time for the job assigned. Using Hadoop on datacenter is the best and most efficient analytical service for any corporates. To provide effective and reliable performance analytical computing interface to the client, various cloud service providers host Hadoop clusters. The previous works done by many scholars were aimed at execution of workflows on Hadoop platform which also minimizes the cost of virtual machines and other computing resources. Earlier stochastic hill climbing technique was applied for single parameter and now we are working to optimize multiple parameters in the cloud data centers with proposed heuristic hill climbing. As many users try to priorities their job simultaneously in the cluster, resource optimized workflow scheduling technique should be very reliable to complete the task assigned before the deadlines and also to optimize the usage of the resources in cloud.