• 제목/요약/키워드: VM Allocation

검색결과 28건 처리시간 0.021초

Efficient Virtual Machine Resource Management for Media Cloud Computing

  • Hassan, Mohammad Mehedi;Song, Biao;Almogren, Ahmad;Hossain, M. Shamim;Alamri, Atif;Alnuem, Mohammed;Monowar, Muhammad Mostafa;Hossain, M. Anwar
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제8권5호
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    • pp.1567-1587
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    • 2014
  • Virtual Machine (VM) resource management is crucial to satisfy the Quality of Service (QoS) demands of various multimedia services in a media cloud platform. To this end, this paper presents a VM resource allocation model that dynamically and optimally utilizes VM resources to satisfy QoS requirements of media-rich cloud services or applications. It additionally maintains high system utilization by avoiding the over-provisioning of VM resources to services or applications. The objective is to 1) minimize the number of physical machines for cost reduction and energy saving; 2) control the processing delay of media services to improve response time; and 3) achieve load balancing or overall utilization of physical resources. The proposed VM allocation is mapped into the multidimensional bin-packing problem, which is NP-complete. To solve this problem, we have designed a Mixed Integer Linear Programming (MILP) model, as well as heuristics for quantitatively optimizing the VM allocation. The simulation results show that our scheme outperforms the existing VM allocation schemes in a media cloud environment, in terms of cost reduction, response time reduction and QoS guarantee.

Energy-aware Multi-dimensional Resource Allocation Algorithm in Cloud Data Center

  • Nie, Jiawei;Luo, Juan;Yin, Luxiu
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제11권9호
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    • pp.4320-4333
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    • 2017
  • Energy-efficient virtual resource allocation algorithm has become a hot research topic in cloud computing. However, most of the existing allocation schemes cannot ensure each type of resource be fully utilized. To solve the problem, this paper proposes a virtual machine (VM) allocation algorithm on the basis of multi-dimensional resource, considering the diversity of user's requests. First, we analyze the usage of each dimension resource of physical machines (PMs) and build a D-dimensional resource state model. Second, we introduce an energy-resource state metric (PAR) and then propose an energy-aware multi-dimensional resource allocation algorithm called MRBEA to allocate resources according to the resource state and energy consumption of PMs. Third, we validate the effectiveness of the proposed algorithm by real-world datasets. Experimental results show that MRBEA has a better performance in terms of energy consumption, SLA violations and the number of VM migrations.

가격 효율적인 클라우드 가상 자원 중개 기법에 대한 연구 (Cost Efficient Virtual Machine Brokering in Cloud Computing)

  • 강동기;김성환;윤찬현
    • 정보처리학회논문지:컴퓨터 및 통신 시스템
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    • 제3권7호
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    • pp.219-230
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    • 2014
  • 클라우드 컴퓨팅 환경에서, 클라우드 서비스 사용자는 클라우드 자원 제공자로부터 가상화된 컴퓨팅 자원을 사용할 시간만큼 구매하여 할당받는다. 일반적으로 아마존, 고그리드 및 마이크로소프트와 같은 대형 클라우드 자원 제공자들은 자원 과금 정책을 온디맨드와 예약형 기반 가상 자원의 두 가지로 구분하여 제공한다. 예약형 기반 가상 자원은 상대적으로 장기간 할당을 가지므로 단위 시간당 자원 사용 비용이 온디맨드 가상 자원과 비교하여 더 저렴하다. 이러한 과금 정책 특성을 기반으로 클라우드 서비스 사용자의 서비스 요구 사항을 고려하여 적절한 자원 할당을 수행함으로써 클라우드 서비스 제공자는 자원 할당 비용을 효과적으로 절감할 수 있다. 이를 위해서, 기존의 가상 자원 할당 기법들은 서비스 사용자의 요구사항 특성을 미리 예측하여 최적의 자원을 할당하는 방법들을 제안하였다. 그러나 실세계에서는 다양한 클라우드 서비스 사용자가 존재하고 서비스 요구사항이 동적으로 변하기 때문에 정확한 예측을 하기 어려우며, 최적화된 할당을 위한 연산 시간이 추가 오버헤드가 되어 자원 관리 성능을 떨어뜨릴 수 있다. 이를 해결하기 위해, 본 논문에서는 적응적 자원 할당 기법을 제안하여 요구사항 예측 및 최적화 기법을 수행하지 않으면서도 서비스 요구사항에 효과적으로 대응하여 자원을 제공할 수 있도록 한다. 실험 결과를 통해 제안된 기법이 자원 사용 비용을 크게 절감하면서도 클라우드 서비스 사용자의 QoS를 만족함을 보인다.

A Novel Framework for Resource Orchestration in OpenStack Cloud Platform

  • Muhammad, Afaq;Song, Wang-Cheol
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제12권11호
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    • pp.5404-5424
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    • 2018
  • This work is mainly focused on two major topics in cloud platforms by using OpenStack as a case study: management and provisioning of resources to meet the requirements of a service demanded by remote end-user and relocation of virtual machines (VMs) requests to offload the encumbered compute nodes. The general framework architecture contains two subsystems: 1) An orchestrator that allows to systematize provisioning and resource management in OpenStack, and 2) A resource utilization based subsystem for vibrant VM relocation in OpenStack. The suggested orchestrator provisions and manages resources by: 1) manipulating application program interfaces (APIs) delivered by the cloud supplier in order to allocate/control/manage storage and compute resources; 2) interrelating with software-defined networking (SDN) controller to acquire the details of the accessible resources, and training the variations/rules to manage the network based on the requirements of cloud service. For resource provisioning, an algorithm is suggested, which provisions resources on the basis of unused resources in a pool of VMs. A sub-system is suggested for VM relocation in a cloud computing platform. The framework decides the proposed overload recognition, VM allocation algorithms for VM relocation in clouds and VM selection.

Round Robin with Server Affinity: A VM Load Balancing Algorithm for Cloud Based Infrastructure

  • Mahajan, Komal;Makroo, Ansuyia;Dahiya, Deepak
    • Journal of Information Processing Systems
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    • 제9권3호
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    • pp.379-394
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    • 2013
  • Cloud computing is an evolving computing paradigm that has influenced every other entity in the globalized industry, whether it is in the public sector or the private sector. Considering the growing importance of cloud, finding new ways to improve cloud services is an area of concern and research focus. The limitation of the available Virtual Machine Load balancing policies for cloud is that they do not save the state of the previous allocation of a virtual machine to a request from a Userbase and the algorithm requires execution each time a new request for Virtual Machine allocation is received from the Userbase. This problem can be resolved by developing an efficient virtual machine load balancing algorithm for the cloud and by doing a comparative analysis of the proposed algorithm with the existing algorithms.

Heuristic based Energy-aware Resource Allocation by Dynamic Consolidation of Virtual Machines in Cloud Data Center

  • Sabbir Hasan, Md.;Huh, Eui-Nam
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제7권8호
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    • pp.1825-1842
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    • 2013
  • Rapid growth of the IT industry has led to significant energy consumption in the last decade. Data centers swallow an enormous amount of electrical energy and have high operating costs and carbon dioxide excretions. In response to this, the dynamic consolidation of virtual machines (VMs) allows for efficient resource management and reduces power consumption through the live migration of VMs in the hosts. Moreover, each client typically has a service level agreement (SLA), this leads to stipulations in dealing with energy-performance trade-offs, as aggressive consolidation may lead to performance degradation beyond the negotiation. In this paper we propose a heuristic based resource allocation of VM selection and a VM allocation approach that aims to minimize the total energy consumption and operating costs while meeting the client-level SLA. Our experiment results demonstrate significant enhancements in cloud providers' profit and energy savings while improving the SLA at a certain level.

퍼지 분류 및 동적 임계 값을 사용한 적응형 VM 할당 및 마이그레이션 방식 (Adaptive VM Allocation and Migration Approach using Fuzzy Classification and Dynamic Threshold)

  • 존크리스토퍼 마테오;이재완
    • 인터넷정보학회논문지
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    • 제18권4호
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    • pp.51-59
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    • 2017
  • 클라우드 컴퓨팅이 발전하면서, 전체적인 관리 비용을 최소화하기 위해 자원 관리 기술이 중요하다. 클라우드 환경에서 사용자 선호도에 기반한 호스트의 활용과 가상머신들의 요구사항은 본질적으로 자주 바뀐다. 이러한 문제를 해결하기 위해, 호스트와 가상 머신들이 분류가 되지 않은 상황에서 효율적인 자원 할당 방법을 연구할 필요가 있다. 에너지 소비를 절약하기 위해 액티브 호스트를 줄일 때, 가상머신들을 다른 호스트로 이주할때 임계값을 사용한다. 가상머신의 자원 요구량과 호스트의 자원 이용량을 분류할 때 Fuzzy Logic을 이용하여 적응성 가상머신 할당 및 이주 방법을 제안한다. 제안한 방법은 자원의 요구량에 따라 가상머신들을 분류한 뒤 가장 적은 자원활용도를 갖는 호스트에게 자원을 할당하며, 과부하된 호스트들로부터 가상머신을 이주시킬 때 상위 임계치를 설정하기 위해 각 호스트들의 자원 활용도가 사용된다. 이주하기 위한 후보 가상머신들을 선택할 때, 호스트에서 높은 자원을 가진 가상머신을 선택한다. 시뮬레이션을 통해 연구 결과를 평가하였고, 평가 결과 다른 가상머신 할당 방법들보다 효율적임을 증명하였다.

Energy and Service Level Agreement Aware Resource Allocation Heuristics for Cloud Data Centers

  • Sutha, K.;Nawaz, G.M.Kadhar
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제12권11호
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    • pp.5357-5381
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    • 2018
  • Cloud computing offers a wide range of on-demand resources over the internet. Utility-based resource allocation in cloud data centers significantly increases the number of cloud users. Heavy usage of cloud data center encounters many problems such as sacrificing system performance, increasing operational cost and high-energy consumption. Therefore, the result of the system damages the environment extremely due to heavy carbon (CO2) emission. However, dynamic allocation of energy-efficient resources in cloud data centers overcomes these problems. In this paper, we have proposed Energy and Service Level Agreement (SLA) Aware Resource Allocation Heuristic Algorithms. These algorithms are essential for reducing power consumption and SLA violation without diminishing the performance and Quality-of-Service (QoS) in cloud data centers. Our proposed model is organized as follows: a) SLA violation detection model is used to prevent Virtual Machines (VMs) from overloaded and underloaded host usage; b) for reducing power consumption of VMs, we have introduced Enhanced minPower and maxUtilization (EMPMU) VM migration policy; and c) efficient utilization of cloud resources and VM placement are achieved using SLA-aware Modified Best Fit Decreasing (MBFD) algorithm. We have validated our test results using CloudSim toolkit 3.0.3. Finally, experimental results have shown better resource utilization, reduced energy consumption and SLA violation in heterogeneous dynamic cloud environment.

클라우드 컴퓨팅 환경에서 강화학습기반 자원할당 기법 (Reinforcement Learning Approach for Resource Allocation in Cloud Computing)

  • 최영호;임유진;박재성
    • 한국통신학회논문지
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    • 제40권4호
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    • pp.653-658
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    • 2015
  • 다양한 강점을 지닌 클라우드 서비스는 현대 IT 사업에 주요 이슈 중 하나이다. 클라우드 환경에서 서비스 제공자는 사용자의 동적인 자원 요구량을 예측하여 사용자의 QoS를 만족시켜야 한다. 사용자의 자원 요구량을 예측하는 기존 모델들은 사용자의 QoS는 만족시키지만 서비스 제공자의 이득은 보장하지 않는다. 본 논문에서는 Q-learning 기반의 자원 예측 모델을 제안하여 사용자의 QoS 뿐만 아니라 서비스 제공자의 이득을 최대화하였다. 또한 제안 기법의 성능 분석을 위해 실측 데이터를 이용하여 다른 예측 모델들과 비교함으로써 제안 기법의 우수함을 증명하였다.

Honey Bee Based Load Balancing in Cloud Computing

  • Hashem, Walaa;Nashaat, Heba;Rizk, Rawya
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제11권12호
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    • pp.5694-5711
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    • 2017
  • The technology of cloud computing is growing very quickly, thus it is required to manage the process of resource allocation. In this paper, load balancing algorithm based on honey bee behavior (LBA_HB) is proposed. Its main goal is distribute workload of multiple network links in the way that avoid underutilization and over utilization of the resources. This can be achieved by allocating the incoming task to a virtual machine (VM) which meets two conditions; number of tasks currently processing by this VM is less than number of tasks currently processing by other VMs and the deviation of this VM processing time from average processing time of all VMs is less than a threshold value. The proposed algorithm is compared with different scheduling algorithms; honey bee, ant colony, modified throttled and round robin algorithms. The results of experiments show the efficiency of the proposed algorithm in terms of execution time, response time, makespan, standard deviation of load, and degree of imbalance.