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 Hosting Virtual Machines on a Cloud Datacenter: A Matching Theoretic Approach
Tác giả hoặc Nhóm tác giả: Chuan Pham, Nguyen H. Tran, Minh N.H. Nguyen , Shaolei Ren, Walid Saad, Choong Seon Hong*
Nơi đăng: IEEE/IFIP Network Operations and Management Symposium (NOMS 2016); Số: 2016;Từ->đến trang: 659-664;Năm: 2016
Lĩnh vực: Công nghệ thông tin; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
Selling virtual machine (VM) resource on cloud datacenters has become a pressing need in recent years as several businesses realized the benefits and scalability of clouds. To reap the benefit of cloud computing, it is necessary that cloud providers have a dynamic and efficient allocation method to satisfy heterogeneous workloads from users. In this paper, the problem of resource allocation in cloud datacenters, that own highly complex and heterogeneous tasks and servers, is considered. To address this problem, a novel framework, dubbed joint operation cost and network traffic cost (JOT) framework, is proposed. This framework combines notions from Gibbs sampling and matching theory to find an efficient solution addressing the NP-hard problem JOT. The proposed model is shown to be capable of controlling the active server set, in a coordinated manner while allocating VMs in order to reduce both operation cost and network traffic cost of the cloud datacenter. We also conduct a case-study to validate our proposed algorithm and the results show that JOT can reduce the total incurred cost by up to 19% compared to the existing non-coordinated approach.
ABSTRACT
Selling virtual machine (VM) resource on cloud datacenters has become a pressing need in recent years as several businesses realized the benefits and scalability of clouds. To reap the benefit of cloud computing, it is necessary that cloud providers have a dynamic and efficient allocation method to satisfy heterogeneous workloads from users. In this paper, the problem of resource allocation in cloud datacenters, that own highly complex and heterogeneous tasks and servers, is considered. To address this problem, a novel framework, dubbed joint operation cost and network traffic cost (JOT) framework, is proposed. This framework combines notions from Gibbs sampling and matching theory to find an efficient solution addressing the NP-hard problem JOT. The proposed model is shown to be capable of controlling the active server set, in a coordinated manner while allocating VMs in order to reduce both operation cost and network traffic cost of the cloud datacenter. We also conduct a case-study to validate our proposed algorithm and the results show that JOT can reduce the total incurred cost by up to 19% compared to the existing non-coordinated approach.
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