Network-aware embedding of virtual machine clusters onto federated cloud infrastructure

Aral A., Ovatman T.

JOURNAL OF SYSTEMS AND SOFTWARE, cilt.120, ss.89-104, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 120
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1016/j.jss.2016.07.007
  • Dergi Adı: JOURNAL OF SYSTEMS AND SOFTWARE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.89-104
  • Anahtar Kelimeler: Cloud computing, Federated cloud, Infrastructure as a service, VM cluster embedding, Resource allocation, Subgraph isomorphism, COMPUTING ENVIRONMENTS, SERVICE DEPLOYMENT, ALLOCATION, PLACEMENT, ALGORITHM, FRAMEWORK, MODEL
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

Federated clouds are continuously developing as the demands of cloud users get more complicated. Contemporary cloud management technologies like Open-Stack (Sefraoui et al., 2012) and OpenNebula (Milojine et al., 2011) allow users to define network topologies among virtual machines that are requested. Therefore, federated clouds currently face the challenge of network topology mapping in addition to conventional resource allocation problems. In this paper, topology based mapping of virtual machine clusters onto the federated cloud infrastructures is studied. A novel algorithm is presented to perform the mapping operation that work towards minimizing network latency and optimizing bandwidth utilization. To realize and evaluate the algorithm, a widely used cloud simulation environment, CloudSim (Calheiros et al., 2011), is extended to support several additional capabilities in network and cost modeling. Evaluation is performed by comparing the proposed algorithm to a number of conventional heuristics such as least latency first and round-robin. Results under different request characteristics indicate that the proposed algorithm performs significantly better than the compared conventional approaches regarding various QoS parameters such as inter-cloud latency and throughput. (C) 2016 Elsevier Inc. All rights reserved.