A novel distributed architecture of large-scale multimedia storage system using autonomous object-based storage devices

  • Authors:

  • Zeng Zeng;Bharadwaj Veeravalli

  • Affiliations:

  • Computer Networks and Distributed Systems Laboratory, Department of Electrical and Computer Engineering, The National University of Singapore, 10 Kent Ridge Crescent, Singapore 117576, Singapore;Computer Networks and Distributed Systems Laboratory, Department of Electrical and Computer Engineering, The National University of Singapore, 10 Kent Ridge Crescent, Singapore 117576, Singapore

  • Venue:
  • Journal of Parallel and Distributed Computing
  • Year:
  • 2009

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Abstract

In a large-scale multimedia storage system (LMSS) where the user requests for different multimedia objects may have different demands, placement and replication of the objects is an important factor, as it may result in an imbalance in loading across the system. Since replica management and load balancing is a crucial issue in multimedia systems, normally this problem is handled by centralized servers, e.g., metadata servers (MDS) in distributed file systems. Each object-based storage device (OSD) responds to the requests coming from the centralized servers independently and has no communication with other OSDs among the system. In this paper, we design a novel distributed architecture of LMSS, in which the OSDs have some kind of intelligences and can cooperate to achieve a high performance. Such an OSD, named as autonomous object-based storage device (AOSD), can replicate the objects to and balance the requests among other AOSDs, and handle fail-over and recovery autonomously. In the proposed architecture, we move the request balancing from centralized MDS to AOSDs and make the system more scalable, flexible, and robust. Based on the proposed architecture, we propose two different object replication and load balancing algorithms, named as ''Minimum Average Waiting Time'' (MAWT) and ''One of the Best Two Choices'' (OBTC), respectively. We validate the performance of the algorithms via rigorous simulations with respect to several influencing factors. Our findings conclusively demonstrate that the proposed architecture minimizes the average waiting time and at the same time carries out load balancing across servers.