IEEE Transactions on Mobile Computing · 2013 · 24 citations · 25 references
ComputingComputer ScienceComputer SystemsComputer NetworksDistributed SystemsMobile ComputingData ReplicationEnergy-efficient Mobile ComputingMobile NetworkingMobile NetworksBandwidthContent DistributionContent ReplicationMobile Data SharingNetwork AvailabilityPrivacyReplication StrategiesResource Consumption Modeling
Distributed social networking can address data ownership and privacy concerns, but smartphones must overcome high communication costs and battery usage to host and share data. The study proposes a connectivity‑aware replication strategy to reduce these costs to levels comparable with centralized systems. The authors design an algorithm that groups devices into tribes for content replication over low‑cost networks, and evaluate its performance and resource usage using real‑world traces and analytical modeling. Experiments show that the scheme achieves persistent low‑cost network availability with an average of two replicas per content and reduces monetary and energy costs for users compared to non‑mobile‑optimized distributed systems, regardless of the content demand model.
Distributed social networking services show promise to solve data ownership and privacy problems associated with centralized approaches. Smartphones could be used for hosting and sharing users data in a distributed manner, if the associated high communication costs and battery usage issues of the distributed systems could be mitigated. We propose a novel mechanism for reducing these costs to a level comparable with centralized systems by using a connectivity aware replication strategy. We develop an algorithm for grouping devices into tribes for content replication among intended content consumers and serve it using low-cost network connections. We evaluate the performance of the algorithm using three real world trace data sets. The results show that a persistent low-cost network availability can be achieved with an average of two replicas per content. Additionally, cellular bandwidth consumption and energy consumption of users are evaluated analytically using user content creation and consumption modeling. The results show that the proposed mechanism lowers monetary and energy costs for users compared to non-mobile-optimized distributed systems irrespective of the content demand model.
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