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SNSAPI: A Cross-Platform Middleware for Rapid Deployment of Decentralized Social Networks

机译:sNsapI:用于快速部署的跨平台中间件   分散的社交网络

摘要

In this paper, we present the design, implementation and our year-longmaintenance experience of SNSAPI, a Python-based middleware which unifies theinterfaces and data structures of heterogeneous Social Networking Services(SNS). Unlike most prior works, our middleware is user-oriented and requireszero infrastructure support. It enables a user to readily conduct online socialactivities in a programmable, cross-platform fashion while gradually reducingthe dependence on centralized Online Social Networks (OSN). More importantly,as the SNSAPI middleware can be used to support decentralized social networkingservices via conventional communication channels such as RSS or Email, itenables the deployment of Decentralized Social Networks (DSN) in anincremental, ad hoc manner. To demonstrate the viability of such type of DSNs,we have deployed an experimental 6000-node SNSAPI-based DSN on PlanetLab andevaluate its performance by replaying traces of online social activitiescollected from a mainstream OSN. Our results show that, with only mild resourceconsumption, the SNSAPI-based DSN can achieve acceptable forwarding latencycomparable to that of a centralized OSN. We also develop an analytical model tocharacterize the trade-offs between resource consumption and message forwardingdelay in our DSN. Via 20 parameterized experiments on PlanetLab, we have foundthat the empirical measurement results match reasonably with the performancepredicted by our analytical model.
机译:在本文中,我们将介绍SNSAPI的设计,实现和我们长达一年的维护经验,SNSAPI是一种基于Python的中间件,它统一了异构社交网络服务(SNS)的接口和数据结构。与大多数先前的作品不同,我们的中间件是面向用户的,需要零基础架构支持。它使用户能够以可编程的,跨平台的方式轻松地进行在线社交活动,同时逐渐减少对集中式在线社交网络(OSN)的依赖。更重要的是,由于SNSAPI中间件可用于通过常规通信渠道(例如RSS或电子邮件)来支持分散式社交网络服务,因此可以增量,临时的方式启用分散式社交网络(DSN)的部署。为了证明这种类型的DSN的可行性,我们在PlanetLab上部署了一个实验性的基于6000个节点的基于SNSAPI的DSN,并通过重放从主流OSN收集的在线社交活动来评估其性能。我们的结果表明,仅消耗少量资源,基于SNSAPI的DSN可以实现与集中式OSN相当的可接受的转发延迟。我们还开发了一个分析模型来表征DSN中资源消耗与消息转发延迟之间的折衷。通过PlanetLab上的20个参数化实验,我们发现经验测量结果与我们的分析模型预测的性能合理匹配。

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