...
首页> 外文期刊>IEEE transactions on industrial informatics >A Profitable and Energy-Efficient Cooperative Fog Solution for IoT Services
【24h】

A Profitable and Energy-Efficient Cooperative Fog Solution for IoT Services

机译:IOT服务的盈利和节能的合作迷雾解决方案

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

Fog-to-fog communication has been introduced to deliver services to clients with minimal reliance on the cloud through resource and capability sharing of cooperative fogs. Current solutions assume full cooperation among the fogs to deliver simple and composite services. Realistically, each fog might belong to a different network operator or service provider and thus will not participate in any form of collaboration unless self-monetary profit is incurred. In this paper, we introduce a fog collaboration approach for simple and complex multimedia service delivery to cloud subscribers while achieving shared profit gains for the cooperating fogs. The proposed work dynamically creates short-term service-level agreements (SLAs) offered to cloud subscribers for service delivery while maximizing user satisfaction and fog profit gains. The solution provides a learning mechanism that relies on online and offline simulation results to build guaranteed workflows for new service requests. The configuration parameters of the short-term SLAs are obtained using a modified tabu-based search mechanism that uses previous solutions when selecting new optimal choices. Performance evaluation results demonstrate significant gains in terms of service delivery success rate, service quality, reduced power consumption for fog and cloud datacenters, and increased fog profits.
机译:已经引入了雾化通信,以通过合作雾的资源和能力共享为客户提供对客户的依赖性最小的服务。目前的解决方案承担雾气中的全部合作,以提供简单和复合服务。现实地,每个雾可能属于不同的网络运营商或服务提供商,因此除非产生自我货币利润,否则不会以任何形式的合作参与。在本文中,我们向云订阅者提供简单和复杂的多媒体服务交付的雾协作方法,同时实现合作雾的共同利润收益。拟议的工作动态创造了向云订阅者提供的短期服务级别协议(SLA)以获得服务交付,同时最大限度地提高用户满意度和雾利润收益。该解决方案提供了一种依赖于在线和离线模拟结果的学习机制,以构建新的服务请求的保证工作流程。使用基于修改的禁忌的搜索机制获得短期SLA的配置参数,该机制在选择新的最佳选择时使用先前的解决方案。绩效评估结果表明,在服务交付成功率,服务质量,雾和云数据中心的功耗降低以及迷雾利润增加的情况下,展示了显着的提升。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号