The Internet of Things (IoT) requires a new processing paradigm that inherits the scalability of the cloud while minimizing network latency using resources closer to the network edge. Building up such flexibility within the edge-to-cloud continuum consisting of a distributed networked ecosystem of heterogeneous computing resources is challenging. Furthermore, IoT traffic dynamics and the rising demand for low-latency services foster the need for minimizing the response time and balanced service placement. Load-balancing for fog computing becomes a cornerstone for cost-effective system management and operations. This paper studies two optimization objectives and formulates a decentralized load-balancing problem for IoT service placement: (global) IoT workload balance and (local) quality of service (QoS), in terms of minimizing the cost of deadline violation, service deployment, and unhosted services. The proposed solution, EPOS Fog, introduces a decentralized multi-agent system for collective learning that utilizes edge-to-cloud nodes to jointly balance the input workload across the network and minimize the costs involved in service execution. The agents locally generate possible assignments of requests to resources and then cooperatively select an assignment such that their combination maximizes edge utilization while minimizes service execution cost. Extensive experimental evaluation with realistic Google cluster workloads on various networks demonstrates the superior performance of EPOS Fog in terms of workload balance and QoS, compared to approaches such as First Fit and exclusively Cloud-based. The results confirm that EPOS Fog reduces service execution delay up to 25% and the load-balance of network nodes up to 90%. The findings also demonstrate how distributed computational resources on the edge can be utilized more cost-effectively by harvesting collective intelligence.
翻译:互联网(IoT)需要一种新的处理模式,以继承云的伸缩性,同时利用更接近网络边缘的资源尽量减少网络的延迟。在由分布式网络化的多种计算资源组成的分布式网络生态系统组成的边对角连续运行中,建设这种灵活性是一项挑战。此外,IoT的交通动态和对低纬度服务的不断增长的需求,有助于最大限度地减少反应时间和平衡服务安排。为雾计算而平衡负荷计算成为成本效益高的系统管理和操作的基石。本文研究两个优化目标,并提出了一个分散化的IoT服务配置平衡负担问题:(全球)IoT工作量平衡和(当地)服务质量(QoS),以最大限度地减少最后期限违约、服务部署和未托管服务的成本成本。拟议的解决方案EPOS Fog引入一个分散化的多剂系统,利用边际对网络输入工作量进行联合平衡,并尽量减少服务执行成本成本。 代理当地生成资源需求,然后通过合作性地选择一个高端服务网络的交付结果,从而尽可能降低成本,同时将E-OLLLL的运行率组合。