Optimal Cloud Resource Allocation With Cost Performance Tradeoff Based on Internet of Things
Optimal Cloud Resource Allocation With Cost Performance Tradeoff Based on Internet of Things
复制标题
基于物联网的成本性能权衡云资源优化配置
DOI：
10.1109/jiot.2019.2911978
复制
发表时间：
2019-08-01
影响因子：
10.6
通讯作者：
Li, Feifei
中科院分区：
文献类型：
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作者：
Li, Xuefeng;Tan, Liansheng;Li, Feifei
物联网(IoT)仍然面临着许多挑战，其中之一就是如何高效、有效地分配云资源，以便为物联网用户和通信域获得所需的服务质量(Qos)。面对上述挑战，本文首先确定了物联网系统中各种有限的资源和相互竞争的服务需求所产生的性价比权衡问题。其次，我们提出了一个攻击资源分配问题的非线性优化模型。在资源和服务需求的约束下，该模型寻求最大化建议的物联网性价比。然后我们发现这个问题可以看作是一个拟凹的极大化问题。我们能够将原问题归结为一个伪凹极大化问题，从而识别出一个局部解，这被证明是期望的全局解。因此，我们提出了一种可行的方向法，并设计了相应的算法来得到期望的解。最后，给出了一个数值算例，验证了基于物联网系统的云计算资源分配的理论结果和提出的算法。本文提出的数学结果和方法可以为物联网云基础设施中的资源分配和管理、计算调度和组网协议设计提供设计指导。
Internet of Things (IoT) still faces many challenges, one of which is how to efficiently and effectively allocate the cloud resources in order to obtain the desired quality of service (QoS) for the IoT users and communication domains. To face the above challenges, this paper first identifies that there is an issue of cost performance tradeoff, which is stemmed from various limited resources in the IoT systems and the competing services requirements. We second propose a nonlinear optimization model that attacks the resource allocation problem. Within the constraints of resources and service demands, this model seeks to maximize the suggested IoT cost performance ratio. We then find that the problem can be treated as a quasiconcave maximization problem. We are able to reduce the original problem to a pseudoconcave maximization problem and thereby identify a local solution, which is proved to be exactly the desired global one. We therefore propose a feasible direction method and design its corresponding algorithm to yield the desired solution. Finally, we provide a numerical example to demonstrate the theoretical findings of resource allocation and the proposed algorithm for the cloud computing based on IoT systems. The mathematical results and method proposed in this paper can act as designing guidelines for resource allocation and managements, computing scheduling, and networking protocol designing in the cloud infrastructure of IoT.