Application Aware Workload Allocation for Edge Computing-Based IoT
Application Aware Workload Allocation for Edge Computing-Based IoT
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DOI：
10.1109/jiot.2018.2826006
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发表时间：
2018-06-01
影响因子：
10.6
通讯作者：
Ansari, Nirwan
中科院分区：
文献类型：
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作者：
Fan, Qiang;Ansari, Nirwan
由网络边缘处的计算资源授权，从物联网（IoT）设备感测到的数据可以被处理并存储在其附近的微云中，以减少核心网络中的流量负载，而各种IoT应用可以在微云中运行，以减少IoT用户之间的响应时间（例如，移动的网络中的用户设备）和微云。考虑到每个应用程序的工作负载在小云中的空间和时间动态，每个IoT应用程序的小云中的工作负载分配会影响应用程序请求的响应时间。虽然将IoT用户的请求分配给他们附近的微云可以最小化网络延迟，但是如果微云中的应用的对应虚拟机过载，则一种类型的请求的计算延迟可能是不可承受的。为了解决这个问题，我们为基于边缘计算的物联网设计了一个应用感知的工作负载分配方案，通过为每个物联网用户的不同类型的请求确定目标云以及为每个云中的每个应用分配的计算资源量，来最大限度地减少物联网应用请求的响应时间。该方案同时考虑了网络延迟和计算延迟，物联网用户的请求更有可能被分配到距离更近、负载更轻的小云。同时，该方案将根据每个小云中不同应用的工作负载动态调整其计算资源，从而降低小云中所有请求的计算延迟。所提出的方案的性能已通过大量的仿真验证。
Empowered by computing resources at the network edge, data sensed from Internet of Things (IoT) devices can be processed and stored in their nearby cloudlets to reduce the traffic load in the core network, while various IoT applications can be run in cloudlets to reduce the response time between IoT users (e.g., user equipment in mobile networks) and cloudlets. Considering the spatial and temporal dynamics of each application's workloads among cloudlets, the workload allocation among cloudlets for each IoT application affects the response time of the application's requests. While assigning IoT users' requests to their nearby cloudlets can minimize the network delay, the computing delay of a type of requests may be unbearable if the corresponding virtual machine of the application in a cloudlet is overloaded. To solve this problem, we design an application aware workload allocation scheme for edge computing-based IoT to minimize the response time of IoT application requests by deciding the destination cloudlets for each IoT user's different types of requests and the amount of computing resources allocated for each application in each cloudlet. In this scheme, both the network delay and computing delay are taken into account, i.e., IoT users' requests are more likely assigned to closer and lightly loaded cloudlets. Meanwhile, the scheme will dynamically adjust computing resources of different applications in each cloudlet based on their workloads, thus reducing the computing delay of all requests in the cloudlet. The performance of the proposed scheme has been validated by extensive simulations.