Abstract
The latency of cloud computing is high for the reason that it is far from terminal users. Edge computing can transfer computing from the center to the network edge. However, the problem of load balancing among different edge nodes still needs to be solved. In this paper, we propose a load balancing strategy by task allocation in edge computing based on intermediary nodes. The intermediary node is used to monitor the global information to obtain the real-time attributes of the edge nodes and complete the classification evaluation. First, edge nodes can be classified to three categories (light-load, normal-load, and heavy-load), according to their inherent attributes and real-time attributes. Then, we propose a task assignment model and allocate new tasks to the relatively lightest load node. Experiments show that our method can balance load among edge nodes and reduce the completion time of tasks.
Highlights
The Internet of Things (IoT) can connect a large number of smart devices across regions and it has become part of many advanced application infrastructures
Load balancing strategy by task allocation in edge computing based on intermediary nodes is a dynamic load balancing technique, which considers the previous state of a node while distributing the load[11]
We propose a task allocation model that the relatively lightest nodes and the task arrival node are used as the target node to allocate new tasks, while the other nodes are not assigned tasks temporarily, so as to achieve dynamic balancing of the system
Summary
The Internet of Things (IoT) can connect a large number of smart devices across regions and it has become part of many advanced application infrastructures. Load balancing strategy by task allocation in edge computing based on intermediary nodes is a dynamic load balancing technique, which considers the previous state of a node while distributing the load[11]. For the system with unbalanced initial state, we propose a load balancing strategy by task allocation in edge computing based on intermediary nodes. The new task assignment is completed according to the task assignment model proposed by us In this architecture, we store the intrinsic attributes of the node to the intermediary node before applying the load balancing strategy. Static properties of the nodes, including physical memory, CPU main frequency multiplied by the number of cores, disk size, and network bandwidth.
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