Shulou(Shulou.com)11/24 Report--

The development of the Internet of things has brought people a lot of convenience and applications. With the continuous development and popularization of the Internet of things (Internet of Things,IoT) technology, more and more devices and sensors are connected to the Internet, resulting in a large number of data. In the computing of the Internet of things, data scheduling is an important problem, which affects the performance and resource utilization of the system. In the Internet of things computing environment, each computing node and storage node are independent of each other and connected through a high-speed network. This architecture has many advantages, such as the independent upgrade of storage and computing resources and the seamless connection between storage systems and different computing systems.

However, in the face of massive data processing, the separation of data storage and computing may cause network transmission to become the bottleneck of system performance, and the quality of data scheduling directly affects the performance and resource utilization of the system. The optimization of data scheduling is an important means to improve the performance of Internet of things cluster system. For this reason, the IoT-LocalSense algorithm developed by NASDAQ:WIMI optimizes the data locality and load balancing problems, improves the task localization execution rate, reduces non-local execution and load imbalance, optimizes resource utilization, and further improves the performance of the Internet of things cluster system.

In the Internet of things computing environment, data scheduling involves assigning the input data of the job to each computing node and storage node. If the data matching deviation is serious, it may lead to the non-local execution of data scheduling, which increases the task execution time and resource consumption. At the same time, load imbalance may lead to heavy load on some nodes, while lighter load on other nodes, which affects the overall performance and resource utilization efficiency of the system. The technical principle of the IoT-LocalSense algorithm developed by WIMI Weimei holography:

Data placement module: through the evaluation of the processing capacity of the working nodes of the Internet of things, the data placement algorithm is designed to reasonably distribute the input data of the job in the computing nodes and storage nodes. At the same time, considering the locality of the data, the relevant data is placed near the computing node to reduce the data transmission overhead and delay.

Data scheduling queue optimization module: using the data block storage location information to optimize the data scheduling queue, so that the task is more likely to be executed in the local node, reducing the frequency of non-local execution. And balance the load of each node in the cluster, ensure that the tasks are evenly distributed in the whole cluster, and optimize the utilization efficiency of system resources.

Data prefetching module: a data prefetching method is designed to prefetch the data needed for non-local data scheduling to the local storage of the computing node in advance. By prefetching non-local data, the time for tasks to wait for data transmission is reduced, thus the situation of non-local execution is reduced and the overall execution efficiency is improved.

Advantages of WIMI Weimei holographic IoT-LocalSense algorithm:

Improve task localization execution rate: through data placement algorithm and data scheduling queue optimization, IoT-LocalSense algorithm can effectively improve the local execution rate of tasks on computing nodes. The local storage of related data enables tasks to access data quickly, reducing the need for data transmission, thus speeding up the execution of tasks.

Reduce non-local execution: through the data prefetching method, the IoT-LocalSense algorithm pulls the data needed for non-local data scheduling to the local storage of the computing node in advance. This reduces the time that tasks wait for non-local data transmission, thus reducing the frequency of non-local execution and improving the overall execution efficiency.

Consider the data locality: the algorithm focuses on the locality of the data and places the relevant data near the computing node, which reduces the data transmission across the network, thus reducing the network transmission overhead and delay, and improving the overall performance of the system.

Optimize resource utilization: by reducing non-local execution and optimizing data scheduling queues, IoT-LocalSense algorithm improves the efficiency of system resource utilization. Tasks are performed more locally, reducing resource waste and unnecessary load.

In the large-scale data processing scenario of the Internet of things, the IoT-LocalSense algorithm developed by WIMI can significantly improve system performance and resource utilization efficiency. In the actual Internet of things computing system, the algorithm can be used as the core component of data scheduling to optimize task scheduling and data distribution, in order to improve the overall performance of the system. Through system simulation experiments, compared with other data scheduling algorithms, the performance of IoT-LocalSense algorithm is excellent in task localization execution rate and response time, which is obviously better than the traditional data scheduling algorithm.

In addition, NASDAQ:WIMI IoT-LocalSense algorithm improves the execution rate of task localization, reduces non-local execution and load imbalance, optimizes resource utilization, and significantly improves the performance and efficiency of the Internet of things cluster system by optimizing data placement, scheduling queue and data prefetching. With the continuous development of Internet of things technology, IoT-LocalSense algorithm will continue to be optimized and improved to provide more powerful data scheduling optimization support for Internet of things computing.

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