WiMi Hologram Cloud Inc. (NASDAQ: WIMI) (“WiMi” or the “Company”), a leading global Hologram Augmented Reality (“AR”) Technology provider, today announced that it developed IoT-LocalSense algorithm, which optimizes the load balancing problem, improves the task localization execution rate, reduces non-local execution and load imbalance, optimizes resource utilization, and further enhances the performance of IoT cluster systems.
In IoT computing environments, data scheduling involves distributing the input data of a job to various compute and storage nodes. If the data matching deviation is severe, it may lead to non-local execution of data scheduling, which increases the task execution time and resource consumption. At the same time, load imbalance may lead to overloading of some nodes and light loading of other nodes, which affects the overall performance of the system and resource utilization efficiency. The principle:
Data placement module: Through the processing capacity assessment of the IoT work nodes, the data placement algorithm is designed to reasonably distribute the input data of the job in the computing nodes and storage nodes. Meanwhile, considering the localization of data, relevant data are placed near the computing nodes to reduce data transmission overhead and delay.
Data scheduling optimization module: Optimize the data scheduling by using the data block storage location information to make it more likely that tasks will be executed in local nodes during execution, reducing the frequency of non-local execution. It also balances the load of each node in the cluster, ensures that tasks are evenly distributed throughout the cluster, and optimizes the utilization efficiency of system resources.
Advantages of the IoT-LocalSense algorithm:
Improving task localized execution rate: Through data placement algorithms and data scheduling optimization, the IoT-LocalSense algorithm can effectively improve the local execution rate of tasks on compute nodes. The local storage of relevant data enables tasks to access the data quickly, reducing the need for data transfer and thus speeding up task execution.
Reducing non-local execution: The IoT-LocalSense algorithm puts the data required for non-local data scheduling into the local storage of the compute node in advance through the data prefetching method. This reduces the amount of time a task waits for non-local data transfers, thereby reducing the frequency of non-local execution and improving overall execution efficiency.
Considering data locality: The algorithm focuses on the locality of the data and places the relevant data in the vicinity of the computational nodes, which reduces the data transmission across the network, thus reducing the network transmission overhead and latency, and improving the overall system performance.
Optimized resource utilization: By reducing non-local execution and optimizing data scheduling, the IoT-LocalSense algorithm improves the efficient use of system resources. Tasks are executed more locally, reducing wasted resources and unnecessary load.
In IoT large-scale data processing scenarios, WiMi’s IoT-LocalSense algorithm can significantly improve system performance and resource utilization efficiency. In the real IoT computing system, the algorithm can be used as a core component of data scheduling optimization to optimize the schedule of tasks and the distribution of data to improve the overall performance of the system. The performance of the IoT-LocalSense algorithm is compared with other data scheduling algorithms through system simulation experiments, and the algorithm excels in terms of task localization execution rate and response time, which is significantly better than traditional data scheduling optimization algorithms.
WiMi’s IoT-LocalSense algorithm significantly improves the performance and efficiency of IoT cluster systems by optimizing data placement, data scheduling optimization, and data prefetching, which increases task localization execution, reduces non-local execution and load imbalance, and optimizes resource utilization. With the continuous development of IoT technology, the IoT-LocalSense algorithm will continue to be optimized and improved to provide more powerful data scheduling optimization support for IoT computing.