Spring Buddy: A Self-Adaptive Elastic Memory Management Scheme for Efficient Concurrent Allocation/Deallocation in Cloud Computing Systems

Lu, Yihui; Liu, Weidong; Wu, Chentao; Wang, Jia; Gao, Xiaoming; Li, Jie; Guo, Minyi

doi:10.1109/icpads53394.2021.00056

Cited by 2 publications

(1 citation statement)

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“…This algorithm schedules virtual machines based on the availability of memory and microprocessors, resulting in significant performance improvements compared to other load balancing algorithms. Lu et al [17] designed an elastic memory management scheme called Spring Buddy suitable for highly concurrent environments. It significantly reduces the latency of memory allocation and deallocation by adjusting the focus of concurrent response or resource aggregation based on different memory request patterns to adapt to concurrency pressure.…”

Section: Related Workmentioning

confidence: 99%

Memory Allocation Strategy in Edge Programmable Logic Controllers Based on Dynamic Programming and Fixed-Size Allocation

Cheng,

Wan,

Ding

et al. 2023

Applied Sciences

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With the explosive growth of data at the edge in the Industrial Internet of Things (IIoT), edge devices are increasingly performing more data processing tasks to alleviate the load on cloud servers. To achieve this goal, Programmable Logic Controllers (PLCs) are gradually transitioning into edge PLCs. However, efficiently executing a large number of computational tasks in memory-limited edge PLCs is a significant challenge. Therefore, there is a need to design an efficient memory allocation strategy for edge PLCs. This paper proposes a dynamic memory allocation strategy for edge PLCs. It adopts an approach of organizing memory into small blocks to handle memory requests from real-time tasks and utilizes a well-performing dynamic programming method for resource allocation problems to handle memory requests from non-real-time tasks. This approach ensures real-time performance while improving the efficiency of non-real-time task processing. In the simulation experiments, the algorithm implemented based on this allocation strategy is compared with the default method and several open-source memory allocators. The experimental results demonstrate that the proposed algorithm, on average, improves the speed of real-time task processing by 13.7% and achieves a maximum speed improvement of 17.0% for non-real-time task processing. The experimental results show that the allocation strategy effectively improves memory allocation efficiency in memory-limited environments.

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Section: Related Workmentioning

confidence: 99%