Evaluating machine learning prediction techniques and their impact on proactive resource provisioning for cloud environments

Kirchoff, Dionatrã F.; Meyer, Vinícius; Calheiros, Rodrigo N.; De Rose, Cesar A. F.

doi:10.1007/s11227-024-06303-6

Cited by 1 publication

(1 citation statement)

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“…Therefore, it seems that the problem of raising the upper limit of the server system's carrying traffic can only be solved by replacing the higher performance JobManager server in terms of hardware. For the scheduling problem [1,2] of the same kind of instances in the container chain, after the system receives a large number of requests, in addition to using more and better servers in hardware to improve the performance of the system, better design should also be used in the algorithm of software. When using a suitable scheduling algorithm to respond well to user requests, making the server load more balanced is also an important evaluation indicator to measure the excellence of an algorithm.…”

Section: Introductionmentioning

confidence: 99%

Traffic Carrying and Delay Response Scheduling Algorithm for Distributed E- commerce Platforms

Gao,

Yang,

Chen

et al. 2024

Preprint

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The popularization of digitalization, informatization and the Internet has given birth to the rapid development of e-commerce. Faced with the rapidly expanding user traffic, there are still technical bottlenecks in how e-commerce platforms can carry more user traffic and improve server response performance. This article conducts system optimization performance analysis from both hardware and software aspects, and constructs a high-performance distributed AR-AFSA system. (1) The AR (Application Router, AR) architecture is configured with three JobManager server nodes, each receiving three types of user access requests. A traffic allocation mechanism is used to distribute the system's traffic carrying pressure, and user requests are divided into four traffic queues for scheduling according to different access methods. (2) Improve AFSA for container scheduling, re plan the execution order of various behaviors of artificial fish, reduce ineffective search steps, and influence the direction of artificial fish's movement through the global optimal solution, increasing the possibility of finding the optimal solution and accelerating local convergence speed. (3) Using the CPU, memory performance, and load balancing parameters of the container as the parameters and evaluation indicators for artificial fish, matching sufficient resource containers for user requests while ensuring container resource conservation and system load balancing. Finally, the traffic carrying capacity of the AR system and the single JobManager system was validated using the Taobao user behavior dataset and multiple control experiments. The AR system can withstand three times the traffic pressure of traditional servers. The improved AFSA algorithm can converge to a more optimal solution compared to the control algorithm, and in more complex server resource sizes, it consumes lower latency, reduces iteration times, schedules and uses more reasonable resources, demonstrating greater advantages.

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Section: Introductionmentioning

confidence: 99%