Load-aware switch migration for controller load balancing in edge–cloud architectures

IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-08-19 DOI:10.1016/j.future.2024.107489
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Abstract

As the fundamental infrastructure for edge–cloud architectures, the inter-datacenter elastic optical network is used for data analysis and processing. As the demand for applications increases, the large number of service requests increases the processing overhead in the control plane, resulting in unbalanced controller loads. Existing switch migration mechanisms have been proposed for controller load balancing. Unfortunately, most of the existing mechanisms only consider the switch with the highest flow request rate as the migration object in the process of switch selection, and ignore the migration cost generated in the switch migration activity, such as the update cost of flow request message and the deployment cost of migration rule, which may increase the controller load. Additionally, most of them choose the controller with light load as the target controller to associate with the switch to be migrated, without considering whether the target controller is overloaded after the switch migration, which leads to the low load balancing performance of the controller. In view of the above problems, this paper proposes a Load-Aware Switch Migration (LASM) mechanism in edge–cloud architectures. The LASM mechanism models and analyses the cost metrics affecting switch migration and selects lower-cost switches from overloaded controller-controlled domain networks for migration activities. Besides, the LASM mechanism models switch migration based on the 0-1 knapsack problem and avoids overloading the target controllers through a greedy policy to achieving optimal migration activities. The experimental results show that the proposed LASM mechanism improves controller load balancing performance by an average of 34.3%, eliminates migration costs by 30.2%, and reduces response times by an average of 39.3%, respectively, compared to existing solutions.

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在边缘云架构中实现控制器负载平衡的负载感知交换机迁移
作为边缘云架构的基础架构,数据中心间弹性光网络用于数据分析和处理。随着应用需求的增加,大量服务请求会增加控制平面的处理开销,导致控制器负载不平衡。现有的交换机迁移机制已被提出用于控制器负载平衡。遗憾的是,大多数现有机制在选择交换机的过程中只考虑流量请求率最高的交换机作为迁移对象,而忽略了交换机迁移活动中产生的迁移成本,如流量请求报文的更新成本和迁移规则的部署成本,这可能会增加控制器负载。此外,大多数方案选择负载较轻的控制器作为目标控制器与待迁移交换机关联,而没有考虑交换机迁移后目标控制器是否过载,导致控制器负载均衡性能低下。针对上述问题,本文提出了边缘云架构中的负载感知交换机迁移(LASM)机制。LASM 机制对影响交换机迁移的成本指标进行建模和分析,并从过载的控制器控制域网络中选择成本较低的交换机进行迁移活动。此外,LASM 机制基于 0-1 knapsack 问题对交换机迁移进行建模,并通过贪婪策略避免目标控制器过载,从而实现最优迁移活动。实验结果表明,与现有解决方案相比,所提出的 LASM 机制平均提高了 34.3% 的控制器负载平衡性能,消除了 30.2% 的迁移成本,并平均缩短了 39.3% 的响应时间。
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来源期刊
CiteScore
19.90
自引率
2.70%
发文量
376
审稿时长
10.6 months
期刊介绍: Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
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