| Summary: | 碩士 === 國立交通大學 === 網路工程研究所 === 100 === Cloud computing appears at the fourth season, 2007. It has high scalability and nearly unlimited (e.g., computing) resources. One of the most important issues about cloud computing is how to achieve load balancing among thousands of virtual machines (VMs) in a large datacenter. In this paper, we propose a novel decentralized load balancing architecture, called tldlb (two-level decentralized load balancer). This distributed load balancer takes advantage of the decentralized architecture for providing scalability and high availability capabilities to service more cloud users. We also propose a neural network-based dynamic load balancing algorithm, called nn-dwrr (neural network-based dynamic weighted round-robin), to dispatch a large number of client requests to different VMs, which are actually providing services. In nn-dwrr, we combine of VM load metrics monitoring (CPU, memory, network bandwidth, disk I/O utilization) and neural network to adjust the weight of each VM. Our nn-dwrr algorithm can reduce SLA (service-level agreement) violations. Experimental results support that our proposed load balancing algorithm, nn-dwrr, can be applied to a large cloud datacenter, and it is 1.86 times faster than wrr, 1.49 times faster than capacity-based, and 1.21 times faster than ANN-based load balancing algorithms in terms of average response time in the limited resources. In addition, tldlb can avoid SLA violations via in-time activating VMs in the spare VM pool.
|
|---|
