The scale of cloud services keeps increasing over time, significantly introducing huge challenges in system manageability and reliability. Designing coordination services in cloud is the right track to solve the above...
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Approaching a comprehensive performance benchmark for on-line transaction processing (OLTP) applications in a cloud environment is a challenging task. Fundamental features of clouds, such as the pay-as-you-go pricing ...
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ISBN:
(纸本)9781479954711
Approaching a comprehensive performance benchmark for on-line transaction processing (OLTP) applications in a cloud environment is a challenging task. Fundamental features of clouds, such as the pay-as-you-go pricing model and unknown underlying configuration of the system, are contrary to the basic assumptions of available benchmarks such as TPC-W or RUBiS. In this paper, we introduce a systematic performance benchmark approach for OLTP applications on public clouds that use virtual machines(VMs). We propose WPress benchmark, which is based on the widespread blogging software, WordPress, as a representative OLTP application and implement an open source workload generator. Furthermore, we utilize a CPU micro-benchmark to investigate CPU performance of cloud-based VMs in greater detail. Average response time and total VM cost are the performance metrics measured by WPress. We evaluate small and large instance types of three real-life cloud providers, Amazon EC2, Microsoft Azure and Rackspace cloud. Results imply that Rackspace cloud has better average response times and total VM cost on small instances. However, Microsoft Azure is preferable for large instance type.
The scale of cloud services keeps increasing over time, significantly introducing huge challenges in system manageability and reliability. Designing coordination services in cloud is the right track to solve the above...
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ISBN:
(纸本)9781479955497
The scale of cloud services keeps increasing over time, significantly introducing huge challenges in system manageability and reliability. Designing coordination services in cloud is the right track to solve the above problems. However, existing coordination services (e.g., Chubby and ZooKeeper) only perform well in read-intensive scenario and small ensemble scales. To this end, we propose Giraffe, a scalable distributed coordination service. There are three important contributions in our design. (1) Giraffe organizes coordination servers using interior-node-disjoint trees for better scalability. (2) Giraffe employs a novel Paxos protocol for strong consistency and fault-tolerance. (3) Giraffe supports hierarchical data organization and in-memory storage for high throughput and low latency. We evaluate Giraffe on a highperformancecomputing test-bed. The experimental results show that Giraffe gains much better write performance than ZooKeeper when server ensemble is large. Giraffe is nearly 300% faster than ZooKeeper on update operations when ensemble size is 50 servers. Experiments also show that Giraffe reacts and recovers more quickly than ZooKeeper against node failures.
When multiple instances of an application running on multiple virtual machines, an interesting problem is how to utilize the fault handling result from one application instance to heal the same fault occurred on other...
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Broadband ISDN has made possible a variety of new multimedia services, but also created new problems for congestion control, due to the bursty nature of traffic sources. Lazar and Pacifici (1991) showed that traffic p...
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Broadband ISDN has made possible a variety of new multimedia services, but also created new problems for congestion control, due to the bursty nature of traffic sources. Lazar and Pacifici (1991) showed that traffic prediction is able to alleviate this problem. The traffic prediction model in their framework is a special case of the Box-Jenkins ARIMA model. In this paper, we propose a neural network approach for traffic prediction. A (1,5,1) backpropagation feedforward neural network is trained to capture the linear and nonlinear regularities in several time series. A comparison between the results from the neural network approach and the Box-Jenkins approach is also provided. The nonlinearity used in this paper is chaotic. We have designed a set of experiments to show that a neural network's prediction performance is only slightly affected by the intensity of the stochastic component (noise) in a time series. We have also demonstrated that a neural network's performance should be measured against the variance of the noise, in order to gain more insight into its behavior and prediction performance. Based on experimental results, we then conclude that the neural network approach is an attractive alternative to traditional regression techniques as a tool for traffic prediction.< >
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