Cost savings have become a significant challenge in the management of data centers. In this paper, we show that, besides energy consumption, Service Level Agreement (SLA) violations also severely degrade the cost-efficiency of data centers. We present online VM placement algorithms for increasing cloud provider's revenue. First, First-Fit and Harmonic algorithm are devised for VM placement without considering migrations. Both algorithms get the same performance in the worst-case analysis, and equal to the lower bound of the competitive ratio. However, Harmonic algorithm could create more revenue than First-Fit by more than 10 percent when job arriving rate is greater than 1.0. Second, we formulate an optimization problem of maximizing revenue from VM migration, and prove it as NP-Hard by a reduction from 3-Partition problem. Therefore, we propose two heuristics: Least-Reliable-First (LRF) and Decreased-Density-Greedy (DDG). Experiments demonstrate that DDG yields more revenue than LRF when migration cost is low, yet leads to losses when SLA penalty is low or job arriving rate is high, due to the large number of migrations. Finally, we compare the four algorithms above with algorithms adopted in Openstack using a real trace, and find that the results are consistent with the ones using synthetic data.
Various studies have demonstrated that convolutional neural networks (CNNs) can be directly applied to different levels of text embedding, such as character‐, word‐, or document‐levels. However, the effectiveness of different embeddings is limited in the reported result and there is a lack of clear guidance on some aspects of their use, including choosing the proper level of embedding and switching word semantics from one domain to another when appropriate. In this paper, we propose a new architecture of CNN based on multiple representations for text classification, by constructing multiple planes so that more information can be dumped into the networks, such as different parts of text obtained through named entity recognizer or part‐of‐speech tagging tools, different levels of text embedding, or contextual sentences. Various large‐scale, domain‐specific datasets are used to validate the proposed architecture. Tasks analyzed include ontology document classification, biomedical event categorization, and sentiment analysis, showing that multi‐representational CNNs, which learns to focus attention to specific representations of text, can obtain further gains in performance over state‐of‐the‐art deep neural network models.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.