Edge computing oriented virtual optical network mapping scheme based on fragmentation prediction

As edge computing services soar,the problem of resource fragmentation situation is greatly worsened in elastic optical networks(EON).Aimed to solve this problem,this article proposes the fragmentation prediction model that makes full use of the gate recurrent unit(GRU)algorithm.Based on the fragmentation prediction model,one virtual optical network mapping scheme is presented for edge computing driven EON.With the minimum of fragmentation degree all over the whole EON,the virtual network mapping can be successively conducted.Test results show that the proposed approach can reduce blocking rate,and the supporting ability for virtual optical network services is greatly improved.

Super Node Routing Strategy in Content-Centric Networking

There were two strategies for the data forwarding in the content-centric networking(CCN): forwarding strategy and routing strategy. Forwarding strategy only considered a separated node rather than the whole network performance, and Interest flooding led to the network overhead and redundancy as well. As for routing strategy in CCN, each node was required to run the protocol. It was a waste of routing cost and unfit for large-scale deployment.This paper presents the super node routing strategy in CCN. Some super nodes selected from the peer nodes in CCN were used to receive the routing information from their slave nodes and compute the face-to-path to establish forwarding information base(FIB). Then FIB was sent to slave nodes to control and manage the slave nodes. The theoretical analysis showed that the super node routing strategy possessed robustness and scalability, achieved load balancing,reduced the redundancy and improved the network performance. In three topologies, three experiments were carried out to test the super node routing strategy. Network performance results showed that the proposed strategy had a shorter delay, lower CPU utilization and less redundancy compared with CCN.

Analysis of Review Helpfulness Based on Consumer Perspective

When consumers make purchase decisions, they generally refer to the reviews generated by other consumers who have already purchased similar products in order to get more information. Online transaction platforms provide a highly convenient channel for consumers to generate and retrieve product reviews. In addition, consumers can also vote reviews perceived to be helpful in making their decision. However, due to diverse characteristics, consumers can have different preferences on products and reviews. Their voting behavior can be influenced by reviews and existing review votes. To explore the influence mechanism of the reviewer, the review, and the existing votes on review helpfulness, we propose three hypotheses based on the consumer perspective and perform statistical tests to verify these hypotheses with real review data from Amazon. Our empirical study indicates that review helpfulness has significant correlation and trend with reviewers, review valance, and review votes. In this paper, we also discuss the implications of our findings on consumer preference and review helpfulness.

Quantifying the Influence of Websites Based on Online Collective Attention Flow

The availability of network big data, such as those from online users＇ surfing records, communication records, and e-commerce records, makes it possible for us to probe into and quantify the regular patterns of users＇ long-range and complex interactions between websites. If we see the Web as a virtual living organism, according to the metabolic theory, the websites must absorb ＂energy＂ to grow, reproduce, and develop. We are interested in the following two questions： 1） where does the ＂energy＂ come from？ 2） will the websites generate macro influence on the whole Web based on the ＂energy＂？ Our data consist of more than 30 000 online users＇ surfing log data from China Internet Network Information Center. We would consider the influence as metabolism and users＇ attention flow as the energy for the websites. We study how collective attention distributes and flows among different websites by the empirical attention flow network. Different from traditional studies which focused on information flow, we study users＇ attention flow, which is not only a ＂reversed＂ way to study Web structure and transmission mode, but also the first step to understand the underlying dynamics of the World Wide Web. We find that the macro influence of websites scales sub-linearly against the collective attention flow dwelling time, which is not consistent with the heuristics that the more users＇ dwelling time is, the greater influence a website will have. Further analysis finds a supper-linear scaling relationship between the influence of websites and the attention flow intensity. This is a websites version of Kleiber＇s law. We further notice that the development cycle of the websites can be split into three phases： the uncertain growth phase, the partially accelerating growth phase, and the fully accelerating growth phase. We also find that compared with the widespread hyperlinks analysis models, the attention flow network is an effective theoretical tool to estimate and rank websites.

An accurate selectivity estimation method for window queries and an implementation thereof

Spatial selectivity estimation is crucial to choose the cheapest execution plan for a given query in a query optimizer.This article proposes an accurate spatial selectivity estimation method based on the cumulative density(CD)histograms,which can deal with any arbitrary spatial query window.In this method,the selectivity can be estimated in original logic of the CD histogram,after the four corner values of a query window have been accurately interpolated on the continuous surface of the elevation histogram.For the interpolation of any corner points,we first identify the cells that can affect the value of point(x,y)in the CD histogram.These cells can be categorized into two classes:ones within the range from(0,0)to(x,y)and the other overlapping the range from(0,0)to(x,y).The values of the former class can be used directly,whereas we revise the values of any cells falling in the latter class by the number of vertices in the corresponding cell and the area ratio covered by the range from(0,0)to(x,y).This revision makes the estimation method more accurate.The CD histograms and estimation method have been implemented in INGRES.Experiment results show that the method can accurately estimate the selectivity of arbitrary query windows and can help the optimizer choose a cheaper query plan.