Caching Algorithm with a Novel Cost Model to Deliver Content and Its Interest over Content Centric Networks

Recently the content centric networks(CCNs) have been advocated as a new solution to design future networks. In the CCNs, content and its interest are delivered over the content store and pending interest table, respectively, where both have limited capacities. Therefore, how to design the corresponding algorithms to efficiently deliver content and inertest over them becomes an important issue. In this paper, based on the analysis of content distribution, status of content store, and pending interest, we propose a novel caching algorithm with which the resources of content store and pending interest table can be efficiently used. Simulation results prove that the proposal can outperform the conventional methods.

Algorithms to Calculate the Most Reliable Maximum Flow in Content Delivery Network

Calculating the most reliable maximum flow(MRMF)from the edge cache node to the requesting node can provide an important reference for selecting the best edge cache node in a content delivery network(CDN).However,SDBA,as the current state-of-the-art MRMF algorithm,is too complex to meet real-time computing needs.This paper proposes a set of MRMF algorithms:NWCD(Negative Weight Community Deletion),SCPDAT(Single-Cycle Preference Deletion Approximation algorithm with Time constraint)and SCPDAP(Single-Cycle Preference Deletion Approximation algorithm with Probability constraint).NWCD draws on the“flow-shifting”algorithm of minimum cost and maximum flow,and further defines the concept of negative weight community.This algorithm continuously deletes the negative weight communities,which can increase reliability while keeping the flow constant in the residual graph.It is proven that when all negative weight communities are deleted,the corresponding maximum flow is the MRMF.SCPDAT tries to approach the optimal solution to the greatest extent possible within the limited time,while SCPDAP tries to reach the probability threshold in the shortest amount of time.Both of these adopt the strategy of first deleting single-cycle communities(which contribute more to the reliability with lower time cost).Experiments show that,compared with SDBA,NWCD combined with the probabilistic pruning achieves an order of magnitude improvement in time cost,while SCPDAT and SCPDAP demonstrate better time performance and increased applicability.

Caching Strategies in NDN Based Wireless Ad Hoc Network:A Survey

Wireless Ad Hoc Networks consist of devices that are wirelessly connected.Mobile Ad Hoc Networks(MANETs),Internet of Things(IoT),and Vehicular Ad Hoc Networks(VANETs)are the main domains of wireless ad hoc network.Internet is used in wireless ad hoc network.Internet is based on Transmission Control Protocol(TCP)/Internet Protocol(IP)network where clients and servers interact with each other with the help of IP in a pre-defined environment.Internet fetches data from a fixed location.Data redundancy,mobility,and location dependency are the main issues of the IP network paradigm.All these factors result in poor performance of wireless ad hoc networks.The main disadvantage of IP is that,it does not provide in-network caching.Therefore,there is a need to move towards a new network that overcomes these limitations.Named Data Network(NDN)is a network that overcomes these limitations.NDN is a project of Information-centric Network(ICN).NDN provides in-network caching which helps in fast response to user queries.Implementing NDN in wireless ad hoc network provides many benefits such as caching,mobility,scalability,security,and privacy.By considering the certainty,in this survey paper,we present a comprehensive survey on Caching Strategies in NDN-based Wireless AdHocNetwork.Various cachingmechanism-based results are also described.In the last,we also shed light on the challenges and future directions of this promising field to provide a clear understanding of what caching-related problems exist in NDN-based wireless ad hoc networks.

Improving the precision of the keyword-matching pornographic text filtering method using a hybrid model

With the flooding of pornographic information on the Internet, how to keep people away from that offensive information is becoming one of the most important research areas in network information security. Some applications which can block or filter such information are used. Approaches in those systems can be roughly classified into two kinds: metadata based and content based. With the development of distributed technologies, content based filtering technologies will play a more and more important role in filtering systems. Keyword matching is a content based method used widely in harmful text filtering. Experiments to evaluate the recall and precision of the method showed that the precision of the method is not satisfactory, though the recall of the method is rather high. According to the results, a new pornographic text filtering model based on reconfirming is put forward. Experiments showed that the model is practical, has less loss of recall than the single keyword matching method, and has higher precision.

Dependable Deployment Method for Multiple Applications in Cloud Services Delivery Network

The mutual-interference phenomenon among multiple applications delivered as services through Cloud Services Delivery Network(CSDN)influences their QoS seriously.In order to deploy multiple applications dependably and efficiently,we propose the Multiple Applications Co-Exist(MACE)method.MACE classifies multiple applications into different types and deploys them using isolation to some extent.Meanwhile,resource static allocation,dynamic supplement and resource reserved mechanism to minimize mutual-interference and maximize resource utilization are designed.After MACE is applied to a real large-scale CSDN and evaluated through 6-month measurement,we find that the CSDN load is more balanced,the bandwidth utilization increases by about 20%,the multiple applications'potential statistical multiplexing ratio decreases from 12% to 5%,and the number of complaint events affecting the dependability of CSDN services caused by multiple applications'mutual-interference has dropped to 0.Obviously,MACE offers a tradeoff and improvement for the dependability and efficiency goals of CSDN.

Internet Load and Traffic Balancing

Load and traffic balancing for the Internet are analyzed. An intelligent domain name system(DNS) policy for the control and management of content distribution n etworks (CDN) is presented. An CDNs network based Unitech IntelliDNS is built, a nd performance of the network in realizing traffic and load balancing is measure d. It is proved that CDNs provide a valuable service and the methods are feasibl e.

Impact of public name resolving services on Akamai

To enhance user perceived performance,Akamai' s content distribution network(CDN) utilizes the domain name system(DNS) effectively to redirect users to close-by content replicas over short time scale.The use of DNS redirection has brought a significant advantage to Akamai' s CDN.However,the reliance on DNS also poses a fundamental threat to Akamai' s content distribution model.In particular,systematic evaluation and quantification illustrates the impact of recent emerging public DNS resolving services on Akamai' s CDN,including the degradation of corresponding user perceived performance and the benefit damnification of collaborating internet services providers(ISPs) that host Akamai's edge servers.The measurement demonstrates that a considerable penetration of public DNS resolving services(e.g.,OpenDNS and GoogleDNS) effectively corrupts the Akamai' s serving model,namely the large-scale server distribution and quick DNS redirection.

A regional GNSS-VTEC model over Nigeria using neural networks： A novel approach

A neural network model of the Global Navigation Satellite System - vertical total electron content （GNSS-VTEC） over Nigeria is developed. A new approach that has been utilized in this work is the consideration of the International Reference Ionosphere＇s （IRI＇s） critical plasma frequency （foF2） parameter as an additional neuron for the network＇s input layer. The work also explores the effects of using various other input layer neurons like distur- bance storm time （DST） and sunspot number. All available GNSS data from the Nigerian Permanent GNSS Network （NIGNET） were used, and these cover the period from 2011 to 2015, for 14 stations. Asides increasing the learning accuracy of the networks, the inclusion of the IRI＇s foF2 parameter as an input neuron is ideal for making the networks to learn long-term solar cycle variations. This is important especially for regions, like in this work, where the GNSS data is available for less than the period of a solar cycle. The neural network model developed in this work has been tested for time-varying and spatial per- formances. The latest 10% of the GNSS observations from each of the stations were used to test the forecasting ability of the networks, while data from 2 of the stations were entirely used for spatial performance testing. The results show that root-mean-squared-errors were generally less than 8.5 TEC units for all modes of testing performed using the optimal network. When compared to other models, the model developed in this work was observed to reduce the prediction errors to about half those of the NeQuick and the IRI model.

Edge Video CDN： A Wi-Fi Content Hotspot Solution

The emergence of smart edge-network content item hotspots, which are equipped with huge storage space （e.g., several GBs）, opens up the opportunity to study the possibility of delivering videos at the edge network. Different from both the conventional content item delivery network （CDN） and the peer-to-peer （P2P） scheme, this new delivery paradigm, namely edge video CDN, requires up to millions of edge hotspots located at users＇ homes/offices to be coordinately managed to serve mobile video content item. Specifically, two challenges are involved in building edge video CDN, including how edge content item hotspots should be organized to serve users, and how content items should be replicated to them at different locations to serve users. To address these challenges, we propose our data-driven design as follows. First, we formulate an edge region partition problem to jointly maximize the quality experienced by users and minimize the replication cost, which is NP-hard in nature, and we design a Voronoi-like partition algorithm to generate optimal service cells. Second, to replicate content items to edge-network content item hotspots, we propose an edge request prediction based replication strategy, which carries out the replication in a server peak offioading manner. We implement our design and use trace-driven experiments to verify its effectiveness. Compared with conventional centralized CDN and popularity-based replication, our design can significantly improve users＇ quality of experience, in terms of users＇ perceived bandwidth and latency, up to 40%.

Approach to extracting hot topics based on network traffic content

This article presents the formal definition and description of popular topics on the Internet,analyzes the relationship between popular words and topics,and finally introduces a method that uses statistics and correlation of the popular words in traffic content and network flow characteristics as input for extracting popular topics on the Internet.Based on this,this article adapts a clustering algorithm to extract popular topics and gives formalized results.The test results show that this method has an accuracy of 16.7%in extracting popular topics on the Internet.Compared with web mining and topic detection and tracking(TDT),it can provide a more suitable data source for effective recovery of Internet public opinions.

A Multi-dimensional Index Structure Based on Improved VA-file and CAN in the Cloud

Currently,the cloud computing systems use simple key-value data processing,which cannot support similarity search efectively due to lack of efcient index structures,and with the increase of dimensionality,the existing tree-like index structures could lead to the problem of"the curse of dimensionality".In this paper,a novel VF-CAN indexing scheme is proposed.VF-CAN integrates content addressable network(CAN)based routing protocol and the improved vector approximation fle(VA-fle) index.There are two index levels in this scheme:global index and local index.The local index VAK-fle is built for the data in each storage node.VAK-fle is thek-means clustering result of VA-fle approximation vectors according to their degree of proximity.Each cluster forms a separate local index fle and each fle stores the approximate vectors that are contained in the cluster.The vector of each cluster center is stored in the cluster center information fle of corresponding storage node.In the global index,storage nodes are organized into an overlay network CAN,and in order to reduce the cost of calculation,only clustering information of local index is issued to the entire overlay network through the CAN interface.The experimental results show that VF-CAN reduces the index storage space and improves query performance efectively.

PROSE: Proactive, Selective CDN Participation for P2P Streaming

Many production peer-to-peer （P2P） streaming systems use content delivery networks （CDN） to protect the user＇s quality of experiences. Thus, how to efficiently utilize the capacity of CDN （e.g., which peers receive services from the CDN nodes） is a problem of practical significance. Existing solutions adopt a passive, on-demand approach, which is inefficient in utilizing CDN resources. In this paper, we propose PROSE, a simple, novel scheme to achieve proactive, selective CDN participation for P2P streaming. PROSE introduces novel concepts such as choke point expansion nodes/super nodes and leads to efficient, light-weighted, and distributed algorithms to identify and serve these nodes using CDN. Our experimental results show that PROSE achieves at least 10%~25% performance improvement and 2~4 times overhead reduction compared with existing general CDN-P2P-hybrid schemes.

Transcriptome analysis of near-isogenic lines provides molecular insights into starch biosynthesis in maize kernel

Starch is the major component in maize kernels,providing a stable carbohydrate source for humans and livestock as well as raw material for the biofuel industry.Increasing maize kernel starch content will help meet industry demands and has the potential to increase overall yields.We developed a pair of maize near-isogenic lines（NILs） with different alleles for a starch quantitative trait locus on chromosome 3（q HS3）, resulting in different kernel starch content. To investigate the candidate genes for q HS3 and elucidate their effects on starch metabolism, RNA-Seq was performed for the developing kernels of the NILs at 14 and 21 d after pollination（DAP）. Analysis of genomic and transcriptomic data identified 76 genes with nonsynonymous single nucleotide polymorphisms and 384 differentially expressed genes（DEGs） in the in trogressed fragment, including a hexokinase gene, Zm HXK3 a, which catalyzes the conversion of glucose to glucose-6-phosphate and may play a key role instarch metabolism. The expression pattern of all DEGs in starch metabolism shows that altered expression of the candidate genes for q HS3 promoted starch synthesis,with positive consequences for kernel starch content. These results expand the current understanding of starch biosynthesis and accumulation in maize kernels and provide potential candidate genes to increase starch content.

A Simple Method to Solve the Network Congestion for Spitial Architcture

Scheduling algorithm always plays an important role in the spatial architecture for the contradiction between the finite network bandwidth and the abundant execution resources. This article provides a simple method to solve the contention for network resource in one of the spatial architecture, i.e. the tera-op, reliable, intelligently adaptive processing system(TRIPS) processor. The method improves the performance of network by increasing the bypass bandwidth which can transmit the data in the internal of every execution unit, and converting the proportion of remote communication by the deep scheduling algorithm. The deeply optimized algorithm is realized to verify the validity of the method, and the performance increase 9% for floating point spec2000 benchmark is got.