What to Cache：Differentiated Caching Resource Allocation and Management in Information-Centric Networking

Information-centric networking(ICN) aims to improve the efficiency of content delivery and reduce the redundancy of data transmission by caching contents in network nodes. An important issue is to design caching methods with better cache hit rate and achieve allocating on-demand. Therefore, an in-network caching scheduling scheme for ICN was designed, distinguishing different kinds of contents and dynamically allocating the cache size on-demand. First discussing what was appropriated to be cached in nodes, and then a classification about the contents could be cached was proposed. Furthermore, we used AHP to weight different contents classes through analyzing users' behavior. And a distributed control process was built, to achieve differentiated caching resource allocation and management. The designed scheme not only avoids the waste of caching resource, but also further enhances the cache availability. Finally, the simulation results are illustrated to show that our method has the superior performance in the aspects of server hit rate and convergence.

A Distributed User Association and Resource Allocation Method in Cache-Enabled Small Cell Networks

Cache-enabled small cell networks have been regarded as a promising approach for network operators to cope with the explosive data traffic growth in future 5 G networks. However, the user association and resource allocation mechanism has not been thoroughly studied under given content placement situation. In this paper, we formulate the joint optimization problem of user association and resource allocation as a mixed integer nonlinear programming(MINLP) problem aiming at deriving a balance between the total utility of data rates and the total data rates retrieved from caches. To solve this problem, we propose a distributed relaxing-rounding method. Simulation results demonstrate that the distributed relaxing-rounding method outperforms traditional max-SINR method and range-expansion method in terms of both total utility of data rates and total data rates retrieved from caches in practical scenarios. In addition, effects of storage and backhaul capacities on the performance are also studied.

An Algorithm Based on Markov Chain to Improve Edge Cache Hit Ratio for Blockchain-Enabled IoT

Reasonable allocation of storage and computing resources is the basis of building big data system.With the development of IoT(Internet of Things),more data will be brought.A three-layer architecture includes smart devices layer,edge cloud layer and blockchain-based distributed cloud layer.Blockchain is used in IoT for building a distributed decentralize P2P architecture to deal with the secure issue while edge computing deals with increasing volume of data.Edge caching is one of the important application scenarios.In order to allocate edge cache resources reasonably,to improve the quality of service and to reduce the waste of bandwidth resources,this paper proposes a content selection algorithm of edge cache nodes.The algorithm adopts markov chain model,improves the utilization of cache space and reduces the content transmission delay.The hierarchical caching strategy is adopted and the secondary cache stores slides of contents to expand the coverage of cached content and to reduce user waiting time.Regional node cooperation is adopted to expand the cache space and to support the regional preference of cache content.Compared with the classical substitution algorithm,simulation results show that the algorithm in this paper has higher cache hit ratio and higher space utilization.

Steiner Tree Based Optimal Resource Caching Scheme in Fog Computing

Fog Computing is a new platform that can serve mobile devices in the local area. In Fog Computing, the resources need to be shared or cached in the widely deployed Fog clusters. In this paper, we propose a Steiner tree based caching scheme, in which the Fog servers, when caching resources, first produce a Steiner tree to minimize the total path weight(or cost) such that the cost of resource caching using this tree could be minimized. Then we give a running illustration to show how the Fog Computing works and we compare the traditional shortest path scheme with the proposed one. The outcome shows that the Steiner tree based scheme could work more efficiently.

Intelligent Resources Management System Design in Information Centric Networking

Information centric networking(ICN) is a new network architecture that is centred on accessing content. It aims to solve some of the problems associated with IP networks, increasing content distribution capability and improving users' experience. To analyse the requests' patterns and fully utilize the universal cached contents, a novel intelligent resources management system is proposed, which enables effi cient cache resource allocation in real time, based on changing user demand patterns. The system is composed of two parts. The fi rst part is a fi ne-grain traffi c estimation algorithm called Temporal Poisson traffi c prediction(TP2) that aims at analysing the traffi c pattern(or aggregated user requests' demands) for different contents. The second part is a collaborative cache placement algorithm that is based on traffic estimated by TP2. The experimental results show that TP2 has better performance than other comparable traffi c prediction algorithms and the proposed intelligent system can increase the utilization of cache resources and improve the network capacity.

Eastern gray squirrels are consistent shoppers of seed traits:insights from discrete choice experiments

Seeds of many hardwood trees are dispersed by scatter-hoarding rodents,and this process is often mediated by the traits of seeds.Although numerous studies have linked seed traits to seed preference by rodents,little is known about how rodents forage for seeds when multiple desirable and undesirable seed traits are available simultaneously.Here,we adopt a novel method of designing choice experiments to study how eastern gray squirrels(Sciurus carolinensis)select for 6 traits(caloric value,protein content,tannin concentration,kernel mass,dormancy period and toughness of shell)among seeds.From n=426 seed-pair presentations,we found that squirrels preferentially consumed seeds with short dormancy or tougher shells,and preferentially cached seeds with larger kernel mass,tougher shells and higher tannin concentrations.By incorporating random effects,we found that squirrels exhibited consistent preferences for seed traits,which is likely due to the fitness consequences associated with maintaining cached resources.Furthermore,we found that squirrels were willing to trade between multiple traits when caching seeds,which likely results in more seed species being cached in the fall.Ultimately,our approach allowed us to compute the relative values of different seed traits to squirrels,despite covariance among studied traits across seed species.In addition,by investigating how squirrels trade among different seed traits,important insights can be gleaned into behavioral mechanisms underlying seed caching(and,thus,seed survival)dynamics as well as evolutionary strategies adopted by plants to attract seed dispersers.We describe how discrete choice experiments can be used to study resource selection in other ecological systems.