Mixed Micro/Macro Cache for Device-to-Device Caching Systems in Multi-Operator Environments.

In a device-to-device (D2D) caching system that utilizes a device's available storage space as a content cache, a device called a helper can provide content requested by neighboring devices, thereby reducing the burden on the wireless network. To enhance the efficiency of a limited-size cache, one can consider not only macro caching, which is content-based caching based on content popularity, but also micro caching, which is chunk-based sequential prefetching and stores content chunks slightly behind the one that a nearby device is currently viewing. If the content in a cache can be updated intermittently even during peak hours, the helper can improve the hit ratio by performing micro caching, which stores chunks that are expected to be requested by nearby devices in the near future. In this paper, we discuss the performance and effectiveness of micro D2D caching when there are multiple operators, the helpers can communicate with the devices of other operators, and the operators are under a low load independently of each other. We also discuss the ratio of micro caching in the cache area when the cache space is divided into macro and micro cache areas. Good performance can be achieved by using micro D2D caching in conjunction with macro D2D caching when macro caching alone does not provide sufficient performance, when users are likely to continue viewing the content they are currently viewing, when the content update cycle for the cache is short and a sufficient number of chunks can be updated for micro caching, and when there are multiple operators in the region.

Integrated Quality of Service for Offline and Online Services in Edge Networks via Task Offloading and Service Caching.

Edge servers frequently manage their own offline digital twin (DT) services, in addition to caching online digital twin services. However, current research often overlooks the impact of offline caching services on memory and computation resources, which can hinder the efficiency of online service task processing on edge servers. In this study, we concentrated on service caching and task offloading within a collaborative edge computing system by emphasizing the integrated quality of service (QoS) for both online and offline edge services. We considered the resource usage of both online and offline services, along with incoming online requests. To maximize the overall QoS utility, we established an optimization objective that rewards the throughput of online services while penalizing offline services that miss their soft deadlines. We formulated this as a utility maximization problem, which was proven to be NP-hard. To tackle this complexity, we reframed the optimization problem as a Markov decision process (MDP) and introduced a joint optimization algorithm for service caching and task offloading by leveraging the deep Q-network (DQN). Comprehensive experiments revealed that our algorithm enhanced the utility by at least 14.01% compared with the baseline algorithms.

Implementation and optimization of SpMV algorithm based on SW26010P many-core processor and stored in BCSR format.

The irregular distribution of non-zero elements of large-scale sparse matrix leads to low data access efficiency caused by the unique architecture of the Sunway many-core processor, which brings great challenges to the efficient implementation of sparse matrix-vector multiplication (SpMV) computing by SW26010P many-core processor. To address this problem, a study of SpMV optimization strategies is carried out based on the SW26010P many-core processor. Firstly, we design a memorized data storage transformation strategy to transform the matrix in CSR storage format into BCSR (Block Compressed Sparse Row) storage. Secondly, the dynamic task scheduling method is introduced to the algorithm to realize the load balance between slave cores. Thirdly, the LDM memory is refined and designed, and the slave core dual cache strategy is optimized to further improve the performance. Finally, we selected a large number of representative sparse matrices from the Matrix Market for testing. The results show that the scheme has obviously speedup the processing procedure of sparse matrices with various sizes and sizes, and the master-slave speedup ratio can reach up to 38 times. The optimization method used in this paper has implications for other complex applications of the SW26010P many-core processor.

Evolutionary lability of food caching behaviour in mammals.

Food hoarding provides animals access to resources during periods of scarcity. Studies on mammalian caching indicate associations with brain size, seasonality and diet but are biased to a subset of rodents. Whether the behaviour is generalizable at other taxonomic scales and/or is influenced by other ecological factors is less understood. Population density may influence food caching due to food competition or pilferage, but this remains untested in a comparative framework. Using phylogenetic analyses, we assessed the role of morphology (body and brain size), climate, diet breadth and population density on food caching behaviour evolution at multiple taxonomic scales. We also used a long-term dataset on caching behaviour of red squirrels (Tamiasciurus fremonti) to test key factors (climate and population density) on hoarding intensity. Consistent with previous smaller scale studies, we found the mammalian ancestral state for food caching was larderhoarding, and scatterhoarding was derived. Caching strategy was strongly associated with brain size, population density and climate. Mammals with larger brains and hippocampal volumes were more likely to scatterhoard, and species living at higher population densities and in colder climates were more likely to larderhoard. Finer-scale analyses within families, sub-families and tribes indicated that the behaviour is evolutionary labile. Brain size in family Sciuridae and tribe Marmotini was larger in scatterhoarders, but not in other tribes. Scatterhoarding in tribe Marmotini was more likely in species with lower population densities while scatterhoarding in tribe Sciurini was associated with warmer climates. Red squirrel larderhoarding intensity was positively related to population density but not climate, implicating food competition or pilferage as an important mechanism mediating caching behaviour. Our results are consistent with previous smaller-scale studies on food caching and indicate the evolutionary patterns of mammalian food caching are broadly generalizable. Given the lability of caching behaviour as evidenced by the variability of our results at finer phylogenetic scales, comparative analyses must consider taxonomic scale. Applying our results to conservation could prove useful as changes in population density or climate may select for different food caching strategies and thus can inform management of threatened and endangered species and their habitats.

A Service-Caching Strategy Assisted by Double DQN in LEO Satellite Networks.

Satellite fog computing (SFC) achieves computation, caching, and other functionalities through collaboration among fog nodes. Satellites can provide real-time and reliable satellite-to-ground fusion services by pre-caching content that users may request in advance. However, due to the high-speed mobility of satellites, the complexity of user-access conditions poses a new challenge in selecting optimal caching locations and improving caching efficiency. Motivated by this, in this paper, we propose a real-time caching scheme based on a Double Deep Q-Network (Double DQN). The overarching objective is to enhance the cache hit rate. The simulation results demonstrate that the algorithm proposed in this paper improves the data hit rate by approximately 13% compared to methods without reinforcement learning assistance.

Caching Policy in Low Earth Orbit Satellite Mega-Constellation Information-Centric Networking for Internet of Things.

Information-Centric Networking (ICN) is the emerging next-generation internet paradigm. The Low Earth Orbit (LEO) satellite mega-constellation based on ICN can achieve seamless global coverage and provide excellent support for Internet of Things (IoT) services. Additionally, in-network caching, typically characteristic of ICN, plays a paramount role in network performance. Therefore, the in-network caching policy is one of the hotspot problems. Especially, compared to caching traditional internet content, in-networking caching IoT content is more challenging, since the IoT content lifetime is small and transient. In this paper, firstly, the framework of the LEO satellite mega-constellation Information-Centric Networking for IoT (LEO-SMC-ICN-IoT) is proposed. Then, introducing the concept of "viscosity", the proposed Caching Algorithm based on the Random Forest (CARF) policy of satellite nodes combines both content popularity prediction and satellite nodes location prediction, for achieving good cache matching between the satellite nodes and content. And using the matching rule, the Random Forest (RF) algorithm is adopted to predict the matching relationship among satellite nodes and content for guiding the deployment of caches. Especially, the content is cached in advance at the future satellite to maintain communication with the current ground segment at the time of satellite switchover. Additionally, the policy considers both the IoT content lifetime and the freshness. Finally, a simulation platform with LEO satellite mega-constellation based on ICN is developed in Network Simulator 3 (NS-3). The simulation results show that the proposed caching policy compared with the Leave Copy Everywhere (LCE), the opportunistic (OPP), the Leave Copy down (LCD), and the probabilistic algorithm which caches each content with probability 0.5 (prob 0.5) yield a significant performance improvement, such as the average number of hops, i.e., delay, cache hit rate, and throughput.

Edge Caching Data Distribution Strategy with Minimum Energy Consumption.

In the context of the rapid development of the Internet of Vehicles, virtual reality, automatic driving and the industrial Internet, the terminal devices in the network show explosive growth. As a result, more and more information is generated from the edge of the network, which makes the data throughput increase dramatically in the mobile communication network. As the key technology of the fifth-generation mobile communication network, mobile edge caching technology which caches popular data to the edge server deployed at the edge of the network avoids the data transmission delay of the backhaul link and the occurrence of network congestion. With the growing scale of the network, distributing hot data from cloud servers to edge servers will generate huge energy consumption. To realize the green and sustainable development of the communication industry and reduce the energy consumption of distribution of data that needs to be cached in edge servers, we make the first attempt to propose and solve the problem of edge caching data distribution with minimum energy consumption (ECDDMEC) in this paper. First, we model and formulate the problem as a constrained optimization problem and then prove its NP-hardness. Subsequently, we design a greedy algorithm with computational complexity of O(n2) to solve the problem approximately. Experimental results show that compared with the distribution strategy of each edge server directly requesting data from the cloud server, the strategy obtained by the algorithm can significantly reduce the energy consumption of data distribution.

Genes and gene networks underlying spatial cognition in food-caching chickadees.

Substantial progress has been made in understanding the genetic architecture of phenotypes involved in a variety of evolutionary processes. Behavioral genetics remains, however, among the least understood. We explore the genetic architecture of spatial cognitive abilities in a wild passerine bird, the mountain chickadee (Poecile gambeli). Mountain chickadees cache thousands of seeds in the fall and require specialized spatial memory to recover these caches throughout the winter. We previously showed that variation in spatial cognition has a direct effect on fitness and has a genetic basis. It remains unknown which specific genes and developmental pathways are particularly important for shaping spatial cognition. To further dissect the genetic basis of spatial cognitive abilities, we combine experimental quantification of spatial cognition in wild chickadees with whole-genome sequencing of 162 individuals, a new chromosome-scale reference genome, and species-specific gene annotation. We have identified a set of genes and developmental pathways that play a key role in creating variation in spatial cognition and found that the mechanism shaping cognitive variation is consistent with selection against mildly deleterious non-coding mutations. Although some candidate genes were organized into connected gene networks, about half do not have shared regulation, highlighting that multiple independent developmental or physiological mechanisms contribute to variation in spatial cognitive abilities. A large proportion of the candidate genes we found are associated with synaptic plasticity, an intriguing result that leads to the hypothesis that certain genetic variants create antagonism between behavioral plasticity and long-term memory, each providing distinct benefits depending on ecological context.

Blockchain Based Decentralized and Proactive Caching Strategy in Mobile Edge Computing Environment.

In the mobile edge computing (MEC) environment, the edge caching can provide the timely data response service for the intelligent scenarios. However, due to the limited storage capacity of edge nodes and the malicious node behavior, the question of how to select the cached contents and realize the decentralized security data caching faces challenges. In this paper, a blockchain-based decentralized and proactive caching strategy is proposed in an MEC environment to address this problem. The novelty is that the blockchain was adopted in an MEC environment with a proactive caching strategy based on node utility, and the corresponding optimization problem was built. The blockchain was adopted to build a secure and reliable service environment. The employed methodology is that the optimal caching strategy was achieved based on the linear relaxation technology and the interior point method. Additionally, in a content caching system, there is a trade-off between cache space and node utility, and the caching strategy was proposed to solve this problem. There was also a trade-off between the consensus process delay of blockchain and the caching latency of content. An offline consensus authentication method was adopted to reduce the influence of the consensus process delay on the content caching. The key finding was that the proposed algorithm can reduce latency and can ensure the security data caching in an IoT environment. Finally, the simulation experiment showed that the proposed algorithm can achieve up to 49.32%, 43.11%, and 34.85% improvements on the cache hit rate, the average content response latency, and the average system utility, respectively, compared to the random content caching algorithm, and it achieved up to 9.67%, 8.11%, and 5.95% increases, successively, compared to the greedy content caching algorithm.

Controller-driven vector autoregression model for predicting content popularity in programmable named data networking devices.

Named Data Networking (NDN) has emerged as a promising network architecture for content delivery in edge infrastructures, primarily due to its name-based routing and integrated in-network caching. Despite these advantages, sub-optimal performance often results from the decentralized decision-making processes of caching devices. This article introduces a paradigm shift by implementing a Software Defined Networking (SDN) controller to optimize the placement of highly popular content in NDN nodes. The optimization process considers critical networking factors, including network congestion, security, topology modification, and flowrules alterations, which are essential for shaping content caching strategies. The article presents a novel content caching framework, Popularity-aware Caching in Popular Programmable NDN nodes (PaCPn). Employing a multi-variant vector autoregression (VAR) model driven by an SDN controller, PaCPn periodically updates content popularity based on time-series data, including 'request rates' and 'past popularity'. It also introduces a controller-driven heuristic algorithm that evaluates the proximity of caching points to consumers, considering factors such as 'distance cost,' 'delivery time,' and the specific 'status of the requested content'. PaCPn utilizes customized DATA named packets to ensure the source stores content with a valid residual freshness period while preventing intermediate nodes from caching it. The experimental results demonstrate significant improvements achieved by the proposed technique PaCPn compared to existing schemes. Specifically, the technique enhances cache hit rates by 20% across various metrics, including cache size, Zipf parameter, and exchanged traffic within edge infrastructure. Moreover, it reduces content retrieval delays by 28%, considering metrics such as cache capacity, the number of consumers, and network throughput. This research advances NDN content caching and offers potential optimizations for edge infrastructures.

Understanding Mobile OTT Service Users' Resistance to Participation in Wireless D2D Caching Networks.

With the explosive pace of mobile over-the-top (OTT) video content streaming services, mobile network traffic has seen unprecedented growth in recent years. However, the limitation of antenna performance, the burden of investment cost, and restricted resources hinder improving the current mobile networks' functionality. Accordingly, wireless device-to-device (D2D) caching networks came to the fore as one of the competitive alternatives for alleviating the overloads of mobile network traffic. Wireless D2D caching networks can be a desirable alternative for OTT service providers and telecommunication operators, but the problem is user resistance. User participation is imperative to deliver wireless D2D caching network functionality successfully. Thus, to gain a deeper understanding of user resistance toward wireless D2D caching networks and their underlying sources, this study introduces two perceived cost factors contributing to this resistance and one perceived benefit that mitigates such resistance. Based on an online survey, this study found new theoretical links among perceived costs and benefits, resistance, and participation intention. The findings reveal that user resistance is predicted by perceived costs, encompassing resource sacrifices and privacy concerns, whereas perceived benefits-specifically, perceived usefulness-did not significantly influence resistance. This implies that telecommunication operators should prioritize market requirements over technological advantages, emphasizing the potential for successful commercialization of wireless D2D caching networks.

Hierarchical Cache-Aided Networks for Linear Function Retrieval.

In a hierarchical caching system, a server is connected to multiple mirrors, each of which is connected to a different set of users, and both the mirrors and the users are equipped with caching memories. All the existing schemes focus on single file retrieval, i.e., each user requests one file. In this paper, we consider the linear function retrieval problem, i.e., each user requests a linear combination of files, which includes single file retrieval as a special case. We propose a new scheme that reduces the transmission load of the first hop by jointly utilizing the two layers' cache memories, and we show that our scheme achieves the optimal load for the second hop in some cases.

Fundamental Limits of Coded Caching in Request-Robust D2D Communication Networks.

D2D coded caching, originally introduced by Ji, Caire, and Molisch, significantly improves communication efficiency by applying the multi-cast technology proposed by Maddah-Ali and Niesen to the D2D network. Most prior works on D2D coded caching are based on the assumption that all users will request content at the beginning of the delivery phase. However, in practice, this is often not the case. Motivated by this consideration, this paper formulates a new problem called request-robust D2D coded caching. The considered problem includes K users and a content server with access to N files. Only r users, known as requesters, request a file each at the beginning of the delivery phase. The objective is to minimize the average and worst-case delivery rate, i.e., the average and worst-case number of broadcast bits from all users among all possible demands. For this novel D2D coded caching problem, we propose a scheme based on uncoded cache placement and exploiting common demands and one-shot delivery. We also propose information-theoretic converse results under the assumption of uncoded cache placement. Furthermore, we adapt the scheme proposed by Yapar et al. for uncoded cache placement and one-shot delivery to the request-robust D2D coded caching problem and prove that the performance of the adapted scheme is order optimal within a factor of two under uncoded cache placement and within a factor of four in general. Finally, through numerical evaluations, we show that the proposed scheme outperforms known D2D coded caching schemes applied to the request-robust scenario for most cache size ranges.

Efficient content caching for 5G assisted vehicular networks.

Traffic congestion is one of the major challenges faced by daily commuters in smart cities. An autonomous transportation system with a 5 G-based Cellular Vehicle-to-Everything (C-V2X) communication system is the solution to meet the traffic challenges faced in smart cities. Vehicular networks provide wireless connectivity to enable a large number of connected vehicle applications. Vehicular networks allow vehicles to share their emergency and infotainment traffic by following vehicle-to-vehicle (V2V) or by using vehicle-to-infrastructure (V2I) communication. The infrastructure of vehicular networks mainly comprises multiple Road Side Units (RSUs). Fog computing nodes are placed adjacent to these RSUs to provide quick access to vehicles. For infotainment traffic, vehicles intend to download their required content from the content provider. Caching the same contents from the nearby fog computing node significantly reduces delay with improved quality of service. As there are millions of contents with varying sizes, caching all demanded contents on these fog nodes is not possible due to their limited caching capacity. In this work, we propose an improved content caching scheme for fog nodes to satisfy vehicles and content providers for fair content placement. The proposed algorithm is based on a modified Gale-Shapley technique that considers factors such as content popularity, vehicle connectivity, and quality of the communication channel to optimize the content caching process. Simulation results show that the proposed technique caches a higher number of popular contents with lower downloading time.

QoS-driven resource allocation in fog radio access network: A VR service perspective.

While immersive media services represented by virtual reality (VR) are booming, They are facing fundamental challenges, i.e., soaring multimedia applications, large operation costs and scarce spectrum resources. It is difficult to simultaneously address these service challenges in a conventional radio access network (RAN) system. These problems motivated us to explore a quality-of-service (QoS)-driven resource allocation framework from VR service perspective based on the fog radio access network (F-RAN) architecture. We elaborated details of deployment on the caching allocation, dynamic base station (BS) clustering, statistical beamforming and cost strategy under the QoS constraints in the F-RAN architecture. The key solutions aimed to break through the bottleneck of the network design and to deep integrate the network-computing resources from different perspectives of cloud, network, edge, terminal and use of collaboration and integration. Accordingly, we provided a tailored algorithm to solve the corresponding formulation problem. This is the first design of VR services based on caching and statistical beamforming under the F-RAN. A case study provided to demonstrate the advantage of our proposed framework compared with existing schemes. Finally, we concluded the article and discussed possible open research problems.

Inferring condition in wild mammals: body condition indices confer no benefit over measuring body mass across ecological contexts.

Many studies assume that it is beneficial for individuals of a species to be heavier, or have a higher body condition index (BCI), without accounting for the physiological relevance of variation in the composition of different body tissues. We hypothesized that the relationship between BCI and masses of physiologically important tissues (fat and lean) would be conditional on annual patterns of energy acquisition and expenditure. We studied three species with contrasting ecologies in their respective natural ranges: an obligate hibernator (Columbian ground squirrel, Urocitellus columbianus), a facultative hibernator (black-tailed prairie dog, Cynomys ludovicianus), and a food-caching non-hibernator (North American red squirrel, Tamiasciurus hudsonicus). We measured fat and lean mass in adults of both sexes using quantitative magnetic resonance (QMR). We measured body mass and two measures of skeletal structure (zygomatic width and right hind foot length) to develop sex- and species-specific BCIs, and tested the utility of BCI to predict body composition in each species. Body condition indices were more consistently, and more strongly correlated, with lean mass than fat mass. The indices were most positively correlated with fat when fat was expected to be very high (pre-hibernation prairie dogs). In all cases, however, BCI was never better than body mass alone in predicting fat or lean mass. While the accuracy of BCI in estimating fat varied across the natural histories and annual energetic patterns of the species considered, measuring body mass alone was as effective, or superior in capturing sufficient variation in fat and lean in most cases.

Granivore abundance shapes mutualism quality in plant-scatterhoarder interactions.

Conditional mutualisms involve costs and benefits that vary with environmental factors, but mechanisms driving these dynamics remain poorly understood. Scatterhoarder-plant interactions are a prime example of this phenomenon, as scatterhoarders can either increase or reduce plant recruitment depending on the balance between seed dispersal and predation. We explored factors that drive the magnitude of net benefits for plants in this interaction using a mathematical model, with parameter values based on European beech (Fagus sylvatica) and yellow-necked mice (Apodemus flavicollis). We measured benefits as the percentage of germinating seeds, and examined how varying rodent survival (reflecting, e.g. changes in predation pressure), the rate of seed loss to other granivores, the abundance of alternative food resources, and changes in masting patterns affect the quality of mutualism. We found that increasing granivore abundance can degrade the quality of plant-scatterhoarder mutualism due to increased cache pilferage. Scatterhoarders are predicted to respond by increasing immediate consumption of gathered seeds, leading to higher costs and reduced benefits for plants. Thus, biotic changes that are detrimental to rodent populations can be beneficial for tree recruitment due to adaptive behavior of rodents. When scatterhoarder populations decline too drastically (< 5 individuals ha-1 ); however, tree recruitment may also suffer.

Clark's nutcracker forest community visitation: Whitebark pine maintains a keystone seed disperser.

Clark's nutcrackers (Nucifraga columbiana) are obligate seed dispersers for whitebark pine (Pinus albicaulis), but they frequently use other conifer seed resources because of annual variability in cone production or geographic variation in whitebark pine availability. Whitebark pine is declining from several threats including white pine blister rust, leading to potential population declines in the nutcracker and the pine. We hypothesize that where there are few additional seed resources, whitebark pine becomes the key and limiting resource supporting nutcracker populations. We investigated how nutcrackers use coniferous forest community types within Yellowstone National Park to determine potential seed resources and the importance of whitebark pine. We established sites representing five forest community types, including whitebark pine, lodgepole pine (P. contorta), Engelmann spruce (Picea engelmannii), limber pine (P. flexilis), and Douglas-fir (Pseudotsuga menziesii). Each transect annually generated nutcracker point counts, conifer cone production indices, community composition data, and seed resource use observations. We compared hierarchical distance sampling models, estimating nutcracker density and its relationship to forest community type, seed harvesting time-period, year, study site, and cone seed energy. We found cone production varied across years indicating annual variability in energy availability. Nutcracker density was best predicted by forest community type and survey time-period and was highest in whitebark pine stands during the mid-harvesting season. Nutcracker density was comparatively low for all other forest community types. This finding underscores the importance of whitebark pine as a key seed resource for Clark's nutcracker in Yellowstone National Park. The decline of whitebark pine potentially leads to a downward spiral in nutcrackers and whitebark pine, arguing for continued monitoring of nutcrackers and implementation of restoration treatments for whitebark pine.

Refresh Rate-Based Caching and Prefetching Strategies for Internet of Things Middleware.

One of the research directions in Internet of Things (IoT) is the field of Context Management Platforms (CMPs) which is a specific type of IoT middleware. CMPs provide horizontal connectivity between vertically oriented IoT silos resulting in a noticeable difference in how IoT data streams are processed. As these context data exchanges can be monetised, there is a need to model and predict the context metrics and operational costs of this exchange to provide relevant and timely context in a large-scale IoT ecosystem. In this paper, we argue that caching all transient context information to satisfy this necessity requires large amounts of computational and network resources, resulting in tremendous operational costs. Using Service Level Agreements (SLAs) between the context providers, CMP, and context consumers, where the level of service imperfection is quantified and linked to the associated costs, we show that it is possible to find efficient caching and prefetching strategies to minimize the context management cost. So, this paper proposes a novel method to find the optimal rate of IoT data prefetching and caching. We show the main context caching strategies and the proposed mathematical models, then discuss how a correctly chosen proactive caching strategy and configurations can help to maximise the profit of CMP operation when multiple SLAs are defined. Our model is accurate up to 0.0016 in Root Mean Square Percentage Error against our simulation results when estimating the profits to the system. We also show our model is valid using the t-test value tending to 0 for all the experimental scenarios.