Cached Items Research Articles

An efficient algorithm for data transmission certainty in IIoT sensing network: A priority-based approach.

This paper proposes a novel cache replacement technique based on the notion of combining periodic popularity prediction with size caching. The popularity, size, and time updates characteristics are used to calculate the value of each cache item. When it comes to content replacement, the information with the least value is first eliminated. Simulation results show that the proposed method outperforms the current algorithms in terms of cache hit rate and delay. The hit rate of the proposed scheme is 15.3% higher than GDS, 17.3% higher than MPC, 20.1% higher than LRU, 22.3% higher than FIFO, and 24.8% higher than LFU when 350 different categories of information are present. In real-world industrial applications such as including supply chain management, smart manufacturing, automation energy optimization, intelligent logistics transportation, and e-healthcare applications, it offers a foundation for the selection of caching algorithms.

Quid pro Quo in Streaming Services: Algorithms for Cooperative Recommendations

Recommendations are employed by Content Providers (CPs) of streaming services in order to boost user engagement and their revenues. Recent works suggest that nudging recommendations towards cached items can reduce operational costs in the caching networks, e.g., Content Delivery Networks (CDNs) or edge cache providers in future wireless networks. However, cache-friendly recommendations could deviate from users' tastes, and potentially affect the CP's revenues. Motivated by real-world business models, this work identifies the misalignment of the financial goals of the CP and the caching network provider, and presents a network-economic framework for recommendations. We propose a cooperation mechanism leveraging the Nash bargaining solution that allows the two entities to jointly design the recommendation policy. We consider different problem instances that vary on the extent these entities are willing to share their cost and revenue models, and propose two cooperative policies, CCR and DCR, that allow them to make decisions in a centralized or distributed way. In both cases, our solution guarantees reaching a fair and Pareto optimal allocation of the cooperation gains. Moreover, we discuss the extension of our framework towards caching decisions. A wealth of numerical experiments in realistic scenarios show the policies lead to significant gains for both entities.

An Effective Management Model for Data Caching in MANET Environment

A mobile ad-hoc (MANET) network has the main challenge to provide the needed data for the desired mobile nodes. An efficient on request routing protocol for MANET is Ad-hoc on-demand Distance Vector (AODV), which is based on two main methods: route discovery and route maintenance. Route discovery is the process used to detect a route to the destination from the packet source, while route maintenance is the process used to detect a link failure and repair it. Cooperative caching tends improving data availability in mobile ad-hoc networks, the coordination of cache discovery and cache management strategies is very significant in the cooperative caching of MANETs because requests for data and answers to requested data can be reduced simply due to interference, network congestion, or when a forwarding node is out of reach and the route breaks down. Cooperative cache management is much more complicated in cooperative caching because it also depends on neighbouring nodes to decide what to cache. In this paper, three algorithms were proposed: (1) a combination algorithm for cache admission control based on cache data and location of data to save space and reduce data redundancy, (2) a value-based policy for cache placement and replacement instead of the more common least recently used strategy, depending on metrics that describe cached items to increase the local cache hit ratio, and (3) a combined algorithm for cache consistency that includes time-to-live, pull, and push policies to enhance data availability and system scalability. The proposed algorithm implemented by the NS3 simulation program; which used to create a network using the AODV protocol in several parameters and achieve better system performance.

An Effective Management Model for Data Caching in MANET Environment

A mobile ad-hoc (MANET) network has the main challenge to provide the needed data for the desired mobile nodes. An efficient on request routing protocol for MANET is Ad-hoc on-demand Distance Vector (AODV), which is based on two main methods: route discovery and route maintenance. Route discovery is the process used to detect a route to the destination from the packet source, while route maintenance is the process used to detect a link failure and repair it. Cooperative caching tends improving data availability in mobile ad-hoc networks, the coordination of cache discovery and cache management strategies is very significant in the cooperative caching of MANETs because requests for data and answers to requested data can be reduced simply due to interference, network congestion, or when a forwarding node is out of reach and the route breaks down. Cooperative cache management is much more complicated in cooperative caching because it also depends on neighbouring nodes to decide what to cache. In this paper, three algorithms were proposed: (1) a combination algorithm for cache admission control based on cache data and location of data to save space and reduce data redundancy, (2) a value-based policy for cache placement and replacement instead of the more common least recently used strategy, depending on metrics that describe cached items to increase the local cache hit ratio, and (3) a combined algorithm for cache consistency that includes time-to-live, pull, and push policies to enhance data availability and system scalability. The proposed algorithm implemented by the NS3 simulation program; which used to create a network using the AODV protocol in several parameters and achieve better system performance.

Proof of Optimality based on Greedy Algorithm for Offline Cache Replacement Algorithm

The optimal offline cache replacement algorithm is a MIN algorithm that chooses which data item to remove when a new data item is brought from lower level of cache or main memory. The optimal offline algorithm evicts a cache item whose next request is the furthest away. For a particular program, the performance behavior of the cache memory is determined by memory and block sizes and by the nature of the replacement algorithm. Replacement algorithms, however, deserve analysis because they are based on a variety of assumptions and design considerations. This paper presents a simpler proof based on the greedy algorithm. The paper demonstrates that every ideal solution can be repeatedly transformed into the solution provided by the greedy algorithm without increasing the miss of the optimal solution, hence demonstrating that the greedy solution is providing optimality.This paper also presents a new replacement algorithm named Greedy Weight-based Cache Replacement Algorithm (GWCRA), on the basis of the Greedy algorithm and it also incorporates the weighted access-based parameter, recency and frequency. The GWCRA achieves an average speedup of 57.29% when compared to LRU and SRRIP which is 55.58% and 55.65%, respectively.

Fresh Caching of Dynamic Content Over the Wireless Edge

We introduce a framework and provably-efficient schemes for ‘fresh’ caching at the (front-end) local cache of content that is subject to ‘dynamic’ updates at the (back-end) database. We start by formulating the hard-cache-constrained problem for this setting, which quickly becomes intractable due to the limited cache. To bypass this challenge, we first propose a flexible time-based-eviction model to derive the average system cost function that measures the system’s cost due to the service of aging content in addition to the regular cache miss cost. Next, we solve the cache-unconstrained case, which reveals how the refresh dynamics and popularity of content affect optimal caching. Then, we extend our approach to a soft-cache-constrained version, where we can guarantee that the cache use is limited with arbitrarily high probability. The corresponding solution reveals the interesting insight that ‘whether to cache an item or not in the local cache?’ depends primarily on its popularity level and channel reliability, whereas ‘how long the cached item should be held in the cache before eviction?’ depends primarily on its refresh rate. Moreover, we investigate the cost-cache saving trade-offs and prove that substantial cache gains can be obtained while also asymptotically achieving the minimum cost as the database size grows.

Provisioning behavior of male and female Loggerhead Shrikes

The unique predatory, caching, and impaling behaviors of shrikes (Laniidae) may provide these unusual passerines with alternative provisioning strategies compared to other songbirds, but few studies have determined the impact these unique behaviors have on provisioning rates. Our objectives were to compare the provisioning behavior of male and female Loggerhead Shrikes ( Lanius ludovicianus), and determine how their provisioning behavior was influenced by nestling age. The provisioning behavior of six pairs of Loggerhead Shrikes was videotaped from 15 April to 15 August 2004–2007 in Garrard and Madison counties, Kentucky, USA. For each nest visit by a shrike, the sex of the visiting adult, number of prey items delivered, size of prey, type of prey, and the duration of the nest visit were recorded. Adults made 2169 visits to nests, with males (49%) and females (51%) making similar numbers of visits. Males and females differed in time spent at nests, with males spending less time at nests per visit (mean = 12.0 s, N = 1062) than females (77.0 s, N = 1107). Feeding rates did not differ between the sexes, but feeding rates did increase with nestling age. The size of prey delivered to nestlings also tended to increase with nestling age. Invertebrates comprised 65% of the identified prey items, and cached items made up the remaining 35%. Although many passerine species increase provisioning rates as nestling demand increases, the caching behavior of shrikes may allow adults to reliably increase both feeding rates and prey size as nestling age and demand increase as well as act as buffers when prey availability declines. The caching behavior of shrikes provides them with parental provisioning strategies not available to non-caching songbirds.

Analysis of the Chaotic Dynamics Duopoly Game of ISPs Bounded Rational

We propose in this paper an investigation of the dynamical behaviors of a duopoly model with two Internet services providers (ISPs). The competition between them is assumed to take place in terms of their pricing decisions and their number of cached items. According to the ISPs’ rationality level, we consider a scenario where both ISPs are bounded rational. Each ISP in any period uses the marginal profit observed from the previous period to choose its strategies. We compute explicitly the steady states of the dynamic system induced by bounded rationality and establish a necessary and sufficient condition for the stability of its Nash equilibrium (NE). Numerical simulations show that if some parameters of the model are varied, the stability of the NE point is lost and the complex (periodic or chaotic) behavior occurs. The chaotic behavior of the system is stabilized on the NE point by applying control.

Non-Breeding Behavior and Diet of Loggerhead Shrikes in an Intensive Agricultural Region

As agricultural conversion has transformed the landscape across the central United States, populations of grassland-associated species, such as those of Lanius ludovicianus (Loggerhead Shrike), have declined. Understanding the ecology and behavior of grassland species in these highly anthropogenic, agricultural landscapes provides vital information for conservation efforts. However, few studies have assessed the ecology and behavior of this species in intensive agricultural areas, especially during the potentially critical nonbreeding season. To help fill these knowledge gaps, we used direct behavioral and larder surveys to describe the non-breeding ecology and behavior of Loggerhead Shrikes in the agriculturally dominated Lower Mississippi Alluvial Valley of Arkansas. Over 3 winters, we observed behavior of Loggerhead Shrikes and found that they spent most of their time perched and scanning for prey (81%), mainly from utility wires (68% of used perches). On average, they made 14.9 ± 1.2 foraging attempts/hr and focused their efforts mostly in right-of-way grasses and adjacent agricultural fields. Overall, individual foraging success rates averaged 58%, did not differ amongst microhabitats, and were comparable to rates reported for breeding Loggerhead Shrikes from other habitats at similar latitudes. Arthropods were the most frequently captured prey (76%; based on direct behavioral surveys), and anurans were the most commonly cached prey (43% of observed cached items). In total, our study highlights the importance of utility wires and rights-of-way for foraging and arthropods as food sources, throughout even the coldest months of the year at this latitude, and increases our understanding of the behavior and ecology of the Loggerhead Shrike in these contexts.

Jays are sensitive to cognitive illusions.

Jays hide food caches, steal them from conspecifics and use tactics to minimize cache theft. Jays are sensitive to the content of their own caches, retrieving items depending on their preferences and the perishability of the cached item. Whether jays impose the same content sensitivity when they steal caches is less clear. We adapted the ‘cups-and-balls’ magic routine, creating a cognitive illusion to test whether jays are sensitive to the (i) content of hidden items and (ii) type of displacement. Subjects were presented with two conditions in which hidden food was consistent with their expectations; and two conditions in which food was manipulated to violate their expectations by switching their second preferred food for their preferred food (up-value) or vice versa (de-value). Subjects readily accepted food when it was consistent with their expectations but were more likely to re-inspect the baited cup and alternative cup when their expectations were violated. In the de-value condition, jays exhibited longer latencies to consume the food and often rejected it. Dominant subjects were more likely to reject the food, suggesting that social factors influence their responses to cognitive illusions. Using cognitive illusions offers innovative avenues for investigating the psychological constraints in diverse animal minds.

An efficient Intelligent Cache Replacement Policy Suitable for PACS

An efficient intelligent cache replacement policy suitable for picture archiving and communication systems (PACS) was proposed in this work. By combining the Support vector machine (SVM) with the classic least recently used (LRU) cache replacement policy, we have created a new intelligent cache replacement policy called SVM-LRU. The SVM-LRU policy is unlike conventional cache replacement policies, which are solely dependent on the intrinsic properties of the cached items. Our PACS-oriented SVM-LRU algorithm identifies the variables that affect file access probabilities by mining medical data. The SVM algorithm is then used to model the future access probabilities of the cached items, thus improving cache performance. Finally, a simulation experiment was performed using the trace-driven simulation method. It was shown that the SVM-LRU cache algorithm significantly improves PACS cache performance when compared to conventional cache replacement policies like LRU, LFU, SIZE and GDS.

High-valued seeds are remembered better: evidence for item-based spatial memory of scatter-hoarding rodents

Episodic memory of scatter-hoarding animals involves encoding and storage of memories of cached food items, in conjunction with subsequent retrieval or recovery. Although the reward values mediate the interaction of item memory and source memory, whether nonhuman animals divide their spatial memory resources between cached items of different values has never been tested. Here, we used a unique scatter-hoarder/seed system to test our ‘cache value hypothesis’, which proposes that scatter-hoarding animals will preferentially allocate spatial memory resources towards higher-valued caches. By making Edwards's long-tailed rats, Leopoldamys edwardsi , anosmic, using Triton X-100, we showed that L. edwardsi recovered more caches containing large nuts than those containing small ones, although the two kinds of nuts were equivalently cached. Our results therefore support the hypothesis that scatter-hoarding animals allocate more spatial memory resources to caches containing high-valued items. This is one of the few studies to investigate how item memory regulates source memory (order information) in the context of food storing and recovery by animals, especially by scatter-hoarding rodents. That high-valued items were remembered better than low-valued ones in our study suggests that item memory appears to be contextually associated with order information of items in animal memory processes, which is not in agreement with a previous suggestion that order information is independent of content or item information. • We used a scatter-hoarding rodent to test episodic memory. • Edward's long-tailed rats allocated more memory on high-valued caches. • High-valued items were remembered better than low-valued ones. • The rats know ‘what food’ was cached ‘where’ or ‘when’.

Global cooperative image prefetching in mobile ad hoc networks

Prefetching the data is a popular technique that improves data accessibility in wireless networks. The improvement in access latency and cache-hit-ratio may diminish because of the mobility and limited cache-space of mobile-hosts-(MHs). The proposed algorithm uses a global-cooperative-caching with image-prefetching (GCCIP) which prefetches the image based on data items. If it is a cache hit, the cache manager still needs to validate the consistency of the cached item with the copy at the original server. The cache-manager returns it to the requester and retains a copy in the cache, when the requested item appears. Experiments are done using both MATLAB and Network-Simulator-2 tools to evaluate the performance of the proposed algorithm and also the parameters are compared with global-cooperative-caching (GCC). Compared to GCC, the proposed GCCIP algorithm improved the system's performance in terms of packet loss, delay and high-throughput compared with previous schemes using NS2. MSE increases with decrease in PSNR, in which performances are taken using MATLAB.

GLOBAL COOPERATIVE IMAGE PREFETCHING IN MOBILE AD HOC NETWORKS

Prefetching the data is a popular technique that improves data accessibility in wireless networks. The improvement in access latency and cache-hit-ratio may diminish because of the mobility and limited cache-space of mobile-hosts-(MHs). The proposed algorithm uses a global-cooperative-caching with image-prefetching (GCCIP) which prefetches the image based on data items. If it is a cache hit, the cache manager still needs to validate the consistency of the cached item with the copy at the original server. The cache-manager returns it to the requester and retains a copy in the cache, when the requested item appears. Experiments are done using both MATLAB and Network-Simulator-2 tools to evaluate the performance of the proposed algorithm and also the parameters are compared with global-cooperative-caching (GCC). Compared to GCC, the proposed GCCIP algorithm improved the system's performance in terms of packet loss, delay and high-throughput compared with previous schemes using NS2. MSE increases with decrease in PSNR, in which performances are taken using MATLAB.

Blockchain-Incentivized D2D and Mobile Edge Caching: A Deep Reinforcement Learning Approach

D2D caching assists mobile edge caching in offloading inter-domain traffic by sharing cached items with nearby users, while its performance relies heavily on caching nodes' sharing willingness. In this article, a Blockchain-based CDM is proposed as an incentive mechanism for the distributed caching system. Under given incentive mechanisms, both D2D and mobile edge caching nodes' willingness is guaranteed by satisfying their expected reward for cache sharing. Besides, for the distributed CDM, content delivery related transactions are executed by smart contracts. To achieve consensus on the transactions and prevent frauds, a consensus protocol among the SCENE is necessary. The pPBFT protocol is proposed to minimize the latency of reaching consensus while guaranteeing its confidence level. We build the model of cache sharing and transaction execution consensus, and we further formulate cache placement and SCENE selection as Markov Decision Process problems. Considering the complexity and dynamics of the problems, a deep reinforcement learning approach is adopted to solve the problems. Simulation results show that, compared to conventional solutions, the proposed schemes achieve efficient traffic offloading, and significantly improve the transaction execution consensus speed.

Deep Reinforcement Learning (DRL)-Based Device-to-Device (D2D) Caching With Blockchain and Mobile Edge Computing

Device-to-Device (D2D) caching assists Mobile Edge Computing (MEC) based caching in offloading inter-domain traffic by sharing cached items with nearby users, while its performance relies heavily on caching nodes’ sharing willingness. In this paper, a Blockchain-based Cache and Delivery Market (CDM) is proposed as an incentive mechanism for the distributed caching system. Under given incentive mechanisms, both D2D and MEC caching nodes’ willingness is guaranteed by satisfying their expected reward for cache sharing. Besides, for the distributed CDM, content delivery related transactions are executed by smart contracts. To achieve consensus on transactions and prevent frauds, a consensus protocol among the smart contract execution nodes (SCENE) is necessary. To minimize the latency of reaching consensus while guaranteeing its confidence level, we propose partial Practical Byzantine Fault Tolerance (pPBFT) protocol. Further, the model of cache sharing and transaction execution consensus is proposed, and we further formulate caching placement and SCENE selection as Markov Decision Process problems. Due to the complexity and dynamics of the problems, a deep reinforcement learning approach is adopted to solve the problem. The simulation results show that the proposed schemes outperform conventional solutions in terms of traffic offloading, content retrieval latency, and consensus latency.

Research on Polynomial Regression Prefetching Model

Based on the addition of prefetching function in the Web, the polynomial regression prefetching model is studied, it designed to mitigate low query response times due to network latency. In the prediction module, the polynomial regression technique is used to predict the prefetch probability of each cache item in the semantic cache. In the prefetch module, a prefetch queue is generated. When the N+1th access period comes, the system idle time is used for prefetching to reduce the web access delay. Through the test and analysis of the Web query data added to the polynomial regression prefetch model, the exactness and stability of the polynomial regression prefetch model are proved, which can reduce the network delay and improve the query response speed.

LR-LRU: A PACS-Oriented Intelligent Cache Replacement Policy

An intelligent cache replacement policy suitable for picture archiving and communication systems (PACS) was proposed in this work. By combining the logistic regression (LR) algorithm with the classic least recently used (LRU) cache replacement policy, we have created a new intelligent cache replacement policy called LR-LRU. The LR-LRU policy is unlike conventional cache replacement policies, which are solely dependent on the intrinsic properties of the cached items. Our PACS-oriented LRLRU algorithm identifies the variables that affect file access probabilities by mining medical data. The LR algorithm is then used to model the future access probabilities of the cached items, thus improving cache performance. In addition, l1-regularization was used to reduce the absolute values of the variables' coefficients. This screens some variables that have little influence on the model by causing their coefficients to approach zero, which achieves the effect of screening the variables. Finally, a simulation experiment was performed using the trace-driven simulation method. It was shown that the l1-regularized LR model is superior to the LR and l2-regularized LR models. The LR-LRU cache algorithm significantly improves PACS cache performance when compared to conventional cache replacement policies, such as LRU, LFU, SIZE, GDF, and GDSF.

Proactive cache replacement technique for mobile networks based on genetic programming

In the mobile environment, the movement of users, disconnected modes, many data updates, power battery consumption, limited cache size, and limited bandwidth impose significant challenges to information access. Caching is considered one of the most important concepts to deal with these challenges. There are two general topics related to the client cache policy: cache invalidation method keeps data in the cache up to date; and cache replacement method chooses the cached item(s) which should be deleted from the cache when the cache is full. The aim of this work is to propose a new technique for cache replacement in a mobile database that takes into consideration the impact of invalidation time for enhancing data availability in the mobile environment by using genetic programming. In this case, each client collects information for every cached item in the cache like access probability, cached document size, validation time and uses these factors in a fitness function to determine cached items that will be removed from the cache. The experiments were performed using Network Simulator 2 to evaluate the effectiveness of the proposed approach, and the results are compared with the existing cache replacement algorithms. It is concluded that the proposed approach performs significantly better than other approaches.

An intelligent database proactive cache replacement policy for mobile communication system based on genetic programming

SummaryIn the mobile environment, the movement of the users, disconnected modes, many data updates, power battery consumption, limited cache size, and limited bandwidth impose significant challenges in information access. Caching is considered one of the most important concepts to deal with these challenges. There are 2 general topics related to the client cache policy: cache invalidation method keeps data in the cache up to date and cache replacement method chooses the cached element(s) that would be removed from the cache once the cache stays full. The aim of this work is to introduce a new technique for cache replacement in a mobile database that takes into consideration the impact of invalidation time for enhancing data availability in the mobile environment by using genetic programming. In this case, each client collects information for every cached item in the cache like access probability, cached document size, and validation time and uses these factors in a fitness function to determine cached items that will be removed from the cache. The experiments were carried by NS2 simulator to assess the efficiency of the proposed method, and the outcomes are judged against existing cache replacement algorithms. It is concluded that the proposed approach performs significantly better than other approaches.