Content Store Research Articles

Enhancing computation reuse efficiency in ICN-based edge computing by modifying content store table structure

Enhancing computation reuse efficiency in ICN-based edge computing by modifying content store table structure

Caching Method for Information-Centric Ad Hoc Networks Based on Content Popularity and Node Centrality

In recent years, most internet communications have focused on accessing content such as video, web services, and audio. Conversely, traditional Internet communications are inefficient because they are primarily designed for data transfer between hosts. In response, Information-Centric Networking (ICN) has emerged as a content-oriented networking model. The impact of ICN in reducing the location dependency of data and its high compatibility with ad hoc networks has led to research on realizing Information-Centric ad hoc Networks (ICANET). There has also been extensive research into caching content in the network, which is one of the features of ICN. In static networks, methods have been proposed to cache highly popular content in nodes that are more likely to be used for shortest paths. However, in dynamic networks, content with high popularity should be cached on nodes that are more likely to reach all nodes, as missing nodes need to be taken into account. In this study, we propose a cache control scheme for content caching in ICANET that utilizes both content popularity and the closeness centrality of nodes within the ad hoc network as indicators. To realise the proposed method, a new packet flow based on the Pending Interest Table (PIT) and Content Store (CS) was implemented in the forwarding strategy of ICN. The experiments used ndnSIM, a protocol implementation of NDN based on Network Simulator3, which is widely used in wireless network research. The experimental results showed that the cache hit rate could be increased by up to 4.5% in situations with low content bias. In the same situation, the response delay was also reduced by up to 28.3%.

Evolutionary game based interest forwarding in information centric vehicular networks

In Information Centric Network (ICN) a client generates an interest packet when it is in need of a content. The interest packet carries the content name and propagates through the network till it reaches an appropriate cached content store or the producer of the content. Retrieval of the searched content is faster if the interest packet is forwarded in right direction towards a probable content location. An efficient and optimal interest forwarding technique is the key requirement for the success of any ICN implementation. This paper describes an interest forwarding approach for information centric vehicular networks based on evolutionary game theory. It uses the Public Goods Gaming (PGG) strategy to forward interest packets so that the content can be located at the earliest. The interest packets are modeled as goods and various participating entities are categorized either as cooperator or defector. The purpose of the game is to deliver the goods (the interest packet) to the rightful content store. Cooperator tries to optimally deliver the interest packets and hence they are rewarded with certain incentives. However, defectors are not rewarded as they do not participate in the game. At the beginning, a player selects a strategy and confined to it till the completion of the game. During interest forwarding, a node selects its neighbor(s) having higher credits as next level forwarder. The scheme is analyzed mathematically to establish various claims made in this paper. The mathematically established claims are also validated through simulation in ns-3 based ndnSIM-2.0. The model is compared with four other existing ICN forwarding approaches and simulation results depict that the new algorithm performs better.

A new cache replacement policy in named data network based on FIB table information

Named Data Network (NDN) is proposed for the Internet as an information-centric architecture. Content storing in the router’s cache plays a significant role in NDN. When a router’s cache becomes full, a cache replacement policy determines which content should be discarded for the new content storage. This paper proposes a new cache replacement policy called Discard of Fast Retrievable Content (DFRC). In DFRC, the retrieval time of the content is evaluated using the FIB table information, and the content with less retrieval time receives more discard priority. An impact weight is also used to involve both the grade of retrieval and stale parameters in the discard priority calculation. This weight adjusts the relative impact of these two parameters on the discard priority. The grade of retrieval reflects the content’s retrieval time, and the stale parameter indicates its popularity. The reinforcement Learning method is used to calculate the suitable weight dynamically and based on the network condition. Simulation results show that DFRC improves hit rate and round-trip time compared to Least Recently Used, Optimized Cache Replacement algorithm for Information-Centric Networks, Efficient Popularity-aware Probabilistic Caching, and Shortest Round-Trip Time.

Adaptive Forwarding and Routing of Named Data Networking A Survey

In recent years, Named Data Networking (NDN) has been one of the future internet network architectures by proposing it as a substitute for existing IP networks. NDN assigns addresses/names to data or content, whereas IP assigns addresses to devices. NDN router has a Content Store (CS) component to store requested packets and reducing the time for duplicate content requests. As network architecture, NDN uses several algorithms and strategies in the process. This paper will specifically discuss the forwarding and routing algorithm. Forwarding mechanisms play a significant role in packet delivery on the NDN system. Even though routing was not the main component of the NDN system, the routing mechanism and Routing Information Base remain important, considering Forwarding Information Base was generated after Routing Information Base was generated. Routing has significant control, whereas forwarding can give finer control over the delivery path. Considering the previous statement, the combination of routing and forwarding strategy must be considered to optimize the performance of the packet delivery system on NDN. In addition to getting information about the entire network, centralized routing, and adaptive networking are needed to distribute all network traffic fairly. The challenges and opportunities can also be a reference and a guideline for future Adaptive NDN research.

Low delay fragment forwarding in LEO satellite networks based on named data networking

The LEO satellite network with cache and computing capabilities has become an important evolution of the next generation (6G) mobile communication network. The satellite networks employing Named Data Networking (NDN) can achieve swift content access through in-network caches and data multicast. The data packet is the basic unit of transmission in NDN. In order to adapt to the limited MTU of links, large data packets must be transmitted in fragments. However, due to the issues such as large transmission delay, redundant reassembly, and infeasible fragment reuse, the existing fragmentation methods in NDN cannot be applied to satellite communication. In this paper, we propose a low delay fragment forwarding method in NDN: Direct Forwarding and Reuse of Fragments (DFRF). Specifically, the content in the large data packet will be split and packaged into fragments that conform to NDN semantics. Once the fragment is received by the satellite router, it can be forwarded directly without reassembly, thus saving the forwarding delay. In addition, we design a new fragment format and the list-based cache, so that all fragments generated from the same data packet can be cached intact in a list, and each fragment can be reused as a complete packet in the content store (CS). The proposed approach has been further evaluated and the simulation results show that compared with the NDN Link Protocol (NDNLP), using the DFRF method, the end-to-end transmission delay of large content is reduced by up to 51%, and the content acquisition delay is reduced by up to 60% through fragment reuse.

IBPC: An Approach for Mitigation of Cache Pollution Attack in NDN using Interface-Based Popularity

The performance of named data networking (NDN) depends on the caching efficiency of routers. Cache pollution attack (CPA) refers to colonization of unpopular contents in the content store (CS) of an NDN router, which leads to declined quality of service (QoS) in NDN. CPA has very few solutions proposed for its mitigation. Most of these solutions are based on the statistics of the router itself. However, an attacker can influence these statistics by requesting unpopular contents repeatedly. This article proposes a new parameter for the detection of CPA, which is based on the number of distinct users requesting interest packets for a content over a period of time. The local popularity of the attackers’ content does not affect the proposed approach. The results show that the proposed approach consumes less storage, reduces processing time, and more effectively mitigates the CPA, as compared to the other existing approaches. Compared with the previous approaches, the proposed approach exhibits an improvement of approximately 28.14% to 36.80%.

Cache-MAB: A reinforcement learning-based hybrid caching scheme in named data networks

The presence of content stores in routers enables in-network caching in Named Data Networks (NDN) to improve customer experience, especially when content distribution is considered. Typically, homogeneous caching is assumed in the network where each router operates its cache according to the installed policy. Numerous in-network caching policies that vary in admission logic and caching attributes have been urged. Hence, the policies vary in performance (efficiency) according to unbounded network factors such as caching capacity, users’ request pattern, content popularity distribution, and application type (amongst other contexts). A single scheme may not effectively realize all the network characteristics and vandalize the efficient use of precious cache spaces. Therefore, a new strategy capable of dynamically selecting the optimal caching policy under varying network contexts is inevitable. In this direction, we propose a reinforcement learning (RL)-based hybrid caching strategy, namely Cache-MAB, where the routers work in a distributed manner and learn to pick the most suitable policy for caching a content. The set of policies is formed by picking the policies that assume similar caching attributes for caching decisions. The proposed strategy decouples the caching policy selection from the admission logic used by the selected policy and models the suitable policy selection as a Multi-armed Bandit (MAB) problem. The hybrid strategy equips each router with a diverse set of caching policies and an RL agent that utilizes a reinforcement algorithm to solve the MAB problem and, hence, select the optimal policy in different cases. The optimal policy is the one that maximizes the local performance, for instance, the cache hit rate. Simulation results confirm that the hybrid strategy effectively achieves the optimal policy’s performance, having a performance gap of less than 1%, by adapting to different network scenarios.

An Efficient Pending Interest Table Content Search In NDN through Stable Bloom Filter

AbstractNamed Data Networking (NDN) has gained importance in today’s era due to a paradigm shift in the Internet usage pattern which revolves around the content rather than the respective host addresses. Three important data structures in NDN are Content Store (CS), Pending Interest Table (PIT) and Forwarding Information Base (FIB). The search time of PIT is quite high since its size grows with the addition of new content names, and the Interest packets which are not served by CS are searched in millions of existing entries in the PIT. Hence, lookup time can be improved if, instead of checking all the available entries, initial scanning is done to determine whether the required content name exists in the PIT or not. In this paper, we propose a Stable Bloom Filter (SBF) based PIT called S-PIT, to minimize the PIT search time by identifying the existence of query content through SBF. The various experiments performed show that S-PIT outperforms existing data structures in terms of memory consumption, content insertion time, average search time and false positive rate.

Performance Analysis of Cache Replacement Algorithm using Virtual Named Data Network Nodes

As a future internet candidate, named Data Network (NDN) provides more efficient communication than TCP/IP network. Unlike TCP/IP, consumer requests in NDN are sent based on content, not the address. The previous study evaluated the NDN performance using a simulator. In this research, we modeled the system using virtual NDN nodes, making the model more relevant to the real NDN. As an essential component in every NDN router, the content store (CS) has a function to keep the data. We use First In First Out (FIFO) and Least Recetly Used (LRU) in our nodes as cache replacement algorithms. The in-depth exploration is done using various scenarios. The result shows that the cache hit ratio (CHR) increases if the size of the CS, the number of interests, and the number of consumers increases. CHR decreases as the number of producers and the number of prefixes increase. As CHR increases, round trip time (RTT) decreases. LRU provides better performance for all cases: higher CHR of 5-15% and lower RTT of 1-10% than FIFO.

An Intelligent Caching Strategy Considering Time-Space Characteristics in Vehicular Named Data Networks

In the Internet of Vehicles (IoV), the classic TCP/IP still plays an important role for data transmission, traffic control and address assignment. However, with increasing requirements on content retrieve efficiency in IoV, the drawbacks of traditional TCP/IP stacks, such as weak scalability in large networks, low efficiency in dense environment and unreliable addressing in high mobility circumstance, have incurred significant performance degradations in vehicular environments. Fortunately, the emerging Named Data Network (NDN) technology provides a good choice to address above issues in vehicular environment by proving content caching capability with introduced content store module, and boosts the research activity of Vehicular Named Data Network (VNDN) in the last few years. In this paper, to improve the service performance, e.g., reducing the delay of data acquisition, a data caching scheme is proposed by taking the spatial-temporal characteristics of data into account. At first, we divided the data in a VNDN into emergency safety message, traffic efficiency message and service message, according to the application requirements. Then, we analyze the spatial-temporal characteristics of these three message categories and design the caching strategy according to these characteristics. Experimental results from NDNSim platform show that our designed scheme has an approximately 50% performance enhancement compared with Leave Copy Everywhere (LCE), Pro(0.7), and Pro(0.2) data caching protocols in terms of average hit rate, average hop count and average cache replacement times, which verifies the reliability and effectiveness of our proposed data caching scheme.

Fog-based caching mechanism for IoT data in information centric network using prioritization

In-Network caching is one of the most prominent features of Information-Centric Networks (ICN). This feature, designed to reduce the content delivery time, can improve data availability, especially during the sleeping mode of the original data producer. However, the fact that caching mechanism is simultaneously performed with data forwarding, limited processing power, and limited memory capacity of routers has posed challenges in fully profiting from this feature. The synchronization of caching mechanism with data forwarding limits the speed of decision-making in content placement policies. These challenges as well as the limitations of ICN routers have not been taken into consideration by the existing strategies for content placement of IoT data. In this paper, the restrictions for content placement policies in ICN-based IoT are outlined, and a data prioritization-based approach that prevents ICN nodes from filling their Content Store (CS) with inappropriate and inferior data is proposed to improve cache efficiency. Besides, fog computing is used as a middle layer between IoT devices and ICN networks to resolve the speed limits of decision-making and improve cache performance. In the fog layer, prioritization is performed based on the popularity and freshness of data object, and, thus, low-priority data are removed from the caching mechanism of ICN nodes. As the result shows the total number of cached data is reduced. Freshness is an essential property of multiple IoT data that significantly affects caching performance. That's why we prioritized data based on popularity and freshness. Eventually, the simulation results performed by the NDNsim simulator show that decreasing the number of data objects could improve the cache mechanism efficiency in terms of both delay and hit ratio.

Quality-of-Service-Linked Privileged Content-Caching Mechanism for Named Data Networks

The domain of information-centric networking (ICN) is expanding as more devices are becoming a part of connected technologies. New methods for serving content from a producer to a consumer are being explored, and Named Data Networking (NDN) is one of them. The NDN protocol routes the content from a producer to a consumer in a network using content names, instead of IP addresses. This facility, combined with content caching, efficiently serves content for very large networks consisting of a hybrid and ad hoc topology with both wired and wireless media. This paper addresses the issue of the quality-of-service (QoS) dimension for content delivery in NDN-based networks. The Internet Engineering Task Force (IETF) classifies QoS traffic as (prompt, reliable), prompt, reliable, and regular, and assigns corresponding priorities for managing the content. QoS-linked privileged content caching (QLPCC) proposes strategies for Pending Interest Table (PIT) and content store (CS) management in dedicated QoS nodes for handling priority content. QoS nodes are intermediately resourceful NDN nodes between content producers and consumers which specifically manage QoS traffic. The results of this study are compared with EQPR, PRR probability cache, and Least Frequently Used (LFU) and Least Fresh First (LFF) schemes, and QLPCC outperformed the latter-mentioned schemes in terms of QoS-node CS size vs. hit rate (6% to 47%), response time vs, QoS-node CS size (65% to 90%), and hop count vs. QoS-node CS size (60% to 84%) from the perspectives of priority traffic and overall traffic. QLPCC performed predictably when the NDN node count was increased from 500 to 1000, showing that the strategy is scalable.

NDN Content Store and Caching Policies: Performance Evaluation

Among various factors contributing to performance of named data networking (NDN), the organization of caching is a key factor and has benefited from intense studies by the networking research community. The performed studies aimed at (1) finding the best strategy to adopt for content caching; (2) specifying the best location, and number of content stores (CS) in the network; and (3) defining the best cache replacement policy. Accessing and comparing the performance of the proposed solutions is as essential as the development of the proposals themselves. The present work aims at evaluating and comparing the behavior of four caching policies (i.e., random, least recently used (LRU), least frequently used (LFU), and first in first out (FIFO)) applied to NDN. Several network scenarios are used for simulation (2 topologies, varying the percentage of nodes of the content stores (5–100), 1 and 10 producers, 32 and 41 consumers). Five metrics are considered for the performance evaluation: cache hit ratio (CHR), network traffic, retrieval delay, interest re-transmissions, and the number of upstream hops. The content request follows the Zipf–Mandelbrot distribution (with skewness factor α=1.1 and α=0.75). LFU presents better performance in all considered metrics, except on the NDN testbed, with 41 consumers, 1 producer and a content request rate of 100 packets/s. For the level of content store from 50% to 100%, LRU presents a notably higher performance. Although the network behavior is similar for both skewness factors, when α=0.75, the CHR is significantly reduced, as expected.

An Efficient Distributed Content Store-Based Caching Policy for Information-Centric Networking.

Content store (CS) is one of the main components of information-centric networking (ICN), which enables content objects to be cached and retrieved from any intermediate node in the network. However, in existing ICN designs, CS information is not exploited to coordinate content caching and content retrieval. CS of nodes in the network operates independently while Interest packets forwarding mainly uses forwarding information base (FIB). This paper highlights the importance of CS information for efficient content caching and content retrieval to improve the performance of information-centric networking, especially in resource-constrained environments like the Internet of Things. We propose an efficient caching policy to coordinate the CS of a node with its neighbor nodes in a distributed manner so that more and more popular content objects are cached in the neighborhood of the node. To exploit and coordinate CS information among nodes, we urge to enable CS information in the data plane of the network and design an efficient way for CS information transmission. Each node contributes to the objective of its neighborhood by maximizing its number of unique popular content objects being cached in its CS and not cached in the CS of its neighbors. We implement the proposed policy on top of state-of-the-art popularity-based caching schemes. Through analysis and experiments, we show that the proposed caching policy achieves a significant improvement in terms of cache hit ratio, stretch ratio, content retrieval latency, and energy efficiency significantly compared to state-of-the-art schemes.

A fair dynamic content store-based congestion control strategy for named data networking

In this paper, the congestion control for named data networking (NDN) is studied. A novel dynamic content store-based congestion control strategy is proposed on account of the characteristic of in-network cache in NDN. A queuing network model is constructed to judge whether congestion occurs. If the network has the tendency of congestion or the congestion happened, the buffer of the output queue is dynamically expanded by borrowing NDN content store (CS), and the forwarding rates of data packets and corresponding interest packets are reduced so as to prevent or alleviate network congestion. In order to reflect fairness, the CS to be borrowed by the data output queue in the port is calculated in terms of the data output queue length and its weight. The simulation results based on ndnSIM show that the given scheme improves the bottleneck link utilization and maintains a low packet loss rate and average flow completion time.

SAFT-VNDN: A Socially-Aware Forwarding Technique in Vehicular Named Data Detworking

Vehicular Social Networks (VSNs) is the bridge of social networks and Vehicular Ad-Hoc Networks (VANETs). VSNs are promising as they allow the exchange of various types of contents in large-scale through Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication protocols. Vehicular Named Data Networking (VNDN) is an auspicious communication paradigm for the challenging VSN environment since it can optimize content dissemination by decoupling contents from their physical locations. However, content dissemination and caching represent crucial challenges in VSNs due to short link lifetime and intermittent connectivity caused by vehicles’ high mobility. Our aim with this paper is to improve content delivery and cache hit ratio, as well as decrease the transmission delay between end-users. In this regard, we propose a novel hybrid VNDN-VSN forwarding technique based on social communities, which allows requester vehicles to easily find the most suitable forwarder or producer among the community members in their neighborhood area. Furthermore, we introduce an effective caching mechanism by dividing the content store into two parts, one for community private contents and the second one for public contents. Simulation results show that our proposed forwarding technique can achieve a favorable performance compared with traditional VNDN, in terms of data delivery ratio, average data delivery delay, and cache hit ratio.

An Efficient Content Store-Based Forwarding Scheme for Internet of Things.

One of the main advantages of information-centric networking (ICN) is that a requested piece of content can be retrieved from a content store (CS) at any intermediate node, instead of its original content producer. In existing ICN designs, nodes forward Interest packets mainly based on forwarding information base (FIB). FIB is constructed from name prefixes registered by content producers with a list of next hops to the name prefixes. The ICN forwarding engine uses those information to forward Interest packets towards corresponding content producers. CS information of a node is currently used only for checking the availability of cached content objects at the node and is not considered in the data plane of existing ICN forwarding mechanisms. This paper highlights the importance of CS information in an ICN forwarding mechanism and enables neighbor CS information in the data plane to improve the cache hit ratio and forwarding efficiency, especially for resource-constraint Internet of Things (IoT). We propose an efficient CS-based forwarding scheme for IoT. The proposed forwarding scheme exploits CS information of neighbors to find efficient routes to forward Interest packets toward nearby nodes with corresponding cached content. For that, we carefully design an efficient way for CS information sharing using counting bloom filter. We implement the proposed scheme and compare with state-of-the-art ICN forwarding schemes in IoT. Experimental results indicate that the proposed forwarding scheme achieves a significant improvement in terms of cache hit ratio, energy efficiency, content retrieval latency, and response rate.

NB-Cache: Non-Blocking In-Network Caching for High-Performance Content Routers

Information-Centric Networking (ICN) provides scalable and efficient content distribution at the Internet scale due to in-network caching and native multicast. To support these features, a content router needs high performance at its data plane, which consists of three forwarding steps: checking the Content Store (CS), then the Pending Interest Table (PIT), and finally the Forwarding Information Base (FIB). In this work, we build an analytical model of the router and identify that CS is the actual bottleneck. Then, we propose a novel mechanism called “NB-Cache” to address CS’s performance issue from a network-wide point of view. In NB-Cache, when packets arrive at a router whose CS is fully loaded, instead of being blocked and waiting for the CS, these packets are forwarded to the next-hop router, whose CS may not be fully loaded. This approach essentially utilizes Content Stores of all the routers along the forwarding path in parallel rather than checking each CS sequentially. NB-Cache follows a design pattern of on-demand load balancing and can be formulated into a non-trivial N-queue bypass model. We use the Markov chain to establish its theoretical base and find an algorithm for automated transition rate matrix generation. Experiments show significant improvement of data plane performance: 70% reduction in round-trip time (RTT) and 130% increase in throughput. NB-Cache decouples the fast packet forwarding from the slower content retrieval thus substantially reducing CS’s heavy dependency on fast but expensive memory.

Cache management for large data transfers and multipath forwarding strategies in Named Data Networking

Named Data Networking (NDN) is a promising approach to provide fast in-network access to compact muon solenoid (CMS) datasets. It proposes a content-centric rather than a host-centric approach to data retrieval. Data packets with unique and immutable names are retrieved from a content store (CS) using Interest packets. The current NDN architecture relies on forwarding strategies that are only dependent upon on-path caching. Such a design does not take advantage of the cached content available on the adjacent off-path routers in the network, thus reducing data transfer efficiency. In this work, we propose a software-defined, storage-aware routing mechanism that leverages NDN router cache-states, software defined networking (SDN) and multipath forwarding strategies to improve the efficiency of very large data transfers. First, we propose a novel distributed multipath (D-MP) forwarding strategy and enhancements to the NDN Interest forwarding pipeline. In addition, we develop a centralized SDN-enabled control for the multipath forwarding strategy (S-MP), which leverages the global knowledge of NDN network states that distributes Interests efficiently. We perform extensive evaluations of our proposed methods on an at-scale wide area network (WAN) testbed spanning six geographically separated sites. Our proposed solutions easily outperform the existing NDN forwarding strategies. The D-MP strategy results in performance gains ranging between 10.4x to 12.5x over the default NDN implementation without in-network caching, and 12.2x to 18.4x with in-network caching enabled. For S-MP strategy, we demonstrate a performance improvement of 10.6x to 12.6x, and 12.9x to 18.5x, with in-network caching disabled and enabled, respectively. Further, we also present a comprehensive analysis of NDN cache management for large data transfers and propose a novel prefetching mechanism to improve data transfer performance. Due to the inherent capacity limitations of the NDN router caches, we use SDN to provide an intelligent and efficient solution for data distribution and routing across multiple NDN router caches. We demonstrate how software-defined control can be used to partition and distribute large CMS files based on NDN router cache-state knowledge. Further, SDN control will also configure the router forwarding strategy to retrieve CMS data from the network. Our proposed solution demonstrates that the CMS datasets can be retrieved 28%–38% faster from the NDN routers’ caches than existing NDN approaches. Lastly, we develop a prefetching mechanism to improve the transfer performance of files that are not available in the router’s cache.