Internet Cache Research Articles

Learning-based joint recommendation, caching, and transmission optimization for cooperative edge video caching in Internet of Vehicles

In an era dominated by multimedia information, achieving efficient video transmission in the Internet of Vehicles (IoV) is crucial because of the inherent bandwidth constraints and network volatility within vehicular environments. In this paper, we propose a cooperative edge video caching framework designed to enhance video delivery efficiency in IoV by integrating joint recommendation, caching, and transmission optimization. Leveraging deep reinforcement learning with the discrete soft actor–critic algorithm, our methodology dynamically adapts to fluctuating network conditions and diverse user preferences, aiming to optimize content delivery efficiency and quality of experience. The proposed approach combines recommendation and caching strategies with transmission optimization to provide a comprehensive solution for high-performance video services. Extensive simulation results demonstrate that our framework significantly outperforms traditional baseline methods, achieving superior outcomes in terms of service utility, delivery rate, and delay reduction. These results highlight the robust potential of our solution to facilitate seamless and high-quality video experiences in the complex and dynamic landscape of vehicular networks, advancing the capabilities of IoV content delivery.

Edge Computing Task Scheduling with Joint Blockchain and Task Caching in Industrial Internet

Deploying task caching at edge servers has become an effective way to handle compute-intensive and latency-sensitive tasks on the industrial internet. However, how to select the task scheduling location to reduce task delay and cost while ensuring the data security and reliable communication of edge computing remains a challenge. To solve this problem, this paper establishes a task scheduling model with joint blockchain and task caching in the industrial internet and designs a novel blockchain-assisted caching mechanism to enhance system security. In this paper, the task scheduling problem, which couples the task scheduling decision, task caching decision, and blockchain reward, is formulated as the minimum weighted cost problem under delay constraints. This is a mixed integer nonlinear problem, which is proved to be nonconvex and NP-hard. To solve the optimal solution, this paper proposes a task scheduling strategy algorithm based on an improved genetic algorithm (IGA-TSPA) by improving the genetic algorithm initialization and mutation operations to reduce the size of the initial solution space and enhance the optimal solution convergence speed. In addition, an Improved Least Frequently Used algorithm is proposed to improve the content hit rate. Simulation results show that IGA-TSPA has a faster optimal solution-solving ability and shorter running time compared with the existing edge computing scheduling algorithms. The established task scheduling model not only saves 62.19% of system overhead consumption in comparison with local computing but also has great significance in protecting data security, reducing task processing delay, and reducing system cost.

Joint Design of Multicast Transmission and In-Network Caching for Green Internet of Things

In this paper, the joint design of energy-efficient multicast beamforming and in-network caching in Internet of Things is investigated. We propose the fractional in-network caching scheme. Specifically, parts of the sensed data resources are cached in the helpers, such as small-cell base stations and device-to-device equipments, and the caching fraction for a certain resource is the same among the helpers. The remaining data resources are kept in the sensor nodes. Employing multicast transmission at the helpers, the cached data can be cooperatively transmitted to the clients belonging to multiple multicast groups, where the data requests are identical among the clients in the same multicast group. Based on the proposed fractional caching and multicast transmission protocols, the power consumption model is established, and then we formulate the energy efficiency (EE) optimization problem, which is challenging to solve due to its non-convexity. Thereafter, by exploiting the Dinkelbach ’ s method, semi-definite relaxation, successive convex approximation and other approximation techniques, the original problem is converted into a second-order cone programming problem, which is convex and can be efficiently solved by off-the-shelf CVX solvers. Numerical results exhibit the fast convergence of the proposed algorithm, and demonstrate the superior EE performance of the proposed fractional caching scheme, compared to the benchmark strategies.

Incentive mechanism for competitive edge caching in 5G-enabled Internet of things

The fifth generation (5G) of cellular networks provides the enabling environment for the Internet of Things (IoT) applications. Hence, the vast proliferation of 5G-enabled IoT devices and services led to an overwhelming growth of data traffic that could saturate the core network’s backhaul links. Nowadays, caching is an unavoidable technique to solve this issue, whereby popular contents are stored on edge nodes near to end-users. There exist several initiatives to motivate caching actors for improving the caching process, but not designed for the real-world competitive caching market. In this work, we propose an incentive caching strategy in a 5G-enabled IoT network by considering a completely competitive caching scenario with multiple 5G mobile network operators (MNOs) and multiple content providers (CPs). The MNOs manage a set of edge caches on their base stations and they are competing to fill these caching resources, while the CPs detain a set of popular contents and are in conflict to rent the MNOs’ caches. Each MNO aims to maximize its monetary profit and offload its backhaul links, as each CP aims to improve the quality of experience (QoE) of its end-users. Then, we formulate a multi-leader multi-follower Stackelberg game to model the interaction between MNOs and CPs and define the different players’ utilities. Subsequently, we propose an iterative algorithm based on the convex optimization method to investigate the Stackelberg equilibrium. Finally, the numerical results of the different experimentations demonstrate that our game-based incentive strategy can significantly alleviate the backhaul links while improving the user QoE.

Joint Computing and Caching in 5G-Envisioned Internet of Vehicles: A Deep Reinforcement Learning-Based Traffic Control System

Recent developments of edge computing and content caching in wireless networks enable the Intelligent Transportation System (ITS) to provide high-quality services for vehicles. However, a variety of vehicular applications and time-varying network status make it challenging for ITS to allocate resources efficiently. Artificial intelligence algorithms, owning the cognitive capability for diverse and time-varying features of Internet of Connected Vehicles (IoCVs), enable an intent-based networking for ITS to tackle the above-mentioned challenges. In this paper, we develop an intent-based traffic control system by investigating Deep Reinforcement Learning (DRL) for 5G-envisioned IoCVs, which can dynamically orchestrate edge computing and content caching to improve the profits of Mobile Network Operator (MNO). By jointly analyzing MNO's revenue and users' quality of experience, we define a profit function to calculate the MNO's profits. After that, we formulate a joint optimization problem to maximize MNO's profits, and develop an intelligent traffic control scheme by investigating DRL, which can improve system profits of the MNO and allocate network resources effectively. Experimental results based on real traffic data demonstrate our designed system is efficient and well-performed.

Convergence of computing, communication, and caching in Internet of Things

Internet of Things (IoTs) is a big world of connected objects, including the small and low-resources devices, like sensors, as well as the full-functional computing devices, such as servers and routers in the core network. With the emerging of new IoT-based applications, such as smart transportation, smart agriculture, healthcare, and others, there is a need for making great efforts to achieve a balance in using the IoT resources, including Computing, Communication, and Caching. This paper provides an overview of the convergence of Computing, Communication, and Caching (CCC) by covering the IoT technology trends. At first, we give a snapshot of technology trends in communication, computing, and caching. As well, we describe the convergence in sensors, devices, and gateways. Addressing the aspect of convergence, we discuss the relationship between CCC technologies in collecting, indexing, processing, and storing data in IoT. Also, we introduce the three dimensions of the IoTs based on CCC. We explore different existing technologies that help to solve bottlenecks caused by a large number of physical devices in IoT. Finally, we propose future research directions and open problems in the convergence of communication, computing, and cashing with sensing and actuating devices.

Joint Multioperator Virtual Network Sharing and Caching in Energy Harvesting-Aided Environmental Internet of Things

Environmental monitoring is one of the fundamental applications of the Internet of Things (IoT), and caching in energy harvesting-aided IoT is a promising solution to handle the energy charging of the IoT nodes in the vast monitoring area. However, the growth of the requirements for monitoring area and accuracy brings huge infrastructure costs to the network operators (OP), especially for the multiple OPs scenario. In this article, we utilize wireless virtualization to enable the IoT node sharing between multiple OPs in cache-enabled energy harvesting-aided IoT, so as to improve the utility of the OPs. A Stackelberg game is formulated to jointly handle the IoT node sharing and energy transmission incentives between OPs and energy transmitters. Then, the knapsack problem, convex and linear programming are utilized to approximate the game through problem transformation and derivations. On the basis of that, an alternative direction algorithm is proposed to solve the equilibrium efficiently. The simulation results verify the advantages of the proposed algorithm in utility improvement and fairness maintenance between multiple OPs.

Random Caching Optimization in Large-Scale Cache-Enabled Internet of Things Networks

The introduction of cache can reduce unnecessary traffic load and improve latency in the wireless access networks, especially for wireless video broadcasting. But how cache impacts video broadcasting with scalable video coding (SVC) is still an open problem. In this paper, we analyze the optimization of random caching in a large-scale cache-enabled Internet of Things networks. Specifically, we propose to optimize the random caching strategy that aims to maximize the successful transmission probability (STP) of the video contents at edge base stations (BSs). To this end, by using the stochastic geometry theory, we derive analytical expression of STP by considering SVC to satisfy different levels of quality of service requirements. We develop a gradient-based iterative algorithm to search the local optimal solution for the general random caching strategy optimization problem. The asymptotical optimal caching strategy is obtained with a lower complexity. The closed-form STP is also obtained in high signal-to-noise ratio and particular cache size. Based on the closed-form STP expressions, the random caching strategy, i.e., caching probability of different video contents, is further optimized to enhance STP performance. Compare to different reference schemes, the proposed caching strategy improves the STP up to 18% and 21.6% with the low density of BSs and the high density of BSs, respectively.

Design of Binary Erasure Code With Triple Simultaneous Objectives for Distributed Edge Caching in Industrial Internet of Things Networks

For one moving Internet of Things (IoT) collector to download files from nearby industrial IoT devices, storing network coded files into multiple IoT devices can achieve good reliability performance if the following $(n, k)$ erasure property (EP) is fulfilled: $k$ source packets are encoded into $n$ packets, and $k$ packets chosen from these $n$ packets in an arbitrary manner can reconstruct all the source packets. Besides, low decoding complexity is desired for time-sensitive applications and energy-limited moving IoT collectors, and binary zigzag decoding (BZD) achieves significantly low decoding complexity. The objective of previous EP-BZD designs is unilateral, which limits its application scenarios. In this article, a novel EP-BZD code is designed, which achieves good tradeoff among largest storage room overhead (SRO), SRO variance, and wide range of $(n, k)$ . To implement such a code, the source packets are shifted by several bits, respectively, and then Xored together. The numbers of bits shifted are represented by a matrix, which is obtained from a specially constructed triangle by taking a certain maximal submatrix. The triangle is obtained by a series of steps, including the construction of base vector, parallelogram, trapezoid, etc. The proof that the proposed code possesses EP and BZD simultaneously is also provided. A series of comparisons verify that the proposed code achieves significantly better tradeoff among triple objectives than existing EP-BZD codes.

Incentive edge caching in software‐defined internet of vehicles: A Stackelberg game approach

SummaryIn this paper, we investigate an incentive edge caching mechanism for an internet of vehicles (IoV) system based on the paradigm of software‐defined networking (SDN). We start by proposing a distributed SDN‐based IoV architecture. Then, based on this architecture, we focus on the economic side of caching by considering competitive cache‐enablers market composed of one content provider (CP) and multiple mobile network operators (MNOs). Each MNO manages a set of cache‐enabled small base stations (SBS). The CP incites the MNOs to store its popular contents in cache‐enabled SBSs with highest access probability to enhance the satisfaction of its users. By leasing their cache‐enabled SBSs, the MNOs aim to make more monetary profit. We formulate the interaction between the CP and the MNOs, using a Stackelberg game, where the CP acts first as the leader by announcing the popular content quantity that it which to cache and fixing the caching popularity threshold, a minimum access probability under it a content cannot be cached. Then, MNOs act subsequently as followers responding by the content quantity they accept to cache and the corresponding caching price. A noncooperative subgame is formulated to model the competition between the followers on the CP's limited content quantity. We analyze the leader and the follower's optimization problems, and we prove the Stackelberg equilibrium (SE). Simulation results show that our game‐based incentive caching model achieves optimal utilities and outperforms other incentive caching mechanisms with monopoly cache‐enablers whilst enhancing 30% of the user's satisfaction and reducing the caching cost.

Possession of Child Exploitation Material in Computer Temporary Internet Cache

Possession of Child Exploitation Material in Computer Temporary Internet Cache

Digital forensics: Understanding the development of criminal law in England and Wales on images depicting child sexual abuse

In 2015, offences surrounding the possession, distributing, creation and publication of images depicting child sexual abuse (IDCSA) are prevalent. As a result, it is well publicised that law enforcement and associated digital forensic organisations are incurring substantial case backlogs in this area. As more investigations of this type are faced, it is becoming increasingly essential for practitioners to maintain an understanding of current legislative developments, as a digital forensic investigation of suspected offences surrounding IDCSA does not just involve the blanket recovery of all digital imagery on a device. Governed by this complex area of law, practitioners must appreciate the intricacies of these offences, ensuring any examination policies are correctly defined whilst recovering information that will support criminal justice processes. In addition, as triage strategies are increasingly employed in an effort to speed up investigations, it is crucial to recognise the types of evidence that are of use to a prosecuting authority in order to ensure these examination techniques are both efficient and effective. This paper offers a comprehensive analysis of legislative developments for offences surrounding IDCSA in the United Kingdom, bringing together the disciplines of law and digital forensics. Evidence of value to a prosecution for these offences is also considered taking into account existing case law precedents in line with contentious areas including the Internet cache and unallocated clusters.

Traffic Management of Video on Demand: An Analysis of Investments for Improving the End User's Quality of Experience

The current escalation in user demand for web contents, particularly Video on Demand (VoD), is causing a continuing increase in both the types of web traffic and the volumes of data transmitted. The greater demand arises from the new means of communication employed by individuals and companies, as well as the development of readily usable applications distributed by ‘app stores’. In this paper, we suggest that the stakeholders of a VoD framework, the Content Providers (CPs) and the Internet Service Providers (telcos/ISPs), should guarantee a solid Quality of Experience (QoE) to the end user through two potential investments: either in ultra-broadband (UBB) or in the technologies for the acceleration of web content, known as the Content Delivery Network (CDN) and Transparent Internet Caching (TIC). The aim of the paper is to analyse these investments in terms of providers' profits. The base hypothesis is that the investments are subsidized by the CPs, which, in recent years, have indeed been directing a large part of their revenues towards investments in network infrastructure.

Network Services and Applications: Web Caching

3 Abstract: As Internet content today grows and the number of user increase thus, the Internet traffic also increase. As one of the most popular applications running on Internet, the World Wide Web (WWW) experiences network congestion and overloading of the origin server. The existence of Web caches plays important roles in reducing network traffic between clients and server. In this paper, we discuss the overview of Web caching generally including the types of Web cache, the Web cache architecture, Internet Cache Protocols (ICP), Hyper Text Caching Protocols (HTCP), the advantages and disadvantages of Web caching and also the dynamic data caching.

Automated event and social network extraction from digital evidence sources with ontological mapping

The sharp rise in consumer computing, electronic and mobile devices and data volumes has resulted in increased workloads for digital forensic investigators and analysts. The number of crimes involving electronic devices is increasing, as is the amount of data for each job. This is becoming unscaleable and alternate methods to reduce the time trained analysts spend on each job are necessary.This work leverages standardised knowledge representations techniques and automated rule-based systems to encapsulate expert knowledge for forensic data. The implementation of this research can provide high-level analysis based on low-level digital artefacts in a way that allows an understanding of what decisions support the facts. Analysts can quickly make determinations as to which artefacts warrant further investigation and create high level case data without manually creating it from the low-level artefacts. Extraction and understanding of users and social networks and translating the state of file systems to sequences of events are the first uses for this work.A major goal of this work is to automatically derive ‘events’ from the base forensic artefacts. Events may be system events, representing logins, start-ups, shutdowns, or user events, such as web browsing, sending email. The same information fusion and homogenisation techniques are used to reconstruct social networks. There can be numerous social network data sources on a single computer; internet cache can locate Facebook, LinkedIn, Google Plus caches; email has address books and copies of emails sent and received; instant messenger has friend lists and call histories. Fusing these into a single graph allows a more complete, less fractured view for an investigator.Both event creation and social network creation are expected to assist investigator-led triage and other fast forensic analysis situations.

Modeling Energy-Delay Tradeoffs in Single Base Station with Cache

With the explosive increase of mobile data traffic, the energy efficiency issue in cellular networks is a growing concern. Recently, the advantages of in-network caching in Internet have been widely investigated, for example, speeding up content distribution and improving network resource utilization. In this paper, we analyze the energy-delay tradeoff problem in the context of single base station (BS), which has a cache capacity to buffer the contents through it. Although additional power is consumed by the cache, work load of BS and network delay will be improved, which makes a tradeoff between network power consumption and delay. Simulation results reveal that, by introducing the cache in a BS, the network power and delay can be obviously reduced in different network conditions compared to the scenario without a cache. In addition, we find that a large cache size does not always mean a less network cost because of the more cache power consumption.

Capacity and Security of Heterogeneous Distributed Storage Systems

We study the capacity of heterogeneous distributed storage systems under repair dynamics. Examples of these systems include peer-to-peer storage clouds, wireless, and Internet caching systems. Nodes in a heterogeneous system can have different storage capacities and different repair bandwidths. We give lower and upper bounds on the system capacity. These bounds depend on either the average resources per node, or on a detailed knowledge of the node characteristics. Moreover, we study the case in which nodes may be compromised by an eavesdropper, and give bounds on the system secrecy capacity. One implication of our results is that symmetric repair maximizes the capacity of a homogeneous system, which justifies the model widely used in the literature.

Combining JPEG2000 Compressed Formats and OGC Standards for Fast and Easy Dissemination of Large Satellite Data

This article explores ways for disseminating huge dataset imagery compressed in JPEG2000 using OGC standards. Some web service protocols are analyzed and tested (Web Map Service, Web Feature Service, Web Coverage Service, etc) but special attention is given to the new Web Map Tile Service (WMTS) standard that has been recently approved by the OGC Technical Committee. A WMTS server exposes a structure of resolution levels that are cut in small and uniform tiles in a very similar way that JPEG2000 internally does. We discuss how JPEG2000 and WMTS can be combined together and the advantages and disadvantages versus a more common approach based on prerendered classical JPEG or PNG tiles or versus JPIP approaches. Among the different architectures, it is worth mentioning a WMTS server that uses one or some JPEG2000 images internally and serves images to a thin client (a web browser) in a more classical format like JPEG, PNG etc. In this case, WMTS tile matrix set structure can be used to describe the internal structure of the tiled JPEG2000 images to the client, guaranteeing an optimum performance and taking profit of the Internet caching mechanisms.

Estimating evolution of freshness in Internet cache directories under the capture–recapture methodology

In this paper, we describe a new web sampling scheme for measuring the evolution of freshness in search engines. The methodology used is the capture-recapture, which is mainly applied for estimating evolution rates in wildlife biological studies. After modifications and amendments, necessary for web paradigm application, we conducted three capture-recapture experiments of different duration over the caches of Google and MSN. In parallel, we used a typical sampling scheme, similar to many other web sampling approaches used in the literature, to evaluate the robustness of our proposal. The paper provides the implementation details of a web-based capture-recapture model along with its assessment. The results show that through the capture-recapture methodology we are able not only to measure the freshness of the tested search services but also to monitor its evolution over time, with a substantially lower amount of required sampling instances. It was not our intention to compare the performance of Google and MSN. However, through our experiments, we observed that although one sometimes presents better refresh rates than the other, in general both search services have virtually equal capabilities in refreshing their directories and providing new and up-to-date results to their users.

Location and time do matter: A long tail study of website requests

There has been a tremendous growth in the amount and range of information available on the Internet. The users' requests for online information can be captured by a long tail model. A few popular websites enjoy a high number of visitations while the majority of the rest are less frequently requested. In this study we use real world data to investigate this phenomenon and show that both users' physical location and time of access affect the heterogeneity of website requests. The effect can partially be explained by differences in demographic characteristics at locations and diverse user browsing behavior in weekdays and weekends. These results can be used to design better online marketing strategies, affiliate advertising models, and Internet caching algorithms with sensitivities to user location and time of access differences.