In-network Storage Research Articles

Enhancing Cache Robustness in Information-Centric Networks: Per-Face Popularity Approaches

Information-Centric Networking (ICN) is a new paradigm of network architecture that focuses on content rather than hosts as first-class citizens of the network. As part of these architectures, in-network storage devices are essential to provide end users with close copies of popular content, to reduce latency and improve the overall experience for the user but also to reduce network congestion and load on the content producers. To be effective, in-network storage devices, such as content storage routers, should maintain copies of the most popular content objects. Adversaries that wish to reduce this effectiveness can launch cache pollution attacks to eliminate the benefit of the in-network storage device caches. Therefore, it is crucial to protect these devices and ensure the highest hit rate possible. This paper demonstrates Per-Face Popularity approaches to reducing the effects of cache pollution and improving hit rates by normalizing assessed popularity across all faces of content storage routers. The mechanisms that were developed prevent consumers, whether legitimate or malicious, on any single face or small number of faces from overwhelmingly influencing the content objects that remain in the cache. The results demonstrate that per-face approaches generally have much better hit rates than currently used cache replacement techniques.

Towards Encrypted In-Network Storage Services with Secure Near-Duplicate Detection

In-network storage is recognized as a vital component of many emerging network architectures, which facilitates high-quality and efficient content-centric services. In this trend, providing content-based near-duplicate detection (NDD) services among in-network storage becomes naturally necessary for network traffic alleviation and resource optimization. However, due to the increasing attacking surfaces, storing data in the networked environment inevitably raises new concerns about user privacy exposure and unauthorized data access. Therefore, we aim to design a secure NDD service in the context of encrypted in-network storage. For efficiency, we first leverage the fingerprint techniques and locality-sensitive hashing to convert the problem of NDD into the keyword search. We then adopt an efficient multi-key searchable encryption scheme, which requires only one encrypted query from the user even the data are from multiple content providers encrypted with different keys. As simply combining the above methods does not appear to directly locate accurate results, we then devise a secure result refining scheme via Yao's garbled circuits to avoid user-side post-processing. Furthermore, we enhance our design to address the potential malicious behavior of in-network servers. Extensive evaluations of real-world image dataset demonstrate that our design can achieve comparable accuracy to the plaintext with modest security overhead.

Service Oriented Virtual Machine for Maximising Quality of Service in Wireless Networks

A traditional big data network is a wireless multimedia network, as multiple video/audio streaming account for 70 percent of mobile traffic and the fifth generation wireless networks are projected to rise 500 times. However, it does raise various new and transparent challenges for multimedia big data communication over 5G wireless systems because multimedia big data services provide timely, throughput broadcasts through time-sensitive communication networks with restricted wireless resources. In order to address the above issues, we suggest in this paper information-centric virtualization architectures for a statistical latency consistency of content delivery across 5G wireless large media platforms for technology. In particular, three successful nominee strategies to promise the mathematical time limit for interactive large-data communications are implemented in our proposed scheme: information-centered network which extracts an optimum in-network storage location for multimedia big data; a virtualization of the network feature which transforms PHY architecture into many virtualized channels. In our architectures we build the 3 primary computer-generated machine collection besides power distribution plans to collectively simplify the application of NFV and SDN techniques in the ICN architecture: to improve performance for single users, to integrate effective combining capability with equity allocation for all users besides under non-cooperative betting Via models and computational assesses, we prove that our suggested designs and applications greatly perform better other existing systems to enable QoS provisioning with statistical delays over 5G immersive Large Data Wireless communication.

What do information centric networks, trusted execution environments, and digital watermarking have to do with privacy, the data economy, and their future?

What if instead of having to implement controversial user tracking techniques, Internet advertising & marketing companies asked explicitly to be granted access to user data by name and category, such as Alice→Mobility→05-11-2020? The technology for implementing this already exists, and is none other than the Information Centric Networks (ICN), developed for over a decade in the framework of Next Generation Internet (NGI) initiatives. Beyond named access to personal data, ICN's in-network storage capability can be used as a substrate for retrieving aggregated, anonymized data, or even for executing complex analytics within the network, with no personal data leaking outside. In this opinion article we discuss how ICNs combined with trusted execution environments and digital watermarking, can be combined to build a personal data overlay inter-network in which users will be able to control who gets access to their personal data, know where each copy of said data is, negotiate payments in exchange for data, and even claim ownership, and establish accountability for data leakages due to malfunctions or malice. Of course, coming up with concrete designs about how to achieve all the above will require a huge effort from a dedicated community willing to change how personal data are handled on the Internet. Our hope is that this opinion article can plant some initial seeds towards this direction.

Scalable and efficient routing protocol for internet of things by clustering cache and diverse paths

This paper proposes SENR, a scalable and efficient named-based routing protocol for the internet of things (IoT). SENR explores semantic notification and opportunistic caching strategy to enhance the data retrieval efficiency for smart systems. First, SENR performs data aggregation according to the hierarchy of namespace, and stores data over in-network storage node. It also marks each route notification at the edge nodes, and allows the interest with the same name prefix to choose the same edge routing node towards to the storage node. Second, SENR adopts link-state routing protocols to collect network topology and compute the shortest path to the content source nodes, moreover, it uses distance-vector routing protocols to select multiple alternative paths to cope with node failures. Third, SENR employs partial routing update to explicit support of dynamic paths and caching capabilities. Simulation results demonstrate that SENR can significantly reduce the content retrieval delay and routing overheads.

The Quest for Information-Centric Networking

The articles in this special section focus on the topic of information centric networking (ICN), an approach that shifts the Internet infrastructure from a host-centric paradigm, based on perpetual connectivity and the end-to-end principle, to a new paradigm where named data becomes independent from location, application, storage, and means of transportation, enabling in-network storage and replication. The expected benefits include improved effi ciency, better scalability with respect to information/bandwidth demand, and better robustness in challenging communication scenarios. As the most promising clean-slate approach for future Internet, ICN has attracted much attention in the past few years, and created a trajectory that is to replace the current IP-based Internet. There have been many prototypes of ICN, but more research is still ongoing, which includes naming, forwarding/routing, and caching policy, deployment strategy, security policy, and so on.

In-network data storage protocols for wireless sensor networks: A state-of-the-art survey

In wireless sensor networks, the aim of storage protocols is to efficiently replicate data across nodes and to improve data collection and querying by sinks. Among them, in-network storage protocol...

Compressed Sensing-Based Cached Content Locating for ICN

Locating the cached content accurately and timely is critical for utilizing in-network storage in information-centric networks (ICN). However, an enormous number of cached contents and their volatility nature make it a challenge to collect and maintain a consistent view of the network cache information. In this letter, we propose a novel cached content locating mechanism for ICN, which jointly uses the bloom filter and compressed sensing to efficiently represent the cached contents and leverages the ability of centralized control of software-defined networking (SDN) to make the cache information consistent in the SDN controller. Simulation results show a large saving in announcement cost with a tolerable decrease in locating accuracy.

A Per-Face Popularity Scheme to Increase Cache Robustness in Information-Centric Networks

In-network storage is an essential element of nearly all Information-Centric Networking (ICN) architectures. Thus, protecting the cache of popular items in the network storage devices is crucial. With cache pollution attacks, an attacker seeks to push legitimately popular items out of the cache in favor of less popular items. This paper presents a per-face popularity (PFP) scheme to mitigate effects of cache pollution attacks and to increase overall cache robustness in Information-Centric Networks. The scheme normalizes the contributions from each face so that Interests arriving over any one face has no more effect on the popularity than any other face. As a result, the items that remain in the cache are those that have legitimate popularity, represented across all faces. The experimental results confirm its effectiveness against cache pollution attacks over one or a relatively small percentage of the faces, and demonstrate its overall effectiveness in preserving popular items and increasing hit rate.

Video Delivery in Dense 5G Cellular Networks

One of the main challenges for mobile network operators today is the efficient delivery of mobile VoD services. In order to satisfy their requirements, we must revolutionize the way we manage the densely deployed resources in 5G wireless networks, and tailor their operation for video content delivery. Two key approaches that leverage the density of the 5G infrastructure are the systematic usage of in-network storage resources available in BSs for the deployment of smart caching policies, and the video delivery optimization techniques that leverage the novel multi-tier, dense, and heterogeneous structure of 5G systems. We first provide an overview of the main features of the densely deployed 5G networks that are expected to shape mobile VoD services. Next, we present specific solutions that have recently been proposed for each one of the approaches above, and discuss the main lessons learned. Finally, we discuss a number of open problems for video delivery in dense 5G systems.

Distributed Storage Control Algorithms for Dynamic Networks

Recent technological advances have rendered storage a readily available resource, yet there exist few examples that use it for enhancing network performance. We revisit in-network storage and we evaluate its usage as an additional degree of freedom in network optimization. We consider the network design problem of maximizing the volume of end-to-end transferred data and we derive storage allocation (placement) solutions. We show that different storage placements have different impact on the performance of the network and we introduce a systematic methodology for the derivation of the optimal one. Accordingly, we provide a framework for the joint optimization of routing and storage control (usage) in dynamic networks for the case of a single commodity transfer. The derived policies are based on time-expanded graphs and ensure maximum performance improvement with minimum possible storage usage. We also study the respective multiple commodity problem, where the network link capacities and node storage resources are shared by the different commodities. A key advantage of our methodology is that it employs algorithms that are applicable to both centralized as well as to distributed execution in an asynchronous fashion, and thus, no tight synchronization is required among the various involved storage and routing devices in an operational network. We also present an extensive performance evaluation study using the backbone topology and actual traffic traces from a large European Internet Service Provider, and a number of synthetic network topologies. Our results show that indeed our approach offers significant improvements in terms of delivery time and transferred traffic volume.

Optimal Content Prefetching in NDN Vehicle-to-Infrastructure Scenario

Data replication and in-network storage are two basic principles of the information-centric networking (ICN) framework in which caches spread out in the network can be used to store the most popular contents. This paper shows how one of the ICN architectures, i.e., Named Data Networking (NDN), with content prefetching can maximize the probability that a user retrieves the desired content in a vehicle-to-infrastructure scenario. We give an integer linear programming formulation of the problem of optimally distributing content in the network nodes while accounting for the available storage capacity and the available link capacity. The optimization framework is then leveraged to evaluate the impact on content retrievability of topology- and network-related parameters as the number and mobility models of moving users, the size of the content catalog, and the location of the available caches. Moreover, we show how the proposed model can be modified to find the minimum storage occupancy to achieve a given content retrievability level. The results obtained from the optimization model are finally validated against an NDN architecture through simulations in ndnSIM.

Neighborhood-Based In-Network Caching for Information-Centric Networks

The current Internet is based on host-centric networking, and a user needs to know the host address before reaching a data target in the network. The new architecture of information-centric networking (ICN) facilitates users to locate data targets by giving their data names without any information about host addresses. In-network caching is one of the prominent features in ICN, which allows network routers to cache data contents. In this paper, we emphasize the management of in-network cache storage, and this includes the mechanisms of cache replacement and cache replication. A new cost function is then proposed to evaluate each cache content and the least valuable content is evicted when cache is full. To increase cache utilization, a cooperative caching policy among neighboring routers is proposed. The proper network locations to cache data contents are also discussed in the paper. Experimental results show the superiority of the proposed caching policy than some traditional caching polices.

On the Distance-Sensitive and Load-Balanced Information Storage and Retrieval for 3D Sensor Networks

Efficient in-network information storage and retrieval is of paramount importance to sensor networks and has attracted a large number of studies while most of them focus on 2D fields. In this paper, we propose novel Reeb graph based information storage and retrieval schemes for 3D sensor networks. The key is to extract the line-like skeleton from the Reeb graph of a network, based on which two distance-sensitive information storage and retrieval schemes are developed: one devoted to shorter retrieval path and the other devoted to more balanced load. Desirably, the proposed algorithms have no reliance on the geographic location or boundary information, and have no constraint on the network shape or communication graph. The extensive simulations also show their efficiency in terms of sensor storage load and retrieval path length.

Towards Robust Surface Skeleton Extraction and Its Applications in 3D Wireless Sensor Networks

The in-network data storage and retrieval are fundamental functions of sensor networks. Among many proposals, geographical hash table (GHT) is perhaps most appealing as it is very simple yet powerful with low communication cost, where the key is to correctly define the bounding box. It is envisioned that the skeleton has the power to facilitate computing a precise bounding box. In existing works, the focus has been on skeleton extraction algorithms targeting for 2D sensor networks, which usually deliver a 1-manifold skeleton consisting of 1D curves. It faces a set of non-trivial challenges when 3D sensor networks are considered, in order to properly extract the surface skeleton composed of a set of 2-manifolds and possibly 1D curves. In this paper, we study the problem of surface skeleton extraction in 3D sensor networks. We propose a scalable and distributed connectivity-based algorithm to extract the surface skeleton of 3D sensor networks. First, we propose a novel approach to identifying surface skeleton nodes by computing the extended feature nodes such that it is robust against boundary noise, etc. We then find the maximal independent set of the identified skeleton nodes and triangulate them to form a coarse-grained surface skeleton, followed by a refining process to generate the fine-grained surface skeleton. Furthermore, we design an efficient updating scheme to react to the network dynamics caused by node failure, insertion, etc. We also investigate the impact of boundary incompleteness and present a scheme to extract the surface skeleton under incomplete boundary. Finally, we apply the extracted surface skeleton to facilitate the design of data storage protocol and curve skeleton extraction algorithm. Extensive simulations show the robustness of the proposed algorithm to shape variation, node density, node distribution, communication radio model and boundary incompleteness, and its effectiveness for data storage and retrieval application with respect to load balancing.

Coding-based storage design for continuous data collection in wireless sensor networks

In-network storage is an effective technique for avoiding network congestion and reducing power consumption in continuous data collection in wireless sensor networks. In recent years, network coding based storage design has been proposed as a means to achieving ubiquitous access that permits any query to be satisfied by a few random (nearby) storage nodes. To maintain data consistency in continuous data collection applications, the readings of a sensor over time must be sent to the same set of storage nodes. In this paper, we present an efficient approach to updating data at storage nodes to maintain data consistency at the storage nodes without decoding out the old data and re-encoding with new data. We studied a transmission strategy that identifies a set of storage nodes for each source sensor that minimizes the transmission cost and achieves ubiquitous access by transmitting sparsely using the sparse matrix theory. We demonstrate that the problem of minimizing the cost of transmission with coding is NPhard. We present an approximation algorithm based on regarding every storage node with memory size B as B tiny nodes that can store only one packet. We analyzed the approximation ratio of the proposed approximation solution, and compared the performance of the proposed coding approach with other coding schemes presented in the literature. The simulation results confirm that significant performance improvement can be achieved with the proposed transmission strategy.

How Much to Coordinate? Optimizing In-Network Caching in Content-Centric Networks

In content-centric networks, it is challenging how to optimally provision in-network storage to cache contents, to balance the tradeoffs between the network performance and the provisioning cost. To address this problem, we first propose a holistic model for intradomain networks to characterize the network performance of routing contents to clients and the network cost incurred by globally coordinating the in-network storage capability. We then derive the optimal strategy for provisioning the storage capability that optimizes the overall network performance and cost, and analyze the performance gains via numerical evaluations on real network topologies. Our results reveal interesting phenomena; for instance, different ranges of the Zipf exponent can lead to opposite optimal strategies, and the tradeoffs between the network performance and the provisioning cost have great impacts on the stability of the optimal strategy. We also demonstrate that the optimal strategy can achieve significant gain on both the load reduction at origin servers and the improvement on the routing performance. Moreover, given an optimal coordination level $\ell^\ast$ , we design a routing-aware content placement (RACP) algorithm that runs on a centralized server. The algorithm computes and assigns contents to each CCN router to store, which can minimize the overall routing cost, e.g., transmission delay or hop counts, to deliver contents to clients. By conducting extensive simulations using a large-scale trace dataset collected from a commercial 3G network in China, our results demonstrate that our caching scheme can achieve 4% to 22% latency reduction on average over the state-of-the-art caching mechanisms.

BufferBank storage: an economic, scalable and universally usable in-network storage model for streaming data applications

Large-scale streaming media distribution services impart unprecedented pressures and challenges to existing Internet architecture. Researchers have proposed CDN, P2P Cache, ICN and other solutions to alleviate the pressure of the core network, as well as improve the user experience (QoE). All these solutions achieve their goals by deploying storage resources close to the end users to cache the hot data. Based on the advantages of P2P service, which takes into account end-users resources, we proposed the BufferBank Storage (BBS). It is a new streaming media distribution-oriented storage model by aggregating end-users resources. This provides a novel approach for implementation of economic, scalable, dynamically deploy streaming media distribution applications. However, the dynamic character of the end-user behavior brings challenges to BBS designation. In our previous work, we have analyzed the basic principle of BBS and its feasibility. There is lack of substantial research on resources management and reliability assessment, which are the core issue of BBS implementation. After carefully analyzing the reliability and security issue in BBS deployment, this paper has proposed the implementation model of BBS and studied the performance of different buffer allocation mechanism through simulation. Our work mainly provides a new way of thinking for the dynamic, universal scalable storage system in the Internet, that suffers “weak reliability”.

English

This thesis presents an investigation of the designand evaluation of the generalized storage aware routing (GSTAR) protocol proposed for use in the Mobility First future Internet architecture. The GSTAR protocol uses innetwork storage to improve service quality and throughput in wireless access networks with varying radio link quality and/or disconnection. These gains are achieved using a combination of short-term buffering at routers to smooth out fluctuations in path quality along with delay-tolerant storage, to overcome total disconnection of the mobile device. The performance of the GSTAR protocol is evaluated for exemplary wireless access network scenarios using ns-3 based simulation models, and key design parameters are investigated. Each node in GSTAR maintains two kinds of topology information. The intrapartition graph contains information about path quality between no des in the current partition of the network. The path quality is determined using two metrics: short term and long term expected transmission time. Every no de compares these two metrics using the store/forward decision threshold and stores the data on finding that the path is degraded with the expectation that it may improve in the future. Inter-partition graph gives a probabilistic view of the connection patterns between no des in the network. It is used in the event of disconnections or partitions. The results show that if link quality fluctuations are random, the moving average scheme works well, while an exponentially weighted moving average is recommended for on-off channels with periodic outages. Simulation results are provided in each case, showing the benefit of adaptive threshold settings over the baseline non-adaptive case considered in earlier work.

Replication vs erasure coding in data centric storage for wireless sensor networks

In-network storage of data in wireless sensor networks contributes to reduce the communications inside the network and to favor data aggregation. In this paper, we consider the use of n out of m codes and data dispersal in combination to in-network storage. In particular, we provide an abstract model of in-network storage to show how n out of m codes can be used, and we discuss how this can be achieved in five cases of study. We also define a model aimed at evaluating the probability of correct data encoding and decoding, we exploit this model and simulations to show how, in the cases of study, the parameters of the n out of m codes and the network should be configured in order to achieve correct data coding and decoding with high probability.