Content Delivery Latency of Caching Strategies for Information-Centric IoT

In-network caching is a central aspect of Information-Centric Networking (ICN). It enables the rapid distribution of content across the network, alleviating strain on content producers and reducing content delivery latencies. ICN has emerged as a promising candidate for use in the Internet of Things (IoT). However, IoT devices operate under severe constraints, most notably limited memory. This means that nodes cannot indiscriminately cache all content; instead, there is a need for a caching strategy that decides what content to cache. Furthermore, many applications in the IoT space are timesensitive; therefore, finding a caching strategy that minimises the latency between content request and delivery is desirable. In this paper, we evaluate a number of ICN caching strategies in regards to latency and hop count reduction using IoT devices in a physical testbed. We find that the topology of the network, and thus the routing algorithm used to generate forwarding information, has a significant impact on the performance of a given caching strategy. To the best of our knowledge, this is the first study that focuses on latency effects in ICN-IoT caching while using real IoT hardware, and the first to explicitly discuss the link between routing algorithm, network topology, and caching effects.Comment: 10 pages, 9 figures, journal pape

Load Imbalance and Caching Performance of Sharded Systems

Sharding is a method for allocating data items to nodes of a distributed caching or storage system based on the result of a hash function computed on the item’s identifier. It is ubiquitously used in key-value stores, CDNs and many other applications. Despite considerable work that has focused on the design and implementation of such systems, there is limited understanding of their performance in realistic operational conditions from a theoretical standpoint. In this paper we fill this gap by providing a thorough modeling of sharded caching systems, focusing particularly on load balancing and caching performance aspects. Our analysis provides important insights that can be applied to optimize the design and configuration of sharded caching systems

Understanding Sharded Caching Systems

Sharding is a method for allocating data items to nodes of a distributed caching or storage system based on the result of a hash function computed on the item identifier. It is ubiquitously used in key-value stores, CDNs and many other applications. Despite considerable work has focused on the design and the implementation of such systems, there is limited understanding of their performance in realistic operational conditions from a theoretical standpoint. In this paper we fill this gap by providing a thorough modeling of sharded caching systems, focusing particularly on load balancing and caching performance aspects. Our analysis provides important insights that can be applied to optimize the design and configuration of sharded caching systems

A Survey of Deep Learning for Data Caching in Edge Network

The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network as well as reducing latency to access popular content. In that respect end user demand for popular content can be satisfied by proactively caching it at the network edge, i.e, at close proximity to the users. In addition to model based caching schemes learning-based edge caching optimizations has recently attracted significant attention and the aim hereafter is to capture these recent advances for both model based and data driven techniques in the area of proactive caching. This paper summarizes the utilization of deep learning for data caching in edge network. We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure. Then, a number of key types of deep learning algorithms are presented, ranging from supervised learning to unsupervised learning as well as reinforcement learning. Furthermore, a comparison of state-of-the-art literature is provided from the aspects of caching topics and deep learning methods. Finally, we discuss research challenges and future directions of applying deep learning for cachin

HEC: Collaborative Research: SAM^2 Toolkit: Scalable and Adaptive Metadata Management for High-End Computing

The increasing demand for Exa-byte-scale storage capacity by high end computing applications requires a higher level of scalability and dependability than that provided by current file and storage systems. The proposal deals with file systems research for metadata management of scalable cluster-based parallel and distributed file storage systems in the HEC environment. It aims to develop a scalable and adaptive metadata management (SAM2) toolkit to extend features of and fully leverage the peak performance promised by state-of-the-art cluster-based parallel and distributed file storage systems used by the high performance computing community. There is a large body of research on data movement and management scaling, however, the need to scale up the attributes of cluster-based file systems and I/O, that is, metadata, has been underestimated. An understanding of the characteristics of metadata traffic, and an application of proper load-balancing, caching, prefetching and grouping mechanisms to perform metadata management correspondingly, will lead to a high scalability. It is anticipated that by appropriately plugging the scalable and adaptive metadata management components into the state-of-the-art cluster-based parallel and distributed file storage systems one could potentially increase the performance of applications and file systems, and help translate the promise and potential of high peak performance of such systems to real application performance improvements. The project involves the following components: 1. Develop multi-variable forecasting models to analyze and predict file metadata access patterns. 2. Develop scalable and adaptive file name mapping schemes using the duplicative Bloom filter array technique to enforce load balance and increase scalability 3. Develop decentralized, locality-aware metadata grouping schemes to facilitate the bulk metadata operations such as prefetching. 4. Develop an adaptive cache coherence protocol using a distributed shared object model for client-side and server-side metadata caching. 5. Prototype the SAM2 components into the state-of-the-art parallel virtual file system PVFS2 and a distributed storage data caching system, set up an experimental framework for a DOE CMS Tier 2 site at University of Nebraska-Lincoln and conduct benchmark, evaluation and validation studies

Enhancing Cache Robustness in Named Data Networks

Information-centric networks (ICNs) are a category of network architectures that focus on content, rather than hosts, to more effectively support the needs of today’s users. One major feature of such networks is in-network storage, which is realized by the presence of content storage routers throughout the network. These content storage routers cache popular content object chunks close to the consumers who request them in order to reduce latency for those end users and to decrease overall network congestion. Because of their prominence, network storage devices such as content storage routers will undoubtedly be major targets for malicious users. Two primary goals of attackers are to increase cache pollution and decrease hit rate by legitimate users. This would effectively reduce or eliminate the advantages of having in-network storage. Therefore, it is crucial to defend against these types of attacks. In this thesis, we study a specific ICN architecture called Named Data Networking (NDN) and simulate several attack scenarios on different network topologies to ascertain the effectiveness of different cache replacement algorithms, such as LRU and LFU (specifically, LFU-DA.) We apply our new per-face popularity with dynamic aging (PFP-DA) scheme to the content storage routers in the network and measure both cache pollution percentages as well as hit rate experienced by legitimate consumers. The current solutions in the literature that relate to reducing the effects of cache pollution largely focus on detection of attacker behavior. Since this behavior is very unpredictable, it is not guaranteed that any detection mechanisms will work well if the attackers employ smart attacks. Furthermore, current solutions do not consider the effects of a particularly aggressive attack against any single or small set of faces (interfaces.) Therefore, we have developed three related algorithms, namely PFP, PFP-DA, and Parameterized PFP-DA. PFP ensures that interests that ingress over any given face do not overwhelm the calculated popularity of a content object chunk. PFP normalizes the ranks on all faces and uses the collective contributions of these faces to determine the overall popularity, which in turn determines what content stays in the cache and what is evicted. PFP-DA adds recency to the original PFP algorithm and ensures that content object chunks do not remain in the cache longer than their true, current popularity dictates. Finally, we explore PFP-β, a parameterized version of PFP-DA, in which a β parameter is provided that causes the popularity calculations to take on Zipf-like characteristics, which in turn reduces the numeric distance between top rated items, and lower rated items, favoring items with multi-face contribution over those with single-face contributions and those with contributions over very few faces. We explore how the PFP-based schemes can reduce impact of contributions over any given face or small number of faces on an NDN content storage router. This in turn, reduces the impact that even some of the most aggressive attackers can have when they overwhelm one or a few faces, by normalizing the contributions across all contributing faces for a given content object chunk. During attack scenarios, we conclude that PFP-DA performs better than both LRU and LFU-DA in terms of resisting the effects of cache pollution and maintaining strong hit rates. We also demonstrate that PFP-DA performs better even when no attacks are being leveraged against the content store. This opens the door for further research both within and outside of ICN-based architectures as a means to enhance security and overall performance.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/145175/1/John Baugh Final Dissertation.pdfDescription of John Baugh Final Dissertation.pdf : Dissertatio

Cloud transactions and caching for improved performance in clouds and DTNs

In distributed transactional systems deployed over some massively decentralized cloud servers, access policies are typically replicated. Interdependencies ad inconsistencies among policies need to be addressed as they can affect performance, throughput and accuracy. Several stringent levels of policy consistency constraints and enforcement approaches to guarantee the trustworthiness of transactions on cloud servers are proposed. We define a look-up table to store policy versions and the concept of Tree-Based Consistency approach to maintain a tree structure of the servers. By integrating look-up table and the consistency tree based approach, we propose an enhanced version of Two-phase validation commit (2PVC) protocol integrated with the Paxos commit protocol with reduced or almost the same performance overhead without affecting accuracy and precision. A new caching scheme has been proposed which takes into consideration Military/Defense applications of Delay-tolerant Networks (DTNs) where data that need to be cached follows a whole different priority levels. In these applications, data popularity can be defined not only based on request frequency, but also based on the importance like who created and ranked point of interests in the data, when and where it was created; higher rank data belonging to some specific location may be more important though frequency of those may not be higher than more popular lower priority data. Thus, our caching scheme is designed by taking different requirements into consideration for DTN networks for defense applications. The performance evaluation shows that our caching scheme reduces the overall access latency, cache miss and usage of cache memory when compared to using caching schemes --Abstract, page iv