Analytical Investigation of On-Path Caching Performance in Information Centric Networks

Information Centric Networking (ICN) architectures are proposed as a solution to address the shift from host-centric model toward an information centric model in the Internet. In these architectures, routing nodes have caching functionality that can influence the network traffic and communication quality since the data items can be sent from nodes far closer to the requesting users. Therefore, realizing effective caching networks becomes important to grasp the cache characteristics of each node and to manage system resources, taking into account networking metrics (e.g., higher hit ratio) as well as user’s metrics (e.g. shorter delay). This thesis studies the methodologies for improving the performance of cache management in ICNs. As individual sub-problems, this thesis investigates the LRU-2 and 2-LRU algorithms, geographical locality in distribution of users’ requests and efficient caching in ICNs. As the first contribution of this thesis, a mathematical model to approximate the behaviour of the LRU-2 algorithm is proposed. Then, 2-LRU and LRU-2 cache replacement algorithms are analyzed. The 2-LRU caching strategy has been shown to outperform LRU. The main idea behind 2-LRU and LRU-2 is considering both frequency (i.e. metric used in LFU) and recency (i.e. metric used in LRU) together for cache replacement process. The simulation as well as numeric results show that the proposed LRU-2 model precisely approximates the miss rate for LRU-2 algorithm. Next, the influence of geographical locality in users’ requests on the performance of network of caches is investigated. Geographically localized and global request patterns have both been observed to possess Zipf (i.e. a power-law distribution in which few data items have high request frequencies while most of data items have low request frequencies) properties, although the local distributions are poorly correlated with the global distribution. This suggests that several independent Zipf distributions combine to form an emergent Zipf distribution in real client request scenarios. An algorithm is proposed that can generate realistic synthetic traffic to regional caches that possesses Zipf properties as well as produces a global Zipf distribution. The simulation results show that the caching performance could have different behaviour based on what distribution the users’ requests follow. Finally, the efficiency of cache replacement and replication algorithms in ICNs are studied since ICN literature still lacks an empirical and analytical deep understanding of benefits brought by in-network caching. An analytical model is proposed that optimally distributes a total cache budget among the nodes of ICN networks for LRU cache replacement and LCE cache replication algorithms. The results will show how much user-centric and system-centric benefits could be gained through the in-network caching compared to the benefits obtained through caching facilities provided only at the edge of the network

Service Migration from Cloud to Multi-tier Fog Nodes for Multimedia Dissemination with QoE Support.

A wide range of multimedia services is expected to be offered for mobile users via various wireless access networks. Even the integration of Cloud Computing in such networks does not support an adequate Quality of Experience (QoE) in areas with high demands for multimedia contents. Fog computing has been conceptualized to facilitate the deployment of new services that cloud computing cannot provide, particularly those demanding QoE guarantees. These services are provided using fog nodes located at the network edge, which is capable of virtualizing their functions/applications. Service migration from the cloud to fog nodes can be actuated by request patterns and the timing issues. To the best of our knowledge, existing works on fog computing focus on architecture and fog node deployment issues. In this article, we describe the operational impacts and benefits associated with service migration from the cloud to multi-tier fog computing for video distribution with QoE support. Besides that, we perform the evaluation of such service migration of video services. Finally, we present potential research challenges and trends

Information-centric networking for machine-to-machine data delivery: A case study in smart grid applications

Largely motivated by the proliferation of content-centric applications in the Internet, information-centric networking has attracted the attention of the research community. By tailoring network operations around named information objects instead of end hosts, ICN yields a series of desirable features such as the spatiotemporal decoupling of communicating entities and the support of in-network caching. In this article, we advocate the introduction of such ICN features in a new, rapidly transforming communication domain: the smart grid. With the rapid introduction of multiple new actors, such as distributed (renewable) energy resources and electric vehicles, smart grids present a new networking landscape where a diverse set of multi-party machine-to-machine applications are required to enhance the observability of the power grid, often in real time and on top of a diverse set of communication infrastructures. Presenting a generic architectural framework, we show how ICN can address the emerging smart grid communication challenges. Based on real power grid topologies from a power distribution network in the Netherlands, we further employ simulations to both demonstrate the feasibility of an ICN solution for the support of real-time smart grid applications and further quantify the performance benefits brought by ICN against the current host-centric paradigm. Specifically, we show how ICN can support real-time state estimation in the medium voltage power grid, where high volumes of synchrophasor measurement data from distributed vantage points must be delivered within a very stringent end-to-end delay constraint, while swiftly overcoming potential power grid component failures. © 1986-2012 IEEE

Hardware accelerated redundancy elimination in network system

With the tremendous growth in the amount of information stored on remote locations and cloud systems, many service providers are seeking ways to reduce the amount of redundant information sent across networks by using data de-duplication techniques. Data de-duplication can reduce network traffic without the loss of information, and consequently increase available network bandwidth by reducing redundant traffic. However, due to the heavy computation required for detecting and reducing redundant data transmission, de-duplication itself can become a bottleneck in high capacity links. We completed two parts of work in this research study, Hardware Accelerated Redundancy Elimination in Network Systems (HARENS) and Distributed Redundancy Elimination System Simulation (DRESS). HARENS can significantly improve the performance of redundancy elimination algorithm in a network system by leveraging General Purpose Graphic Processing Unit (GPGPU) techniques as well as other big data optimizations such as the use of a hierarchical multi-threaded pipeline, single machine Map-Reduce, and memory efficiency techniques. Our results indicate that throughput can be increased by a factor of 9 times compared to a naive implementation of the data de-duplication algorithm, providing a net transmission increase of up to 3.0 Gigabits per second (Gbps). DRESS provides further acceleration to the redundancy elimination in network system by deploying HARENS as the server\u27s side redundancy elimination module, and four cooperative distributed byte caches on the clients\u27 side. A client\u27s side distributed byte cache broadcast its cached chunks by sending hash values to other byte caches, so that they can keep a record of all the chunks in the cooperative distributed cache system. When duplications are detected, a client\u27s side byte cache can fetch a chunk directly from either its own cache or peer byte caches rather than server\u27s side redundancy elimination module. Our results indicate that bandwidth savings of the redundancy elimination system with cooperative distributed byte cache can be increased by 12% compared to the one without distributed byte cache, when transferring about 48 Gigabits of data

Quaestor: Query web caching for database-as-a-service providers

Today, web performance is primarily governed by round-trip latencies between end devices and cloud services. To improve performance, services need to minimize the delay of accessing data. In this paper, we propose a novel approach to low latency that relies on existing content delivery and web caching infrastructure. The main idea is to enable application-independent caching of query results and records with tunable consistency guarantees, in particular bounded staleness. Q uaestor (Query Store) employs two key concepts to incorporate both expiration-based and invalidation-based web caches: (1) an Expiring Bloom Filter data structure to indicate potentially stale data, and (2) statistically derived cache expiration times to maximize cache hit rates. Through a distributed query invalidation pipeline, changes to cached query results are detected in real-time. The proposed caching algorithms offer a new means for data-centric cloud services to trade latency against staleness bounds, e.g. in a database-as-a-service. Q uaestor is the core technology of the backend-as-a-service platform Baqend, a cloud service for low-latency websites. We provide empirical evidence for Q uaestor 's scalability and performance through both simulation and experiments. The results indicate that for read-heavy workloads, up to tenfold speed-ups can be achieved through Q uaestor 's caching. </jats:p