Impact of traffic mix on caching performance in a content-centric network

For a realistic traffic mix, we evaluate the hit rates attained in a two-layer cache hierarchy designed to reduce Internet bandwidth requirements. The model identifies four main types of content, web, file sharing, user generated content and video on demand, distinguished in terms of their traffic shares, their population and object sizes and their popularity distributions. Results demonstrate that caching VoD in access routers offers a highly favorable bandwidth memory tradeoff but that the other types of content would likely be more efficiently handled in very large capacity storage devices in the core. Evaluations are based on a simple approximation for LRU cache performance that proves highly accurate in relevant configurations

Improving the Performance of SQL Join Operation in the Distributed Enterprise Information System by Caching

The enterprise information system (EIS) contains databases and other data sources in multiple data centers. Users query the EIS via clients. The client has a working space in the cloud. Caching data in client space will reduce the total execution time of the query. However, the client space has limited resources to store data. There are two options for caching data at the client space: caching the final results of query operations, or caching the source data tables. The problem is that some query operations such as “joining multiple big tables” will simply produce a result too big to store in cache in some cases. By contrast, caching source data tables may be a better choice in those situations. This paper presents an algorithm that combines active caching and passive caching to improve the cache hit, thus improving performance of the SQL join query in the cloud computing environment

On the Intrinsic Locality Properties of Web Reference Streams

There has been considerable work done in the study of Web reference streams: sequences of requests for Web objects. In particular, many studies have looked at the locality properties of such streams, because of the impact of locality on the design and performance of caching and prefetching systems. However, a general framework for understanding why reference streams exhibit given locality properties has not yet emerged. In this work we take a first step in this direction, based on viewing the Web as a set of reference streams that are transformed by Web components (clients, servers, and intermediaries). We propose a graph-based framework for describing this collection of streams and components. We identify three basic stream transformations that occur at nodes of the graph: aggregation, disaggregation and filtering, and we show how these transformations can be used to abstract the effects of different Web components on their associated reference streams. This view allows a structured approach to the analysis of why reference streams show given properties at different points in the Web. Applying this approach to the study of locality requires good metrics for locality. These metrics must meet three criteria: 1) they must accurately capture temporal locality; 2) they must be independent of trace artifacts such as trace length; and 3) they must not involve manual procedures or model-based assumptions. We describe two metrics meeting these criteria that each capture a different kind of temporal locality in reference streams. The popularity component of temporal locality is captured by entropy, while the correlation component is captured by interreference coefficient of variation. We argue that these metrics are more natural and more useful than previously proposed metrics for temporal locality. We use this framework to analyze a diverse set of Web reference traces. We find that this framework can shed light on how and why locality properties vary across different locations in the Web topology. For example, we find that filtering and aggregation have opposing effects on the popularity component of the temporal locality, which helps to explain why multilevel caching can be effective in the Web. Furthermore, we find that all transformations tend to diminish the correlation component of temporal locality, which has implications for the utility of different cache replacement policies at different points in the Web.National Science Foundation (ANI-9986397, ANI-0095988); CNPq-Brazi

Flexpop: A popularity-based caching strategy for multimedia applications in information-centric networking

Information-Centric Networking (ICN) is the dominant architecture for the future Internet. In ICN, the content items are stored temporarily in network nodes such as routers. When the memory of routers becomes full and there is no room for a new arriving content, the stored contents are evicted to cope with the limited cache size of the routers. Therefore, it is crucial to develop an effective caching strategy for keeping popular contents for a longer period of time. This study proposes a new caching strategy, named Flexible Popularity-based Caching (FlexPop) for storing popular contents. The FlexPop comprises two mechanisms, i.e., Content Placement Mechanism (CPM), which is responsible for content caching, and Content Eviction Mechanism (CEM) that deals with content eviction when the router cache is full and there is no space for the new incoming content. Both mechanisms are validated using Fuzzy Set Theory, following the Design Research Methodology (DRM) to manifest that the research is rigorous and repeatable under comparable conditions. The performance of FlexPop is evaluated through simulations and the results are compared with those of the Leave Copy Everywhere (LCE), ProbCache, and Most Popular Content (MPC) strategies. The results show that the FlexPop strategy outperforms LCE, ProbCache, and MPC with respect to cache hit rate, redundancy, content retrieval delay, memory utilization, and stretch ratio, which are regarded as extremely important metrics (in various studies) for the evaluation of ICN caching. The outcomes exhibited in this study are noteworthy in terms of making FlexPop acceptable to users as they can verify the performance of ICN before selecting the right caching strategy. Thus FlexPop has potential in the use of ICN for the future Internet such as in deployment of the IoT technology

INTELLIGENT CACHE FARMING ARCHITECTURE WITH THE RECOMMENDER SYSTEM

The Quality of Services (QoS) guaranteed by the Internet Service Providers (ISPs) is an important factor for users’ satisfaction in using the Internet. The implementation of the web proxy caching has been implemented to support this objective and also support the security procedure of the organizations. However, the success of guaranteeing the QoS of each ISP must be depended on the cache size and efficient caching policy. This paper proposes a new architecture of cache farming with the recommender system concept to manage users’ requirements. This solution helps reducing the retrieval time and also increasing the hit rate although the number of users increases without expanding the size of caches in the farm

Routing and Caching in Information-Centric Networking

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 2. 권태경.When the Internet was designed decades ago, main applications are resource sharing such as remote login and file transfer. To support such applications, the key principle in the Internet architecture is point-to-point communications, and the key element is an IP address that identifies a host. Due to the flexible design of the Internet, a wide range of new applications and services have been introduced over the decades. The recent surge of Internet traffic is mainly attributed to applications such as web, P2P file sharing, and video streaming. In such applications, an end user is mostly interested in content itself, not in a particular host or its location. Over the past few years, there have been many efforts to address the above issues from a content centric perspective. Those proposals are collectively called Information Centric Networking (ICN), which is largely deemed as a clean-slate approach. Most of the ICN studies think of content as a key element and hence assume a new paradigm by shifting from host-oriented communications to content-oriented i communications. Consequently, instead of locator-based routing, most ICN proposals consider name-based routing, which decouples content production and consumption in time and space domains. The decoupling enhances content availability and naming persistency, and supports in-network caching, multicast and mobility. Most of ICN proposals use content names as routing entries, and thus the routing scalability is primary concern. ICN allows in-network caching as a built-in functionality. However, if network nodes make caching decisions individually, duplicate copies of the same content may exist among nearby nodes. To address these problems, this dissertation proposes a unified framework named Coordinated Routing and Caching (CoRC) that mitigates routing scalability and enhances the efficiency of the in-network storage.Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 II. Design Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1 How to Make FIBs Scalable? . . . . . . . . . . . . . . . . . . . . . 4 2.2 Where to Place the Cached Item? . . . . . . . . . . . . . . . . . . . 5 2.3 How to Coordinate between Routing and Caching? . . . . . . . . . 5 2.4 How to Reflect the Current Internet Infrastructure and Business? . . 6 III. RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 IV. CoRC: Coordinated Routing and Caching . . . . . . . . . . . . . . 9 4.1 Name Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 4.2 Routing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 4.2.1 Intra-domain Routing . . . . . . . . . . . . . . . . . . . . . 11 4.2.2 Inter-domain Routing . . . . . . . . . . . . . . . . . . . . . 12 4.3 Caching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 V. Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 5.1 Assigning PID prefix to RR . . . . . . . . . . . . . . . . . . . . . . 15 5.2 Hybrid Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 VI. Routing Scalability . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 6.1 AS-FIB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 6.2 PAR-FIB and PIB . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 6.3 Numbers of Entries of Three Tables . . . . . . . . . . . . . . . . . 22 VII. Network Performance . . . . . . . . . . . . . . . . . . . . . . . . . . 24 7.1 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . 24 7.2 Compared Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . 25 7.3 Experimental Setting . . . . . . . . . . . . . . . . . . . . . . . . . 26 7.4 Average Cache Hit Ratio . . . . . . . . . . . . . . . . . . . . . . . 27 7.5 Content Delivery Latency . . . . . . . . . . . . . . . . . . . . . . . 29 7.6 Traffic Load . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 7.7 Route Stretch vs. Topology . . . . . . . . . . . . . . . . . . . . . . 36 VIII.Packet Processing Time in a Router . . . . . . . . . . . . . . . . . . 38 8.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 8.2 Drop Rate vs. Interest Packet Rate . . . . . . . . . . . . . . . . . . 39 IX. Discussions and Future Work . . . . . . . . . . . . . . . . . . . . . . 41 9.1 Hashing by Publisher Name . . . . . . . . . . . . . . . . . . . . . 41 9.2 Dealing with Router Failure . . . . . . . . . . . . . . . . . . . . . 42 9.3 Resolution System and Multihoming . . . . . . . . . . . . . . . . . 42 X. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44Docto