Comprehensive Analysis of Caching Performance under Probabilistic Traffic Patterns for Content Centric Networking

The phenomenon of data explosion represents a severe challenge for the upcoming big data era.However,the current Internet architecture is insufficient for dealing with a huge amount of traffic owing to an increase in redundant content transmission and the end-point-based communication model.Information-centric networking(ICN)is a paradigm for the future Internet that can be utilized to resolve the data explosion problem.In this paper,we focus on content-centric networking(CCN),one of the key candidate ICN architectures.CCN has been studied in various network environments with the aim of relieving network and server burden,especially in name-based forwarding and in-network caching functionalities.This paper studies the effect of several caching strategies in the CCN domain from the perspective of network and server overhead.Thus,we comprehensively analyze the in-network caching performance of CCN under several popular cache replication methods(i.e.,cache placement).We evaluate the performance with respect to wellknown Internet traffic patterns that follow certain probabilistic distributions,such as the Zipf/Mandelbrot–Zipf distributions,and flashcrowds.For the experiments,we developed an OPNET-based CCN simulator with a realistic Internet-like topology.

Degree-Based Probabilistic Caching in Content-Centric Networking

Content-Centric Networking is a novel future network architecture that attracts increasing research interests in recent years. In-network caching has been regarded as a prominent feature of Content-Centric Networking since it is able to reduce the network traffic, alleviate the server bottleneck and decrease the user access latency. However, the CCN default caching scheme results in a high caching redundancy, causing an urgent need for an efficient caching scheme. To address this issue, we propose a novel implicit cooperative caching scheme to efficiently reduce the caching redundancy and improve the cache resources utilization. The simulation results show that our design achieves a higher hit ratio and a shorter cache hit distance in comparison with the other typical caching schemes.

Efficient Execution of Multiple Queries on Deep Memory Hierarchy

This paper proposes a complementary novel idea, called MiniTasking to further reduce the number of cache misses by improving the data temporal locality for multiple concurrent queries. Our idea is based on the observation that, in many workloads such as decision support systems （DSS）, there is usually significant amount of data sharing among different concurrent queries. MiniTasking exploits such data sharing to improve data temporal locality by scheduling query execution at three levels： query level batching, operator level grouping and mini-task level scheduling. The experimental results with various types of concurrent TPC-H query workloads show that, with the traditional N-ary Storage Model （NSM） layout, MiniTasking significantly reduces the L2 cache misses by up to 83%, and thereby achieves 24% reduction in execution time. With the Partition Attributes Across （PAX） layout, MiniTasking further reduces the cache misses by 65% and the execution time by 9%. For the TPC-H throughput test workload, MiniTasking improves the end performance up to 20%.

Structure splitting framework for data-layout optimization

To improve data cache performance, optimizing program data layout by data reorganization has become an important method of decreasing the impact of increasing gap of speed between processor and memory. In this article, a structure splitting framework with an analysis model named structure field relation graph （SFRG） is presented to optimize program data layout. The SFRG can be used to quantify relationship between fields. It helps to find an optimal layout for structure as well as the optimal program data layout. And the data cache performance is improved through SFRG-based structure splitting. Experiments show that this framework is effective in optimizing program data layout and improving the performance of data cache and whole program.