458 research outputs found
Adaptive Caching Strategy Based on Big Data Learning in ICN
In-network caching, a typical feature of information centric networking (ICN) architecture, has played an important role on the network performance. Existing caching management strategies mainly focus on minimizing the redundancy content by exploiting either node data or content data respectively, which may not lead to effectively improve the caching performance, as there is no consideration on supplementary action of these two types of data. In this paper, the correlation between node data and content data brought by the big data are analyzed and mined to determine whether the selected content are cached in a few suitable nodes, and a Big data driven Adaptive In-network Caching management strategy (BAIC) is proposed. Driven by the current state of node and content, a novel multidimensional state attribution data model including network, node and content data is proposed. Based on the data model, the mapping relationship between the status data and the matching relationship value is further analyzed and mined. And then utilizing this mapping relationship function, the matching algorithm to predict the matching relationship between the node and the content in the next time period is proposed. The simulation experiments demonstrate that the proposed BAIC has significantly improved the network performance
A survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
Distributed Caching for Complex Querying of Raw Arrays
As applications continue to generate multi-dimensional data at exponentially
increasing rates, fast analytics to extract meaningful results is becoming
extremely important. The database community has developed array databases that
alleviate this problem through a series of techniques. In-situ mechanisms
provide direct access to raw data in the original format---without loading and
partitioning. Parallel processing scales to the largest datasets. In-memory
caching reduces latency when the same data are accessed across a workload of
queries. However, we are not aware of any work on distributed caching of
multi-dimensional raw arrays. In this paper, we introduce a distributed
framework for cost-based caching of multi-dimensional arrays in native format.
Given a set of files that contain portions of an array and an online query
workload, the framework computes an effective caching plan in two stages.
First, the plan identifies the cells to be cached locally from each of the
input files by continuously refining an evolving R-tree index. In the second
stage, an optimal assignment of cells to nodes that collocates dependent cells
in order to minimize the overall data transfer is determined. We design cache
eviction and placement heuristic algorithms that consider the historical query
workload. A thorough experimental evaluation over two real datasets in three
file formats confirms the superiority -- by as much as two orders of magnitude
-- of the proposed framework over existing techniques in terms of cache
overhead and workload execution time
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