Management is a complex task in today's heterogeneous and large scale networks like Cloud, IoT, vehicular and MPLS networks. Likewise, researchers and developers envision the use of artificial intelligence techniques to create cognitive and autonomic management tools that aim better assist and enhance the management process cycle. Bandwidth allocation models (BAMs) are a resource allocation solution for networks that need to share and optimize limited resources like bandwidth, fiber or optical slots in a flexible and dynamic way. This paper proposes and evaluates the use of Case-Based Reasoning (CBR) for the cognitive management of BAM reconfiguration in MPLS networks. The results suggest that CBR learns about bandwidth request profiles (LSPs requests) associated with the current network state and is able to dynamically define or assist in BAM reconfiguration. The BAM reconfiguration approach adopted is based on switching among available BAM implementations (MAM, RDM and ATCS). The cognitive management proposed allows BAMs self-configuration and results in optimizing the utilization of network resources.
Resource allocation management for communications has been researched for many years @cite_26 . Despite years of struggle, investment and results achieved, the subject keeps its importance mainly due to the communication network's evolution.
@cite_26
2068193298
Scientific research is highly dynamic. New areas of science continually evolve; others gain or lose importance, merge, or split. Due to the steady increase in the number of scientific publications, it is hard to keep an overview of the structure and dynamic development of one's own field of science, much less all scientific domains. However, knowledge of "hot" topics, emergent research frontiers, or change of focus in certain areas is a critical component of resource allocation decisions in research laboratories, governmental institutions, and corporations. This paper demonstrates the utilization of Kleinberg's burst detection algorithm, co-word occurrence analysis, and graph layout techniques to generate maps that support the identification of major research topics and trends. The approach was applied to analyze and map the complete set of papers published in PNAS in the years 1982-2001. Six domain experts examined and commented on the resulting maps in an attempt to reconstruct the evolution of major research areas covered by PNAS. We review the rationale behind the current design of the Delay Disruption Tolerant Networking (DTN) Architecture and highlight some remaining open issues. Its evolution, from a focus on deep space to a broader class of heterogeneous networks that may suffer disruptions, affected design decisions spanning naming and addressing, message formats, data encoding methods, routing, congestion management and security. Having now achieved relative stability with the design, additional experience is required in long-running operational environments in order to fine tune our understanding of DTN concepts and the types of capabilities that are worth the investment in implementation complexity. We expect key management, handling of congestion, multicasting capability, and routing to remain active areas of research and development, and that DTN may continue to be an active research endeavor for at least the next few years. In recent years, the paradigm of cloud computing has gained an increasing interest from the academic community as well as from the commercial point of view. The cloud is a very appealing concept both for the providers (who can benefit from hiring out their extra computation and storage resources) and for the users (who can avoid the initial investment on resources by outsourcing their processes and data to a cloud). Previous research on Recommender Systems (RS), especially the continuously popular approach of Collaborative Filtering (CF), has been mostly focusing on the information resource of explicit user numerical ratings or implicit (still numerical) feedbacks. However, the ever-growing availability of textual user reviews has become an important information resource, where a wealth of explicit product attributes features and user attitudes sentiments are expressed therein. This information rich resource of textual reviews have clearly exhibited brand-new approaches to solving many of the important problems that have been perplexing the research community for years, such as the paradox of cold-start, the explanation of recommendation, and the automatic generation of user or item profiles. However, it is only recently that the fundamental importance of textual reviews has gained wide recognition, perhaps mainly because of the difficulty in formatting, structuring and analyzing the free-texts. In this research, we stress the importance of incorporating textual reviews for recommendation through phrase-level sentiment analysis, and further investigate the role that the texts play in various important recommendation tasks.