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Skyline service selection method based on mapreduce and multi-objective simulated annealing

A mapreduce framework and simulated annealing algorithm technology, applied in the field of network services, can solve problems such as low efficiency, difficulty in service selection technology, and difficulty in ensuring the quality of the optimal solution, so as to achieve global QoS optimization and improve efficiency and effectiveness

Active Publication Date: 2018-05-01
李金忠
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Problems solved by technology

With the increasing number of services and QoS attributes in the Internet, as well as the distributed nature of services in the real world, traditional service selection techniques are difficult to deal with, and its efficiency is low and the quality of its optimal solution is difficult to guarantee

Method used

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  • Skyline service selection method based on mapreduce and multi-objective simulated annealing
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  • Skyline service selection method based on mapreduce and multi-objective simulated annealing

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specific Embodiment

[0103] Such as Figure 5 As shown, suppose there is an abstract composite service instance composed of several abstract services, and each abstract service AS i Can have n i Candidate concrete services complete the abstract service AS i The functions of these candidate specific services WS i It is indexed in the HDFS massive service pool. After the first stage of "screening massive services", each type of candidate specific WS i From the original n i Reduced to m i First, the non-Skyline services of all kinds of services are removed, and the Skyline services of all kinds of services are left to form a Skyline service library. After the second stage of "optimized Skyline service", each abstract service AS i Select the corresponding specific service WS i After that, they are combined into a specific combined service. At this stage, since each abstract service AS i Specific service selected WS i If they are different, they can be combined into a large number of specific composite ...

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Abstract

The present invention relates to a Skyline service selection method based on MapReduce and multi-objective simulated annealing, comprising the following steps: Step 1: screening massive services: under the framework of MapReduce, using block nesting algorithm and Skyline calculation of divide-and-conquer algorithm, from massive services Select the service with better QoS from the pool to generate the Skyline service library; Step 2: Optimizing the Skyline service: Under the MapReduce framework, use the multi-objective simulated annealing algorithm to optimize the Skyline service from the Skyline service library generated in step 1, and generate Pareto composite service set; Step 3: Optimizing Pareto composite services: using the Top-k query processing technology, combined with the user's personalized QoS preference, from the Pareto composite service set generated in step 2, select k Pareto composite services that meet the user's QoS constraints . Compared with the prior art, the present invention has the advantages of greatly improving the efficiency and effect of mass service selection.

Description

Technical field [0001] The invention relates to the technical field of network services, in particular to a Skyline service selection method based on MapReduce and multi-objective simulated annealing. Background technique [0002] With the rapid development of service computing, cloud computing, big data and other related technologies, the types of services available on the Internet (including grid services, web services, cloud services, etc.) have grown rapidly in type and explosive growth in number. Massive services distributed in different geographical locations and on different servers may have the same or similar functions, rather than different services with different functional attributes (QoS). How to choose a service with better QoS from a large number of services with equivalent functions to form a combined service with the best QoS to meet the user’s QoS constraints and recommend it to users in a personalized way has become academic Issues of common concern to industr...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L29/08
CPCH04L41/5019H04L41/5041H04L67/51
Inventor 李金忠夏洁武谭云兰曾小荟李满华吴玉春王巧玲胡运全
Owner 李金忠
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