scholarly journals Matching-based cache placement decision for 5G network caching

2021 ◽  
Author(s):  
Shadi Sadeghpour Kharkan
Keyword(s):  
Graph Matching ◽  
Placement Problem ◽  
Matching Problem ◽  
Matching Algorithm ◽  
Cell Network ◽  
5G Network ◽  
Backhaul Link ◽  
Random Placement ◽  
Cache Placement ◽  
Bottleneck Link

In this thesis, we present a cache placement scheme to deal with backhaul link constraint in Small Cell Network for 5G wireless network. We formulated the cache placement problem as a graph matching problem and presented an optimal file-helper matching algorithm. We defined stability criterion for the matching and found that our matching solution is stable in the sense that every helper finds at least one file to cache given that no file exceed minimum cache size. We achieved a unique placement of a file within a cluster of helpers to increase the number of files cached within a cluster. Further, our experimental evaluation demonstrates that our algorithm increases local and neighbor hit ratios as compared to a random placement, which in turn significantly decreases the traffic that goes over the backhaul bottleneck link.

scholarly journals Matching-based cache placement decision for 5G network caching

2021 ◽  
Author(s):  
Shadi Sadeghpour Kharkan
Keyword(s):  
Graph Matching ◽  
Placement Problem ◽  
Matching Problem ◽  
Matching Algorithm ◽  
Cell Network ◽  
5G Network ◽  
Backhaul Link ◽  
Random Placement ◽  
Cache Placement ◽  
Bottleneck Link

In this thesis, we present a cache placement scheme to deal with backhaul link constraint in Small Cell Network for 5G wireless network. We formulated the cache placement problem as a graph matching problem and presented an optimal file-helper matching algorithm. We defined stability criterion for the matching and found that our matching solution is stable in the sense that every helper finds at least one file to cache given that no file exceed minimum cache size. We achieved a unique placement of a file within a cluster of helpers to increase the number of files cached within a cluster. Further, our experimental evaluation demonstrates that our algorithm increases local and neighbor hit ratios as compared to a random placement, which in turn significantly decreases the traffic that goes over the backhaul bottleneck link.

scholarly journals POOR TEXTURAL IMAGE MATCHING BASED ON GRAPH THEORY

2016 ◽  
Vol XLI-B3 ◽  
pp. 741-747 ◽  
Author(s):  
Shiyu Chen ◽  
Xiuxiao Yuan ◽  
Wei Yuan ◽  
Yang Cai
Keyword(s):  
Computer Vision ◽  
Image Matching ◽  
Urban Areas ◽  
Graph Matching ◽  
Recall Rate ◽  
Edge Weight ◽  
Matching Problem ◽  
Matching Algorithm ◽  
Low Contrast ◽  
Series Of Experiments

Image matching lies at the heart of photogrammetry and computer vision. For poor textural images, the matching result is affected by low contrast, repetitive patterns, discontinuity or occlusion, few or homogeneous textures. Recently, graph matching became popular for its integration of geometric and radiometric information. Focused on poor textural image matching problem, it is proposed an edge-weight strategy to improve graph matching algorithm. A series of experiments have been conducted including 4 typical landscapes: Forest, desert, farmland, and urban areas. And it is experimentally found that our new algorithm achieves better performance. Compared to SIFT, doubled corresponding points were acquired, and the overall recall rate reached up to 68%, which verifies the feasibility and effectiveness of the algorithm.

scholarly journals Energy-Delay Tradeoff for Virtual Machine Placement in Virtualized Multi-Access Edge Computing: A Two-Sided Matching Approach

2020 ◽  
Author(s):  
Long Zhang ◽  
Shanshan Zhuge ◽  
Yao Wang ◽  
Haitao Xu ◽  
Enchang Sun
Keyword(s):  
Virtual Machines ◽  
Cost Minimization ◽  
Cost Effective ◽  
Edge Computing ◽  
Vm Placement ◽  
Placement Problem ◽  
Matching Problem ◽  
Matching Algorithm ◽  
Effective Manner ◽  
Multi Access

By decoupling network functions from the underlying physical machines (PMs) at the edge of the networks, the virtualized multi-access edge computing (MEC) enables deployment of new network services and elastic network scaling to reduce maintenance costs in a more flexible, scalable and cost-effective manner. Although there are appealing performance gains to be achieved, the placement of virtual machines (VMs) on top of the sharing PMs to support computation-intensive applications for the smart mobile devices becomes a major challenge, especially for an increasing network scale. In this paper, we attempt to deal with the VM placement problem in virtualized MEC system, which is targeted for finding a performance balance between energy consumption and computing/offloading delay. To capture such a tradeoff for VM placement, we formulate a weighted sum based cost minimization problem as a pure 0-1 integer linear programming problem, which is NP-complete and very complex to solve with lower complexity. Based on the one-to-one mapping relation constraint, the VM placement problem is converted into a many-to-many two-sided matching problem between the VM instances and the PMs. Motivated by the student project allocation problem, we develop an extended two-sided matching algorithm with lower computational complexity for solving the many-to-many matching problem. Simulation results are presented to demonstrate the effectiveness of our proposed matching algorithm, and the normalization factor is of great significance to obtain lower total cost.

scholarly journals Energy-Delay Tradeoff for Virtual Machine Placement in Virtualized Multi-Access Edge Computing: A Two-Sided Matching Approach

2021 ◽  
Author(s):  
Long Zhang ◽  
Shanshan Zhuge ◽  
Yao Wang ◽  
Haitao Xu ◽  
Enchang Sun
Keyword(s):  
Virtual Machines ◽  
Cost Minimization ◽  
Cost Effective ◽  
Edge Computing ◽  
Vm Placement ◽  
Placement Problem ◽  
Matching Problem ◽  
Matching Algorithm ◽  
Effective Manner ◽  
Multi Access

By decoupling network functions from the underlying physical machines (PMs) at the edge of the networks, the virtualized multi-access edge computing (MEC) enables deployment of new network services and elastic network scaling to reduce maintenance costs in a more flexible, scalable and cost-effective manner. Although there are appealing performance gains to be achieved, the placement of virtual machines (VMs) on top of the sharing PMs to support computation-intensive applications for the smart mobile devices becomes a major challenge, especially for an increasing network scale. In this paper, we attempt to deal with the VM placement problem in virtualized MEC system, which is targeted for finding a performance balance between energy consumption and computing/offloading delay. To capture such a tradeoff for VM placement, we formulate a weighted sum based cost minimization problem as a pure 0-1 integer linear programming problem, which is NP-complete and very complex to solve with lower complexity. Based on the one-to-one mapping relation constraint, the VM placement problem is converted into a many-to-many two-sided matching problem between the VM instances and the PMs. Motivated by the student project allocation problem, we develop an extended two-sided matching algorithm with lower computational complexity for solving the many-to-many matching problem. Simulation results are presented to demonstrate the effectiveness of our proposed matching algorithm, and the normalization factor is of great significance to obtain lower total cost.

Research on Random Walk Rough Matching Algorithm of Attribute Sub-Graph

2011 ◽  
Vol 474-476 ◽  
pp. 297-302
Author(s):  
Chun Ying Zhang ◽  
Jing Feng Guo ◽  
Xiao Chen
Keyword(s):  
Social Network ◽  
Random Walk ◽  
Graph Matching ◽  
Matching Problem ◽  
Matching Algorithm

In the analysis of social network, the attribute values of an entity change constantly as time goes by and the corresponding attribute graph changes accordingly. However, the essence of the entity is invariable. The problem is how to discover essence from the change of mining frequent rough matching attribute sub-graph in an attribute graph in this paper. The problem of the attribute sub-graph rough matching is defined and described, then the random walk rough matching algorithm of attribute sub-graph is designed so that the problem of incomplete coincide attribute sub-graph rough matching would be solved. Attribute sub-graph is the expansion of the traditional sub-graph; rough matching problem is the extension of traditional sub-graph matching problem. With the help of the random walk rough matching algorithm of attribute sub-graph, more potential frequent attribute sub-graphs can be discovered, and more valuable information mined.

Speeding up Fast Bipartite Graph Matching Through a New Cost Matrix

2015 ◽  
Vol 29 (02) ◽  
pp. 1550010 ◽  
Author(s):  
Francesc Serratosa
Keyword(s):  
Linear Problem ◽  
Graph Matching ◽  
Matching Problem ◽  
Quadratic Problem ◽  
Matching Algorithm ◽  
Cost Matrix ◽  
Specific Cost ◽  
Hungarian Method ◽  
Convergence Problems ◽  
Reduced Time

Bipartite (BP) has been seen to be a fast and accurate suboptimal algorithm to solve the Error-Tolerant Graph Matching problem. Recently, Fast Bipartite (FBP) has been presented that obtains the same distance value and node labelings but in a reduced time. Both algorithms approximate the quadratic problem in a linear problem and they do it through a specific cost matrix. FBP imposes the Edit costs to be defined such as the Edit distance is a distance function. Originally, the Hungarian method was used but it has been seen the Jonker–Volgenant linear solver obtains similar results than the Hungarian method but with an important run time reduction. Nevertheless, this second solver has some convergence problems on some specific cost matrices. The aim of this paper is to define a new cost matrix such that the Jonker–Volgenant solver converges and the matching algorithm obtains the same distance value than the BP algorithm.

scholarly journals Energy-Delay Tradeoff for Virtual Machine Placement in Virtualized Multi-Access Edge Computing: A Two-Sided Matching Approach

2020 ◽  
Author(s):  
Long Zhang ◽  
Shanshan Zhuge ◽  
Yao Wang ◽  
Haitao Xu ◽  
Enchang Sun
Keyword(s):  
Virtual Machines ◽  
Cost Minimization ◽  
Cost Effective ◽  
Edge Computing ◽  
Vm Placement ◽  
Placement Problem ◽  
Matching Problem ◽  
Matching Algorithm ◽  
Effective Manner ◽  
Multi Access

By decoupling network functions from the underlying physical machines (PMs) at the edge of the networks, the virtualized multi-access edge computing (MEC) enables deployment of new network services and elastic network scaling to reduce maintenance costs in a more flexible, scalable and cost-effective manner. Although there are appealing performance gains to be achieved, the placement of virtual machines (VMs) on top of the sharing PMs to support computation-intensive applications for the smart mobile devices becomes a major challenge, especially for an increasing network scale. In this paper, we attempt to deal with the VM placement problem in virtualized MEC system, which is targeted for finding a performance balance between energy consumption and computing/offloading delay. To capture such a tradeoff for VM placement, we formulate a weighted sum based cost minimization problem as a pure 0-1 integer linear programming problem, which is NP-complete and very complex to solve with lower complexity. Based on the one-to-one mapping relation constraint, the VM placement problem is converted into a many-to-many two-sided matching problem between the VM instances and the PMs. Motivated by the student project allocation problem, we develop an extended two-sided matching algorithm with lower computational complexity for solving the many-to-many matching problem. Simulation results are presented to demonstrate the effectiveness of our proposed matching algorithm, and the normalization factor is of great significance to obtain lower total cost.

scholarly journals POOR TEXTURAL IMAGE MATCHING BASED ON GRAPH THEORY

2016 ◽  
Vol XLI-B3 ◽  
pp. 741-747
Author(s):  
Shiyu Chen ◽  
Xiuxiao Yuan ◽  
Wei Yuan ◽  
Yang Cai
Keyword(s):  
Computer Vision ◽  
Image Matching ◽  
Urban Areas ◽  
Graph Matching ◽  
Recall Rate ◽  
Edge Weight ◽  
Matching Problem ◽  
Matching Algorithm ◽  
Low Contrast ◽  
Series Of Experiments

Image matching lies at the heart of photogrammetry and computer vision. For poor textural images, the matching result is affected by low contrast, repetitive patterns, discontinuity or occlusion, few or homogeneous textures. Recently, graph matching became popular for its integration of geometric and radiometric information. Focused on poor textural image matching problem, it is proposed an edge-weight strategy to improve graph matching algorithm. A series of experiments have been conducted including 4 typical landscapes: Forest, desert, farmland, and urban areas. And it is experimentally found that our new algorithm achieves better performance. Compared to SIFT, doubled corresponding points were acquired, and the overall recall rate reached up to 68%, which verifies the feasibility and effectiveness of the algorithm.

scholarly journals Large-scale Semantic Parsing without Question-Answer Pairs

2014 ◽  
Vol 2 ◽  
pp. 377-392 ◽  
Author(s):  
Siva Reddy ◽  
Mirella Lapata ◽  
Mark Steedman
Keyword(s):  
Natural Language ◽  
Large Scale ◽  
Graph Matching ◽  
State Of The Art ◽  
The State ◽  
Semantic Parsing ◽  
Matching Problem ◽  
Weak Supervision ◽  
Benchmark Datasets

In this paper we introduce a novel semantic parsing approach to query Freebase in natural language without requiring manual annotations or question-answer pairs. Our key insight is to represent natural language via semantic graphs whose topology shares many commonalities with Freebase. Given this representation, we conceptualize semantic parsing as a graph matching problem. Our model converts sentences to semantic graphs using CCG and subsequently grounds them to Freebase guided by denotations as a form of weak supervision. Evaluation experiments on a subset of the Free917 and WebQuestions benchmark datasets show our semantic parser improves over the state of the art.

Sign in / Sign up

Export Citation Format

Share Document