When computing meets heterogeneous cluster: Workload assignment in graph computation

2015 ◽  
Author(s):  
Jilong Xue ◽  
Zhi Yang ◽  
Shian Hou ◽  
Yafei Dai
Keyword(s):  
Heterogeneous Cluster ◽  
Graph Computation ◽  
Cluster Workload

REDUCING THE APPROXIMATION TIME OF CLUSTER WORKLOAD BY USING METHOD OF MOMENTS ON HYPEREXPONENTIAL DISTRIBUTION

2017 ◽  
Vol 37 (1) ◽  
pp. 094-105 ◽  
Author(s):  
Sergey V. Gaevoy ◽  
◽  
Wesam M. A. Ahmed ◽  
Dmitriy V. Bykov ◽  
Sergey A. Fomenkov ◽  
...  
Keyword(s):  
Method Of Moments ◽  
Hyperexponential Distribution ◽  
Cluster Workload

Research on Short-term Load Forecasting Based Graph Computation in Power Supply Areas

2021 ◽  
Author(s):  
Lizong Zhang ◽  
Dong Xie ◽  
Gang Luo ◽  
Gang Qian ◽  
Meiya Song ◽  
...  
Keyword(s):  
Power Supply ◽  
Load Forecasting ◽  
Short Term ◽  
Graph Computation ◽  
Short Term Load Forecasting

Customer Review Provision Policies with Heterogeneous Cluster Preferences

2021 ◽  
Author(s):  
Shihong Xiao ◽  
Ying-Ju Chen ◽  
Christopher S. Tang
Keyword(s):  
Operations Management ◽  
Consumer Welfare ◽  
Geographic Region ◽  
Heterogeneous Cluster ◽  
Heterogeneous Preferences ◽  
Experience Goods ◽  
The Third ◽  
Experience Good ◽  
Common User

Companies often post user-generated reviews online so that potential buyers in different clusters (age, geographic region, occupation, etc.) can learn from existing customers about the quality of an experience good and cluster preferences before purchasing. In this paper, we evaluate two common user-generated review provision policies for selling experience goods to customers in different clusters with heterogeneous preferences. The first policy is called the association-based policy (AP) under which a customer in a cluster can only observe the aggregate review (i.e., average rating) generated by users within the same cluster. The second policy is called the global-based policy (GP) under which each customer is presented with the aggregate review generated by all users across clusters. We find that, in general, the firm benefits from a policy that provides a larger number of “relevant reviews” to customers. When customers are more certain about the product quality and when clusters are more diverse, AP is more profitable than GP because it provides cluster-specific reviews to customers. Otherwise, GP is more profitable as it provides a larger number of less relevant reviews. Moreover, we propose a third provision policy that imparts the union of the information by AP and GP and show that it is more profitable for the firm. Although the third policy always renders a higher consumer welfare than GP, it may generate a lower consumer welfare than AP. This paper was accepted by Martínez-de-Albéniz Victor, operations management.

Local Graph Edge Partitioning

2021 ◽  
Vol 12 (5) ◽  
pp. 1-25
Author(s):  
Shengwei Ji ◽  
Chenyang Bu ◽  
Lei Li ◽  
Xindong Wu
Keyword(s):  
Real World ◽  
Graph Partitioning ◽  
Large Scale ◽  
Complete Information ◽  
Local Information ◽  
Experimental Results ◽  
Two Stage ◽  
Graph Computation ◽  
Local Graph ◽  
Edge Partitioning

Graph edge partitioning, which is essential for the efficiency of distributed graph computation systems, divides a graph into several balanced partitions within a given size to minimize the number of vertices to be cut. Existing graph partitioning models can be classified into two categories: offline and streaming graph partitioning models. The former requires global graph information during the partitioning, which is expensive in terms of time and memory for large-scale graphs. The latter creates partitions based solely on the received graph information. However, the streaming model may result in a lower partitioning quality compared with the offline model. Therefore, this study introduces a Local Graph Edge Partitioning model, which considers only the local information (i.e., a portion of a graph instead of the entire graph) during the partitioning. Considering only the local graph information is meaningful because acquiring complete information for large-scale graphs is expensive. Based on the Local Graph Edge Partitioning model, two local graph edge partitioning algorithms—Two-stage Local Partitioning and Adaptive Local Partitioning—are given. Experimental results obtained on 14 real-world graphs demonstrate that the proposed algorithms outperform rival algorithms in most tested cases. Furthermore, the proposed algorithms are proven to significantly improve the efficiency of the real graph computation system GraphX.

A comprehensive reconfigurable computing approach to memory wall problem of large graph computation

2016 ◽  
Vol 70 ◽  
pp. 59-69 ◽  
Author(s):  
Xu Wang ◽  
Yongxin Zhu ◽  
Linan Huang
Keyword(s):  
Reconfigurable Computing ◽  
Memory Wall ◽  
Large Graph ◽  
Graph Computation ◽  
Computing Approach

GraphA: Efficient Partitioning and Storage for Distributed Graph Computation

2019 ◽  
pp. 1-1 ◽  
Author(s):  
Yiming Zhang ◽  
Dongsheng Li ◽  
Chengfei Zhang ◽  
Jinyan Wang ◽  
Ling Liu
Keyword(s):  
Graph Computation ◽  
And Storage

scholarly journals Methods for division of road traffic network for distributed simulation performed on heterogeneous clusters

2013 ◽  
Vol 10 (1) ◽  
pp. 321-348 ◽  
Author(s):  
Tomas Potuzak
Keyword(s):  
Road Traffic ◽  
Distributed Simulation ◽  
Computation Time ◽  
Traffic Network ◽  
Traffic Networks ◽  
Computing Environment ◽  
Heterogeneous Cluster ◽  
Heterogeneous Clusters ◽  
The Road ◽  
Road Traffic Networks

The computer simulation of road traffic is an important tool for control and analysis of road traffic networks. Due to their requirements for computation time (especially for large road traffic networks), many simulators of the road traffic has been adapted for distributed computing environment where combined power of multiple interconnected computers (nodes) is utilized. In this case, the road traffic network is divided into required number of sub-networks, whose simulation is then performed on particular nodes of the distributed computer. The distributed computer can be a homogenous (with nodes of the same computational power) or a heterogeneous cluster (with nodes of various powers). In this paper, we present two methods for road traffic network division for heterogeneous clusters. These methods consider the different computational powers of the particular nodes determined using a benchmark during the road traffic network division.

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