Name
Abstract | ||
|---|---|---|
In data center networks, resource allocation refers to mapping a large number of workloads to substrate networks. Existing heuristic mapping algorithms evaluate the resources of the nodes according to one resource factor or a product of resource factors, which will probably lead to an imbalance of the resource allocation. Furthermore, neglecting the hops of the substrate paths in the resource allocation may result in low resource utilization. In this paper, we adopt a top- k dominating model to rank the nodes, aiming at balancing these factors to improve resource allocation. Moreover, we propose a novel mapping algorithm TK-Match, which consists of a node mapping stage and link mapping stage. In the node mapping stage, TK-Match maps the virtual nodes to the substrate nodes in terms of the node ranking and the hops of the substrate paths. In the link mapping stage, TK-Match adopts the k -shortest path algorithm to map the virtual links. Extensive simulation experiments show that TK-Match can greatly increase the long-term average revenue and the acceptance ratio. We apply a top- k dominating model to compute the RankScore values of the nodes.TK-Match considers the balance of different substrate resources.We consider the hops of the substrate paths in the node mapping stage.Extensive experiments demonstrate the effectiveness and efficiency of the proposals. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.future.2013.09.028 | Future Generation Comp. Syst. |
Keywords | Field | DocType |
node mapping stage,heuristic mapping algorithm,substrate node,low resource utilization,resource allocation,multi-factor node ranking,tk-match map,link mapping stage,novel mapping algorithm tk-match,substrate path,resource factor,data center network | Revenue,Heuristic,Ranking,Workload,Computer science,Real-time computing,Resource allocation,Mapping algorithm,Data center,Distributed computing,Dijkstra's algorithm | Journal |
Volume | Issue | ISSN |
32 | C | 0167-739X |
Citations | PageRank | References |
8 | 0.51 | 34 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoling Li | 1 | 80 | 8.02 |
| Wang Huaimin | 2 | 1025 | 121.31 |
| bo | 3 | 18 | 2.30 |
| Xiaoyong Li | 4 | 19 | 3.76 |
| Dawei Feng | 5 | 11 | 2.57 |