Version 1
: Received: 19 February 2017 / Approved: 20 February 2017 / Online: 20 February 2017 (04:56:24 CET)
How to cite:
Abbasi, A.A.; Jin, H. v-Mapper: An Application-Aware Resource Consolidation Scheme for Cloud Data Centres. Preprints2017, 2017020074. https://doi.org/10.20944/preprints201702.0074.v1
Abbasi, A.A.; Jin, H. v-Mapper: An Application-Aware Resource Consolidation Scheme for Cloud Data Centres. Preprints 2017, 2017020074. https://doi.org/10.20944/preprints201702.0074.v1
Abbasi, A.A.; Jin, H. v-Mapper: An Application-Aware Resource Consolidation Scheme for Cloud Data Centres. Preprints2017, 2017020074. https://doi.org/10.20944/preprints201702.0074.v1
APA Style
Abbasi, A.A., & Jin, H. (2017). v-Mapper: An Application-Aware Resource Consolidation Scheme for Cloud Data Centres. Preprints. https://doi.org/10.20944/preprints201702.0074.v1
Chicago/Turabian Style
Abbasi, A.A. and Hai Jin. 2017 "v-Mapper: An Application-Aware Resource Consolidation Scheme for Cloud Data Centres" Preprints. https://doi.org/10.20944/preprints201702.0074.v1
Abstract
Cloud computing refers to applications delivered as services over the Internet. Cloud systems employ policies that are inherently dynamic in nature and that depend on temporal conditions defined in terms of external events, such as the measurement of bandwidth, use of hosts, intrusion detection or specific time events. In this paper, we investigate an optimized resource management scheme named v-Mapper. The basic premise of v-Mapper is to exploit application-awareness concepts using software-defined networking (SDN) features. This paper makes three key contributions to the field: (1) We propose a virtual machine (VM) placement scheme that can effectively mitigate the VM placement issues for data-intensive applications; (2) We propose a validation scheme that will ensure that a service is entertained only if there are sufficient resources available for its execution and (3) We present a scheduling policy that aims to eliminate network load constraints. An evaluation was carried out with various benchmarks and demonstrated that v-Mapper shows improved performance over other state-of-the-art approaches in terms of average task completion time, service delay time and bandwidth utilization. Given the growing importance of supporting large-scale data processing and analysis in datacentres, the v-Mapper system has the potential to make a positive impact in improving datacentre performance in the future.
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
