TY - JOUR
T1 - Optimizing MapReduce Task Scheduling on Virtualized Heterogeneous Environments Using Ant Colony Optimization
AU - Jeyaraj, Rathinaraja
AU - Paul, Anand
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2022
Y1 - 2022
N2 - Consuming Hadoop MapReduce via virtual infrastructure as a service is becoming common practice as cloud service providers (CSP) offers relevant applications and scalable resources. One of the predominant requirements of cloud users is to improve resource utilization in the virtual cluster during the service period. However, it may not be possible when MapReduce workloads and virtual machines (VM) are highly heterogeneous. Therefore, in this paper, we addressed these heterogeneities and proposed an efficient MapReduce scheduler to improve resource utilization by placing the right combination of the map and reduce tasks in each VM in the virtual cluster. To achieve this, we transformed the MapReduce task scheduling problem into a 2-Dimensional (2D) bin packing model and obtained an optimal schedule using the ant colony optimization (ACO) algorithm. As an added advantage, our proposed ACO based bin packing (ACO-BP) scheduler minimized the makespan for a batch of jobs. To showcase the performance improvement, we compared our proposed scheduler with three existing schedulers that work well in a heterogeneous environment. As expected, results show that ACO-BP significantly outperformed the existing schedulers while dealing with workload and VM level heterogeneities.
AB - Consuming Hadoop MapReduce via virtual infrastructure as a service is becoming common practice as cloud service providers (CSP) offers relevant applications and scalable resources. One of the predominant requirements of cloud users is to improve resource utilization in the virtual cluster during the service period. However, it may not be possible when MapReduce workloads and virtual machines (VM) are highly heterogeneous. Therefore, in this paper, we addressed these heterogeneities and proposed an efficient MapReduce scheduler to improve resource utilization by placing the right combination of the map and reduce tasks in each VM in the virtual cluster. To achieve this, we transformed the MapReduce task scheduling problem into a 2-Dimensional (2D) bin packing model and obtained an optimal schedule using the ant colony optimization (ACO) algorithm. As an added advantage, our proposed ACO based bin packing (ACO-BP) scheduler minimized the makespan for a batch of jobs. To showcase the performance improvement, we compared our proposed scheduler with three existing schedulers that work well in a heterogeneous environment. As expected, results show that ACO-BP significantly outperformed the existing schedulers while dealing with workload and VM level heterogeneities.
KW - Ant colony optimization
KW - Bin packing
KW - Heterogeneity
KW - MapReduce
KW - Resource utilization
KW - Task scheduling
UR - http://www.scopus.com/inward/record.url?scp=85130457430&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2022.3176729
DO - 10.1109/ACCESS.2022.3176729
M3 - Article
AN - SCOPUS:85130457430
SN - 2169-3536
VL - 10
SP - 55842
EP - 55855
JO - IEEE Access
JF - IEEE Access
ER -