Service Replication Strategies with MapReduce in Clouds

The current implementations of cloud environment do not have suitable mechanism through which services can be managed to make use of cloud resources. The services in these environments can passively serve users' request only. If a service receives more requests than it can handle in a certain time period, it is subject to malfunctioning. This paper proposes a new approach to service replications that allows a cloud to adjust its service instance deployments in response to existing and projected service request loads. This approach defines an optimal service replication strategy based on MapReduce, a processing model used extensively in GAE (Google App Engine) to solve huge data processing tasks. This service replication strategy is implemented by Service Level MapReduce (SLMR). To better support for SLMR, service replication technology is introduced, which include dynamic service replication and pre-deployed service replication. Furthermore, a passive SLMR approach that depends on the cloud management service (CMS) and a positive SLMR approach that does not need the support from CMS will be introduced.