User-Centric Performance Analysis of Market-Based Cluster Batch Schedulers
CCGRID '02 Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid
Balancing Risk and Reward in a Market-Based Task Service
HPDC '04 Proceedings of the 13th IEEE International Symposium on High Performance Distributed Computing
Profitable services in an uncertain world
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Future Generation Computer Systems
SLA-based admission control for a Software-as-a-Service provider in Cloud computing environments
Journal of Computer and System Sciences
Future Generation Computer Systems
Solidifying the foundations of the cloud for the next generation Software Engineering
Journal of Systems and Software
Hi-index | 0.00 |
Admission Control has been proven essential to avoid overloading of resources and for meeting user service demands in utility driven grid computing. Recent emergence of Cloud based services and the popularity of MapReduce paradigm in Cloud Computing environments make the problem of admission control intriguing. We propose a model that allows one to offer MapReduce jobs in the form of on-demand services. We present a learning based opportunistic algorithm that admits MapReduce jobs only if they are unlikely to cross the overload threshold set by the service provider. The algorithm meets deadlines negotiated by users in more than 80% of cases. We employ an automatically supervised Naive Bayes Classifier to label incoming jobs as admissible and non-admissible. From the list of jobs classified as admissible, we then pick a job that is expected to maximize service provider utility. An external supervision rule automatically evaluates decisions made by the algorithm in retrospect, and trains the classifier. We evaluate our algorithm by simulating a MapReduce cluster hosted in the Cloud that offers a set of MapReduce jobs as services to its users. Our results show that admission control is useful in minimizing losses due to overloading of resources, and by choosing jobs that maximize revenue of the service provider.