Self-economy in cloud data centers: statistical assignment and migration of virtual machines

Authors:
Carlo Mastroianni;Michela Meo;Giuseppe Papuzzo
Affiliations:
ICAR-CNR, Rende, CS, Italy;Politecnico di Torino, Italy;ICAR-CNR, Rende, CS, Italy
Venue:
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
Year:
2011

Citing 12
Cited 2

The dynamics of collective sorting robot-like ants and ant-like robots

Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Managing server energy and operational costs in hosting centers

SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
The Case for Energy-Proportional Computing

Computer
So-Grid: A self-organizing Grid featuring bio-inspired algorithms

ACM Transactions on Autonomous and Adaptive Systems (TAAS)
The cost of a cloud: research problems in data center networks

ACM SIGCOMM Computer Communication Review
pMapper: power and migration cost aware application placement in virtualized systems

Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware
Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility

Future Generation Computer Systems
A Live Storage Migration Mechanism over WAN for Relocatable Virtual Machine Services on Clouds

CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Energy Efficient Allocation of Virtual Machines in Cloud Data Centers

CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Maximizing Cloud Providers' Revenues via Energy Aware Allocation Policies

CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
Online Self-Reconfiguration with Performance Guarantee for Energy-Efficient Large-Scale Cloud Computing Data Centers

SCC '10 Proceedings of the 2010 IEEE International Conference on Services Computing
A bio-inspired algorithm for energy optimization in a self-organizing data center

SOAR'09 Proceedings of the First international conference on Self-organizing architectures

Efficient lower bounding procedures with application in the allocation of virtual machines to data centers

WSEAS Transactions on Information Science and Applications
Multi-resource Workload Consolidation in Cloud Data Centers

UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

The success of Cloud computing has led to the establishment of large data centers to serve the increasing need for on-demand computational power, but data centers consume a huge amount of electrical power. The problem can be alleviated by mapping virtual machines, VMs, which run client applications, on as few servers as possible, so that some servers with low traffic can be put in low consuming sleep modes. This paper presents a new approach for the adaptive assignment of VMs to servers and their dynamic migration, with a twofold goal: reduce the energy consumption and meet the Service Level Agreements established with users. The approach, based on ant-inspired algorithms, founds on statistical processes: the mapping and migration of VMs are driven by Bernoulli trials whose success probability depends on the utilization of single servers. Experiments highlight the two main advantages with respect to the state of the art: the approach is self-organizing and mostly decentralized, since each server locally decides whether or not a new VM can be served, and the migration process is continuous and adaptive, thus avoiding the need for the simultaneous reassignment of many VMs.