TY - JOUR
T1 - Live VM Migration under Time-Constraints in Share-Nothing IaaS-Clouds
AU - Tsakalozos, Konstantinos
AU - Verroios, Vasilis
AU - Roussopoulos, Mema
AU - Delis, Alex
N1 - Funding Information:
A preliminary version of the paper appeared in [42]. This work has been partially supported by i-Marine and Sucre EU FP7 projects as well as ERC Starting Grant # 279237.
Publisher Copyright:
© 1990-2012 IEEE.
PY - 2017/8/1
Y1 - 2017/8/1
N2 - Live VM migration helps attain both cloud-wide load balancing and operational consolidation while the migrating VMs remain accessible to users. To avoid periods of high-load for the involved resources, IaaS-cloud operators assign specific time windows for such migrations to occur in an orderly manner. Moreover, providers typically rely on share-nothing architectures to attain scalability. In this paper, we focus on the real-time scheduling of live VM migrations in large share-nothing IaaS clouds, such that migrations are completed on time and without adversely affecting agreed-upon SLAs. We propose a scalable, distributed network of brokers that oversees the progress of all on-going migration operations within the context of a provider. Brokers make use of an underlying special purpose file system, termed MigrateFS, that is capable of both replicating and keeping in sync virtual disks while the hypervisor live-migrates VMs (i.e., RAM and CPU state). By limiting the resources consumed during migration, brokers implement policies to reduce SLA violations while seeking to complete all migration tasks on time. We evaluate two such policies, one based on task prioritization and a second that considers the financial implications set by migration deadline requirements. Using our MigrateFS prototype operating on a real cloud, we demonstrate the feasibility of performing migrations within time windows. By simulating large clouds, we assess the effectiveness of our proposed broker policies in a share-nothing configuration; we also demonstrate that our approach stresses 24 percent less an already saturated network if compared to an unsupervised set up.
AB - Live VM migration helps attain both cloud-wide load balancing and operational consolidation while the migrating VMs remain accessible to users. To avoid periods of high-load for the involved resources, IaaS-cloud operators assign specific time windows for such migrations to occur in an orderly manner. Moreover, providers typically rely on share-nothing architectures to attain scalability. In this paper, we focus on the real-time scheduling of live VM migrations in large share-nothing IaaS clouds, such that migrations are completed on time and without adversely affecting agreed-upon SLAs. We propose a scalable, distributed network of brokers that oversees the progress of all on-going migration operations within the context of a provider. Brokers make use of an underlying special purpose file system, termed MigrateFS, that is capable of both replicating and keeping in sync virtual disks while the hypervisor live-migrates VMs (i.e., RAM and CPU state). By limiting the resources consumed during migration, brokers implement policies to reduce SLA violations while seeking to complete all migration tasks on time. We evaluate two such policies, one based on task prioritization and a second that considers the financial implications set by migration deadline requirements. Using our MigrateFS prototype operating on a real cloud, we demonstrate the feasibility of performing migrations within time windows. By simulating large clouds, we assess the effectiveness of our proposed broker policies in a share-nothing configuration; we also demonstrate that our approach stresses 24 percent less an already saturated network if compared to an unsupervised set up.
KW - Distributed systems
KW - IaaS clouds
KW - cloud computing
KW - virtual machine migration
UR - http://www.scopus.com/inward/record.url?scp=85029054884&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029054884&partnerID=8YFLogxK
U2 - 10.1109/TPDS.2017.2658572
DO - 10.1109/TPDS.2017.2658572
M3 - Article
AN - SCOPUS:85029054884
SN - 1045-9219
VL - 28
SP - 2285
EP - 2298
JO - IEEE Transactions on Parallel and Distributed Systems
JF - IEEE Transactions on Parallel and Distributed Systems
IS - 8
M1 - 7833184
ER -