Network-centric Performance Improvement for Live VM Migration

Nasim, Robayet; Kassler, Andreas

doi:10.1109/cloud.2015.24

Cited by 12 publications

(9 citation statements)

References 8 publications

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“…Recent contributions along the (previously mentioned) second research line are reported in 16,17,18 . Specifically, the authors of 16 propose a nearby VM-based cloudlet for boosting the performance of real-time resource-stressing applications.…”

Section: Related Workmentioning

confidence: 99%

“…Interestingly, almost zero downtimes are experienced at the destination cloudlets after the VM migrations are completed. The contribution in 17 proposes an adaptive queue-management scheduler, in order to exploit at the maximum the bandwidth aggregation capability offered by MPTCP when intra-data center live migration of VM must be carried out under delay-constraints.…”

Section: Related Workmentioning

confidence: 99%

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Fog-Supported Delay-Constrained Energy-Saving Live Migration of VMs Over MultiPath TCP/IP 5G Connections

Baccarelli¹,

Scarpiniti²,

Momenzadeh³

2018

IEEE Access

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The incoming era of the Fifth Generation (5G) Fog Computing (FC)-supported Radio Access Networks (shortly, 5G FOGRANs) aims at exploiting the virtualization of the computing/networking resources. The goal is to allow battery-powered wireless devices to augment their limited native resources through the seamless (e.g., live) migration of Virtual Machines (VMs) towards nearby Fog data centers. For this purpose, the bandwidths of the multiple heterogeneous Wireless Network Interface Cards (WNICs) typically equipping the current wireless devices may be used in parallel under the control of the emerging MultiPathTCP (MPTCP) protocol. However, due to the fading and mobility-induced phenomena impairing the underlying RANs, the energy consumptions and migration delays of the current state-of-the-art VM migration techniques may drastically reduce their expected benefits. Motivated by these considerations, in this paper, we analytically characterize, implement in software and numerically test the optimal minimum-energy Settable-Complexity Bandwidth Manager (SCBM) for the live migration of VMs over MPTCP-supported 5G FOGRAN connections. The key features of the proposed SCBM are that: (i) its implementation complexity is settable on-line, in order to attain the targeted energy consumption-vs.-implementation complexity tradeoff; (ii) it minimizes the energy consumed by the migration process under hard QoS constraints on the allowed migration time and downtime;and, (iii) by leveraging a suitably designed adaptive mechanism, it is capable to quickly react to (possibly, unpredicted) fading and/or mobility-induced abrupt changes of the operating conditions without requiring and forecasting support. We numerically test and compare the energy and delay performances of the proposed adaptive SCBM under several 3G/4G/WiFi FOGRAN scenarios by considering a number of synthetic and real-world workloads. The obtained numerical results point out that: (i) the energy savings of the proposed adaptive SCBM over the benchmark one currently implemented by legacy Xen, KWM and VMware hypervisors are typically over 25% and approach 70% when the migrated applications are memory-write intensive; and, (ii) the MPTCP may reduce the energy consumption of the proposed SCBM over legacy SinglePathTCP (SPTCP) more than 50% under strict real-time limits on the allowed migration times.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Fog-Supported Delay-Constrained Energy-Saving Live Migration of VMs Over MultiPath TCP/IP 5G Connections

Baccarelli¹,

Scarpiniti²,

Momenzadeh³

2018

IEEE Access

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“…precopy or postcopy) and other network traffic during migrations (such as VM-to-VM communication traffic, management traffic etc. ), the temporary downtime due to VM migration can vary [33] significantly. Therefore, these settings also play a critical role for selecting a proper value for a.…”

Section: Impact Of Weights (A) On the Objective Functionmentioning

confidence: 99%

“…Therefore, these settings also play a critical role for selecting a proper value for a. For example, if a datacenter has implemented an efficient migration strategy [33] which tries to minimize the impact of migration traffic on VM-to-VM communication or separate migration traffic in order to not disrupt ongoing VM-to-VM latency sensitive traffic, then the cloud Fig. 21 Relative risk adjusted power consumption for 30% CPU uncertainty with different level of weight (a) on the objective Fig.…”

Section: Impact Of Weights (A) On the Objective Functionmentioning

confidence: 99%

Robust optimization for energy-efficient virtual machine consolidation in modern datacenters

2018

Self Cite

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Energy efficient virtual machine (VM) consolidation in modern data centers is typically optimized using methods such as Mixed Integer Programming, which typically require precise input to the model. Unfortunately, many parameters are uncertain or very difficult to predict precisely in the real world. As a consequence, a once calculated solution may be highly infeasible in practice. In this paper, we use methods from robust optimization theory in order to quantify the impact of uncertainty in modern data centers. We study the impact of different parameter uncertainties on the energy efficiency and overbooking ratios such as e.g. VM resource demands, migration related overhead or the power consumption model of the servers used. We also show that setting aside additional resource to cope with uncertainty of workload influences the overbooking ration of the servers and the energy consumption. We show that, by using our model, Cloud operators can calculate a more robust migration schedule leading to higher total energy consumption. A more risky operator may well choose a more opportunistic schedule leading to lower energy consumption but also higher risk of SLA violation.

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“…Previous work [24] looks at combining hierarchical token bucket scheduling with multi-path TCP and SDNs while looking at VM migration. Their emphasis is solely on migrations within a LAN which does not apply to the WAN context of edge clouds since it can not provide transparency when the VM's IP address changes across sites.…”

Section: Related Workmentioning

confidence: 99%

Fast transparent virtual machine migration in distributed edge clouds

Chaufournier

Sharma

et al. 2017

Proceedings of the Second ACM/IEEE Symposium on Edge Computing

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Edge clouds are emerging as a popular paradigm of computation. In edge clouds, computation and storage can be distributed across a large number of locations, allowing applications to be hosted at the edge of the network close to the end-users. Virtual machine live migration is a key mechanism which enables applications to be nimble and nomadic as they respond to changing user locations and workload. However, VM live migration in edge clouds poses a number of challenges. Migrating VMs between geographically separate locations over slow wide-area network links results in large migration times and high unavailability of the application. This is due to network reconfiguration delays as user traffic is redirected to the newly migrated location. In this paper, we propose the use of multi-path TCP to both improve VM migration time and network transparency of applications. We evaluate our approach in a commercial public cloud environment and an emulated lab based edge cloud testbed using a variety of network conditions and show that our approach can reduce migration times by up to 2X while virtually eliminating downtimes for most applications.

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Network-centric Performance Improvement for Live VM Migration

Cited by 12 publications

References 8 publications

Fog-Supported Delay-Constrained Energy-Saving Live Migration of VMs Over MultiPath TCP/IP 5G Connections

Fog-Supported Delay-Constrained Energy-Saving Live Migration of VMs Over MultiPath TCP/IP 5G Connections

Robust optimization for energy-efficient virtual machine consolidation in modern datacenters

Fast transparent virtual machine migration in distributed edge clouds

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