| 1. |
- Svärd, Petter, 1977-, et al.
(författare)
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Continuous Datacenter Consolidation
- 2014
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Rapport (refereegranskat)abstract
- Efficient mapping of Virtual Machines (VMs) onto physical servers is a key problem for cloud infrastructure providers as hardware utilization directly im- pacts revenue. Today, this mapping is commonly only performed when new VMs are created, but as VM workloads fluctuate and server availability varies, any ini- tial mapping is bound to become suboptimal over time. We introduce a set of heuristic methods for continuous optimization of the VM-to-server mapping based on combina- tions of fundamental management actions, namely suspending and resuming physical machines, migrating VMs, and suspending and resuming VMs. Using these methods cloud infrastructure providers can continuously optimize their server resources regard- less of the predictability of the workload. To verify that our approach is applicable in real-world scenarios, we build a proof-of-concept datacenter management system that implements the proposed algorithms. The feasibility of our approach is evaluated through a combination of simulations and real experiments where our system provi- sions a workload of benchmark applications. Our results indicate that the proposed algorithms are feasible, that the combined management approach achieves the best results, and that the VM suspend and resume mechanism has the largest impact.
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| 2. |
- Svärd, Petter, 1977-
(författare)
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Dynamic Cloud Resource Management : Scheduling, Migration and Server Disaggregation
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A key aspect of cloud computing is the promise of infinite, scalable resources, and that cloud services should scale up and down on demand. This thesis investigates methods for dynamic resource allocation and management of services in cloud datacenters, introducing new approaches as well as improvements to established technologies.Virtualization is a key technology for cloud computing as it allows several operating system instances to run on the same Physical Machine, PM, and cloud services normally consists of a number of Virtual Machines, VMs, that are hosted on PMs. In this thesis, a novel virtualization approach is presented. Instead of running each PM isolated, resources from multiple PMs in the datacenter are disaggregated and exposed to the VMs as pools of CPU, I/O and memory resources. VMs are provisioned by using the right amount of resources from each pool, thereby enabling both larger VMs than any single PM can host as well as VMs with tailor-made specifications for their application. Another important aspect of virtualization is live migration of VMs, which is the concept moving VMs between PMs without interruption in service. Live migration allows for better PM utilization and is also useful for administrative purposes. In the thesis, two improvements to the standard live migration algorithm are presented, delta compression and page transfer reordering. The improvements can reduce migration downtime, i.e., the time that the VM is unavailable, as well as the total migration time. Postcopy migration, where the VM is resumed on the destination before the memory content is transferred is also studied. Both userspace and in-kernel postcopy algorithms are evaluated in an in-depth study of live migration principles and performance.Efficient mapping of VMs onto PMs is a key problem for cloud providers as PM utilization directly impacts revenue. When services are accepted into a datacenter, a decision is made on which PM should host the service VMs. This thesis presents a general approach for service scheduling that allows for the same scheduling software to be used across multiple cloud architectures. A number of scheduling algorithms to optimize objectives like revenue or utilization are also studied. Finally, an approach for continuous datacenter consolidation is presented. As VM workloads fluctuate and server availability varies any initial mapping is bound to become suboptimal over time. The continuous datacenter consolidation approach adjusts this VM-to-PM mapping during operation based on combinations of management actions, like suspending/resuming PMs, live migrating VMs, and suspending/resuming VMs. Proof-of-concept software and a set of algorithms that allows cloud providers to continuously optimize their server resources are presented in the thesis.
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| 3. |
- Svärd, Petter, 1977-, et al.
(författare)
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Hecatonchire : Towards Multi-Host Virtual Machines by Server Disaggregation
- 2014
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Ingår i: EuroPar 2014. - Cham : Springer. - 9783319143125 - 9783319143132 ; , s. 519-529
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Konferensbidrag (refereegranskat)abstract
- Horizontal elasticity through scale-out is the current dogma for scaling cloud applications but requires a particular application architecture. Vertical elasticity is transparent to applications but less used as scale-up is limited by the size of a single physical server. In this paper, we propose a novel approach, server disaggregation, that aggregates memory, compute and I/O resources from multiple physical machines in resource pools. From these pools, virtual machines can be seamlessly provisioned with the right amount of resources for each application and more resources can be added to vertically scale a virtual machine as needed, regardless of the bound of any single physical machine. We present our proposed architecture and implement key functionality such as transparent memory scale-out and cloud management integration. Our approach is validated by a demonstration using benchmarks and a real-world big-data application and results indicate a low overhead in using memory scale-out in both test cases.
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| 4. |
- Svärd, Petter, 1977-, et al.
(författare)
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Hecatonchire: Enabling Multi-Host Virtual Machines by Resource Aggregation and Pooling
- 2014
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Vertical elasticity, or scale-up of individual virtual ma- chines is hard to perform in today’s cloud environments due to limitations in the amount of hardware resources available in single servers. We propose a novel approach that allows aggregation of memory, compute and I/O resources from multiple physical machines in resource pools which in turn are used to seamlessly provision vir- tual machines with the right amount of resources. We present our architecture and highlight key functionality such as transparent and resilient memory aggregation and fast live migration. Our approach is validated by a demonstration using benchmarks and a real-world big- data application. Performance results indicate a very low overhead in using aggregated memory as well as a sig- nificant improvement in live migration performance.
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