VM consolidation: A real case based on OpenStack Cloud

Corradi, Antonio; Fanelli, Mario; Foschini, Luca

doi:10.1016/j.future.2012.05.012

Cited by 204 publications

(98 citation statements)

References 19 publications

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“…These efforts are not only centered on increasing the utilization but also to keep a more balanced usage among nodes [11], and/or delivering fairness among users, projects or virtual organizations [12]. For data centers, the efficient use of resources is mainly motivated by hardware and operational costs [13], and lately also by power consumption and environmental concerns [14], becoming a critical issue for large scale data centers. The very large scale and multi-tenant nature of cloud infrastructures offers great potential for efficient multiplexing of different services and applications, allowing a much higher resource utilization [15].…”

Section: Data Center Utilization Problemsmentioning

confidence: 99%

An Autonomic Approach to Risk-Aware Data Center Overbooking

Tomás

Tordsson

2014

IEEE Trans. Cloud Comput.

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Abstract-Elasticity is a key characteristic of cloud computing that increases the flexibility for cloud consumers, allowing them to adapt the amount of physical resources associated to their services over time in an on-demand basis. However, elasticity creates problems for cloud providers as it may lead to poor resource utilization, specially in combination with other factors, such as user overestimations and pre-defined VM sizes. Admission control mechanisms are thus needed to increase the number of services accepted, raising the utilization without affecting services performance. This work focuses on implementing an autonomic risk-aware overbooking architecture capable of increasing the resource utilization of cloud data centers by accepting more virtual machines than physical available resources. Fuzzy logic functions are used to estimate the associated risk to each overbooking decision. By using a distributed PID controller approach, the system is capable of self-adapting over time -changing the acceptable level of risk -depending on the current status of the cloud data center. The suggested approach is extensively evaluated using a combination of simulations and experiments executing real cloud applications with real-life available workloads. Our results show a 50% increment at both resource utilization and capacity allocated with acceptable performance degradation and more stable resource utilization over time.

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Section: Data Center Utilization Problemsmentioning

confidence: 99%

An Autonomic Approach to Risk-Aware Data Center Overbooking

Tomás

Tordsson

2014

IEEE Trans. Cloud Comput.

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“…Generally, the aim is to reduce the number of PMs needed to pack different VMs across various resources such as CPU, memory, network I/O etc. Bin packing is known to be an NP hard problem, therefore, several heuristics have been applied when it comes to VM consolidation, including the well established First Fit Decreasing (FFD), Best Fit Decreasing (BFD) or variants of these algorithms [61,80,81,82,83,84]. Other researchers have resorted to novel bio-inspired solutions, and perform the bin packing with algorithms such as the Ant Colony Optimization [85,86].…”

Section: Infrastructure As a Service And Resource Consolidationmentioning

confidence: 99%

A Novel Query Caching Scheme for Dynamic InfiniBand Subnets

Tasoulas

Gran

Johnsen³

et al. 2015

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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The advent of the Internet of Things, sensor and social networks, to mention just a few examples, all contribute towards the solid establishment of the Big Data era. High Performance Computing (HPC) becomes necessary for the efficient processing of the massive amounts of data our society generates, and cloud computing is a critical component to deliver this processing power to a broader audience that cannot afford to acquire and maintain such complex computing systems themselves. However, HPC specific technology and performance is not yet apt to be delivered efficiently over highly flexible and dynamic environments, as typically are the virtualized cloud infrastructures.In this thesis, we address challenges that arise in high performance dynamic cloud environments, that are equipped with HPC specific technology, in the context of networking and virtualization. We use InfiniBand, a high performance lossless interconnection network as the basis of our research, and first show that lossless networks pose prime challenges when the nature of the infrastructure is very dynamic, i.e. exhibits continuous changes. Then we propose a network I/O virtualization architecture, the InfiniBand vSwitch architecture, that can make lossless network technologies more favorable in the cloud. Moreover, we propose different network reconfiguration methods to enable performance-driven reconfigurations in very large network topologies that are commonly found in data centers. Performance-driven reconfigurations are frequently needed to adapt to unpredictable workload changes resulting from the shared and on-demand nature of a cloud platform, or when cloud providers employ live migration of virtual machines to optimize the resource usage of their infrastructure. Last but not least, we propose a new Quality-of-Service metric, called delay, to capture the directly observable service degradation in consolidated cloud environments. We suggest that the delay can be used as a direct service level agreement metric between cloud providers and cloud tenants. AcknowledgementsFirst, I would like to express my gratitude to my three supervisors; Ernst Gunnar Gran, Tor Skeie and Kyrre Begnum. Without their positiveness, supervision, suggestions and encouragement, my PhD journey would not have been so fruitful and enjoyable during both the good and hard times. Then I would like to thank Bjørn Dag Johnsen from Oracle Norway, our closest collaborate in the ERAC project, the project that mainly funded this thesis, for his enthusiastic attitude and participation in the important discussions that shaped the direction of my work and introduced industrial relevance. My appreciation goes as well to Feroz Zahid for being the most easygoing colleague whom I could imagine sharing office with, and a brilliant and ambitious associate, and Sven-Arne Reinemo for his supervision during my early days.A special thank you must be directed to Hårek Haugerud, Anis Yazidi, Hugo Lewi Hammer, Laurence Marie Anna Habib and the whole NETSYS group at the Oslo and Akershus...

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“…In workload aware VM placement mechanism, the different combination of workload mix such as CPU, I/O intensive, etc., are investigated in order to find optimal VM placement. The benefits of workload mixing can be proactively predicted by using machine learning algorithms which can be used for VM placement decisions [21] [22].…”

Section: State-of-the Art In Vm Placementmentioning

confidence: 99%