Chameleon

Panneerselvam, Sankaralingam; Swift, Michael M.

doi:10.1145/2150976.2150988

Cited by 17 publications

(1 citation statement)

References 39 publications

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“…In spite of the lack of support from various operating systems, as noted above, research has begun to focus on hot plug support (e.g. [64,66]) and on vertical scaling (e.g. [67]).…”

Section: Horizontal and Vertical Scalingmentioning

confidence: 99%

Performance-Oriented VM Provisioning in Clouds

Shoaib¹

2021

Preprint

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Managing applications on the cloud requires extensive decision making on the part of the Application Provider (AP). When an application faces changing workload, the services of the application are either scaled up or down in response. The services run on Virtual Machines (VM) or container instances. APs decide on how the application scales through VM provisioning and the placement of the services on the VMs. Various drivers guide this decision making. Application performance and cost are two such drivers. This thesis answers the question of how APs can meet the performance constraints of their applications while minimizing the cost of the running VMs. Two versions of the problem are presented. The first version expects to meet mean response time constraints given a deployment configuration through the replication of VMs and addition of virtual processors. The presented solution is based on layered bottlenecks. A case study shows the solution meets response time constraints and uses fewer resources in comparison to a simple utilization based approach. The second version adds the minimization of cost as an objective, where VM-types having different cost rates are used. This problem does not require a deployment configuration and provides a complete solution, where resources can be added and removed. A novel solution based on the layered bottleneck strength value with genetic algorithm has been presented. For the case study, a decision maker is implemented for a web application. The proposed solution is compared with three algorithms, all of which run within the decision maker. The results from the case study show that the proposed solution provides shorter runtime than the exhaustive search, and is able to meet response time constraints with near optimal minimization of cost. The solution also results in better cost than a plain genetic algorithm and random search, at the expense of slightly longer runtime.

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“…In spite of the lack of support from various operating systems, as noted above, research has begun to focus on hot plug support (e.g. [64,66]) and on vertical scaling (e.g. [67]).…”

Section: Horizontal and Vertical Scalingmentioning

confidence: 99%

Performance-Oriented VM Provisioning in Clouds

Shoaib¹

2021

Preprint

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Prebaked µVMs: Scalable, Instant VM Startup for IaaS Clouds

Razavi¹,

Kolk

Kielmann³

2015

2015 IEEE 35th International Conference on Distributed Computing Systems

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IaaS clouds promise instantaneously available resources to elastic applications. In practice, however, virtual machine (VM) startup times are in the order of several minutes, or at best, several tens of seconds, negatively impacting the elasticity of applications like Web servers that need to scale out to handle dynamically increasing load.VM startup time is strongly influenced by booting the VM's operating system. In this work, we propose using so-called prebaked µVMs to speed up VM startup. µVMs are snapshots of minimal VMs that can be quickly resumed and then configured to application needs by hot-plugging resources. To serve µVMs, we extend our VM boot cache service, Squirrel, allowing to store µVMs for large numbers of VM images on the hosts of a data center. Our experiments show that µVMs can start up in less than one second on a standard file system. Using 1000+ VM images from a production cloud, we show that the respective µVMs can be stored in a compressed and deduplicated file system within 50 GB storage per host, while starting up within 2-3 seconds on average.

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