Squirrel

Razavi, Kaveh; Ion, Ana; Kielmann, Thilo

doi:10.1145/2600212.2600221

Cited by 14 publications

(1 citation statement)

References 31 publications

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“…Razavi et al study both approaches in combination. They dealt with transferring the VM image from the storage node to the host in [25] and presented Squirrel, a system that stores all VM images of a data center on all physical servers. Then, in [27], the actual booting process of the VM is studied, proposing to pre-boot a VM image in advance and take a snapshot with minimal hardware resources that can be resumed very fast (2-3 s on average).…”

Section: Related Workmentioning

confidence: 99%

Effects of Reducing VMs Management Times on Elastic Applications

Pascual¹,

Lozano

Miguel-Alonso

2018

J Grid Computing

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Cloud infrastructures provide computing resources to applications in the form of Virtual Machines (VMs). Many applications deployed in cloud resources have an elastic behavior, that is, they change the number of servers (VMs) dynamically, adapting the application to the workload. Scaling-out and scaling-in operations are managed by an auto-scaler module, which can be reactive (adapting the number of VMs to the current workload) or proactive (adapting to the expected future workload). The cloud infrastructure provides a management interface to create (deploy) and destroy (shutdown) server instances, operations that require some time to complete. In this work we evaluate to what extent the reduction of the time required by VM management operations, namely deployment and shutdown, impacts the performance of applications and the behavior of reactive and proactive auto-scaling policies. After establishing several ideal boundaries on the use of resources, we carry out a set of experiments that show how short management times drastically reduce the use of resources, while allowing the application to operate within the required performance bounds.

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Section: Related Workmentioning

confidence: 99%

Effects of Reducing VMs Management Times on Elastic Applications

Pascual¹,

Lozano

Miguel-Alonso

2018

J Grid Computing

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VMBKS: a shared memory cache system based on booting kernel in cloud

et al. 2015

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In Infrastructure-as-a-Service clouds, virtual machine provisioning time is an important metric for evaluating the quality of service (QoS) provided by the service providers. VM provisioning is usually time-consuming, especially when many VMs boot up simultaneously. We call such phenomenons "Boot Storm". In practice, many VMs would run on a single compute node for higher resource utilization. This multiple VMs and single compute node scenario easily encounters Boot Storm. Boot Storm leads to extremely high latency of VM provisioning and badly affects user's experience. In this paper, we propose VMBKS, which is a cloud shared memory cache system based on booting kernel. We exploit VMs' correlations to organize VM image files, to reduce cache size and mitigate the effect of Boot Storm. We use booting kernel to construct cache and share the booting kernel to correlative VMs. Evaluation shows that VMBKS can speed up VM provisioning time by up to 60 % and mitigate the effect of Boot Storm significantly.

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Discovering and Leveraging Content Similarity to Optimize Collective on-Demand Data Access to IaaS Cloud Storage

Nicolae

Kochut²,

Karve³

2015

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

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A critical feature of IaaS cloud computing is the ability to quickly disseminate the content of a shared dataset at large scale. In this context, a common pattern is collective on-demand read, i.e., accessing the same VM image or dataset from a large number of VM instances concurrently. There are various techniques that avoid I/O contention to the storage service where the dataset is located without relying on pre-broadcast. Most such techniques employ peer-to-peer collaborative behavior where the VM instances exchange information about the content that was accessed during runtime, such that it is possible to fetch the missing data pieces directly from each other rather than the storage system. However, such techniques are often limited within a group that performs a collective read. In light of high data redundancy on large IaaS data centers and multiple users that simultaneously run VM instance groups that perform collective reads, an important opportunity arises: enabling unrelated VM instances belonging to different groups to collaborate and exchange common data in order to further reduce the I/O pressure on the storage system. This paper deals with the challenges posed by such a solution, which prompt the need for novel techniques to efficiently detect and leverage common data pieces across groups. To this end, we introduce a low-overhead fingerprint based approach that we evaluate and demonstrate to be efficient in practice for a representative scenario on dozens of nodes and a variety of group configurations.

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Squirrel

Cited by 14 publications

References 31 publications

Effects of Reducing VMs Management Times on Elastic Applications

Effects of Reducing VMs Management Times on Elastic Applications

VMBKS: a shared memory cache system based on booting kernel in cloud

Discovering and Leveraging Content Similarity to Optimize Collective on-Demand Data Access to IaaS Cloud Storage

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