Vertelas - Automated User-Controlled Vertical Elasticity in Existing Commercial Clouds

Qazi, Kashifuddin

doi:10.1109/cccs.2019.8888039

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…The suspended containers/VMs are restored when memory becomes available again, if not on the same host, on another with spare capacity. [Qazi 2019] considers the execution of containers in a number of heterogeneous VMs with different memory configurations on Amazon EC2. If the memory allocated to a container becomes insufficient, their strategy suspends and checkpoints it, and then re-starts the container in another instance with sufficient memory.…”

Section: Related Workmentioning

confidence: 99%

Making the most of what you pay for by delaying tasks to improve overall cloud instance performance

Sodré¹,

Boeres²,

Rebello³

2022

Anais Estendidos Do XXIII Simpósio Em Sistemas Computacionais De Alto Desempenho (SSCAD Estendido 2022)

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Resource elasticity and server consolidation have long been among two of cloud computing’s most relevant management tools. Yet, exemplified with a scientific application use case, this paper highlights how judicious scheduling of tasks can help maximize resource utilization and improve performance and costs for both users and cloud providers. Developing an efficient cloud service for DNA sequence comparisons is adopted as a motivating use case. Using the bioinformatics tool MASA that finds an optimal pair-wise sequence alignment, we propose a model for co-scheduling multiple alignments on a single cloud instance. The resulting, practically optimal, non-preemptive schedule can effectively double the throughput of MASA-based sequence alignment workflows.

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Section: Related Workmentioning

confidence: 99%