Empirical Comparison of Power-efficient Virtual Machine Assignment Algorithms
| Autor: | Jordi Arjona Aroca, Antonio Fernández Anta |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Computer Networks and Communications
Computer science Energy Efficiency Computation Distributed computing Cloud computing 02 engineering and technology computer.software_genre Outsourcing Scheduling (computing) Datacenters Load Balancing Cloud testing 0202 electrical engineering electronic engineering information engineering business.industry Scheduling Power efficient 020206 networking & telecommunications Load balancing (computing) Task (computing) Virtual machine Algorithm design 020201 artificial intelligence & image processing Virtual Machine Assignment business computer Algorithm Efficient energy use |
| Zdroj: | IMDEA Networks Institute Digital Repository IMDEA Networks Institute instname SustainIT |
| Popis: | The advent of cloud computing has changed the way many companies do computation, allowing them to outsource it to the cloud. This has given origin to a new kind of business, the cloud providers, which run large datacenters. In order to be competitive, cloud providers must keep their operational costs low. One way to reduce these costs is reducing the energy consumed with smart task assignment algorithms, which decide where tasks are to be placed upon their arrival. Unfortunately, almost no task assignment algorithm used is power aware. In this paper we compare the performance of multiple task assignment algorithms for saving energy. We assume that tasks are in fact virtual machines that have to be assigned to physical machines, and we assume that the physical machines have a power consumption that increases superlinearly with the load. First, we propose two tunable power-aware task assignment algorithms (that subsume the algorithms studied in Arjona et al., Power-efficient assignment of virtual machines to physical machines, Future Generation Computer Systems, 2015). These algorithms are then compared with multiple state-of-the-art algorithms in different meaningful scenarios. Both algorithms prove themselves as interesting assignment algorithms since, properly configured, they outperform the other algorithms in most of the cases. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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