Makespan and Energy Based Virtual Machine Scheduling in Cloud Systems

Yıl 2024, Cilt: 39 Sayı: 3, 1661 - 1672

Alper Kızıl Korhan Karabulut

https://doi.org/10.17341/gazimmfd.1202336

Öz

Cloud computing is one of the newest computing paradigms that emerged after worldwide development of Internet infrastructure. It is a technology that saves both large companies and small and medium scale companies as well as independent developers from the cost of keeping infrastructure hardware up to date, and operational while also providing flexibility on resource use as well providing additional opportunity to minimize data losses. While in the future, it is evident that demand for cloud computing will be on the rise. These kinds of datacenters, due to their nature, consume large amount of energy and even the savings on smallest scales will enable these gigantic centers to save a significant amount of energy in total. If we have a look at the literature, we can see green computing is gaining immense popularity over the years. The Cloud Scheduling problem is a proven problem to be NP-Hard, aiming to find the best solution for a limited number of cloud resources, which could theoretically be serving an unlimited number of user demands. In this study, firstly, an experimental workload / power consumption model is proposed for a server computer, and then two genetic algorithms optimizing makespan and energy consumption are compared on these metrics at different server loads. As a result, it has been seen that these two criteria are closely related to each other, and it has been determined that optimizing the energy criterion has a more positive effect between 10% and 13% compared to the time criterion optimization at full or near full server loads. In this way, it has been shown that significant energy savings can be achieved by using energy optimization as an objective function at high server loads.

Anahtar Kelimeler

Energy Consumption, Makespan, Evolutionary algorithm, Virtual machine scheduling

Bulut sistemlerinde toplam tamamlanma ve enerji tabanlı sanal makine çizelgelemesi

Öz

Bulut bilişim, internet altyapısının son yıllarda dünya çapında gelişmesiyle önem kazanmış yeni bilişim paradigmalardandır. Hem büyük şirketlere hem de gelişme aşamasındaki küçük ve orta ölçekli şirketlere hem de bağımsız geliştiricilere kendi donanım altyapılarını güncel ve işler tutma maliyetlerinden kurtaran, kaynak kullanımında esneklik sağlayan, veri kayıplarını minimize edebilmeleri için ek olanak sağlayan bir teknolojidir. Gelecekte de bulut bilişime olan talebin artacağı ortadadır. Bu tür veri merkezleri yapıları gereği oldukça yüklü miktarda enerji tüketimi yapmaktadırlar, bu tüketimde yapılacak en küçük tasarruflar bile bu devasa merkezlerin çok önemli miktarda enerji tasarrufu yapmalarını sağlayacaktır. Literatüre baktığımızda da Yeşil bilişim (Green Computing) yıldan yıla gitgide önem kazanmaktadır. Bulut Çizelgeleme problemi, kısıtlı sayıdaki bulut kaynağının teoride sınırsız sayıda olabilecek kullanıcı talebine en uygun, en iyi çözümün bulunmasını amaçlayan NP- Zor olduğu kanıtlanmış bir problemdir. Bu çalışmada, öncelikle bir sunucu bilgisayarı için deneye dayalı bir iş yükü / güç tüketimi modeli önerilmiş, sonra da toplam bitiş süresi ve enerji tüketimi eniyileme yapan iki genetik algoritma, farklı sunucu yüklerinde bu ölçütler üzerinden kıyaslanmıştır. Sonuçta bu iki kriterin birbirleriyle yakın ilişkide olduğu görülmüş, ayrıca enerji kriterini eniyilemenin tam ya da tama yakın sunucu yüklerinde, zaman kriteri eniyilemeye göre %10 – %13 arasında daha olumlu bir etkisi olduğu saptanmıştır. Bu sayede, yüksek sunucu yüklerinde, enerji eniyilemenin amaç fonksiyonu olarak kullanılmasını ile ciddi oranda enerji tasarrufunun mümkün olabileceği gösterilmiştir.

Anahtar Kelimeler

Enerji tüketimi, Toplam bitiş süresi, Evrimsel Algoritma, Sanal makine çizelgeleme

Kaynakça

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