Capacity Management of Hyperscale Data Centers Using Predictive Modelling
نویسندگان
چکیده
منابع مشابه
Towards Hyperscale Process Management
Scalability of software systems has been a research topic for many years and is as relevant as ever with the dramatic increases in digitization of business operations and data. This relevance also applies to process management systems, most of which are currently incapable of scaling horizontally, i.e., over multiple servers. This paper discusses an approach towards hyperscale workflows, using ...
متن کاملEnergy Aware Resource Management of Cloud Data Centers
Cloud Computing, the long-held dream of computing as a utility, has the potential to transform a large part of the IT industry, making software even more attractive as a service and shaping the way IT hardware is designed and purchased. Virtualization technology forms a key concept for new cloud computing architectures. The data centers are used to provide cloud services burdening a significant...
متن کاملModelling Customer Attraction Prediction in Customer Relation Management using Decision Tree: A Data Mining Approach
In Today’s quality- based competitive world, known as knowledge age, customer attraction is of ultimate importance. In respect to the slogan “customer is always right”, customer relation management is the core of an organizational strategy playing an important role in four aspects of customer identification, customer attraction, customer retaining, and customer satisfaction. Commercial organiza...
متن کاملStochastic Model Predictive Control for Data Centers
Datacenters operations are notoriously energy-hungry with the computing and cooling infrastructures drawing comparable amount of power. A direction to improve their efficiency is to jointly control the Information Technology (IT) and Cooling Techniques (CT) components so that less cooling power has to be spent for the same Quality of Service (QoS) level. This work investigates minimum cost cont...
متن کاملPredictive modelling using neuroimaging data in the presence of confounds
When training predictive models from neuroimaging data, we typically have available non-imaging variables such as age and gender that affect the imaging data but which we may be uninterested in from a clinical perspective. Such variables are commonly referred to as 'confounds'. In this work, we firstly give a working definition for confound in the context of training predictive models from samp...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Energies
سال: 2019
ISSN: 1996-1073
DOI: 10.3390/en12183438