Intel Corporation

Grigorios Katsaros

Joseph Butler

Thijs Metsch

Alexander J Leckey

Connor Upton

Radhika Loomba

Perumal Kuppuudaiyar

Kevin Mullery

Suryanarayanan Natarajan

Apparatuses and methods associated with management of computing infrastructure resources and workloads are disclosed herein. In embodiments, an apparatus may comprise a resource-workload manager having: a placement solution generator to generate a plurality of potential resource placement solutions for allocation of various resources of the computing infrastructure to various workloads; one or more utility function calculators to calculate one or more values for one or more provider-centric attributes, and one or more values for one or more customer-centric attributes, for the plurality of potential solutions; an analyzer to analyze the attributes and select one of the potential resource placement solutions; and a resource allocator to allocate the resources, based at least in part on the selected resource placement solution. Other embodiments may be described and/or claimed.

Computing infrastructure resource-workload management methods and apparatuses

Techniques for dynamically adapting cloud applications are described. A current value of a Key Performance Indicator (KPI) of an executing cloud service instance of a cloud application is detected. A determination is made as to whether the current value of the KPI is unacceptable (e.g., either it is outside of an acceptable range or is within an adaptation region). If the current value of the KPI is unacceptable, a first application programming interface (API) is invoked to request a cloud service instance monitor to adapt the cloud service instance to operate within an acceptable KPI range. If the cloud service instance monitor was unsuccessful, the unacceptable KPI is escalated to the cloud service monitor for the type of the cloud service instance. If the cloud service monitor is unsuccessful, the unacceptable KPI is escalated to the application monitor.

Dynamically adapting cloud applications

Devices and techniques for estimating power consumption and efficiency of application containers configured to run on a server system, such as a cloud server, are provided. In an example, a method can include creating a benchmark application container, naming the benchmark application container on a host server, collecting power consumption information of the host server, collecting resource utilization information of the benchmark application container using an artificial workload, building a statistical model using the power consumption information and the resource utilization information, and generating a first power model of the host server.

Control device for estimation of power consumption and energy efficiency of application containers

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London, United Kingdom

Grigorios Katsaros