Entities such as data centers are typically used to house computer systems and associated components, such as telecommunications and storage systems. Such entities also typically include redundant or backup power supplies, redundant data communications connections, environmental controls (e.g., air conditioning, fire suppression, etc.) and security devices. The implementation and operation of such components factor into aspects such as capital and operational expenditures associated with an entity. Further, the implementation and operation of such components factor into the carbon footprint associated with an entity.
Features of the present disclosure are illustrated by way of example and not limited in the following figure(s), in which like numerals indicate like elements, in which:
For simplicity and illustrative purposes, the present disclosure is described by referring mainly to examples. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. It will be readily apparent however, that the present disclosure may be practiced without limitation to these specific details. In other instances, some methods and structures have not been described in detail so as not to unnecessarily obscure the present disclosure.
Throughout the present disclosure, the terms “a” and “an” are intended to denote at least one of a particular element. As used herein, the term “includes” means includes but not limited to, the term “including” means including but not limited to. The term “based on” means based at least in part on.
Entities such as data centers, buildings, electronics cabinets, etc., typically implement and operate components so as to reduce energy usage and the associated carbon footprint. For example, an entity may use renewable on-site power supplies and alternative cooling approaches to reduce energy usage and the associated carbon footprint. While such solutions may provide significant environmental benefits, the high costs associated with such solutions often limit their adaption in practice. In this regard, the high costs may be reduced by more effective usage of such renewable resources during the operation of entities. The high costs may also be reduced by optimizing the design and operation of entities to minimize the total cost across the entity lifecycle. For example, the high costs may be reduced by determining the appropriate mix and size of renewable power sources to minimize the capital expense, and optimizing IT workload management combined with energy supply provisioning to minimize operational energy cost.
According to an example, a power infrastructure sizing and workload management apparatus, and a method for power infrastructure sizing and workload management of an entity are disclosed herein. The apparatus and method disclosed herein may be implemented to minimize energy costs of entities, including capital and operational costs, by integrating energy supply provisioning with information technology (IT) workload demand management across the entity lifecycle. The apparatus and method disclosed herein may provide for the design and operation of an entity consuming net-zero energy from a grid over the lifetime of the entity at a minimal cost.
The apparatus and method disclosed herein may integrate the management of power supply and demand for an entity to minimize the lifetime cost, while maintaining the environmental impact target of an entity. For example, the apparatus and method disclosed herein may determine the optimal mix and size of power sources to minimize capital cost. Further, the apparatus and method disclosed herein may schedule IT workloads based on power supply availability to minimize operational cost. By using local renewable generation and optimizing the power micro-grid with demand management, the apparatus and method disclosed herein may provide for the design and operation of entities using renewable energy while minimizing total cost.
The apparatus and method disclosed herein may provide for integrated optimization of power infrastructure sizing and workload management from design to operation. The total lifetime energy cost of an entity, including capital expenditures and operational expenditures, may be reduced, while maintaining the environmental impact target of an entity. In addition, entities may be designed and operated to consume net-zero energy from a grid over the entity lifetime at a minimal cost.
The power infrastructure sizing and workload management model 106 may be used to determine the optimal mix and size of power sources to minimize capital cost for an entity, and schedule IT workloads based on supply availability to minimize operational cost for the entity. The power infrastructure sizing and workload management model 106 may use the input parameter specifications 104 to evaluate cost of entity power generation at 118, entity capital expenditure at 120, entity operational expenditure at 122, and cost of energy storage at 124. The power infrastructure sizing and workload management model 106 may be used to generate power supply and IT workload demand output parameter specifications 126 (hereinafter “output parameter specifications 126”) for the entity. The output parameter specifications 126 may include data for parameters related to onsite power generation 128, power from grid 130, energy storage 132, and IT workload scheduling 134. A power infrastructure sizing and workload management implementation module 136 may receive the output parameter specifications 126 to implement the optimal mix and size of power sources to minimize capital cost, and schedule IT workloads based on supply availability to minimize operational cost. The power infrastructure sizing and workload management implementation module 136 may be provided as a component of the apparatus 100 or separately from the apparatus 100 to implement the output parameter specifications 126.
The modules 102 and 136, and other components of the apparatus 100 that perform various other functions in the apparatus 100, may include machine readable instructions stored on a non-transitory computer readable medium. In addition, or alternatively, the modules 102 and 136, and other components of the apparatus 100, may include hardware or a combination of machine readable instructions and hardware.
The power infrastructure sizing and workload management apparatus 100 may generally provide for integration of the management of resource supply and demand for an entity to deliver sustainable entities. The apparatus 100 may generally integrate the management of power supply and demand for an entity in order to minimize the lifetime cost of the entity, while maintaining the environmental impact target of the entity. This may be accomplished by optimizing the power infrastructure size and managing IT workloads based on resource availability. The apparatus 100 may provide for the proper design and correct provisioning of the power supply infrastructure to minimize the capital cost, while providing sufficient renewable resources to meet the carbon footprint target of an entity. Further, the apparatus 100 may provide for balancing of the entity workload, and thus operational energy demand within given supply-side constraints to minimize the operational cost of an entity. In this regard, the power infrastructure sizing and workload management model 106 may characterize the power supply and demand of an entity and generate a general capacity management solution that integrates supply-aware workload planning with supply-side sizing to optimize the power supply infrastructure and workload management from design to operation.
In order to integrate power supply sizing and IT workload capacity planning, the power infrastructure sizing and workload management modeling module 102 may receive the power supply and IT workload demand input parameter specifications 104. The input parameter specifications 104 may include specifications for parameters related to onsite power generation 108, power from grid 110, energy storage 112, IT workload demand and SLAs 114, and cooling 116. The input parameter specifications 104 may generally account for energy supply options and related parameters, location specific environmental data, IT workload demand, and operational goals. For example, the input parameter specifications 104 may be based on receipt of power source options and costs (e.g., electricity price, renewable supplies), energy storage parameters, environmental data (e.g., weather data), IT workload and SLAs, and operational goals (e.g., carbon emission reduction target for an entity).
The power infrastructure sizing and workload management modeling module 102 may utilize the input parameter specifications 104 for the power infrastructure sizing and workload management model 106 that generates, for example, an optimal mix and size of power sources, a detailed power generation and consumption profile, a cost report, and a workload scheduling plan. For example, the optimal mix and size of power sources may provide optimal power infrastructure sizes. The detailed power generation and consumption profile may provide, for example, projections for energy consumption, and energy supply. The detailed cost report and comparison of different solutions may provide, for example, a total cost breakdown (e.g., capital expenditures and operational expenditures), carbon footprint for an entity, and payback period. Further, the workload scheduling plan may provide, for example, a detailed IT workload and capacity allocation plan.
Referring to
With respect to the power supply parameters at 202, the power infrastructure sizing and workload management model 106 may consider two categories of power generation options, that is, onsite power generation at 206 and power from the grid at 208. Onsite power generation at 206 may include renewable or non-renewable power generated by an entity's own facilities. For example, the onsite power generation at 206 may include parameter Cc that may represent installed capacity of onsite power generation including units of kW (e.g., 500 kW of solar power), parameter fc(t) that may represent a capacity factor of onsite power generation at time t, where 0≦fc(t)≦1, parameter ec(t) that may represent a carbon emission factor of onsite power generation at time t including units of CO2-eq kg/kWh, parameter Ic that may represent an amortized capital cost of onsite power generation including units of $/kW, and parameter pc(t) that may represent operational and maintenance cost of onsite power generation including units of $/kWh. The parameters ec(t), IC, and pc(t) may represent input parameters that receive the input parameter specifications 104 for the power infrastructure sizing and workload management model 106, and the parameters Cc and fc(t) may represent output parameters that generate output parameter specifications 126 using the power infrastructure sizing and workload management model 106.
Power from the grid at 208 may include, for example, electricity from traditional power plants and renewable energy sources. For example, the power from the grid at 208 may include parameter Cg that may represent an installed capacity of power from the grid including units of kW, parameter pg(t) that may represent an electricity price of power from the grid at time t including units of $/kWh, parameter pb(t) that may represent a sell-back price of power from the grid at time t including units of $/kWh, parameter cg(t) that may represent an energy consumption of power from the grid at time t including units of kWh, and parameter eg(t) that may represent a carbon emission factor of power from the grid at time t including units of CO2-eq kg/kWh. The parameters pg(t), pb(t), and eg(t) may represent input parameters that receive the input parameter specifications 104 for the power infrastructure sizing and workload management model 106, and the parameters Cg and cg(t) may represent output parameters that generate output parameter specifications 126 using the power infrastructure sizing and workload management model 106.
The power supply parameters at 202 may further include parameters related to energy storage devices at 210. For example, the energy storage devices at 210 may include parameter Ce that may represent an installed capacity of energy storage including units of kW, parameter die(t) that may represent a power discharge of energy storage at time t including units of kWh, parameter che(t) that may represent a power charge of energy storage at time t including units of kWh, parameter ρ that may represent an energy storage loss rate, parameter ue(t) that may represent an emerge storage at time t including units of kWh, where 0≦ue(t)≦Ce, parameter Ie that may represent an amortized capital cost of energy storage including units of $/kWh, and parameter pe(t) may represent operational and maintenance cost of energy storage at time t including units of $/kWh. The parameters ρ, ue(t), Ie, and pe(t) may represent input parameters that receive the input parameter specifications 104 for the power infrastructure sizing and workload management model 106, and the parameters Ce, die(t), and che(t) may represent output parameters that generate output parameter specifications 126 using the power infrastructure sizing and workload management model 106.
With respect to the IT workload demand input parameters at 204, the power infrastructure sizing and workload management model 106 may consider that entities generally support a range of IT workloads (i.e., at 212), including both primary interactive applications that may run 24 hrs/day and 7 days/week (e.g., Internet services), and non-interactive, delay tolerant, batch-style applications (e.g., scientific applications, financial analysis, and image processing), which may be referred to as secondary workloads. Thus, primary workloads may be defined by their IT demand, and the secondary workloads may be defined in terms of IT demand and completion time. Generally, secondary workloads may be scheduled to run anytime as long as such workloads finish before their deadlines. These aspects may provide flexibility for workload management.
The IT workloads at 212 may include parameter ai(t) that may represent a demand of primary workload i at time t, parameter Bj that may represent a total capacity demand of secondary workload j, parameter bj(t) that may represent a capacity of secondary workload j at time t, and parameter Ej that may represent a capacity of secondary workload j at time t. The parameters ai(t), Bj, and Ej may represent input parameters that receive the input parameter specifications 104 for the power infrastructure sizing and workload management model 106, and the parameter bj(t) may represent an output parameter that generates output parameter specifications 126 using the power infrastructure sizing and workload management model 106.
With respect to the IT workload demand input parameters at 204, cooling power demand at 214 may be derived from IT power demand, e.g., via power usage effectiveness (PUE). IT power demand may include demand from both primary and secondary workloads, i.e., CIT(t)=Σiai(t)+Σjbj(t), where a and b respectively represent primary and secondary workloads. The cooling power demand at 214 may include parameter f(CIT(t)) that may represent cooling power consumption at time t. The parameter f(CIT(t)) may represent an input parameter that receives the input parameter specifications 104 for the power infrastructure sizing and workload management model 106.
The power infrastructure sizing and workload management model 106 may optimize the power supply infrastructure size and operation to minimize the total entity cost while meeting specified operational goals by formulating the power supply parameters at 202 and the IT workload demand parameters at 204 as a constrained optimization model. For example, the power infrastructure sizing and workload management model 106 may optimize the power supply infrastructure size and operation as follows:
With respect to Equations (1)-(7), each of the parameters are listed and described in
Referring to
At block 304, the power supply and IT workload demand input parameter specifications may be used for a power infrastructure sizing and workload management model for the entity. For example, referring to
At block 306, the power infrastructure sizing and workload management model may be used to generate power supply and IT workload demand output parameter specifications for the entity to provide optimal power infrastructure sizing for the entity to minimize capital cost of the entity, and IT workload management to minimize operational cost of the entity. For example, referring to
The computer system 400 includes a processor 402 that may implement or execute machine readable instructions performing some or all of the methods, functions and other processes described herein. Commands and data from the processor 402 are communicated over a communication bus 404. The computer system also includes a main memory 406, such as a random access memory (RAM), where the machine readable instructions and data for the processor 402 may reside during runtime, and a secondary data storage 408, which may be non-volatile and stores machine readable instructions and data. The memory and data storage are examples of computer readable mediums. The memory 406 may include a power infrastructure sizing and workload management module 420 including machine readable instructions residing in the memory 406 during runtime and executed by the processor 402. The power infrastructure sizing and workload management module 420 may include the modules 102 and 136 of the apparatus shown in
The computer system 400 may include an I/O device 410, such as a keyboard, a mouse, a display, etc. The computer system may include a network interface 412 for connecting to a network. Other known electronic components may be added or substituted in the computer system.
What has been described and illustrated herein is an example along with some of its variations. The terms, descriptions and figures used herein are set forth by way of illustration only and are not meant as limitations. Many variations are possible within the spirit and scope of the subject matter, which is intended to be defined by the following claims—and their equivalents—in which all terms are meant in their broadest reasonable sense unless otherwise indicated.