EP3332303A1 - Methods and systems for workload distribution - Google Patents
Methods and systems for workload distributionInfo
- Publication number
- EP3332303A1 EP3332303A1 EP15751063.7A EP15751063A EP3332303A1 EP 3332303 A1 EP3332303 A1 EP 3332303A1 EP 15751063 A EP15751063 A EP 15751063A EP 3332303 A1 EP3332303 A1 EP 3332303A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- server
- servers
- temperature
- job
- load
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
- G06F1/16—Constructional details or arrangements
- G06F1/20—Cooling means
- G06F1/206—Cooling means comprising thermal management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
- G06F1/26—Power supply means, e.g. regulation thereof
- G06F1/32—Means for saving power
- G06F1/3203—Power management, i.e. event-based initiation of a power-saving mode
- G06F1/3234—Power saving characterised by the action undertaken
- G06F1/329—Power saving characterised by the action undertaken by task scheduling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
- G06F9/505—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the load
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5094—Allocation of resources, e.g. of the central processing unit [CPU] where the allocation takes into account power or heat criteria
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Definitions
- the present invention relates to methods and systems for workload distribution. It is particularly, but not exclusively, concerned with workload distribution in data centers, in particular data centers that use fresh air cooling.
- Fresh air cooling [7] [1] is a rapidly spreading technique [6] to lower the PUE (Power Usage Effectiveness) of data-centres [2] by lowering the requirements for active cooling [5] with powered chillers [4]. It essentially combines using hardware capable of operating at higher temperatures with a ventilating system that relies solely or primarily on fresh air (i.e. at ambient outside temperature [9]) [8].
- the "shortest queue” or “round-robin” strategy may be sub-optimal. This is intuitively understandable; for instance, it is typical for a server to have a short queue at the end of a period of intense activity. However, it will also be running very hot and potentially close to initiating a "cool down" phase, making it a poor choice for the allocation of a newly arrived job. Yet considering the temperature alone is also a bad idea: at the end of its idling period ("cool down" phase), a server will be close to its lower resumption threshold temperature, and may therefore appear as an optimal choice. However, it may still have a long queue of (partially) unprocessed jobs, which would also cause unwanted delays in the execution of the newcomer.
- An object of the present invention is to provide a method to balance the workload between multiple servers so as to minimise their aggregated cooling-related idling time (and hence the delay incurred by queuing jobs) where the serves use exclusively fresh air cooling.
- a further object of the present invention is to provide a method for distributing incoming jobs across a population of servers so as to maximise their aggregated processing capacity over a period of time in the absence of active refrigeration.
- aspects of the present invention provide for methods and systems devices for distributing jobs between a plurality of servers which take account of both the temperature of the servers and the amount of tasks allocated to the servers.
- a first aspect of the present invention provides a method for allocating jobs to a plurality of servers, wherein the jobs are tasks to be performed by the servers, the method comprising the steps of: receiving a job to be allocated; determining information relating to the state of each of the servers, the information comprising the temperature of each server and the amount of tasks allocated to the server and still to be performed by the server; calculating a thermal ioad of each server using the information from the servers, choosing the server to allocate the job to according to the calculated thermal loads of the servers; and allocating the job to the chosen server.
- Calculating a thermal load of each server which is based on both the temperature of the server and the amount of tasks allocated to the server, allows the method to take account of not only the current temperature state of the server (which is directly measurable) but also the likely temperature state of the server when the server reaches the stage of processing the job being allocated. By comparing these thermal loads, the allocation of incoming jobs between the servers can be improved and preferably optimized.
- the jobs will typically be data processing tasks which are to be processed by the processors or processor cores of the servers.
- the servers may be identical in their processing speed/capacity, but if there are differences (for example because some servers have more processors/processor cores than others), then these differences can be taken into account when calculating the thermal load. Such differences may form part of the information determined in the method of the present aspect.
- the calculation of the thermal load and/or the allocation of the job may take into account further information or further factors, such as the state of each server, any planned downtime for the server or whether certain jobs are required to be performed on certain servers.
- the amount of tasks allocated to a server may be defined in terms of the number of jobs or, more preferably, the expected processing time. In most situations, jobs arriving for processing will have different processing requirements in terms of CPU time and so it is advantageous if the allocation process is able to take account of this.
- the servers are located in a data centre which is cooled by fresh air cooling alone (no active cooling apparatus is used).
- the method of this aspect may be used to prevent the servers from entering an active/idling oscillation of the servers as they cool down or at least increase the job processing capacity of the data centre which can be reached before such oscillation commences.
- the method of this aspect may also be used in conjunction with servers which are cooled by active cooling, by reducing the amount of active cooling required by determining the job allocation between the servers to ensure that they do not overheat and/or that the amount of active cooling required is reduced (compared to other allocation methods) or preferably optimized.
- the step of choosing chooses the server with the lowest calculated thermal load. This should ensure that, at least in approximate terms, the thermal load is balanced across the servers and will therefore allocate the incoming job to the server. Thereby the likelihood of one or more of the servers having to enter an idle state to cool down or require active cooling can be reduced and preferably minimized.
- the temperature of the server which is determined is the temperature of a CPU in the server. However, other temperatures, such as the temperature of the motherboard or bios, may also be used.
- the calculation of the thermal load may be carried out by each server based on the information determined about that server and the thermal loads communicated to a central load balancing device (or to a selected one of the servers which has been selected to carry out job allocation).
- the servers may send the information to the central load balancing device (or selected server) which performs the calculations for all of the servers.
- the method may include the further step of storing the thermal load of each server.
- the information and/or the thermal loads may be updated each time that a new job arrives, or it may be periodically determined. This may be by way of "polling" the servers, or by the servers sending the information and/or thermal loads out on a regular basis.
- the method may also include the further steps of: switching a server to an idle state if the temperature of that server exceeds an upper temperature threshold; and switching a server in an idle state to an active state when the server temperature reaches a lower temperature threshold.
- Idling a server which is above an upper threshold temperature allows the server to cool down and is the usual option adopted where fresh air cooling is used. The server can then be reactivated once it reaches a lower temperature threshold and continue processing tasks. However, unless the rate of job arrival decreases, idling a server will typically lead to a continuous oscillation between the busy and idling states (and an ever-increasing queue of unprocessed jobs).
- queueLength may be the number of tasks awaiting processing by the server, which will be an effective measure where the jobs are of essentially the same or similar processing times. However, where the jobs can be of variable lengths, queueLength is preferably the scheduled or estimated processing time of all the tasks awaiting processing, thus allowing the thermal load calculation to take account of the varying length of the jobs that might be assigned to different servers.
- This particular calculation of the thermal load provides an estimation of the server temperature after the queue is cleared or, in other words, provides an estimation of the total thermal energy of the server which includes both the explicit thermal energy in terms of the temperature and the implicit thermal energy which is condensed in the queue of allocated but unprocessed jobs. Therefore, by using this calculation of the thermal load, the method can allocate the job to the server which currently has the coolest 'effective' temperature or minimum total thermal energy and avoid allocations to servers which have either a short queue but high temperature, or which are coolest, but have a lot of stored processing (and therefore stored temperature).
- the method of the present aspect may include any combination of some, all or none of the above described preferred and optional features.
- the method of the above aspect is preferably implemented by a system or a load balance device according to the second or third aspects of this invention, as described below, but need not be.
- a second aspect of the present invention provides a system for allocating jobs to a plurality of servers, wherein the jobs are tasks to be performed by the servers, the system including: a load balancer; a plurality of servers, each having a temperature sensor arranged to measure the temperature of the server; and a network connecting said servers, wherein the system is arranged to: determine information relating to the state of each of the servers, the information comprising the temperature of each server and the amount of tasks allocated to the server and still to be completed by the server; and calculate a thermal load of each server using the information, and the load balancer is arranged to: receive a job to be allocated; choose a server to allocate the job to according to the calculated thermal loads of the servers; and allocate the job to the chosen server.
- Calculating a thermal load of each server which is based on both the temperature of the server and the amount of tasks allocated to the server, allows the system to take account of not only the current temperature state of the server (which is directly measurable) but also the likely temperature state of the server when the server reaches the stage of processing the job being allocated. By comparing these thermal loads, the allocation of incoming jobs between the servers can be improved and preferably optimized.
- the jobs will typically be data processing tasks which are to be processed by the processors or processor cores of the servers.
- the servers may be identical in their processing speed/capacity, but if there are differences (for example because some servers have more processors/processor cores than others), then these differences can be taken into account when calculating the thermal load. Such differences may form part of the information determined in the system of the present aspect.
- the calculation of the thermal load and/or the allocation of the job may take into account further information or further factors, such as the state of each server, any planned downtime for the server or whether certain jobs are required to be performed on certain servers.
- the amount of tasks allocated to a server may be defined in terms of the number of jobs or, more preferably, the expected processing time. In most situations, jobs arriving for processing will have different processing requirements in terms of CPU time and so it is advantageous if the allocation process is able to take account of this.
- the load balancer chooses the server with the lowest calculated thermal load to allocate the job to. This should ensure that, at least in approximate terms, the thermal load is balanced across the servers and will therefore allocate the incoming job to the server.
- the temperature sensors preferably measure the temperature of a CPU in each server.
- thermos temperature such as the motherboard or bios temperature
- Each server may have a processor which performs the calculation of the thermal ioad based on the information determined about that server.
- the thermal loads thus calculated can be communicated to the load balancer in order for the load balancer to allocate the incoming job.
- the servers may send the information to the load balancer which has a processor which performs the calculations for all of the servers.
- the load balancer may further include a memory for storing the thermal load of each server.
- the information and/or the thermal loads may be updated each time that a new job arrives, or it may be periodically determined. This may be by way of "polling" the servers, or by the servers sending the information and/or thermal loads out on a regular basis.
- the server may be configured to switch to an idle state until the server temperature reaches a lower temperature threshold when it is configured to switch to an active state.
- This switching may be performed automatically by the server, or may be subject to central control. The switching may take account of further factors (for example, switching to an idle state may only occur between the processing of jobs so that the server never leaves a job partially complete before switching to the idle state).
- Idling a server which is above an upper threshold temperature allows the server to cool down and is the usual option adopted where fresh air cooling is used.
- the server can then be reactivated once it reaches a lower temperature threshold and continue processing tasks.
- the servers are located in a data centre which is cooled by fresh air cooling alone (no active cooling apparatus is used).
- the system of this aspect may prevent the servers from entering an active/idling oscillation of the servers as they cool down or at least increase the job processing capacity of the data centre which can be reached before such oscillation commences.
- the system of this aspect may also include active cooling apparatus which actively cools a server which has exceeded a temperature threshold.
- the amount of active cooling required can be reduced by determining the job allocation between the servers to ensure that they do not overheat and/or that the amount of active cooling required is reduced (compared to other allocation methods) or preferably optimized.
- queueLength may be the number of tasks awaiting processing by the server, which will be an effective measure where the jobs are of essentially the same or similar processing times. However, where the jobs can be of variable lengths, queueLength is preferably the scheduled or estimated processing time of all the tasks awaiting processing, thus allowing the thermal load calculation to take account of the varying length of the jobs that might be assigned to different servers.
- This particular calculation of the thermal load provides an estimation of the server temperature after the queue is cleared or, in other words, provides an estimation of the total thermal energy of the server which includes both the explicit thermal energy in terms of the temperature and the implicit thermal energy which is condensed in the queue of allocated but unprocessed jobs. Therefore, by using this calculation of the thermal load, the system can allocate the job to the server which currently has the coolest 'effective' temperature or minimum total thermal energy and avoid allocations to servers which have either a short queue but high temperature, or which are coolest, but have a lot of stored processing (and therefore stored temperature).
- the load balancer may be a separate device, or it may form part of a management computer or similar computer which is connected to the servers. Alternatively, the load balancer may be part of one of the servers which has been selected to carry out job allocation.
- the system of the present aspect may include any combination of some, all or none of the above described preferred and optional features.
- a third aspect of the present invention provides a load balancing device for allocating jobs to a plurality of servers, wherein the jobs are tasks to be performed by the servers, the load balancing device having a processor and being arranged to: receive a job to be allocated to a server, and receive information from each of the servers, the information comprising the temperature of the server, and the amount of tasks allocated to the server and still to be completed by the server, and wherein the processor: calculates a thermal load of each server using the information from the servers; chooses a server to allocate the job to according to the calculated thermal loads of the servers; and allocates the job to said chosen server.
- the jobs will typically be data processing tasks which are to be processed by the processors or processor cores of the servers.
- the servers may be identical in their processing speed/capacity, but if there are differences (for example because some servers have more processors/processor cores than others), then these differences can be taken into account when calculating the thermal load. Such differences may form part of the information determined in the system of the present aspect.
- the calculation of the thermal load and/or the allocation of the job may take into account further information or further factors, such as the state of each server, any planned downtime for the server or whether certain jobs are required to be performed on certain servers.
- the amount of tasks allocated to a server may be defined in terms of the number of jobs or, more preferably, the expected processing time. In most situations, jobs arriving for processing will have different processing requirements in terms of CPU time and so it is advantageous if the allocation process is able to take account of this.
- the load balancing device chooses the server with the lowest calculated thermal load to allocate the job to. This should ensure that, at least in approximate terms, the thermal load is balanced across the servers and will therefore allocate the incoming job to the server. Thereby the likelihood of one or more of the servers having to enter an idle state to cool down or require active cooling can be reduced and preferably minimized.
- the temperatures of the servers are preferably the temperature of a CPU in the server. However, other temperatures, such as the motherboard or bios temperature, may also be used.
- the load balancing device may further include a memory for storing the thermal load of each server.
- the information and/or the thermal loads may be updated each time that a new job arrives, or it may be periodically determined. This may be by way of "polling" the servers, or by the servers sending the information out on a regular basis.
- the servers are located in a data centre which is cooled by fresh air cooling alone (no active cooling apparatus is used).
- the load balancing device of this aspect may prevent the servers from entering an active/idling oscillation of the servers as they cool down or at least increase the job processing capacity of the data centre which can be reached before such oscillation commences.
- the load balancing device of this aspect may also operate in data centres which have active cooling apparatus which actively cools the servers.
- the amount of active cooling required can be reduced by determining the job allocation between the servers to ensure that they do not overheat and/or that the amount of active cooling required is reduced (compared to other allocation methods) or preferably optimized.
- queueLength may be the number of tasks awaiting processing by the server, which will be an effective measure where the jobs are of essentially the same or similar processing times. However, where the jobs can be of variable lengths, queueLength is preferably the scheduled or estimated processing time of all the tasks awaiting processing, thus allowing the thermal load calculation to take account of the varying length of the jobs that might be assigned to different servers.
- This particular calculation of the thermal load provides an estimation of the server temperature after the queue is cleared or, in other words, provides an estimation of the total thermal energy of the server which includes both the explicit thermal energy in terms of the temperature and the implicit thermal energy which is condensed in the queue of allocated but unprocessed jobs.
- the system can allocate the job to the server which currently has the coolest 'effective' temperature or minimum total thermal energy and avoid allocations to servers which have either a short queue but high temperature, or which are coolest, but have a lot of stored processing (and therefore stored temperature).
- the load balancing device may be a separate device, or it may form part of a management computer or similar computer which is connected to the servers. Alternatively, the load balancing device may be part of one of the servers which has been selected to carry out job allocation.
- the load balance device of the present aspect may include any combination of some, all or none of the above described preferred and optional features.
- Figure 1 shows, in schematic form, a system according to an embodiment of the present invention
- Figures 2a-2c show the longest aggregated queue per cycle for simulations of four allocation algorithms at different workloads
- Figure 3 shows a performance comparison between the three best allocation algorithms from the simulations in Figure 2 tested under variable workload conditions; and Figures 4a and 4b show an alternative performance comparison between the three allocation algorithms considered in Figure 3, showing the average aggregated queue over the first two cycles for each of the algorithms at different average capacities.
- Figure 1 shows the system diagram of a typical system according to an embodiment of the present invention which is controlled by a method according to an embodiment of the present invention, such as that set out below.
- a plurality of servers 1 are connected to a network (not shown).
- a load balancer 10 is responsible for allocating arriving jobs between the servers.
- the load balancer 10 may be a selected one of said servers, or it may be a separate computer with a dedicated
- Each of the servers 1 has a processor (or multiple processors) 20 which carries out jobs assigned to the server 1 by the load balancer 10. These jobs are carried out by main process 12 and, where multiple jobs are assigned to the server, subsequent jobs are stored in a queue 14.
- the processor runs a daemon 1 , which continuously receives an input from a temperature sensor 13 which reads the temperature from the the server. Ideally the temperature sensor 13 will measure the temperature of the least temperature tolerant and/or most temperature impacted electronic component (this will be the CPU temperature in most situations, but more heat-tolerant CPUs are being developed and so it may be the motherboard or bios).
- the daemon 1 also communicates with the main process 12 (via interprocess
- the daemon 1 1 calculates its load value X and sends it to the central load balancer 10, either via regular push updates or on-demand pull requests from the balancer 10. This information is sent by a protocol such as SNMP.
- the daemon 1 1 may provide the raw information (temperature, queue length and state) to the central load balancer 10 by a similar mechanism.
- the load balancer stores the load values of each server 1 in a table in its memory.
- the load balancer 10 may use the aggregated information from each server to compute individual load values X either when the data is received (in which case these values are stored in a table in the balancer's memory) or may compute the individual load values X of each server in real-time.
- the load balancer 10 consults the information held and sends the job to the server 1 with the smallest X.
- Servers have a maximum safe operational temperature (critical threshold) [3] [10]. When a server reaches/exceeds it, the server immediately enters idling mode until it has cooled down to a lower temperature (resumption threshold)
- Allocation is immediate and final: upon arrival, a job is directly sent to a server and will remain in the corresponding queue until processed (i.e. no central queue or transfer between servers)
- the determination of the load value in methods according to an embodiment of the invention includes the two variables, queue length (which may be defined in terms of the number of jobs or, more preferably, the expected processing time of the allocated jobs) and
- the load value of each server is calculated as:
- Figures 2a-2c show a performance comparison between the above four allocation algorithms for increasing constant workload.
- the bars show the longest aggregated queue per cycle which, in these tests was a 1 day period.
- Figure 2a when the simulated data-centre operates at 28% capacity, all four strategies are roughly equivalent.
- Figure 2b At 50% (Figure 2b), the "Coolest” strategy is breaking down, as evidenced by substantially longer aggregated queue (jobs waiting to be processed). It should be noted however that for this workload, the situation is almost stabilizing (i.e. the length of the queue only increases marginally in the last few days of the numerical experiment).
- Figure 3 shows a performance comparison between the three best allocation algorithms from the previous simulations ("Idlest”, “Round Robin” and “Smart”) tested under variable workload conditions (sine-wave function, varying between 50% and 150% of the average demand).
- Figure 3 plots the longest aggregated queue in the 7 th cycle (i.e. on day 7 of the simulation) for each algorithm at a range of average workloads.
- Figures 4a and 4b show an alternative performance comparison between the three allocation algorithms considered in Figure 3 under the same variable workload conditions (sine-wave function, amplitude equal to average demand).
- Figure 4a shows the average aggregated queue over the first two cycles for each of the algorithms at an average 52% capacity and Figure 4b shows the same results at 55% capacity.
- simulations relate to specific conditions, in particular that they simulate the case of a data-centre relying exclusively on fresh-air cooling and in which "forced" idling time is used to dissipate heat.
- Equation [1 ] provides an estimation of the processor temperature after the queue is cleared or, in other words, it expresses the total thermal energy of the processor system which includes both the explicit thermal energy in terms of temperature and the implicit thermal energy which is condensed in the queue. Therefore, by using the load value calculated according to equation [1], the load balancer allocates the job to the server with the coolest 'effective' temperature or minimum total thermal energy.
- the present invention is not limited to calculations of load values according to equation [1]. Alternative ways of calculating a load value which exhibit similar properties are also usable in embodiments of the present invention.
- computer system includes the hardware, software and data storage devices for embodying a system or carrying out a method according to the above described
- a computer system may comprise a central processing unit (CPU), input means, output means and data storage.
- the computer system has a monitor to provide a visual output display (for example in the design of the business process).
- the data storage may comprise RAM, disk drives or other computer readable media.
- the computer system may include a plurality of computing devices connected by a network and able to communicate with each other over that network.
- the methods of the above embodiments may be provided as computer programs or as computer program products or computer readable media carrying a computer program which is arranged, when run on a computer, to perform the method(s) described above.
- computer readable media includes, without limitation, any non-transitory medium or media which can be read and accessed directly by a computer or computer system.
- the media can include, but are not limited to, magnetic storage media such as floppy discs, hard disc storage media and magnetic tape; optical storage media such as optical discs or CD- ROMs; electrical storage media such as memory, including RAM, ROM and flash memory; and hybrids and combinations of the above such as magnetic/optical storage media.
Abstract
Description
Claims
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
PCT/GB2015/052292 WO2017025696A1 (en) | 2015-08-07 | 2015-08-07 | Methods and systems for workload distribution |
Publications (1)
Publication Number | Publication Date |
---|---|
EP3332303A1 true EP3332303A1 (en) | 2018-06-13 |
Family
ID=53879721
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP15751063.7A Withdrawn EP3332303A1 (en) | 2015-08-07 | 2015-08-07 | Methods and systems for workload distribution |
Country Status (2)
Country | Link |
---|---|
EP (1) | EP3332303A1 (en) |
WO (1) | WO2017025696A1 (en) |
Families Citing this family (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11042406B2 (en) * | 2018-06-05 | 2021-06-22 | Intel Corporation | Technologies for providing predictive thermal management |
CN111174375B (en) * | 2019-12-11 | 2021-02-02 | 西安交通大学 | Data center energy consumption minimization-oriented job scheduling and machine room air conditioner regulation and control method |
CN111752710B (en) * | 2020-06-23 | 2023-01-31 | 中国电力科学研究院有限公司 | Data center PUE dynamic optimization method, system and equipment and readable storage medium |
CN113342665B (en) * | 2021-06-17 | 2023-10-20 | 北京百度网讯科技有限公司 | Task allocation method and device, electronic equipment and computer readable medium |
CN114265695A (en) * | 2021-12-26 | 2022-04-01 | 特斯联科技集团有限公司 | Energy control device and system based on decision technology |
CN116600553B (en) * | 2023-07-18 | 2023-09-19 | 科瑞特空调集团有限公司 | Dynamic cooling control method and system for indoor server |
Family Cites Families (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6363490B1 (en) * | 1999-03-30 | 2002-03-26 | Intel Corporation | Method and apparatus for monitoring the temperature of a processor |
US8260928B2 (en) * | 2008-05-05 | 2012-09-04 | Siemens Industry, Inc. | Methods to optimally allocating the computer server load based on the suitability of environmental conditions |
-
2015
- 2015-08-07 EP EP15751063.7A patent/EP3332303A1/en not_active Withdrawn
- 2015-08-07 WO PCT/GB2015/052292 patent/WO2017025696A1/en unknown
Non-Patent Citations (1)
Title |
---|
JILONG KUANG ET AL: "Predictive Model-Based Thermal Management for Network Applications", ARCHITECTURES FOR NETWORKING AND COMMUNICATIONS SYSTEMS (ANCS), 2011 SEVENTH ACM/IEEE SYMPOSIUM ON, IEEE, 3 October 2011 (2011-10-03), pages 57 - 68, XP032068403, ISBN: 978-1-4577-1454-2, DOI: 10.1109/ANCS.2011.16 * |
Also Published As
Publication number | Publication date |
---|---|
WO2017025696A1 (en) | 2017-02-16 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US9930109B2 (en) | Methods and systems for workload distribution | |
WO2017025696A1 (en) | Methods and systems for workload distribution | |
EP3183629B1 (en) | Methods and apparatus to estimate power performance of a job that runs on multiple nodes of a distributed computer system | |
Ilager et al. | ETAS: Energy and thermal‐aware dynamic virtual machine consolidation in cloud data center with proactive hotspot mitigation | |
US9568975B2 (en) | Power balancing to increase workload density and improve energy efficiency | |
US9880887B2 (en) | Method, computer program and device for allocating computer resources of a cluster for executing a task submitted to said cluster | |
US7127625B2 (en) | Application management based on power consumption | |
EP2277092B1 (en) | Arrangement for managing data center operations to increase cooling efficiency | |
US20180107503A1 (en) | Computer procurement predicting device, computer procurement predicting method, and recording medium | |
US10305974B2 (en) | Ranking system | |
Dong et al. | Energy-aware scheduling schemes for cloud data centers on google trace data | |
Liu et al. | Task and server assignment for reduction of energy consumption in datacenters | |
CN112533436A (en) | Electronics rack of data center and method for determining optimized pump speed of liquid pump | |
Parolini et al. | Model predictive control of data centers in the smart grid scenario | |
Alhammadi et al. | Multi-objective algorithms for virtual machine selection and placement in cloud data center | |
US11744040B2 (en) | Optimal control logic in liquid cooling solution for heterogeneous computing | |
CN113498300A (en) | Electronic rack of data center and method for determining operating parameters thereof | |
CN107729141B (en) | Service distribution method, device and server | |
Addya et al. | A hybrid queuing model for virtual machine placement in cloud data center | |
WO2022171262A1 (en) | Scheduling tasks for execution by a computer based on a reinforcement learning model | |
Vincent et al. | Using platform level telemetry to reduce power consumption in a datacenter | |
Faraci et al. | An analytical model for electricity-price-aware resource allocation in virtualized data centers | |
US10127087B2 (en) | Capacity based distribution of processing jobs to computing components | |
US11307627B2 (en) | Systems and methods for reducing stranded power capacity | |
KR101718206B1 (en) | Method of dynamic spectrum allocation for load balancing |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
STAA | Information on the status of an ep patent application or granted ep patent |
Free format text: STATUS: THE INTERNATIONAL PUBLICATION HAS BEEN MADE |
|
PUAI | Public reference made under article 153(3) epc to a published international application that has entered the european phase |
Free format text: ORIGINAL CODE: 0009012 |
|
STAA | Information on the status of an ep patent application or granted ep patent |
Free format text: STATUS: REQUEST FOR EXAMINATION WAS MADE |
|
17P | Request for examination filed |
Effective date: 20180301 |
|
AK | Designated contracting states |
Kind code of ref document: A1 Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR |
|
AX | Request for extension of the european patent |
Extension state: BA ME |
|
DAV | Request for validation of the european patent (deleted) | ||
DAX | Request for extension of the european patent (deleted) | ||
STAA | Information on the status of an ep patent application or granted ep patent |
Free format text: STATUS: EXAMINATION IS IN PROGRESS |
|
17Q | First examination report despatched |
Effective date: 20190117 |
|
RAP1 | Party data changed (applicant data changed or rights of an application transferred) |
Owner name: BRITISH TELECOMMUNICATIONS PUBLIC LIMITED COMPANY Owner name: KHALIFA UNIVERSITY OF SCIENCE AND TECHNOLOGY Owner name: EMIRATES TELECOMMUNICATIONS CORPORATION |
|
STAA | Information on the status of an ep patent application or granted ep patent |
Free format text: STATUS: THE APPLICATION HAS BEEN WITHDRAWN |
|
18W | Application withdrawn |
Effective date: 20200203 |