CN111447113B - System monitoring method and device - Google Patents
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- CN111447113B CN111447113B CN202010219283.4A CN202010219283A CN111447113B CN 111447113 B CN111447113 B CN 111447113B CN 202010219283 A CN202010219283 A CN 202010219283A CN 111447113 B CN111447113 B CN 111447113B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L43/0876—Network utilisation, e.g. volume of load or congestion level
- H04L43/0888—Throughput
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
Abstract
The invention discloses a system monitoring method and device, and relates to the technical field of computers. One embodiment of the method comprises: subscribing real-time data of a decision factor of a real-time acquisition system through an event; calculating a throughput strategy according to the real-time data of the decision factor, the current upper limit value of the decision factor and the peak value parameter of the system; and feeding back the throughput strategy to the system so that the system adjusts according to the throughput strategy. The implementation mode can solve the technical problem of lack of comprehensive dynamic regulation and control management on the system.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a system monitoring method and device.
Background
Assume that service A calls service B, service B calls service C, which in turn calls other services. If the call response time of a service on the link is too long or unavailable, the call to the service a will occupy more and more system resources, causing a system crash. In order to prevent the system from running, the currently used methods mainly include:
1) flow control: according to the indexes of flow, the number of concurrent threads, response time and the like, the randomly arrived flow is adjusted to be in a proper shape, and the application is prevented from being washed down by an instantaneous flow peak, so that the high availability of the application is ensured, and the effects of peak clipping and valley filling are achieved.
2) Fusing degradation: due to the complexity of the call relations, if a resource in the call chain is unstable, the request is accumulated. The fusing downgrade limits the calling of a certain resource when the resource in a calling link is in an unstable state (for example, calling is overtime or abnormal proportion is increased), so that the request is failed quickly, and cascade errors caused by other resources are avoided.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
the existing flow control mainly controls a threshold value of a request inlet according to a set strategy (non-functional test result or experience), and cannot dynamically and adaptively adjust the conditions of a system and a rear-end system; the existing fusing mechanism mainly performs threshold control aiming at the calling of a back-end remote calling service according to a set strategy (non-functional test result or experience), and lacks of performing dynamic adaptation on the self condition of a system.
Therefore, the conventional flow control and fuse-tripping mechanism only controls the respective management ranges according to a predetermined strategy (non-functional test result or experience), and lacks comprehensive dynamic regulation and control management on the system itself.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for monitoring a system, so as to solve the technical problem of lack of comprehensive dynamic regulation and control management on the system itself.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a system monitoring method including:
subscribing real-time data of a decision factor of a real-time acquisition system through an event;
calculating a throughput strategy according to the real-time data of the decision factor, the current upper limit value of the decision factor and the peak value parameter of the system;
and feeding back the throughput strategy to the system so that the system adjusts according to the throughput strategy.
Optionally, the peak parameter comprises: processing the upper limit and the lower limit of the number of the access requests per second, and processing the upper limit and the lower limit of the number of the outbound requests per second;
the throughput strategy comprises: the number of access requests processed per second and the number of outbound requests processed per second.
Optionally, calculating a throughput policy according to the real-time data of the decision factor, the current upper limit value of the decision factor, and the peak parameter of the system, includes:
calculating a throughput strategy calculation decision coefficient according to the real-time data of the decision factor and the current upper limit value of the decision factor and based on a weighting algorithm;
judging whether the system is normal or not based on the decision coefficient;
if so, taking the product of the upper limit of the access request processing number per second and the decision coefficient as the access request processing number per second in the throughput strategy, and taking the product of the upper limit of the outbound request processing number per second and the decision coefficient as the outbound request processing number per second in the throughput strategy;
if not, the lower limit of the access request processing number per second is used as the access request processing number per second in the throughput strategy, and the lower limit of the outbound request processing number per second is used as the outbound request processing number per second in the throughput strategy.
Optionally, calculating a throughput policy calculation decision coefficient according to the real-time data of the decision factor and the current upper limit value of the decision factor and based on a weighting algorithm, includes:
the decision coefficient P is calculated using the following formula:
wherein Ei is real-time data of the decision factor i, Eimax is the current upper limit value of the decision factor i, and Wi is the weight of the decision factor i.
Optionally, after subscribing to the real-time data of the decision factor of the real-time acquisition system through the event, the method further includes:
and if the real-time data of the decision factor is larger than the current upper limit value of the decision factor, resetting the current upper limit value of the decision factor as the real-time data of the decision factor.
Optionally, the determining whether the coefficient is normal based on the decision coefficient includes: and judging whether the decision coefficient is less than 1.
Optionally, the decision factor includes a number of transactions processed per second, a query rate per second, an average response time, a failure proportion, and a system load.
In addition, according to another aspect of the embodiments of the present invention, there is provided a system monitoring apparatus including:
the subscription module is used for subscribing the real-time data of the decision factor of the real-time acquisition system through an event;
the calculation module is used for calculating a throughput strategy according to the real-time data of the decision factor, the current upper limit value of the decision factor and the peak value parameter of the system;
and the feedback module is used for feeding the throughput strategy back to the system so as to enable the system to adjust according to the throughput strategy.
Optionally, the peak parameter comprises: processing the upper limit and the lower limit of the number of the access requests per second, and processing the upper limit and the lower limit of the number of the outbound requests per second;
the throughput strategy comprises: the number of access requests processed per second and the number of outbound requests processed per second.
Optionally, the computing module is further configured to:
calculating a throughput strategy calculation decision coefficient according to the real-time data of the decision factor and the current upper limit value of the decision factor and based on a weighting algorithm;
judging whether the system is normal or not based on the decision coefficient;
if so, taking the product of the upper limit of the access request processing number per second and the decision coefficient as the access request processing number per second in the throughput strategy, and taking the product of the upper limit of the outbound request processing number per second and the decision coefficient as the outbound request processing number per second in the throughput strategy;
if not, the lower limit of the access request processing number per second is used as the access request processing number per second in the throughput strategy, and the lower limit of the outbound request processing number per second is used as the outbound request processing number per second in the throughput strategy.
Optionally, the computing module is further configured to:
the decision coefficient P is calculated using the following formula:
wherein Ei is real-time data of the decision factor i, Eimax is the current upper limit value of the decision factor i, and Wi is the weight of the decision factor i.
Optionally, the computing module is further configured to:
after subscribing the real-time data of the decision factor of the real-time acquisition system through an event, if the real-time data of the decision factor is larger than the current upper limit value of the decision factor, resetting the current upper limit value of the decision factor to the real-time data of the decision factor.
Optionally, the computing module is further configured to: and judging whether the decision coefficient is less than 1.
Optionally, the decision factor includes a number of transactions processed per second, a query rate per second, an average response time, a failure proportion, and a system load.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including:
one or more processors;
a storage device for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any of the embodiments described above.
According to another aspect of the embodiments of the present invention, there is also provided a computer readable medium, on which a computer program is stored, which when executed by a processor implements the method of any of the above embodiments.
One embodiment of the above invention has the following advantages or benefits: because the throughput strategy is calculated according to the real-time data of the decision factor, the current upper limit value of the decision factor and the peak value parameter of the system, and the throughput strategy is fed back to the system, the system is adjusted according to the throughput strategy, and the technical problem that comprehensive dynamic regulation and control management of the system is lacked in the prior art is solved. The embodiment of the invention dynamically acquires the current operating condition of the system and calculates the throughput strategy, so that the system dynamically adjusts the flow control and fuses the degradation strategy. The embodiment of the invention does not depend on a light-weight embedded mechanism of a specific application framework, and improves the maximum throughput of the system under the condition of hardly increasing the processing delay.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of a main flow of a system monitoring method according to an embodiment of the invention;
FIG. 2 is a schematic view of a main flow of a system monitoring method according to a referential embodiment of the present invention;
FIG. 3 is a schematic diagram of the major modules of a system monitoring apparatus according to an embodiment of the present invention;
FIG. 4 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 5 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram of a main flow of a system monitoring method according to an embodiment of the present invention. As an embodiment of the present invention, as shown in fig. 1, the system monitoring method may include:
step 101, subscribing to real-time data of a decision factor of a real-time acquisition system through an event.
The embodiment of the invention acquires real-time data corresponding to each decision factor of the system in a quasi-real-time manner through event subscription. The system is a monitored system, and real-time data of decision factors of the system can be acquired in a quasi-real-time manner through an event subscription mode.
Optionally, the decision factor may include a transaction number per second (TPS), a query rate per second (QPS), an average Response Time (RT), a failure rate, a system load, and the like. The decision factors influence the throughput of the system, so that the real-time data of the decision factors are collected in a quasi-real-time manner in an event subscription manner, and the flow control and fusing degradation strategies are adjusted in time.
And 102, calculating a throughput strategy according to the real-time data of the decision factor, the current upper limit value of the decision factor and the peak value parameter of the system.
Since the real-time data of each decision factor is collected in quasi-real time in step 101, the throughput strategies for flow control and fusing degradation can be calculated based on the collected real-time data, the current upper limit value of each decision factor and the peak parameter of the system, which is helpful for adjusting the flow control and fusing degradation strategies in time.
Optionally, the peak parameter comprises: the upper and lower limits of the number of access requests processed per second and the upper and lower limits of the number of outbound requests processed per second. Optionally, the peak parameter of the system is determined according to a system peak parameter provided by a server manufacturer.
For example, the peak parameters may be as shown in the following table:
optionally, step 102 may comprise: calculating a throughput strategy calculation decision coefficient according to the real-time data of the decision factor and the current upper limit value of the decision factor and based on a weighting algorithm; judging whether the system is normal or not based on the decision coefficient; if so, taking the product of the upper limit of the access request processing number per second and the decision coefficient as the access request processing number per second in the throughput strategy, and taking the product of the upper limit of the outbound request processing number per second and the decision coefficient as the outbound request processing number per second in the throughput strategy; if not, the lower limit of the access request processing number per second is used as the access request processing number per second in the throughput strategy, and the lower limit of the outbound request processing number per second is used as the outbound request processing number per second in the throughput strategy.
The upper limit value of each decision factor may be preconfigured, and may be maintained unchanged or changed in real time, which is not limited in the embodiment of the present invention. The weighting policy of each decision factor also needs to be configured in advance, and the weighting policy of each decision factor may be maintained unchanged or may be changed in real time, which is not limited in the embodiment of the present invention.
For example, the upper limit value and weighting strategy of each decision factor can be shown in the following table:
optionally, the throughput policy includes: the number of access requests processed per second and the number of outbound requests processed per second are beneficial to the system to timely adjust the flow control and fuse the degradation strategy based on the throughput strategy.
Optionally, the determining whether the coefficient is normal based on the decision coefficient includes: and judging whether the decision coefficient is less than 1. If the decision factor is less than 1, the system is normal; otherwise, the system is abnormal.
Optionally, calculating a throughput policy calculation decision coefficient according to the real-time data of the decision factor and the current upper limit value of the decision factor and based on a weighting algorithm, includes:
the decision coefficient P is calculated using the following formula:
wherein Ei is real-time data of the decision factor i, Eimax is the current upper limit value of the decision factor i, and Wi is the weight of the decision factor i.
If P > is 1, representing a system anomaly, then:
H’＝Hmin
K’＝Kmin
if P <1, representing that the system is normal:
H’＝Hmax*P
K’＝Kmax*P
hmin is the lower limit of the number of access requests processed per second of a preconfigured system, Kmin is the lower limit of the number of outbound requests processed per second of the preconfigured system, Hmax is the upper limit of the number of access requests processed per second of the preconfigured system, Kmax is the upper limit of the number of outbound requests processed per second of the preconfigured system, H 'is the number of access requests processed per second in a calculated throughput policy, and K' is the number of outbound requests processed per second in the calculated throughput policy.
Optionally, after subscribing to the real-time data of the decision factor of the real-time acquisition system through the event, the method further includes: and if the real-time data of the decision factor is larger than the current upper limit value of the decision factor, resetting the current upper limit value of the decision factor as the real-time data of the decision factor. In order to improve the accuracy of throughput adjustment, after the real-time data of the decision factor of the system is collected, the sizes of the real-time data of the decision factor and the current upper limit value can be further judged, and the current upper limit value of the decision factor is reset according to the judgment result.
As follows:
Eimax’＝max(Ei,Eimax)
wherein Ei is real-time data of the decision factor i, Eimax is the current upper limit value of the decision factor i, and Eimax' is the current upper limit value after the decision factor i is reset.
And 103, feeding back the throughput strategy to the system so that the system adjusts according to the throughput strategy.
And after the throughput strategy is calculated, the throughput strategy is fed back to the monitored system, so that the system can adjust the flow control and fuse the degradation strategy in time according to the throughput strategy. Therefore, the embodiment of the invention can adjust the flow control and fusing degradation strategies in time according to the throughput strategy on the premise of not adjusting or fine-tuning the existing flow control and fusing degradation function codes.
According to the various embodiments, it can be seen that the embodiment of the present invention calculates the throughput policy according to the real-time data of the decision factor, the current upper limit value of the decision factor, and the peak parameter of the system, and feeds the throughput policy back to the system, so that the system performs an adjustment according to the throughput policy. The embodiment of the invention dynamically acquires the current operating condition of the system and calculates the throughput strategy, so that the system dynamically adjusts the flow control and fuses the degradation strategy. The embodiment of the invention does not depend on a light-weight embedded mechanism of a specific application framework, and improves the maximum throughput of the system under the condition of hardly increasing the processing delay.
Fig. 2 is a schematic diagram of a main flow of a system monitoring method according to a referential embodiment of the present invention. As still another embodiment of the present invention, as shown in fig. 2, the system monitoring method may include:
step 201, subscribing to real-time data of a decision factor of a real-time acquisition system through an event.
The embodiment of the invention acquires real-time data corresponding to each decision factor of the system in a quasi-real-time manner through event subscription. Optionally, the decision factor may include a transaction number per second (TPS), a query rate per second (QPS), an average Response Time (RT), a failure rate, a system load, and the like. The decision factors influence the throughput of the system, so that the real-time data of the decision factors are collected in a quasi-real-time manner in an event subscription manner, and the flow control and fusing degradation strategies are adjusted in time.
Step 202, judging whether the real-time data of the decision factor is larger than the current upper limit value of the decision factor; if yes, go to step 203; if not, go to step 204.
Step 203, resetting the current upper limit value of the decision factor to the real-time data of the decision factor.
And if the real-time data of the decision factor is larger than the current upper limit value of the decision factor, resetting the current upper limit value of the decision factor as the real-time data of the decision factor. In order to improve the accuracy of throughput adjustment, after the real-time data of the decision factor of the system is collected, the sizes of the real-time data of the decision factor and the current upper limit value can be further judged, and the current upper limit value of the decision factor is reset according to the judgment result.
As follows:
Eimax’＝max(Ei,Eimax)
wherein Ei is real-time data of the decision factor i, Eimax is the current upper limit value of the decision factor i, and Eimax' is the current upper limit value after the decision factor i is reset.
In the initialization stage, the upper limit values of the decision factors may be pre-configured, and then the upper limit values of the decision factors may be changed according to the comparison result in step 202, which is helpful for timely adjusting the flow control and fusing the degradation policy.
And 204, calculating a throughput strategy calculation decision coefficient according to the real-time data of the decision factor and the current upper limit value of the decision factor and based on a weighting algorithm.
The decision coefficient P may be calculated using the following formula:
wherein Ei is real-time data of the decision factor i, Eimax is the current upper limit value of the decision factor i, and Wi is the weight of the decision factor i.
The weighting strategies of the decision factors need to be configured in advance, and the weighting strategies of the decision factors can be kept unchanged.
Step 205, judging whether the decision coefficient is less than 1; if yes, go to step 206; if not, go to step 207.
And step 206, taking the product of the upper limit of the access request processing number per second and the decision coefficient as the access request processing number per second in the throughput strategy, and taking the product of the upper limit of the outbound request processing number per second and the decision coefficient as the outbound request processing number per second in the throughput strategy.
If P <1, representing that the system is normal:
H’＝Hmax*P
K’＝Kmax*P
hmax is the upper limit of the number of access requests processed per second of the preconfigured system, Kmax is the upper limit of the number of outbound requests processed per second of the preconfigured system, H 'is the number of access requests processed per second in the calculated throughput policy, and K' is the number of outbound requests processed per second in the calculated throughput policy.
It should be noted that the upper limit of the number of access requests processed per second and the upper limit of the number of outbound requests processed per second of the system are both determined according to the system peak parameters provided by the server manufacturer.
Step 207, the lower limit of the number of access requests processed per second is taken as the number of access requests processed per second in the throughput policy, and the lower limit of the number of outbound requests processed per second is taken as the number of outbound requests processed per second in the throughput policy.
If P > is 1, representing a system anomaly, then:
H’＝Hmin
K＝Kmin
hmin is the lower limit of the number of access requests processed per second of the preconfigured system, Kmin is the lower limit of the number of outbound requests processed per second of the preconfigured system, H 'is the number of access requests processed per second in the calculated throughput policy, and K' is the number of outbound requests processed per second in the calculated throughput policy.
It should be noted that the lower limit of the number of access requests processed per second and the lower limit of the number of outbound requests processed per second of the system are both determined according to the system peak parameters provided by the server manufacturer.
And step 208, feeding back the throughput strategy to the system so that the system adjusts according to the throughput strategy.
And after the throughput strategy is calculated, the throughput strategy is fed back to the monitored system, so that the system can adjust the flow control and fuse the degradation strategy in time according to the throughput strategy.
Therefore, the embodiment of the invention can adjust the flow control and fusing degradation strategies in time according to the throughput strategy on the premise of not adjusting or fine-tuning the existing flow control and fusing degradation function codes.
According to the various embodiments, it can be seen that the embodiment of the present invention calculates the throughput policy according to the real-time data of the decision factor, the current upper limit value of the decision factor, and the peak parameter of the system, and feeds the throughput policy back to the system, so that the system performs an adjustment according to the throughput policy. The embodiment of the invention dynamically acquires the current operating condition of the system and calculates the throughput strategy, so that the system dynamically adjusts the flow control and fuses the degradation strategy. The embodiment of the invention does not depend on a light-weight embedded mechanism of a specific application framework, and improves the maximum throughput of the system under the condition of hardly increasing the processing delay.
Fig. 3 is a schematic diagram of main modules of a system monitoring apparatus according to an embodiment of the present invention, and as shown in fig. 3, the system monitoring apparatus 300 includes a subscription module 301, a calculation module 302, and a feedback module 303; the subscription module 301 is configured to subscribe to real-time data of a decision factor of the real-time acquisition system through an event; the calculating module 302 is configured to calculate a throughput policy according to the real-time data of the decision factor, the current upper limit value of the decision factor, and the peak parameter of the system; the feedback module 303 is configured to feed back the throughput policy to the system, so that the system adjusts according to the throughput policy.
Optionally, the peak parameter comprises: processing the upper limit and the lower limit of the number of the access requests per second, and processing the upper limit and the lower limit of the number of the outbound requests per second;
the throughput strategy comprises: the number of access requests processed per second and the number of outbound requests processed per second.
Optionally, the calculation module 302 is further configured to:
calculating a throughput strategy calculation decision coefficient according to the real-time data of the decision factor and the current upper limit value of the decision factor and based on a weighting algorithm;
judging whether the system is normal or not based on the decision coefficient;
if so, taking the product of the upper limit of the access request processing number per second and the decision coefficient as the access request processing number per second in the throughput strategy, and taking the product of the upper limit of the outbound request processing number per second and the decision coefficient as the outbound request processing number per second in the throughput strategy;
if not, the lower limit of the access request processing number per second is used as the access request processing number per second in the throughput strategy, and the lower limit of the outbound request processing number per second is used as the outbound request processing number per second in the throughput strategy.
Optionally, the calculation module 302 is further configured to:
the decision coefficient P is calculated using the following formula:
wherein Ei is real-time data of the decision factor i, Eimax is the current upper limit value of the decision factor i, and Wi is the weight of the decision factor i.
Optionally, the calculation module 302 is further configured to:
after subscribing the real-time data of the decision factor of the real-time acquisition system through an event, if the real-time data of the decision factor is larger than the current upper limit value of the decision factor, resetting the current upper limit value of the decision factor to the real-time data of the decision factor.
Optionally, the calculation module 302 is further configured to: and judging whether the decision coefficient is less than 1.
Optionally, the decision factor includes a number of transactions processed per second, a query rate per second, an average response time, a failure proportion, and a system load.
According to the various embodiments, it can be seen that the embodiment of the present invention calculates the throughput policy according to the real-time data of the decision factor, the current upper limit value of the decision factor, and the peak parameter of the system, and feeds the throughput policy back to the system, so that the system performs an adjustment according to the throughput policy. The embodiment of the invention dynamically acquires the current operating condition of the system and calculates the throughput strategy, so that the system dynamically adjusts the flow control and fuses the degradation strategy. The embodiment of the invention does not depend on a light-weight embedded mechanism of a specific application framework, and improves the maximum throughput of the system under the condition of hardly increasing the processing delay.
It should be noted that, in the system monitoring apparatus according to the present invention, the implementation content is already described in detail in the system monitoring method, and therefore, the repeated content is not described herein.
Fig. 4 shows an exemplary system architecture 400 to which the system monitoring method or system monitoring apparatus of an embodiment of the present invention may be applied.
As shown in fig. 4, the system architecture 400 may include terminal devices 401, 402, 403, a network 404, and a server 405. The network 404 serves as a medium for providing communication links between the terminal devices 401, 402, 403 and the server 405. Network 404 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.
A user may use terminal devices 401, 402, 403 to interact with a server 405 over a network 404 to receive or send messages or the like. The terminal devices 401, 402, 403 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 401, 402, 403 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 405 may be a server providing various services, such as a background management server (for example only) providing support for shopping websites browsed by users using the terminal devices 401, 402, 403. The background management server may analyze and otherwise process the received data such as the item information query request, and feed back a processing result (for example, target push information, item information — just an example) to the terminal device.
It should be noted that the system monitoring method provided by the embodiment of the present invention is generally executed by the server 405, and accordingly, the system monitoring apparatus is generally disposed in the server 405.
It should be understood that the number of terminal devices, networks, and servers in fig. 4 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 5, shown is a block diagram of a computer system 500 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU)501 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM503, various programs and data necessary for the operation of the system 500 are also stored. The CPU 501, ROM 502, and RAM503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output portion 507 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The driver 510 is also connected to the I/O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted into the storage section 508 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 501.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer programs according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes a subscription module, a computation module, and a feedback module, where the names of the modules do not in some cases constitute a limitation on the modules themselves.
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, implement the method of: subscribing real-time data of a decision factor of a real-time acquisition system through an event; calculating a throughput strategy according to the real-time data of the decision factor, the current upper limit value of the decision factor and the peak value parameter of the system; and feeding back the throughput strategy to the system so that the system adjusts according to the throughput strategy.
According to the technical scheme of the embodiment of the invention, the technical means that the throughput strategy is calculated according to the real-time data of the decision factor, the current upper limit value of the decision factor and the peak parameter of the system and fed back to the system so that the system is adjusted according to the throughput strategy is adopted, so that the technical problem that the comprehensive dynamic regulation and control management of the system is lacked in the prior art is solved. The embodiment of the invention dynamically acquires the current operating condition of the system and calculates the throughput strategy, so that the system dynamically adjusts the flow control and fuses the degradation strategy. The embodiment of the invention does not depend on a light-weight embedded mechanism of a specific application framework, and improves the maximum throughput of the system under the condition of hardly increasing the processing delay.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for system monitoring, comprising:
subscribing real-time data of a decision factor of a real-time acquisition system through an event;
calculating a throughput strategy according to the real-time data of the decision factor, the current upper limit value of the decision factor and the peak value parameter of the system;
feeding back the throughput strategy to the system so that the system adjusts according to the throughput strategy;
wherein the peak parameter comprises: processing the upper limit and the lower limit of the number of the access requests per second, and processing the upper limit and the lower limit of the number of the outbound requests per second;
the throughput strategy comprises: processing the number of access requests per second and the number of outbound requests per second;
calculating a throughput strategy according to the real-time data of the decision factor, the current upper limit value of the decision factor and the peak parameter of the system, wherein the method comprises the following steps:
calculating a throughput strategy calculation decision coefficient according to the real-time data of the decision factor and the current upper limit value of the decision factor and based on a weighting algorithm;
judging whether the system is normal or not based on the decision coefficient;
if so, taking the product of the upper limit of the access request processing number per second and the decision coefficient as the access request processing number per second in the throughput strategy, and taking the product of the upper limit of the outbound request processing number per second and the decision coefficient as the outbound request processing number per second in the throughput strategy;
if not, taking the lower limit of the access request processing number per second as the access request processing number per second in the throughput strategy, and taking the lower limit of the outbound request processing number per second as the outbound request processing number per second in the throughput strategy;
calculating a throughput strategy calculation decision coefficient according to the real-time data of the decision factor and the current upper limit value of the decision factor and based on a weighting algorithm, wherein the method comprises the following steps:
the decision coefficient P is calculated using the following formula:
wherein Ei is real-time data of the decision factor i, Eimax is the current upper limit value of the decision factor i, and Wi is the weight of the decision factor i.
2. The method of claim 1, further comprising, after subscribing to real-time data of a decision factor of a real-time acquisition system through an event:
and if the real-time data of the decision factor is larger than the current upper limit value of the decision factor, resetting the current upper limit value of the decision factor as the real-time data of the decision factor.
3. The method of claim 1, wherein determining whether the coefficient is normal based on the decision coefficient comprises: and judging whether the decision coefficient is less than 1.
4. The method of claim 1, wherein the decision factors include a number of transactions processed per second, a query rate per second, an average response time, a failure rate, and a system load.
5. A system monitoring device, comprising:
the subscription module is used for subscribing the real-time data of the decision factor of the real-time acquisition system through an event;
the calculation module is used for calculating a throughput strategy according to the real-time data of the decision factor, the current upper limit value of the decision factor and the peak value parameter of the system;
a feedback module, configured to feed back the throughput policy to the system, so that the system adjusts according to the throughput policy;
wherein the peak parameter comprises: processing the upper limit and the lower limit of the number of the access requests per second, and processing the upper limit and the lower limit of the number of the outbound requests per second;
the throughput strategy comprises: processing the number of access requests per second and the number of outbound requests per second;
the calculation module is further to:
calculating a throughput strategy calculation decision coefficient according to the real-time data of the decision factor and the current upper limit value of the decision factor and based on a weighting algorithm;
judging whether the system is normal or not based on the decision coefficient;
if so, taking the product of the upper limit of the access request processing number per second and the decision coefficient as the access request processing number per second in the throughput strategy, and taking the product of the upper limit of the outbound request processing number per second and the decision coefficient as the outbound request processing number per second in the throughput strategy;
if not, taking the lower limit of the access request processing number per second as the access request processing number per second in the throughput strategy, and taking the lower limit of the outbound request processing number per second as the outbound request processing number per second in the throughput strategy;
the calculation module is further to:
the decision coefficient P is calculated using the following formula:
wherein Ei is real-time data of the decision factor i, Eimax is the current upper limit value of the decision factor i, and Wi is the weight of the decision factor i.
6. The apparatus of claim 5, wherein the computing module is further configured to:
after subscribing the real-time data of the decision factor of the real-time acquisition system through an event, if the real-time data of the decision factor is larger than the current upper limit value of the decision factor, resetting the current upper limit value of the decision factor to the real-time data of the decision factor.
7. The apparatus of claim 5, wherein the computing module is further configured to: and judging whether the decision coefficient is less than 1.
8. The apparatus of claim 5, wherein the decision factors comprise a number of transactions processed per second, a query rate per second, an average response time, a failure fraction, and a system load.
9. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
the one or more programs, when executed by the one or more processors, implement the method of any of claims 1-4.
10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-4.
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| CN117459418B (en) * | 2023-12-25 | 2024-03-08 | 天津神州海创科技有限公司 | Real-time data acquisition and storage method and system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101097625A (en) * | 2006-06-29 | 2008-01-02 | ***股份有限公司 | Method for enhancing bank card transaction processing system RAS performance and system |
| CN103577600A (en) * | 2013-11-18 | 2014-02-12 | 华为技术有限公司 | Method, device and system for adjusting target system performance |
| CN104363300A (en) * | 2014-11-26 | 2015-02-18 | 浙江宇视科技有限公司 | Compute task distributed dispatching device in server cluster |
| CN105591964A (en) * | 2015-10-14 | 2016-05-18 | ***股份有限公司 | Device and method for overload protection for Internet system |
| CN109167735A (en) * | 2018-11-12 | 2019-01-08 | 四川长虹电器股份有限公司 | A kind of Web firewall jamming control method based on nginx request forwarding |
Family Cites Families (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101576846A (en) * | 2008-05-09 | 2009-11-11 | 北京世纪拓远软件科技发展有限公司 | Method for testing software performance |
| CN101605144B (en) * | 2009-07-03 | 2013-04-17 | 复旦大学 | Web software system throughput optimization method |
| KR101427757B1 (en) * | 2012-12-20 | 2014-08-07 | 현대오트론 주식회사 | Tire pressure monitoring method and apparatus thereof |
| CN103902739B (en) * | 2014-04-22 | 2017-06-16 | 郑州悉知信息科技股份有限公司 | Integrated databases data classification method |
| US9755921B2 (en) * | 2015-01-01 | 2017-09-05 | Bank Of America Corporation | Electronic health and welfare monitoring of network server operations |
| CN106547478A (en) * | 2015-09-23 | 2017-03-29 | 北京奇虎科技有限公司 | The I/O throughput monitoring method of process and device |
| CN106815141A (en) * | 2015-12-02 | 2017-06-09 | 北京奇虎科技有限公司 | A kind of method for testing software and device |
| CN106095571B (en) * | 2016-06-07 | 2019-06-04 | 中国建设银行股份有限公司 | More RAC group systems, data access method and device |
| EP3327990B1 (en) * | 2016-11-28 | 2019-08-14 | Deutsche Telekom AG | Radio communication network with multi threshold based sla monitoring for radio resource management |
| CN107911375A (en) * | 2017-11-28 | 2018-04-13 | 四川长虹电器股份有限公司 | Operation system safety protecting method based on flow monitoring |
| CN108376112B (en) * | 2018-02-09 | 2021-03-26 | 腾讯科技（深圳）有限公司 | Pressure testing method, device and readable medium |
| CN108923996B (en) * | 2018-05-11 | 2021-01-05 | ***股份有限公司 | Capacity analysis method and device |
| CN108764863B (en) * | 2018-05-24 | 2021-10-29 | 腾讯科技（深圳）有限公司 | Data transfer method, device, server and storage medium |
| CN108848196B (en) * | 2018-09-25 | 2021-01-26 | 四川长虹电器股份有限公司 | General service monitoring method based on tcp connection number |
| CN110502431B (en) * | 2019-07-16 | 2023-01-17 | 创新先进技术有限公司 | System service evaluation method and device and electronic equipment |
| CN110377519B (en) * | 2019-07-22 | 2023-06-23 | 中国工商银行股份有限公司 | Performance capacity test method, device and equipment of big data system and storage medium |
-
2020
- 2020-03-25 CN CN202010219283.4A patent/CN111447113B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101097625A (en) * | 2006-06-29 | 2008-01-02 | ***股份有限公司 | Method for enhancing bank card transaction processing system RAS performance and system |
| CN103577600A (en) * | 2013-11-18 | 2014-02-12 | 华为技术有限公司 | Method, device and system for adjusting target system performance |
| CN104363300A (en) * | 2014-11-26 | 2015-02-18 | 浙江宇视科技有限公司 | Compute task distributed dispatching device in server cluster |
| CN105591964A (en) * | 2015-10-14 | 2016-05-18 | ***股份有限公司 | Device and method for overload protection for Internet system |
| CN109167735A (en) * | 2018-11-12 | 2019-01-08 | 四川长虹电器股份有限公司 | A kind of Web firewall jamming control method based on nginx request forwarding |
Non-Patent Citations (1)
| Title |
|---|
| 云计算资源容量评估与调度方法研究;高秋生等;《大众用电》;20171230;全文 * |
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