CN112202688A - Data evacuation method and system suitable for cloud data center network - Google Patents
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Abstract
The present disclosure provides a data evacuation method and system suitable for a cloud data center network, including: dividing time slots according to disaster diffusion characteristics, and switching the time slots according to state changes of the data center; optimizing in two stages in each time slot, selecting a safety data center and performing bandwidth time-varying scheduling on a plurality of parallel sparse streams to obtain a data evacuation scheme; when the safety data center selects, the sum of the products of the routing hop count of all evacuation tasks and evacuation data is minimized as a target; the bandwidth time-varying scheduling of multiple parallel sparse streams aims at maximizing the sum of the bandwidth allocated to all the sparse tasks. Factors such as routing hop number, evacuation data volume and the like are considered in the selection stage of the safety data center, so that the time delay of data evacuation can be better reduced; the sign-based sparse route time-varying searching method is utilized to improve the fairness of bandwidth allocation and can better support bandwidth allocation with personalized proportion.
Description
Technical Field
The disclosure belongs to the technical field of post-disaster data evacuation of cloud data centers, and particularly relates to a data evacuation method and system suitable for a cloud data center network.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Cloud data centers, which bear mass data storage and various user services, have become important infrastructures for supporting data-intensive and computing-intensive applications. Using google corporation as an example, cloud data centers process data up to 100PB per day. Such massive data not only plays an important role in cloud service provision, but also has a great economic value. However, cloud data centers are known to be vulnerable to natural disasters or man-made attacks. According to recent statistics, U.S. companies lose up to $ 7000 million annually due to unexpected outages of cloud data centers.
Data evacuation is intended to avoid the loss of data due to a catastrophic event. In a cloud data center network, limited residual network resources should be utilized to transfer affected data from a disaster area within a certain period of time. Especially as catastrophic events propagate, more and more network nodes will fail and the available network resources for evacuation transmission will become more limited.
Therefore, there is a problem that a large number of concurrent transmissions compete for the remaining bandwidth in the evacuation, and the bandwidth allocation proportion and multi-path routing problem of different evacuation transfers should be considered. On the other hand, the catastrophic effects tend to spread over time. Under its influence, the network topology is also time-varying. In a plurality of time slots, progressive evacuation transmission is required to fully utilize the transmission capability of the network.
The inventor finds in research that existing research does not consider the selection problem of the security data center and the reasonable allocation problem of the bandwidth proportion together, and the network transmission capacity cannot be fully utilized.
Disclosure of Invention
In order to overcome the defects of the prior art, the data evacuation method applicable to the cloud data center network is provided, and a data evacuation scheme with better evacuation completion time, network utilization rate, evacuation success rate and the like is obtained.
In order to achieve the above object, one or more embodiments of the present disclosure provide the following technical solutions:
in a first aspect, a data evacuation method applicable to a cloud data center network is disclosed, which includes: constructing an evacuation transmission model, dividing time slots, controlling switching and optimizing network resource allocation in the time slots in sequence;
based on the constructed evacuation transmission model, predicting disaster-suffered time and degree of different areas of a network topology according to early warning information of a disaster, dividing the whole disaster diffusion stage into different optimized time slots, and controlling the switching of subsequent time slots in an optimization algorithm according to the state change of a data center;
optimizing in two stages in each time slot, and sequentially selecting a safety data center and performing bandwidth time-varying scheduling on a plurality of parallel sparse streams to obtain a data sparse scheme;
when the safety data center selects, the sum of the products of the routing hop count of all evacuation tasks and evacuation data is minimized as a target;
the bandwidth time-varying scheduling of multiple parallel sparse streams aims at maximizing the sum of the bandwidth allocated to all the sparse tasks.
The further technical scheme is that time slots are divided according to disaster spread characteristics, and specifically comprises the following steps:
and dividing the evacuation time slots according to the gradual diffusion characteristic of the disaster, entering the next evacuation time slot once the data center is damaged by the diffused disaster in the current time slot, and updating the state of the network resources accordingly.
According to a further technical scheme, when the security data center is selected, the method further comprises the following steps: the received evacuation data cannot exceed the available storage space of the data centers alternatives, and at least one available data center is found for each evacuation task.
According to the further technical scheme, the aim of maximizing the sum of the bandwidths allocated to all the evacuation tasks is to ensure that the total flow in each link does not exceed the maximum capacity of the link, and the bandwidth allocated to each evacuation task is not lower than the minimum bandwidth required for completing the evacuation tasks.
In a further technical scheme, an evacuation route is searched for each evacuation task and the bandwidth proportion is adjusted by adopting a mark-based evacuation route time-varying selection method, so that the reasonable utilization rate of the bandwidth is improved
Further technical solution, the evacuation route time-varying selection method based on the mark is specifically:
in each time slot, continuously searching evacuation routes for the evacuation tasks which are not marked until all the evacuation tasks obtain more than one available evacuation path or the number of search rounds exceeds a preset maximum number; if the evacuation task has been marked, a route search for the next evacuation task is carried over.
In a further technical scheme, the bandwidth time-varying scheduling of a plurality of parallel sparse streams: for each evacuation task, calculating the proportion of the evacuation data volume carried by the evacuation task to the total data volume of all the evacuation tasks, namely the data volume proportion, and adjusting the bandwidth allocated to the evacuation task according to the proportion to ensure that the proportion of the bandwidth allocated to the evacuation task to the total bandwidth allocated to all the evacuation tasks, namely the bandwidth proportion for short, is as close to the data volume proportion as possible.
In a second aspect, a data evacuation system suitable for a cloud data center network is disclosed, including:
a slot partitioning and switching module configured to: dividing time slots according to disaster diffusion characteristics, and switching the time slots according to state changes of the data center;
a two-stage optimization module configured to: optimizing in two stages in each time slot, selecting a safety data center and performing bandwidth time-varying scheduling on a plurality of parallel sparse streams to obtain a data evacuation scheme;
when the safety data center selects, the sum of the products of the routing hop count of all evacuation tasks and evacuation data is minimized as a target;
the bandwidth time-varying scheduling of multiple parallel sparse streams aims at maximizing the sum of the bandwidth allocated to all the sparse tasks.
The above one or more technical solutions have the following beneficial effects:
the technical scheme of the disclosure expands network division and switches evacuation time slots by using disaster-aware time, and can better reflect the change situation of network resources along with disaster diffusion; the two-stage optimization is carried out in each time slot, so that time-varying network resources can be more fully utilized; factors such as routing hop number, evacuation data volume and the like are considered in the selection stage of the safety data center, so that the time delay of data evacuation can be better reduced; the sign-based sparse route time-varying searching method is utilized to improve the fairness of bandwidth allocation and can better support bandwidth allocation with personalized proportion.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
FIG. 1 is a method flow diagram of an embodiment of the present disclosure;
FIG. 2 is an example of a disaster-aware evacuation transmission model in accordance with an embodiment of the present disclosure;
FIG. 3 is a flow diagram of a secure data center selection method in accordance with an exemplary embodiment of the present disclosure;
fig. 4 is a flowchart of an evacuation route search method based on labels according to an embodiment of the present disclosure.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
The implementation example of the application discloses an efficient data evacuation method suitable for a cloud data center network under a time-varying network state.
Example one
The embodiment discloses a data evacuation method suitable for a cloud data center network, aiming at maximizing the utilization of disaster evacuation capacity in the background of disaster diffusion;
dividing the whole disaster diffusion stage into different optimized time slots according to the prediction of the disaster time and degree of different areas, controlling the switching of subsequent time slots in an optimization algorithm according to the state change of a data center, updating the residual network resources in each time slot, establishing a disaster-aware evacuation transmission model, and optimizing the utilization of the evacuation capacity in the current time slot; and two-step optimization is carried out in each time slot, the two-step optimization comprises the selection of a safety data center and the bandwidth allocation with the designated proportion, the former ensures the safety of evacuation data in the next time slot by selecting a storage forwarding node, and the latter allocates the bandwidth with the designated proportion to the evacuation tasks by adopting the evacuation route time-varying search based on the marks according to the data volume carried by the evacuation tasks, so that the utilization rate of the evacuation capacity is jointly improved, and higher data evacuation efficiency can be obtained in the disaster diffusion scene.
Specifically, as shown in fig. 1, for the current status features of data evacuation, such as time-varying network resources, a large amount of concurrent transmission competing for limited residual bandwidth, etc., existing research does not consider the problem of selection of a security data center and the problem of reasonable allocation of bandwidth proportions, and cannot fully utilize the network transmission capability. The invention discloses a high-efficiency data evacuation method suitable for a cloud data center network, which mainly comprises the following steps: aiming at the characteristic that the disaster spreads along with the time, an evacuation transmission model for disaster perception is established, so that time slots are divided according to the characteristic of disaster spread, and the time slots are switched according to the state change of a data center; and performing two-stage time-varying optimization in each time slot, including the selection of the security data center and the time-varying bandwidth scheduling of a plurality of parallel sparse streams, respectively realizing the minimization of the sum of the product of the routing hop count of all the sparse tasks and the sparse data and the maximization of the sum of the bandwidth allocated to all the sparse tasks, and finally obtaining a data evacuation scheme which is better in the aspects of evacuation completion time, network utilization rate, evacuation success rate and the like.
The disaster-aware evacuation transmission model divides evacuation time slots according to the gradual diffusion characteristics of disasters, and once a data center is damaged by diffused disasters in the current time slot, the next evacuation time slot is entered, and the state of network resources is updated accordingly.
Specifically, as shown in fig. 2, an embodiment of an evacuation transmission model is constructed in consideration of the progressive diffusion characteristic of a disaster event, and the implementation description is as follows: dividing the data evacuation process into time slots t according to the characteristics of disaster diffusion1Time slot t2Time slot t3. In time slot t1From the data center ediData of medium evacuation endangered by v1And v3Representing candidate store-and-forward nodes; selecting v in consideration of the progressive diffusion characteristic of a disaster event3As a store-and-forward node; albeit byiWill be at t2Initially destroyed by disaster, but v can be assured3In time slot t2Is safe. In time slot t2In (1), select v4Instead of sdjAs store-and-forward nodes, since the latter will be in the next time slot t3Can be destroyed by disaster. In thatTime slot t3In transmitting the evacuation data to sdk. Based on the evacuation transmission model for disaster perception, an evacuation route ed is obtainedi→v3→v4→sdk. Although the evacuation route with the largest bandwidth is not selected, reasonable storage-forwarding nodes are selected as the safety data center in each time slot, the safety of the evacuation data in the transmission process can be ensured, and the evacuation capacity in each time slot is utilized to the maximum extent through bandwidth time-varying scheduling.
And selecting the safe data center, wherein the sum of the routing hop counts of all evacuation tasks and the product of the evacuation data is minimized, the received evacuation data cannot exceed the available storage space of the alternative data center, and each evacuation task needs to find at least one available alternative data center.
As shown in fig. 3, the method mainly comprises the following steps: the evacuation transmissions are ordered in descending order of volume of evacuation data, meaning that transmissions with larger volume of evacuation data will have higher priority in selecting a secure data center; placing the evacuated data in a nearest safety data center by using a greedy strategy to obtain an initial selection solution; through iterative loop, searching an optimal solution in parallel, specifically, in each iteration, calculating alternative node selection probability by combining heuristic information and pheromone, selecting a security data center by adopting a roulette algorithm, updating a network state and a solution set, evaluating the solution, updating the optimal solution, and entering the next iterative loop; and after the circulation is finished, obtaining the solution selected by the safety data center. The method ends.
In this embodiment, the secure data center is selected as the destination for finding evacuation routes for evacuation tasks.
Regarding the bandwidth time-varying scheduling of a plurality of parallel sparse streams, the total flow in each link cannot exceed the maximum capacity of the link with the aim of maximizing the sum of the bandwidths allocated to all the sparse tasks, and the bandwidth allocated to each sparse task cannot be lower than the minimum bandwidth required for completing the sparse task; in order to improve the reasonable utilization rate of the bandwidth, an evacuation route is searched for each evacuation task and the bandwidth proportion is adjusted by adopting a mark-based evacuation route time-varying selection method.
In each time slot, the time-varying selection method based on the labeled evacuation route continues to search the evacuation route for the evacuation tasks which are not labeled until all the evacuation tasks obtain more than one available evacuation path or the number of search rounds exceeds the preset maximum number; if the evacuation task has been marked, a route search for the next evacuation task is carried over.
When the bandwidth proportion is adjusted: for each evacuation task, calculating the proportion (data volume proportion for short) of the evacuation data volume carried by the evacuation task to the total data volume of all the evacuation tasks, and adjusting the bandwidth allocated to the evacuation task according to the proportion to ensure that the proportion (bandwidth proportion for short) of the total bandwidth allocated to the evacuation task is as close to the data volume proportion as possible; if some evacuation tasks cannot be allocated enough bandwidth to make the bandwidth proportion thereof close to the data volume proportion, the bandwidth is allocated to the evacuation tasks as much as possible, thereby improving the efficiency of evacuation transmission.
Specifically, as shown in fig. 4, the method mainly includes the following steps: firstly, each evacuation task is set to be in an unmarked state; then, calculating the selection probability of alternative nodes by combining heuristic information and pheromone aiming at all the unmarked evacuation tasks within the range of the appointed iteration times, and selecting the next node by adopting a roulette algorithm until the evacuation tasks are set to be marked or the next node does not exist after the evacuation tasks reach the safety data center, and ending the iteration; ensuring that at least one evacuation path is found for each evacuation task by utilizing a marked evacuation route searching method, adjusting the bandwidth proportion of the evacuation tasks according to the carried data volume, and updating the network state; evaluating the solution, updating the optimal solution, and entering the next iteration cycle; and after the circulation is finished, obtaining the solution of the bandwidth time-varying scheduling. The method ends.
Example two
The object of this embodiment is to provide a computing device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the processor implements the steps of the specific method in the first embodiment.
EXAMPLE III
An object of the present embodiment is to provide a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method as specified in the first embodiment.
Example four
The present embodiment aims at providing a data evacuation system suitable for a cloud data center network, including:
a slot partitioning and switching module configured to: dividing time slots according to disaster diffusion characteristics, and switching the time slots according to state changes of the data center;
a two-stage optimization module configured to: optimizing in two stages in each time slot, selecting a safety data center and performing bandwidth time-varying scheduling on a plurality of parallel sparse streams to obtain a data evacuation scheme;
when the safety data center selects, the sum of the products of the routing hop count of all evacuation tasks and evacuation data is minimized as a target;
the bandwidth time-varying scheduling of multiple parallel sparse streams aims at maximizing the sum of the bandwidth allocated to all the sparse tasks.
The steps involved in the apparatuses of the above second, third and fourth embodiments correspond to the first embodiment of the method, and the detailed description thereof can be found in the relevant description of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media containing one or more sets of instructions; it should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any of the methods of the present disclosure.
Those skilled in the art will appreciate that the modules or steps of the present disclosure described above can be implemented using general purpose computer means, or alternatively, they can be implemented using program code executable by computing means, whereby the modules or steps may be stored in memory means for execution by the computing means, or separately fabricated into individual integrated circuit modules, or multiple modules or steps thereof may be fabricated into a single integrated circuit module. The present disclosure is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.
Claims (10)
1. A data evacuation method suitable for a cloud data center network is characterized by comprising the following steps: constructing an evacuation transmission model, dividing time slots, controlling switching and optimizing network resource allocation in the time slots in sequence;
based on the constructed evacuation transmission model, predicting disaster-suffered time and degree of different areas of a network topology according to early warning information of a disaster, dividing the whole disaster diffusion stage into different optimized time slots, and controlling the switching of subsequent time slots in an optimization algorithm according to the state change of a data center;
optimizing in two stages in each time slot, and sequentially selecting a safety data center and performing bandwidth time-varying scheduling on a plurality of parallel sparse streams to obtain a data sparse scheme;
when the safety data center selects, the sum of the products of the routing hop count of all evacuation tasks and evacuation data is minimized as a target;
the bandwidth time-varying scheduling of multiple parallel sparse streams aims at maximizing the sum of the bandwidth allocated to all the sparse tasks.
2. The data evacuation method applicable to the cloud data center network according to claim 1, wherein the time slots are divided according to disaster spread characteristics, and specifically, the method comprises the following steps:
and dividing the evacuation time slots according to the gradual diffusion characteristic of the disaster, entering the next evacuation time slot once the data center is damaged by the diffused disaster in the current time slot, and updating the state of the network resources accordingly.
3. The data evacuation method suitable for the cloud data center network according to claim 1, wherein when the security data center is selected, the method further comprises: the received evacuation data cannot exceed the available storage space of the data centers alternatives, and at least one available data center is found for each evacuation task.
4. A data evacuation method applicable to a cloud data center network according to claim 1, wherein the goal of maximizing the sum of the bandwidth allocated to all evacuation tasks is to maximize the sum of the bandwidth allocated to all evacuation tasks, wherein the total flow in each link does not exceed the maximum capacity of the link, and wherein the bandwidth allocated to each evacuation task is not lower than the minimum bandwidth required for completing the evacuation task.
5. The data evacuation method suitable for the cloud data center network according to claim 1, wherein the label-based evacuation route time-varying selection method is adopted to find an evacuation route for each evacuation task and adjust the bandwidth ratio, so as to improve the reasonable utilization rate of the bandwidth.
6. The data evacuation method suitable for the cloud data center network according to claim 1, wherein a time-varying selection method of evacuation paths based on the markers is adopted, and specifically:
in each time slot, continuously searching evacuation routes for the evacuation tasks which are not marked until all the evacuation tasks obtain more than one available evacuation path or the number of search rounds exceeds a preset maximum number; if the evacuation task has been marked, a route search for the next evacuation task is carried over.
7. The data evacuation method applicable to the cloud data center network as claimed in claim 1, wherein the bandwidth time-varying scheduling of the plurality of parallel evacuation streams is: for each evacuation task, calculating the proportion of the evacuation data volume carried by the evacuation task to the total data volume of all the evacuation tasks, namely the data volume proportion, and adjusting the bandwidth allocated to the evacuation task according to the proportion to ensure that the proportion of the bandwidth allocated to the evacuation task to the total bandwidth allocated to all the evacuation tasks, namely the bandwidth proportion for short, is as close to the data volume proportion as possible.
8. A data evacuation system suitable for a cloud data center network is characterized by comprising:
a slot partitioning and switching module configured to: based on the constructed evacuation transmission model, predicting disaster-suffered time and degree of different areas of a network topology according to early warning information of a disaster, dividing the whole disaster diffusion stage into different optimized time slots, and controlling the switching of subsequent time slots in an optimization algorithm according to the state change of a data center;
a two-stage optimization module configured to: optimizing in two stages in each time slot, and sequentially selecting a safety data center and performing bandwidth time-varying scheduling on a plurality of parallel sparse streams to obtain a data sparse scheme;
when the safety data center selects, the sum of the products of the routing hop count of all evacuation tasks and evacuation data is minimized as a target;
the bandwidth time-varying scheduling of multiple parallel sparse streams aims at maximizing the sum of the bandwidth allocated to all the sparse tasks.
9. A computing device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the method of any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, is adapted to carry out the steps of the method according to any one of the preceding claims 1 to 7.
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