CN114896039A - High-energy-efficiency edge computing unloading decision and resource allocation method - Google Patents
High-energy-efficiency edge computing unloading decision and resource allocation method Download PDFInfo
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Abstract
The invention discloses an energy-efficient edge computing unloading decision and resource allocation method, which comprises the following steps: establishing an edge computing network; initializing unloading decisions of N wireless nodes based on a binary system, and acquiring corresponding maximum energy values and a calculation resource distribution result; according to offload decision M l Generating N candidate offload decisions; acquiring the maximum energy-efficient value and the calculation resource distribution result of each candidate unloading decision; obtaining an optimal candidate offload decision M l (j * ) (ii) a Judgment M l (j * ) Corresponding maximum effective value theta l (j * ) Whether or not it is greater than M l Corresponding maximum effective value theta l If so, adding M l (j * ) And theta l (j * ) Respectively as M l+1 And corresponding maximum energy value, and returning to execute the next stepA second iteration, otherwise, M l And taking the corresponding calculation resource distribution result as a final decision distribution result. The method ensures the inseparability of the calculation task, avoids the communication interference among the nodes, and can quickly solve the optimal decision distribution result, thereby improving the energy efficiency of the wireless node.
Description
Technical Field
The invention belongs to the technical field of edge computing, and particularly relates to an energy-efficient edge computing unloading decision and resource allocation method.
Background
With the rapid development of the internet of things, more and more real-time interactive applications are derived, such as automatic driving, intelligent warehousing and the like. However, these applications tend to have large amounts of computing while requiring low latency to meet real-time, but many wireless devices of the internet of things have limited computing resources and operating power, and thus have unacceptable delays in handling such tasks. While Mobile Edge Computing (MEC) technology allows local users to offload some or all of the computing load to the edge computing servers, thereby greatly enhancing the task processing capabilities of the wireless nodes.
Most of the edge computing network research works in the prior art adopt a partial offload mode, that is, the computing tasks of the wireless devices are considered to be freely split, and part of the tasks are offloaded to the edge server for computing, and part of the tasks are left in the local computing. However, in practical applications, many tasks are often not separable, and the task offloading communication between nodes often has a mutual interference phenomenon. In addition, because the edge servers often have a stable energy source, such as grid power. However, wireless nodes often have no reliable energy source, use more batteries with limited electric quantity, and the energy consumption problem cannot be ignored.
Disclosure of Invention
The invention aims to provide an energy-efficient edge computing unloading decision and resource allocation method, which can avoid the mutual interference of task unloading communication among nodes while ensuring the inseparability of computing tasks, and can quickly solve the optimal unloading decision and computing resource allocation result, thereby improving the energy efficiency of wireless nodes.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the invention provides an energy-efficient edge computing unloading decision and resource allocation method, which comprises the following steps:
s1, establishing an edge computing network, wherein the edge computing network comprises an edge server and N wireless nodes;
s2, initializing unloading decision M of N wireless nodes 0 ＝[m 1 ,m 2 ,…,m i ,…,m N ]And obtaining M 0 And the corresponding calculation resource allocation result, wherein m i E.g. {0,1}, if m i 1, the ith wireless node selects to completely unload the computing task to the edge server, and the wireless node is marked as a first wireless node if m i When the load is equal to 0, the ith wireless node selects complete local calculation and is marked as a second type wireless node, and the calculation resource allocation result comprises the unloading transmission power of the first type wireless node and the CPU calculation frequency of the second type wireless node;
s3 unloading decision M according to the I iteration l Generating N candidate offload decisions { M l (1),M l (2),…,M l (j),…,M l (N) }, wherein,
represents a binary summation, i.e., the decision to transform the jth wireless node during the ith iteration, where l is 0,1,2, …;
s4, acquiring respective maximum energy values of the N candidate unloading decisions and corresponding calculation resource distribution results;
s5, taking the candidate unloading decision corresponding to the maximum value in the N maximum energy values as the best candidate unloading decision M l (j * ) Wherein, in the step (A),
θ l (j) the maximum energy-efficient value of the jth candidate unloading decision in the ith iteration process is obtained;
s6, judgment M l (j * ) Corresponding maximum effective value theta l (j * ) Whether or not it is greater than M l Corresponding maximum effective value theta l If so, adding M l (j * ) AsOffload decision M for next iteration l+1 And will be theta l (j * ) Offload decision M as next iteration l+1 The corresponding maximum energy value returns to the step S3 to execute the next iteration, otherwise, M is added l And taking the corresponding calculation resource distribution result as a final decision distribution result.
Preferably, each first-class wireless node completely offloads the computing task to the edge server in a frequency division multiplexing manner, and the bandwidth is W/O, where O is the number of the first-class wireless nodes and W is the total bandwidth.
Preferably, the maximum energy-efficient value and the corresponding calculation resource allocation result are obtained as follows:
s31, initializing an energy value according to the current unloading decision or the current candidate unloading decision
S32 energy efficiency value according to t iteration
Calculating the unloading transmitting power of the first type wireless node and the CPU calculating frequency of the second type wireless node, wherein:
the unloaded transmission power of the first type wireless node is as follows:
in the formula (I), the compound is shown in the specification,
the offloaded transmit power for the o-th wireless node, W is the total bandwidth,
is the number h of first-class wireless nodes under the current unloading decision or the current candidate unloading decision o Channel gain, n, for the o-th wireless node to the edge server 0 Is a gaussian white noise power spectral density,t＝0,1,2,…；
the CPU of the second type wireless node calculates the frequency according to the following formula:
in the formula (I), the compound is shown in the specification,
calculating frequency for a CPU of a kth wireless node, wherein k is a coefficient of a calculated energy efficiency value of the wireless node, and phi is the number of CPU cycles required by the wireless node to locally calculate a bit task;
s33, presetting convergence accuracy xi, wherein the CPU calculation frequency of the first type wireless node and the unloading transmission power of the second type wireless node are both equal to 0, and judging whether the requirements are met
If so, acquiring the energy efficiency value of the next iteration
Returning to the step S32 to execute the next iteration, if not, taking the unloading emission power of the first type wireless node and the CPU calculation frequency of the second type wireless node of the iteration as the calculation resource distribution result corresponding to the unloading decision, and taking the energy efficiency value of the iteration at this time
As the maximum energy value for the corresponding offloading decision, wherein:
compared with the prior art, the invention has the following beneficial effects:
the method takes the energy efficiency of the wireless node as an optimization target, adopts a binary unloading mode of the node, namely considers that the computing task of the wireless equipment can only be completely calculated locally or completely unloaded to an edge server for computing, and utilizes the orthogonality of the FDMA communication technology to equally divide the total bandwidth of the network for task unloading, ensures the inseparability of the computing task, avoids the mutual interference phenomenon of task unloading communication among the nodes, and simultaneously optimizes the unloading decision and computing resource allocation scheme of the wireless node The offloading transmit power of the wireless node is completely offloaded to task), thereby improving the energy efficiency of the wireless node and solving a complex mixed integer programming problem.
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FIG. 1 is a flow chart of an energy efficient edge computing offload decision and resource allocation method of the present invention;
FIG. 2 is a schematic diagram of an edge computing network according to the present invention.
Description of reference numerals: 1. an edge server; 2. and a base station.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It is to be noted that, 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 application belongs. The terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
As shown in fig. 1-2, an energy-efficient method for offloading decision and resource allocation for edge computing includes the following steps:
s1, establishing an edge computing network, wherein the edge computing network comprises an edge server and N wireless nodes;
s2, initializing unloading decision M of N wireless nodes 0 ＝[m 1 ,m 2 ,…,m i ,…,m N ]And obtaining M 0 And the corresponding calculation resource allocation result, wherein m i E.g. {0,1}, if m i 1, the ith wireless node selects to completely unload the computing task to the edge server, and the wireless node is marked as a first wireless node if m i When the load is equal to 0, the ith wireless node selects complete local calculation and is marked as a second type wireless node, and the calculation resource allocation result comprises the unloading transmission power of the first type wireless node and the CPU calculation frequency of the second type wireless node;
s3 unloading decision M according to the I iteration l Generating N candidate offload decisions { M l (1),M l (2),…,M l (j),…,M l (N) }, wherein,
represents a binary summation, i.e., the decision to transform the jth wireless node during the ith iteration, where l is 0,1,2, …;
s4, acquiring respective maximum energy values of the N candidate unloading decisions and corresponding calculation resource distribution results;
s5, taking the candidate unloading decision corresponding to the maximum value in the N maximum energy values as the best candidate unloading decision M l (j * ) Wherein, in the step (A),
θ l (j) the maximum energy-efficient value of the jth candidate unloading decision in the ith iteration process is obtained;
s6, judgment M l (j * ) Corresponding maximum effective value theta l (j * ) Whether or not it is greater than M l Corresponding maximum effective value theta l If it isWill M l (j * ) Offload decision M as next iteration l+1 And will be theta l (j * ) Offload decision M as next iteration l+1 The corresponding maximum energy value returns to the step S3 to execute the next iteration, otherwise, M is added l And taking the corresponding calculation resource distribution result as a final decision distribution result.
In an embodiment, each first-class wireless node completely offloads the computing task to the edge server in a frequency division multiplexing manner, and the bandwidth is W/O, where O is the number of the first-class wireless nodes and W is the total bandwidth.
In an embodiment, the maximum energy-efficient value and the corresponding calculation resource allocation result are obtained as follows:
s31, initializing an energy value according to the current unloading decision or the current candidate unloading decision
S32 energy efficiency value according to t iteration
Calculating the unloading transmitting power of the first type wireless node and the CPU calculating frequency of the second type wireless node, wherein:
the unloaded transmission power of the first type wireless node is as follows:
in the formula (I), the compound is shown in the specification,
the offloaded transmit power for the o-th wireless node, W is the total bandwidth,
is the number h of first-class wireless nodes under the current unloading decision or the current candidate unloading decision o Channel to edge server for No. o wireless nodeGain, n 0 Is gaussian white noise power spectrum density, t is 0,1,2, …;
the CPU of the second type wireless node calculates the frequency according to the following formula:
in the formula (I), the compound is shown in the specification,
calculating frequency for a CPU of a kth wireless node, wherein k is a coefficient of a calculated energy efficiency value of the wireless node, and phi is the number of CPU cycles required by the wireless node to locally calculate a bit task;
s33, presetting convergence accuracy xi, wherein the CPU calculation frequency of the first type wireless node and the unloading transmission power of the second type wireless node are both equal to 0, and judging whether the requirements are met
If so, acquiring the energy efficiency value of the next iteration
Returning to the step S32 to execute the next iteration, if not, taking the unloading emission power of the first type wireless node and the CPU calculation frequency of the second type wireless node of the iteration as the calculation resource distribution result corresponding to the unloading decision, and taking the energy efficiency value of the iteration at this time
As the maximum energy value for the corresponding offloading decision, wherein:
specifically, as shown in fig. 2, the edge server 1 may be equipped to the base station 2, there are 5 wireless nodes with computing requirements in this embodiment, each wireless node needs to make an offloading decision, that is, select a completely offloaded computing task to the edge server or completely locally compute, and a process of selecting a completely offloaded wireless node to complete a task is mainly divided into three parts: the wireless node transmits the task, the edge server calculates the task, and the edge server returns the task result, which is a conventional operation well known to those skilled in the art.
Initializing offload decisions M for 5 wireless nodes 0 ＝[1,0,1,0,1]Wherein, the 1 st, 3 rd and 5 th wireless nodes select complete task offloading (i.e. the first type wireless nodes), then the 3 wireless nodes perform task offloading communication in a Frequency Division Multiplexing (FDMA) manner, and equally divide the total bandwidth W of the channel in the edge computing network, i.e. the 1 st, 3 th and 5 th wireless nodes which select complete task offloading all divide the offloading transmission bandwidth W/3, and the 2 nd and 4 th wireless nodes select complete local computing (i.e. the second type wireless nodes). Simultaneous determination of offload decisions M 0 The maximum energy-efficient value and the calculation resource distribution result corresponding to the maximum energy-efficient value, wherein the calculation resource distribution result comprises the CPU calculation frequency of the complete local calculation wireless node and the unloading transmission power of the complete task unloading wireless node;
according to the current unloading decision M 0 Generating 5 candidate offload decisions { M } 0 (1),M 0 (2),M 0 (3),M 0 (4),M 0 (5) And (c) the step of (c) in which,
represents a binary summation of (
Or
) I.e. to transform the decision of the jth wireless node during the 0 th iteration, e.g. M 0 (1)＝[0,0,1,0,1]。
And respectively calculating respective maximum energy-efficient values and calculation resource distribution results corresponding to the maximum energy-efficient values for the 5 candidate unloading decisions, wherein the calculation resource distribution results are that for the wireless nodes completely calculated locally, the CPU calculation frequency is calculated, and for the wireless nodes completely unloaded by the task, the unloading transmission power is calculated.
Such as with candidate offload decision M 0 (1)＝[0,0,1,0,1]For example, other candidate offload decisions M 0 (2)、M 0 (3)、M 0 (4)、M 0 (5) And offload decision M 0 Similarly, the maximum energy-efficient value and the corresponding calculation resource allocation result are determined as follows:
1) in candidate offload decision M 0 (1) Next, initialize the energy value
2) According to the current energy efficiency value
If the t iteration is performed, the energy efficiency value is
The offload transmit power of the fully tasked offload wireless node (3 rd, 5 th wireless node) is calculated using the following formula:
wherein the content of the first and second substances,
to decide M for candidate offload 0 (1) The number of wireless nodes to completely task offload is selected next.
The CPU calculation frequency of the wireless node (1 st, 2 nd, 4 th wireless node) which is completely locally calculated is calculated using the following formula:
it is readily understood that the wireless node's offloaded transmit power, which is fully locally computed, is 0 and the wireless node's CPU frequency, which is fully tasked offloaded, is 0.
3) If it is
If the convergence accuracy xi is larger than the preset convergence accuracy xi which can be determined according to actual requirements, the energy efficiency value is obtained according to the following formula
Returning to the step 2) to continue iteration:
if it is
If the current convergence precision xi is less than the preset convergence precision xi, taking the CPU calculation frequency of the current complete local calculation wireless node and the unloading transmission power of the complete task unloading wireless node as M 0 (1) Corresponding to the allocation result of computing resources, ending the iteration and taking the current effective value
As M 0 (1) Corresponding maximum effective value theta 0 (1) And (6) outputting.
Comparing the maximum energy-efficient values of all candidate unloading decisions, and selecting the candidate unloading decision corresponding to the maximum value in the maximum energy-efficient values as the best candidate unloading decision M 0 (j * ) Wherein, in the step (A),
if M is 0 (j * ) Corresponding maximum effective value theta 0 (j * ) Greater than M 0 Corresponding maximum effective value theta 0 Then M will be 0 (j * ) Assign to offload decision M 1 And returning to execute the next iteration, otherwise, outputting the current unloading decision M 0 And the corresponding calculation resource distribution result is used as the final decision distribution result.
The method takes the energy efficiency of the wireless node as an optimization target, adopts a binary unloading mode of the node, namely considers that the computing task of the wireless equipment can only be completely calculated locally or completely unloaded to an edge server for computing, and utilizes the orthogonality of the FDMA communication technology to equally divide the total bandwidth of the network for task unloading, ensures the inseparability of the computing task, avoids the mutual interference phenomenon of task unloading communication among the nodes, and simultaneously optimizes the unloading decision and computing resource allocation scheme of the wireless node The offloading transmit power of the wireless node is completely offloaded to task), thereby improving the energy efficiency of the wireless node and solving a complex mixed integer programming problem.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express the more specific and detailed embodiments described in the present application, but not be construed as limiting the claims. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (3)
1. An energy-efficient method for offloading decisions and resource allocation for edge computing, comprising: the energy-efficient edge computing offloading decision and resource allocation method comprises the following steps:
s1, establishing an edge computing network, wherein the edge computing network comprises an edge server and N wireless nodes;
s2, initializing unloading decision M of N wireless nodes 0 ＝[m 1 ,m 2 ,…,m i ,…,m N ]And obtaining M 0 And the corresponding calculation resource allocation result, wherein m i E.g. {0,1}, if m i 1, the ith wireless node selects to completely unload the computing task to the edge server, and the wireless node is marked as a first wireless node if m i When the number of the wireless nodes is equal to 0, the wireless node i selects complete local calculation, and is marked as a second type of wireless node, and the calculation resource allocation result comprises the unloading transmission power of the first type of wireless node and the CPU calculation frequency of the second type of wireless node;
s3 unloading decision M according to the I iteration l Generating N candidate offload decisions { M l (1),M l (2),…,M l (j),…,M l (N) }, wherein,
represents a binary summation, i.e., the decision to transform the jth wireless node during the ith iteration, where l is 0,1,2, …;
s4, acquiring respective maximum energy values of the N candidate unloading decisions and corresponding calculation resource distribution results;
s5, taking the candidate unloading decision corresponding to the maximum value in the N maximum energy values as the best candidate unloading decision M l (j * ) Wherein, in the step (A),
θ l (j) the maximum energy-efficient value of the jth candidate unloading decision in the ith iteration process is obtained;
s6, judgment M l (j * ) Corresponding maximum effective value theta l (j * ) Whether or not it is greater than M l Corresponding maximum effective value theta l If so, adding M l (j * ) As the next timeIterative offload decision M l+1 And will be theta l (j * ) Offload decision M as next iteration l+1 The corresponding maximum energy value returns to step S3 to execute the next iteration, otherwise, M is added l And taking the corresponding calculation resource distribution result as a final decision distribution result.
2. The energy-efficient edge computing offload decision and resource allocation method of claim 1, wherein: and each first-class wireless node completely unloads the calculation task to an edge server in a frequency division multiplexing mode, wherein the bandwidth is W/O, O is the number of the first-class wireless nodes, and W is the total bandwidth.
3. The energy-efficient method of offloading decision making and resource allocation for edge computing as recited in claim 2, further comprising: the maximum energy efficiency value and the corresponding calculation resource distribution result are obtained by the following steps:
s31, initializing an energy efficiency value according to the current unloading decision or the current candidate unloading decision
S32 energy efficiency value according to t iteration
Calculating the unloading transmitting power of the first type wireless node and the CPU calculating frequency of the second type wireless node, wherein:
the unloaded transmitting power of the first type wireless node is represented by the following formula:
in the formula (I), the compound is shown in the specification,
for the offloaded transmit power of the o-th wireless node, W is the totalThe bandwidth of the communication channel is controlled,
is the number h of first-class wireless nodes under the current unloading decision or the current candidate unloading decision o Channel gain, n, for the o-th wireless node to the edge server 0 Is gaussian white noise power spectrum density, t is 0,1,2, …;
the CPU of the second type wireless node calculates the frequency according to the following formula:
in the formula (I), the compound is shown in the specification,
calculating frequency for a CPU of a kth wireless node, wherein kappa is a calculation energy efficiency value coefficient of the wireless node, and phi is the number of CPU periods required by the wireless node for locally calculating a bit task;
s33, presetting convergence accuracy xi, wherein the CPU calculation frequency of the first type wireless node and the unloading transmission power of the second type wireless node are both equal to 0, and judging whether the requirements are met
If so, acquiring the energy efficiency value of the next iteration
Returning to the step S32 to execute the next iteration, if not, taking the unloading emission power of the first type wireless node and the CPU calculation frequency of the second type wireless node of the iteration as the calculation resource distribution result corresponding to the unloading decision, and taking the energy efficiency value of the iteration at this time
As the maximum energy value for the corresponding offloading decision, wherein:
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