CN104331321A - Cloud computing task scheduling method based on tabu search and load balancing - Google Patents
Cloud computing task scheduling method based on tabu search and load balancing Download PDFInfo
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Abstract
The invention relates to a cloud computing task scheduling method based on tabu search and load balancing. According to the method, a task scheduling initial solution is obtained on the basis of a heuristic priority allocation strategy of the earliest finish time; then, candidate exchange task pairs are generated on the basis of using the tabu search as an instructional neighborhood search optimization strategy, the task pairs with great profit values are exchanged by adopting a greedy principle, and the task scheduling initial solution is optimized, so the completion time span of the whole task is shortened to the greatest degree. After the cloud computing task scheduling method based on tabu search and load balancing is adopted, the task scheduling efficiency is effectively improved, the load balancing of a cloud computing platform is realized, the idle time of resources is also obviously reduced, the utilization benefits of the resources are improved, and better practicability is realized.
Description
Technical field
The present invention relates to the cloud computing method for scheduling task based on tabu search and load balancing under a kind of cloud computing environment.
Background technology
Cloud computing is as the product of the new technique fusion developments such as the conventional arts such as Distributed Calculation, parallel computation, grid computing and network programming model, Distributed Storage technology, Intel Virtualization Technology, be the strategic technology of key and means that lead information industry to innovate, to China's developing new and high-tech industry, there is important strategic importance.Cloud computing, by being divided in by calculation task on large-scale low-cost server cluster, makes people to utilize to be distributed in the unused resource of various places to process comparatively complicated application program, drops into obtain high calculating quality with extremely low cost.
Under cloud computing environment the essence of task scheduling be by task matching separate for n on the physical resource of m unused isomery, make the general assignment deadline minimum and available resources are fully used.Task scheduling is as the important component part of cloud computing platform, and its efficiency directly has influence on performance and the service quality of whole cloud computing platform.Such as, serial task dispatching method is sequentially assigned to one group of virtual machine a group task, ensures that each virtual machine runs the task of equal number with balanced load as far as possible, but does not consider the difference between the demand of task and virtual machine.Mission Scheduling has been proved to be a np complete problem, at m
nindividually the solution space of task scheduling may find approximate optimal solution, make the execution time of general assignment and load balancing degrees minimum, wherein the execution time is minimum is to improve service quality, and load balancing degrees is minimum is stability in order to ensure cloud environment.
In the cloud computing environment that the present invention relates to, task scheduling refers to the scheduling of Meta task, namely separate between task, and data correlation between task and priority constraint relationship are not considered in its scheduling.At present, the method for scheduling task of cloud computing does not also form unified standards and norms, but due to the importance of this problem, domestic and international researcher proposes the approximate optimal solution that a large amount of cloud computing method for scheduling task carrys out calculation task scheduling, the heuristic mutation operations method such as Min-Min, Max-Min, dynamic programming during existing conventional mesh calculates, also has based on intelligent dispatching methods such as genetic algorithm, particle cluster algorithm, ant group algorithm, immune algorithm and differential evolution algorithms.Wherein, the Min-Min algorithm of tradition heuristic mutation operations method adopts easy first and difficult later strategy, the task that first the complete time is short, the task that then the complete time is long, and take Greedy strategy each priority of task to be assigned to the computational resource performing it and complete the earliest; Max-Min algorithm is on the contrary, adopts difficult at first and quite easy afterwards and Greedy strategy, chooses deadline the longest task at every turn, is preferentially assigned to the computational resource performing it and complete the earliest.Tradition heuristic mutation operations method is that target is dispatched with earliest finish time, have good load-balancing performance, but the actual execution time of general assignment is not minimum.Intelligent dispatching method is by the coding to task scheduling approach, and the intelligent algorithm thoughts such as foundation genetic algorithm, particle cluster algorithm, ant group algorithm, immune algorithm and differential evolution algorithm, at m
nequilibrium establishment between the solution space Diversification of size and Intensification, finally significantly reduces the execution time of task.But intelligent dispatching method is carrying out in magnanimity task scheduling process, and be easily absorbed in locally optimal solution, the effect in speed of convergence and load balancing has much room for improvement.
Summary of the invention
The object of the invention is to the deficiency existed for prior art, a kind of cloud computing method for scheduling task based on tabu search and load balancing is provided, solve the optimization problem of execution time and load balancing in task scheduling under cloud computing environment, effectively shorten the time span that task completes, achieve the Appropriate application of cloud computing resources, for cloud computing provides efficient Task Scheduling Mechanism.For achieving the above object, design of the present invention is as follows.
On the formalized description basis of the Load Balancing Task Scheduling problem one, under cloud computing environment, obtained the heuristic priority allocation strategy on earliest finish time by the formalizing deduction of dynamic programming method, and try to achieve the initial solution of task scheduling based on this allocation strategy.
Two, by the financial value concept of task switching, candidate's switching task pair is produced as guiding neighborhood search optimisation strategy based on tabu search, greedy principle is adopted to select the large task of financial value to exchanging, optimize the initial solution of task scheduling, thus farthest shorten the time span that whole task completes, and realize the load balancing of cloud computing platform.The initial solution optimization of task scheduling is as the key link of cloud computing Task Scheduling Mechanism, and the operational efficiency of its result on whole cloud computing environment has important impact, can effectively reduce resource free time, improves the utilization benefit of resource.
According to above-mentioned inventive concept, technical scheme of the present invention is achieved in that a kind of based on multilevel partitioning and the cloud computing method for scheduling task composing power Directed Hypergraph, and it is characterized in that, concrete steps are as follows.
Step 1,
class types degree is analyzed, the task that under input cloud computing environment, user submits to, and it is carried out to the analysis of type and class degree, determine parallelization degree and the feature of task.
Step 2,
proceeding graininess decomposes, according to parallelization degree and the feature of user task, and the peculiar property such as the resource sharing allocation scheme of cloud computing, user task is decomposed according to proceeding graininess rank.
Step 3,
resource characteristics is analyzed, according to peculiar properties such as the resource sharing allocation scheme of cloud computing, resource characteristics analysis is carried out to the task after decomposing.
Step 4,
solve task scheduling initial solution, according to the analysis result to task resource characteristic, set up and describe its Resources requirement model, and then try to achieve the initial solution of task scheduling based on this model.
Step 5,
optimize task scheduling initial solution, optimize task scheduling initial solution, shorten the latest finishing time of general assignment and improve the load-balancing performance of virtual machine, obtaining the optimization solution of task scheduling.
Step 6,
duty mapping is dispatched, by MapReduce Task Scheduling Model, the optimization solution of task scheduling mapped and dispatch, realizing the job invocation in cloud computing environment and execution, effectively balanced cloud computing platform load and shorten the time span that completes of whole task.
In above-mentioned step 4, described
solve task scheduling initial solutionstep as follows.
Step 4.1, the assignment instructions length provided based on Resources requirement model and virtual machine execution instruction strip number per second, calculation task set
tin
nindividual task is in virtual machine set
vM's
mthe expection execution time on individual virtual machine, obtain
n × mexpection execution time matrix
c,wherein expection execution time C
ijrepresent the
iindividual task is
jthe time that individual virtual machine performs, equal
ithe instruction length of individual task is divided by
jthe execution instruction strip number per second of individual virtual machine.
Step 4.2, the present load array of an initialization m virtual machine
vt[1..
m] be zero, namely before starting allocating task, the present load of any virtual machine is zero.
Step 4.3, sequential access set of tasks
tin each task,
based on the heuristic priority allocation strategy on earliest finish time, successively by
kindividual task matching is to having on the virtual machine on earliest finish time
, until all task matching obtain the initial solution of task scheduling after terminating.
In above-mentioned step 5, described
optimize task scheduling initial solutionstep as follows.
Step 5.1, initialization task set
tin
nthe taboo array of individual task
tabu[1..
n] be zero, namely allow all tasks exchanged.
Step 5.2,
select task that financial value is large to exchanging based on tabu search and greedy principle, until the latest finishing time of general assignment cannot be reduced.
In above-mentioned step 4.3, described
based on the heuristic priority allocation strategy on earliest finish time by
kindividual task matching is to having on the virtual machine on earliest finish time
step is as follows.
Step 4.3.1, foundation
mthe present load array of individual virtual machine
vt[1..
m] and expection execution time matrix
c, calculate
kindividual task
t k be dispensed to the corresponding time span of each virtual machine
makespan, Qi Zhong
jthe time span of individual virtual machine is
jthe present load array of individual virtual machine
vt[
j] and the
kindividual task
t k ?
jthe execution time of individual virtual machine
c kj sum.
Step 4.3.2, finds out the virtual machine that time span is minimum
vm x .
Step 4.3.3, allocating task
t k to virtual machine
vm x , upgrade
vm x virtual machine load
vt[
x] be
xthe present load array of individual virtual machine
vt[
x] and the
kindividual task
t k ?
xthe execution time of individual virtual machine
c kx sum.
In above-mentioned step 5.2, described
select task that financial value is large to exchanging based on tabu search and greedy principlestep is as follows.
Step 5.2.1, based on
mthe load array of individual virtual machine
vt[1..
m], find out the virtual machine that load is maximum
vm x with the virtual machine of least-loaded
vm y .
Step 5.2.2, if be dispensed to the maximum virtual machine of load
vm x all tasks be prohibited exchange, then task switching terminates and obtains the optimization solution of task scheduling, goes to step 6; Otherwise at virtual machine
vm x one is selected to allow exchanged task
t k , and by this task
t k corresponding taboo mark
tabu[
k]=0 is revised as
tabu[
k]=1.
Step 5.2.3, if the virtual machine being dispensed to least-loaded
vm y all tasks are prohibited to exchange, then task switching terminates and obtains the optimization solution of task scheduling, goes to step 6; Otherwise at virtual machine
vm y calculate the exchanged task of all permissions and task
t k financial value after exchange.If the exchange financial value that all tasks calculated are right is negative, skip step 5.2.4,5.2.5,5.2.6 and 5.2.7, continue circulation and perform step 5.2.1, otherwise perform step 5.2.4.
Step 5.2.4, select to exchange the maximum task of financial value to (
t l ,
t k ) exchange, i.e. task
t k exchanged to virtual machine
vm y upper execution and task
t l exchanged to virtual machine
vm x upper execution.If virtual machine after exchanging
vm y new load be greater than virtual machine
vm x former load, skip step 5.2.5,5.2.6 and 5.2.7, continue circulation perform step 5.2.1, otherwise perform step 5.2.5.
Step 5.2.5, amendment task
t l taboo mark
tabu[
l]=1.
Step 5.2.6, upgrades
vm x virtual machine load
vt[
x]=
vt[
x]+
c lx -
c kx .
Step 5.2.7, upgrades
vm y virtual machine load
vt[
y]=
vt[
y]+
c ky -
c ly .
The present invention compared with prior art, has following apparent outstanding substantive distinguishing features and remarkable advantage.
1, improve the efficiency of task scheduling.
Cloud computing method for scheduling task based on tabu search and load balancing of the present invention, by the financial value concept of task switching, candidate's switching task pair is produced as guiding neighborhood search optimisation strategy based on tabu search, greedy principle is adopted to select the large task of financial value to exchanging, to mapping and the scheduling of carrying out task after the initial solution optimization of task scheduling again, thus effectively improve the efficiency of task scheduling, the time span that the task of shortening completes, achieve the Appropriate application of cloud computing resources, for cloud computing provides efficient Task Scheduling Mechanism.
By following to the present invention is based on the example of cloud computing method for scheduling task of tabu search and load balancing in conjunction with the description of its accompanying drawing, object of the present invention, specific structural features and advantage can be understood further.
Fig. 1 is the process flow diagram of the cloud computing method for scheduling task based on tabu search and load balancing.
Fig. 2 is the heuristic priority allocation strategy based on earliest finish time
solve task scheduling initial solutionprocess flow diagram.
Fig. 3 is based on tabu search and greedy principle
optimize task scheduling initial solutionprocess flow diagram.
Embodiment.
In order to the technology contents of the cloud computing method for scheduling task that the present invention is based on tabu search and load balancing more clearly can be understood, describe in detail especially exemplified by following instance.
The process flow diagram of the cloud computing method for scheduling task based on tabu search and load balancing of the present embodiment as shown in Figure 1.Under cloud computing environment, the task 101 that input user submits to, carries out the analysis 102 of type and class degree, determines parallelization degree and the feature of task to user task; According to parallelization degree and the feature of user task, and the peculiar property such as the resource sharing allocation scheme of cloud computing, according to proceeding graininess rank, decomposition 103 is carried out to user task; And then resource characteristics analysis 104 is carried out to the task after decomposing; According to the analysis result to task resource characteristic, set up and describe its resource requirement, and then try to achieve the initial solution 105 of task scheduling based on this model; Optimize task scheduling initial solution, shorten the latest finishing time of general assignment and improve the load-balancing performance of virtual machine, obtaining the optimization solution 106 of task scheduling; By MapReduce Task Scheduling Model, the optimization solution of task scheduling is mapped and dispatches 107; In cloud computing environment, to the job invocation dispatched with perform 108, thus effectively shorten time span the load of balanced cloud computing platform that whole task completes.
The related definition that disclosure sets forth the Load Balancing Task Scheduling problem under cloud computing environment is as follows.
definition1: under supposing cloud computing environment, user's submit job resolves into the set of n task, and separate between task, and its scheduling does not need data correlation between consideration task and priority constraint relationship, definition set of tasks
, wherein t
ifor i-th task resolved into
, n is the task quantity after decomposing, and i-th task t
itotal instruction length be MI
i.
definition2: under supposing cloud computing environment, have the set of m virtual resource to participate in task scheduling, and virtual resource is provided by virtual machine mode, namely virtual resource is the virtual machine in cloud computing cluster.The set of defining virtual machine
, wherein vm
jfor a jth resources of virtual machine
, m is virtual machine quantity, and a jth virtual machine vm
jinstruction execution speed (execution instruction strip number per second) be MIPS
j.
definition3: suppose that the task quantity n after decomposing is not less than resources of virtual machine quantity m (n>=m), each task can only be distributed to virtual machine and perform, and a task can only be performed at section virtual machine sometime, multiple task can not be performed simultaneously.The individual different task scheduling of definition n is the matrix of a n × m to the expection execution time C on the individual different virtual machine of m, wherein C
ijrepresent i-th task t
iat a jth virtual machine vm
jthe time of upper execution
, i.e. expection execution time C
ijfor task t
itotal instruction length MI
idivided by virtual machine vm
jexecution instruction strip number MIPS per second
j.
definition4: define n different task
be dispatched to m different virtual machine
upper all possible task matching scheme collection is
.Definition
represent task matching scheme collection
in a kind of allocative decision, i.e. the matrix of a n × m.Wherein, x
ijbe expressed as task t
iwith virtual machine vm
jthe relations of distribution, and
,
,
,
.If i.e. task t
ibe distributed in virtual machine vm
jupper execution, then x
ij=1, otherwise x
ij=0.
definition5: for certain task matching scheme
, the present load of defining virtual machine
for (state that a front k-1 task matching is complete) under current state, distribute to a jth virtual machine vm
jall required by task execution time, namely
.A definition kth task t
kbe distributed in a jth virtual machine vm
jon time span
for task t
kat vm
jthe earliest finish time of upper execution, namely
.
definition6: for certain task matching scheme
, the load of defining virtual machine
for distributing to a jth virtual machine vm
jthe expection deadline of all tasks, namely
.
definition7: define n different task and be dispatched to average load on m different virtual machine, equal total instruction length of n task divided by m virtual machine instructions execution speed cumulative sum, i.e. general assignment optimal finish time
.
definition8: for certain task matching scheme
, the load balancing degrees of defining virtual machine
.Load balancing degrees
numerical value is less, shows that the load in cloud computing system between each virtual machine is more balanced.
definition9: for n different task
be dispatched to m different virtual machine
mission Scheduling be find allocative decision
, make the task latest finishing time of virtual machine in this allocative decision the earliest
, the most long process time of each virtual machine in other words
the shortest, and load balancing degrees
minimum.
According to definition 9, the Mission Scheduling being assigned to m different virtual machine for n different task finds allocative decision
, make the most long process time of virtual machine
the shortest and load balancing degrees
minimum.When only having the scheduling problem of a task,
.When there being the scheduling problem of k-1 task,
.
theorem1: during scheduling problem for k task, suppose a kth task t
kdistribute to z virtual machine vm
z, i.e. z virtual machine vm
ztime span
, and
, then recurrence relation is met
.
prove: provided by definition 9
define known,
.
Can be obtained by theorem 1, a kth task t
kthe virtual machine vm with earliest finish time will be distributed to
z, i.e. the heuristic priority allocation strategy on earliest finish time.
The heuristic priority allocation strategy based on earliest finish time of the present embodiment
solve task scheduling initial solutionprocess flow diagram as shown in Figure 2, step is as follows.
A01, the assignment instructions length provided based on Resources requirement model and virtual machine execution instruction strip number per second, calculation task set
tin
nindividual task is in virtual machine set
vM's
mthe expection execution time on individual virtual machine, obtain
n × mexpection execution time matrix
c,wherein expection execution time C
ijrepresent the
iindividual task is
jthe time that individual virtual machine performs, equal
ithe instruction length of individual task is divided by
jthe execution instruction strip number per second of individual virtual machine.
The present load array of A02, an initialization m virtual machine
vt[1..
m] be zero, namely before starting allocating task, the present load of any virtual machine is zero.
A03, sequential access set of tasks
tin each task and perform steps A 04, A05 and A06, successively by
kindividual task matching is to having on the virtual machine on earliest finish time, until all task matching obtain the initial solution of task scheduling after terminating.
A04, foundation
mthe present load array of individual virtual machine
vt[1..
m] and expection execution time matrix
c, calculate
kindividual task
t k be dispensed to the corresponding time span of each virtual machine
makespan, Qi Zhong
jthe time span of individual virtual machine is
jthe present load array of individual virtual machine
vt[
j] and the
kindividual task
t k ?
jthe execution time of individual virtual machine
c kj sum.
A05, finds out the virtual machine that time span is minimum
vm x .
A06, allocating task
t k to virtual machine
vm x , upgrade
vm x virtual machine load
vt[
x] be
xthe present load array of individual virtual machine
vt[
x] and the
kindividual task
t k ?
xthe execution time of individual virtual machine
c kx sum.
definition10: for certain task matching scheme
, suppose vm
xfor the virtual machine that load is maximum, vm
yfor the virtual machine of least-loaded, namely
and
; Suppose a kth task t
kbe dispensed on virtual machine vm
xupper execution, l task t
lbe dispensed on virtual machine vm
yupper execution, namely
and
.As task t
kwith task t
lexchange, i.e. task t
kexchanged to virtual machine vm
yupper execution and task t
lexchanged to virtual machine vm
xupper execution, virtual machine vm
xexecution time difference before and after exchanging is called the financial value of this exchange
.
The present embodiment based on tabu search and greedy principle
optimize task scheduling initial solutionprocess flow diagram as shown in Figure 3, step is as follows.
B01, initialization task set
tin
nthe taboo array of individual task
tabu[1..
n] be zero, namely allow all tasks exchanged.
B02, circulation performs step B03, B04, B05, B06, B07, B08, B09 and B10, selects the large task of financial value to exchanging, until cannot reduce the latest finishing time of general assignment.
B03, based on
mthe load array of individual virtual machine
vt[1..
m], find out the virtual machine that load is maximum
vm x with the virtual machine of least-loaded
vm y .
B04, if be dispensed to the maximum virtual machine of load
vm x all tasks be prohibited exchange, then task switching terminates and obtains the optimization solution of task scheduling; Otherwise at virtual machine
vm x one is selected to allow exchanged task
t k , by task
t k corresponding taboo mark
tabu[
k]=0 is revised as
tabu[
k]=1.
B05, if the virtual machine being dispensed to least-loaded
vm y all tasks are prohibited to exchange, then task switching terminates and obtains the optimization solution of task scheduling; Otherwise at virtual machine
vm y calculate the exchanged task of all permissions and task
t k financial value after exchange.
B06, if the right exchange financial value of all tasks calculated is negative, skips step B07, B08, B09 and B10, continues circulation and performs step B01, otherwise perform step B07.
B07, select to exchange the maximum task of financial value to (
t l ,
t k ) exchange, i.e. task
t k exchanged to virtual machine
vm y upper execution and task
t l exchanged to virtual machine
vm x upper execution.If virtual machine after exchanging
vm y new load be greater than virtual machine
vm x former load, skip step B08, B09 and B10, continue circulation perform step B01, otherwise perform step B08.
B08, amendment task
t l taboo mark
tabu[
l]=1.
B09, upgrades
vm x virtual machine load
vt[
x]=
vt[
x]+
c lx -
c kx .
B10, upgrades
vm y virtual machine load
vt[
y]=
vt[
y]+
c ky -
c ly .
Claims (1)
1., based on a cloud computing method for scheduling task for tabu search and load balancing, it is characterized in that, concrete steps are as follows:
Step 1,
class types degree is analyzed, the task that under input cloud computing environment, user submits to, and it is carried out to the analysis of type and class degree, determine parallelization degree and the feature of task;
Step 2,
proceeding graininess decomposes, according to parallelization degree and the feature of user task, and the peculiar property such as the resource sharing allocation scheme of cloud computing, user task is decomposed according to proceeding graininess rank;
Step 3,
resource characteristics is analyzed, according to peculiar properties such as the resource sharing allocation scheme of cloud computing, resource characteristics analysis is carried out to the task after decomposing;
Step 4,
solve task scheduling initial solution, according to the analysis result to task resource characteristic, set up and describe its Resources requirement model, and then try to achieve the initial solution of task scheduling based on this model;
Step 5,
optimize task scheduling initial solution, optimize task scheduling initial solution, shorten the latest finishing time of general assignment and improve the load-balancing performance of virtual machine, obtaining the optimization solution of task scheduling;
Step 6,
duty mapping is dispatched, by MapReduce Task Scheduling Model, the optimization solution of task scheduling mapped and dispatch, realizing the job invocation in cloud computing environment and execution, effectively balanced cloud computing platform load and shorten the time span that completes of whole task;
In above-mentioned step 4, described
solve task scheduling initial solutionstep as follows;
Step 4.1, the assignment instructions length provided based on Resources requirement model and virtual machine execution instruction strip number per second, calculation task set
tin
nindividual task is in virtual machine set
vM's
mthe expection execution time on individual virtual machine, obtain
n × mexpection execution time matrix
c,wherein expection execution time C
ijrepresent the
iindividual task is
jthe time that individual virtual machine performs, equal
ithe instruction length of individual task is divided by
jthe execution instruction strip number per second of individual virtual machine;
Step 4.2, the present load array of an initialization m virtual machine
vt[1..
m] be zero, namely before starting allocating task, the present load of any virtual machine is zero;
Step 4.3, sequential access set of tasks
tin each task,
based on the heuristic priority allocation strategy on earliest finish time, successively by
kindividual task matching is to having on the virtual machine on earliest finish time
, until all task matching obtain the initial solution of task scheduling after terminating;
In above-mentioned step 5, described
optimize task scheduling initial solutionstep as follows;
Step 5.1, initialization task set
tin
nthe taboo array of individual task
tabu[1..
n] be zero, namely allow all tasks exchanged;
Step 5.2,
select task that financial value is large to exchanging based on tabu search and greedy principle, until the latest finishing time of general assignment cannot be reduced;
In above-mentioned step 4.3, described
based on the heuristic priority allocation strategy on earliest finish time by
kindividual task matching is to having on the virtual machine on earliest finish time
step is as follows;
Step 4.3.1, foundation
mthe present load array of individual virtual machine
vt[1..
m] and expection execution time matrix
c, calculate
kindividual task
t k be dispensed to the corresponding time span of each virtual machine
makespan, Qi Zhong
jthe time span of individual virtual machine is
jthe present load array of individual virtual machine
vt[
j] and the
kindividual task
t k ?
jthe execution time of individual virtual machine
c kj sum;
Step 4.3.2, finds out the virtual machine that time span is minimum
vm x ;
Step 4.3.3, allocating task
t k to virtual machine
vm x , upgrade
vm x virtual machine load
vt[
x] be
xthe present load array of individual virtual machine
vt[
x] and the
kindividual task
t k ?
xthe execution time of individual virtual machine
c kx sum;
In above-mentioned step 5.2, described
select task that financial value is large to exchanging based on tabu search and greedy principlestep is as follows;
Step 5.2.1, based on
mthe load array of individual virtual machine
vt[1..
m], find out the virtual machine that load is maximum
vm x with the virtual machine of least-loaded
vm y ;
Step 5.2.2, if be dispensed to the maximum virtual machine of load
vm x all tasks be prohibited exchange, then task switching terminates and obtains the optimization solution of task scheduling, goes to step 6; Otherwise at virtual machine
vm x one is selected to allow exchanged task
t k , and by this task
t k corresponding taboo mark
tabu[
k]=0 is revised as
tabu[
k]=1;
Step 5.2.3, if the virtual machine being dispensed to least-loaded
vm y all tasks are prohibited to exchange, then task switching terminates and obtains the optimization solution of task scheduling, goes to step 6; Otherwise at virtual machine
vm y calculate the exchanged task of all permissions and task
t k financial value after exchange; If the exchange financial value that all tasks calculated are right is negative, skip step 5.2.4,5.2.5,5.2.6 and 5.2.7, continue circulation and perform step 5.2.1, otherwise perform step 5.2.4;
Step 5.2.4, select to exchange the maximum task of financial value to (
t l ,
t k ) exchange, i.e. task
t k exchanged to virtual machine
vm y upper execution and task
t l exchanged to virtual machine
vm x upper execution; If virtual machine after exchanging
vm y new load be greater than virtual machine
vm x former load, skip step 5.2.5,5.2.6 and 5.2.7, continue circulation perform step 5.2.1, otherwise perform step 5.2.5;
Step 5.2.5, amendment task
t l taboo mark
tabu[
l]=1;
Step 5.2.6, upgrades
vm x virtual machine load
vt[
x]=
vt[
x]+
c lx -
c kx ;
Step 5.2.7, upgrades
vm y virtual machine load
vt[
y]=
vt[
y]+
c ky -
c ly .
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| CN105468453A (en) * | 2015-08-03 | 2016-04-06 | 汉柏科技有限公司 | Vector scheduling method and apparatus for cloud computing system |
| CN106302224A (en) * | 2016-10-15 | 2017-01-04 | 黄林果 | A kind of task optimization bootstrap technique under cloud service environment |
| CN106991006A (en) * | 2017-03-30 | 2017-07-28 | 浙江天正信息科技有限公司 | Support the cloud workflow task clustering method relied on and the time balances |
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| CN112181658B (en) * | 2020-09-30 | 2024-04-05 | 南京工程学院 | Calculation task allocation method for maximizing network benefits in heterogeneous network |
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