CN111913800A - Resource allocation method for optimizing cost of micro-service in cloud based on L-ACO - Google Patents

Resource allocation method for optimizing cost of micro-service in cloud based on L-ACO Download PDF

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CN111913800A
CN111913800A CN202010678367.4A CN202010678367A CN111913800A CN 111913800 A CN111913800 A CN 111913800A CN 202010678367 A CN202010678367 A CN 202010678367A CN 111913800 A CN111913800 A CN 111913800A
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卢政昊
孙福权
王子健
曹建建
崔志清
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Northeastern University Qinhuangdao Branch
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Abstract

The invention discloses a resource allocation method for optimizing the cost of micro-service in cloud based on L-ACO, which comprises the following steps: distributing the service completion deadline of the whole combined service to each task, and calculating the probability ascending rank of each task to form a sub-deadline; taking the probability ascending rank as heuristic information in the ant colony, performing iterative computation, dynamically updating pheromone weight, heuristic information weight and pheromone volatilization rate in the iterative computation process, and updating pheromone tracks according to local optimal solution in the iterative process; and according to the sub-deadline, sequentially selecting resource allocation meeting the sub-deadline for an executor of each task, namely the service instance, and finding out a global optimal solution of cost optimization. The method aims at a group of specific combined services, and finds a cheap scheme for allocating computing resources for service instances on the premise of guaranteeing the service completion deadline. The algorithm after adjusting the parameters has lower cost and can more effectively distribute the resources.

Description

Resource allocation method for optimizing cost of micro-service in cloud based on L-ACO
Technical Field
The invention belongs to the technical field of cost optimization and resource scheduling, and relates to a resource allocation method for cloud micro-service cost optimization based on L-ACO.
Background
Micro-services are the products of service-oriented architecture and cloud technology development and application to certain mature stages. The traditional single application has many defects, especially under the condition of a large number of concurrent users distributed in different regions and different service requirements, the expansibility, fault tolerance, stability and survivability of the single application obviously cannot meet the concurrent requirements of large-scale distributed users in the mobile internet, so the application research based on the micro service becomes a hotspot. The micro-services are deployed in the cloud, so that each micro-service can be independently realized, deployed and updated without influencing the integrity of the application program. After the micro-service is deployed in the cloud, it is very important to solve the problems of task scheduling and resource allocation of the micro-service.
Typically, a service request will contain several sub-service requests, called composite services. In a particular composite service, different types of service requests need to be performed by different types of microservice instances, each of which may have multiple alternative resource configurations at runtime. Generally, the higher the configuration of service resources, the faster the execution speed and the higher the cost. Conversely, the lower the service resources are configured, the slower the execution speed is, and the lower the cost is. Then, for a specific composite service, how to allocate resources to minimize the operation cost thereof on the premise of guaranteeing the deadline requirement of completing the service is an important issue facing us.
The general approach to solving the above problem is to minimize monetary cost under the constraint of the deadline for completing the service. To date, approaches to this problem can be broadly divided into two categories: local heuristics and meta-heuristics. The local heuristic method adopts a simple heuristic method based on deadline distribution, easily falls into a local optimal solution, and cannot find a global optimal solution; the existing meta-heuristic method does not fully utilize the characteristics of the scheduling problem to carry out effective search.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a resource allocation method for optimizing the cost of micro-service in cloud based on L-ACO, which has lower cost and can allocate resources more effectively.
The invention provides a resource allocation method for optimizing cloud micro-service cost based on L-ACO, which comprises the following steps:
step 1: allocating the service completion deadline of the whole combined service to each task, and calculating the probability ascending rank of each task so as to form a sub-deadline;
step 2: taking the probability ascending rank as heuristic information in the ant colony algorithm, performing iterative computation on the algorithm, dynamically updating three important parameters of pheromone weight, heuristic information weight and pheromone volatilization rate in the iterative computation process, and updating pheromone tracks according to local optimal solutions in the iterative process;
and step 3: and (3) according to the sub-deadline obtained in the step (1), sequentially selecting resource allocation meeting the sub-deadline for an executor of each task, namely the service instance, and finding out a global optimal solution of cost optimization.
In the resource allocation method for optimizing the cost of the L-ACO-based cloud micro-service, the step 1 specifically includes:
step 1-1: computing task tiExecution time ET oftiI.e. is the service instance InsiDistribution vmpCompleting task t after type virtual machineiRequired time period:
Figure BDA0002584836790000021
wherein, wltiRepresentative task tiThe task amount of (2); speedpRepresents vmpThe processing speed of the type virtual machine;
step 1-2: computing task tiTo its subtask tjTransmission time TT ofi,jInstant service instance InsiTo child service instanceInsjData transmission time of (2):
TTi,j＝datai,j/bw
wherein, the datai,jRepresentative task tiSent to the subtask tjBw is the bandwidth between microservice instances;
step 1-3: calculating the Boolean variable gammajIt represents the probability of computing task ti up to rank priWhether or not to consider task tiTo its subtask tjThe transmission time of (c):
Figure BDA0002584836790000031
wherein, theta is a parameter larger than 1, and the larger theta is, the more important the transmission time is; ccrjIs task tjRatio of execution time to transmission time of (ccr)jSmaller gammajThe greater the probability of returning 0, and vice versa;
step 1-4: computing task tiProbability of (3) up rank priIt is an important basis for the deadline assigned to each subtask:
Figure BDA0002584836790000032
wherein, tjIs tiPr is a subtask ofjIs tjThe probability of (d) is up-rank;
step 1-5: allocating a task completion deadline D to the subtask according to the probability ascending order obtained in the step 1-4 to obtain a sub deadline:
Figure BDA0002584836790000033
wherein, two virtual tasks t with zero execution time are respectively added at the starting point and the end point of the resource scheduling processentryAnd texitTo indicate the start and end of the scheduling process, prentryRepresents tentryThe probability of (d) is rank-up.
In the resource allocation method for optimizing the cost of the cloud micro-service based on the L-ACO of the present invention, the step 2 specifically includes:
step 2-1: taking the probability upward rank as heuristic information in the ant colony, and normalizing the heuristic information to ensure that the heuristic information value of each node is distributed between [1 and 2 ]:
Figure BDA0002584836790000034
wherein, prminIs the minimum of all heuristic information, prmaxThe maximum value of all heuristic information is obtained;
step 2-2: dynamically updating pheromone weight alpha, heuristic information weight beta and pheromone volatility rho in the iterative calculation process of the algorithm;
Figure BDA0002584836790000041
Figure BDA0002584836790000042
Figure BDA0002584836790000043
wherein alpha (k) is the updated pheromone weight after k iterations, beta (k) is the updated heuristic information weight after k iterations, rho (k) is the updated pheromone volatility after k iterations, eta is a constant, and lambda means that the global optimal solution is updated after lambda iterations;
step 2-3: and (3) updating pheromone:
Figure BDA0002584836790000044
wherein, Deltaτi,j(k) Is the amount of pheromone, s, accumulated by antsbest(k) The local optimal solution of the kth iteration is obtained; cost (k) is the cost of the local optimal solution for the kth iteration; costmaxThe highest configured cost for all microservice instances; costminThe lowest configured cost is employed for all microservice instances.
In the resource allocation method for optimizing the cost of the cloud micro service based on the L-ACO of the present invention, the step 3 specifically includes:
step 3-1: selecting a resource configuration for a service instance that satisfies its sub-deadlines, and having task tiThe incremental cost increase is minimized at the addition of tiAfter and after addition of tiPrevious execution cost difference;
step 3-2: when no suitable resource allocation can meet the sub-minimum period, the standard of allocating resources from the resource pool is to minimize the completion time of the task;
step 3-3: if the allocated resource configuration is not the fastest processing type, the type is set to a faster level and the completion time of each task on it is redeployed.
The resource allocation method based on the L-ACO cloud micro-service cost optimization provides a scheme for dynamically adjusting parameters on the basis of an L-ACO algorithm. An inexpensive solution for allocating computing resources to service instances can be found for a particular set of composite services while preserving the service completion deadlines. The algorithm after adjusting the parameters has lower cost and can more effectively distribute the resources.
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FIG. 1 is a flowchart of a resource allocation method for L-ACO-based cloud micro-service cost optimization according to the present invention.
Detailed Description
The inspiration of an ant colony optimization Algorithm (ACO) comes from the ability of real ants to find the shortest path between an ant nest and food, and the algorithm can calculate the shortest path starting from an origin, passing through a plurality of given demand points and finally returning to the origin. The L-ACO algorithm utilizes the meta-heuristic method of the ant colony algorithm to refine the task ordering step in the algorithm so as to solve the problem of a resource allocation strategy of cost optimization.
The problem of the research of the invention is how to allocate computing resources for a group of specific combined services of micro-services deployed in a cloud server, so that the total cost is minimum on the premise of meeting the task completion deadline.
A cloud server may be understood as a pool of resources that contains unlimited computing resources, which are allocated to microservice instances in the form of virtual machines. We use { vm1,vm2…vmnRepresents the set of virtual machine types that can be configured by the microservice instance, where vmpRepresents a specific virtual machine type and has two attributes speedpAnd costpEach represents vmpProcessing speed and unit time cost. Generally, the faster the processing speed of a virtual machine, the higher the cost. Conversely, the slower the processing speed of the virtual machine, the lower the cost.
The invention provides a resource allocation method for optimizing the cost of micro-services in a cloud based on L-ACO, which can find a cheap scheme for allocating computing resources for service instances on the premise of ensuring the task deadline requirement for a group of specific combined services in the micro-services.
The L-ACO algorithm is a method for finding a global optimal solution with a better result, and mainly comprises the following steps: establishing an ant colony with a fixed value, and initializing heuristic information; enabling each ant to construct a solution for the problem, and storing the local optimal solution by comparing all solutions; updating the pheromone track; repeating the process until the number of iterations increases to a set maximum value; and returning the global optimal solution.
Because the parameters of the L-ACO algorithm are not reasonably optimized, the result still has a certain optimization space. The invention provides a strategy for dynamically adjusting parameters on the basis of an L-ACO algorithm, which comprises the following steps: normalizing heuristic information; dynamically setting important parameters; a new technical scheme for updating pheromones is provided.
The invention discloses a resource allocation method for optimizing the cost of micro-service in cloud based on L-ACO, which comprises the following steps:
step 1: assigning the service completion deadline of the entire composite service to each task, and calculating an upward rank of probability of each task to form a sub-deadline, wherein the step 1 specifically includes:
step 1-1: computing task tiExecution time ET oftiI.e. is the service instance InsiDistribution vmpCompleting task t after type virtual machineiRequired time period:
Figure BDA0002584836790000061
wherein, wltiRepresentative task tiThe task amount of (2); speedpRepresents vmpThe processing speed of the type virtual machine;
step 1-2: computing task tiTo its subtask tjTransmission time TT ofi,jInstant service instance InsiTo the sub-service instance InsjData transmission time of (2):
TTi,j＝datai,j/bw
wherein, the datai,jRepresentative task tiSent to the subtask tjBw is the bandwidth between microservice instances;
step 1-3: calculating the Boolean variable gammajIt represents the probability of computing task ti up to rank priWhether or not to consider task tiTo its subtask tjThe transmission time of (c):
Figure BDA0002584836790000071
wherein, theta is a parameter larger than 1, and the larger theta is, the more important the transmission time is; ccrjIs task tjRatio of execution time to transmission time of (ccr)jSmaller gammajReturning to 0The greater the probability and vice versa;
step 1-4: computing task tiProbability of (3) up rank priIt is an important basis for the deadline assigned to each subtask:
Figure BDA0002584836790000072
wherein, tjIs tiPr is a subtask ofjIs tjThe probability of (d) is up-rank;
step 1-5: allocating a task completion deadline D to the subtask according to the probability ascending order obtained in the step 1-4 to obtain a sub deadline:
Figure BDA0002584836790000073
wherein, two virtual tasks t with zero execution time are respectively added at the starting point and the end point of the resource scheduling processentryAnd texitTo indicate the start and end of the scheduling process, prentryRepresents tentryThe probability of (d) is rank-up.
Step 2: taking the probability ascending rank as heuristic information in the ant colony algorithm, performing iterative computation on the algorithm, dynamically updating three important parameters, namely pheromone weight, heuristic information weight and pheromone volatilization rate in the iterative computation process, and updating pheromone tracks according to local optimal solutions appearing in the iterative process, wherein the step 2 specifically comprises the following steps:
step 2-1: taking the probability upward rank as heuristic information in the ant colony, and normalizing the heuristic information to ensure that the heuristic information value of each node is distributed between [1 and 2 ]:
Figure BDA0002584836790000074
wherein, prminIs the minimum of all heuristic information, prmaxFor all heuristic informationA maximum value;
step 2-2: dynamically updating pheromone weight alpha, heuristic information weight beta and pheromone volatility rho in the iterative calculation process of the algorithm;
Figure BDA0002584836790000081
Figure BDA0002584836790000082
Figure BDA0002584836790000083
wherein alpha (k) is the updated pheromone weight after k iterations, beta (k) is the updated heuristic information weight after k iterations, rho (k) is the updated pheromone volatility after k iterations, eta is a constant, and lambda means that the global optimal solution is updated after lambda iterations;
step 2-3: and (3) updating pheromone:
Figure BDA0002584836790000084
wherein, Δ τi,j(k) Is the amount of pheromone, s, accumulated by antsbest(k) The local optimal solution of the kth iteration is obtained; cost (k) is the cost of the local optimal solution for the kth iteration; costmaxThe highest configured cost for all microservice instances; costminThe lowest configured cost is employed for all microservice instances.
And step 3: according to the sub-deadline obtained in the step 1, sequentially selecting resource allocation meeting the sub-deadline for an executor of each task, namely a service instance, and finding out a global optimal solution of cost optimization, wherein the step 3 specifically comprises the following steps:
step 3-1: selecting a resource configuration for a service instance that satisfies its sub-deadlines, and having task tiMinimal incremental costChange the cost increment to adding tiAfter and after addition of tiPrevious execution cost difference;
step 3-2: when no suitable resource allocation can meet the sub-minimum period, the standard of allocating resources from the resource pool is to minimize the completion time of the task;
step 3-3: if the allocated resource configuration is not the fastest processing type, the type is set to a faster level and the completion time of each task on it is redeployed.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the scope of the present invention, which is defined by the appended claims.

Claims (4)

1. The resource allocation method for optimizing the cost of the cloud micro-service based on the L-ACO is characterized by comprising the following steps:
step 1: allocating the service completion deadline of the whole combined service to each task, and calculating the probability ascending rank of each task so as to form a sub-deadline;
step 2: taking the probability ascending rank as heuristic information in the ant colony algorithm, performing iterative computation on the algorithm, dynamically updating three important parameters of pheromone weight, heuristic information weight and pheromone volatilization rate in the iterative computation process, and updating pheromone tracks according to local optimal solutions in the iterative process;
and step 3: and (3) according to the sub-deadline obtained in the step (1), sequentially selecting resource allocation meeting the sub-deadline for an executor of each task, namely the service instance, and finding out a global optimal solution of cost optimization.
2. The method for resource allocation based on L-ACO cloud micro-service cost optimization according to claim 1, wherein the step 1 specifically comprises:
step 1-1: computing task tiExecution time ET oftiI.e. as a service instanceInsiDistribution vmpCompleting task t after type virtual machineiRequired time period:
Figure FDA0002584836780000011
wherein, wltiRepresentative task tiThe task amount of (2); speedpRepresents vmpThe processing speed of the type virtual machine;
step 1-2: computing task tiTo its subtask tjTransmission time TT ofi,jInstant service instance InsiTo the sub-service instance InsjData transmission time of (2):
TTi,j＝datai,j/bw
wherein, the datai,jRepresentative task tiSent to the subtask tjBw is the bandwidth between microservice instances;
step 1-3: calculating the Boolean variable gammajIt represents the probability of computing task ti up to rank priWhether or not to consider task tiTo its subtask tjThe transmission time of (c):
Figure FDA0002584836780000021
wherein, theta is a parameter larger than 1, and the larger theta is, the more important the transmission time is; ccrjIs task tjRatio of execution time to transmission time of (ccr)jSmaller gammajThe greater the probability of returning 0, and vice versa;
step 1-4: computing task tiProbability of (3) up rank priIt is an important basis for the deadline assigned to each subtask:
Figure FDA0002584836780000022
wherein, tjIs tiPr is a subtask ofjIs tjThe probability of (d) is up-rank;
step 1-5: allocating a task completion deadline D to the subtask according to the probability ascending order obtained in the step 1-4 to obtain a sub deadline:
Figure FDA0002584836780000023
wherein, two virtual tasks t with zero execution time are respectively added at the starting point and the end point of the resource scheduling processentryAnd texitTo indicate the start and end of the scheduling process, prentryRepresents tentryThe probability of (d) is rank-up.
3. The method for resource allocation based on L-ACO cloud micro-service cost optimization according to claim 1, wherein the step 2 specifically comprises:
step 2-1: taking the probability upward rank as heuristic information in the ant colony, and normalizing the heuristic information to ensure that the heuristic information value of each node is distributed between [1 and 2 ]:
Figure FDA0002584836780000024
wherein, prminIs the minimum of all heuristic information, prmaxThe maximum value of all heuristic information is obtained;
step 2-2: dynamically updating pheromone weight alpha, heuristic information weight beta and pheromone volatility rho in the iterative calculation process of the algorithm;
Figure FDA0002584836780000025
Figure FDA0002584836780000031
Figure FDA0002584836780000032
wherein alpha (k) is the updated pheromone weight after k iterations, beta (k) is the updated heuristic information weight after k iterations, rho (k) is the updated pheromone volatility after k iterations, eta is a constant, and lambda means that the global optimal solution is updated after lambda iterations;
step 2-3: and (3) updating pheromone:
Figure FDA0002584836780000033
wherein, Δ τi,j(k) Is the amount of pheromone, s, accumulated by antsbest(k) The local optimal solution of the kth iteration is obtained; cost (k) is the cost of the local optimal solution for the kth iteration; costmaxThe highest configured cost for all microservice instances; costminThe lowest configured cost is employed for all microservice instances.
4. The method for resource allocation based on L-ACO cloud micro-service cost optimization according to claim 1, wherein the step 3 specifically comprises:
step 3-1: selecting a resource configuration for a service instance that satisfies its sub-deadlines, and having task tiThe incremental cost increase is minimized at the addition of tiAfter and after addition of tiPrevious execution cost difference;
step 3-2: when no suitable resource allocation can meet the sub-minimum period, the standard of allocating resources from the resource pool is to minimize the completion time of the task;
step 3-3: if the allocated resource configuration is not the fastest processing type, the type is set to a faster level and the completion time of each task on it is redeployed.
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