CN110058934B - Method for making optimal task unloading decision in large-scale cloud computing environment - Google Patents
Method for making optimal task unloading decision in large-scale cloud computing environment Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/48—Program initiating; Program switching, e.g. by interrupt
- G06F9/4806—Task transfer initiation or dispatching
- G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
- G06F9/4881—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
- G06F9/4893—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues taking into account power or heat criteria
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2209/00—Indexing scheme relating to G06F9/00
- G06F2209/50—Indexing scheme relating to G06F9/50
- G06F2209/509—Offload
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Abstract
The invention aims to provide a method for obtaining an optimal task unloading decision in a cloud computing environment, and meanwhile, the time delay and the energy consumption are considered to minimize the system overhead. The method for obtaining the optimal task unloading decision in the cloud computing environment mainly comprises the following three steps: A. mathematical modeling is carried out on the system; B. and C, solving a problem formulation text mixed integer programming problem by using a branch-and-bound algorithm. The invention considers the scene that the cloud node and the fog node exist simultaneously, is used for solving the scene that a plurality of tasks need to make unloading decisions, and comprehensively considers the energy consumption and the time delay so as to minimize the overhead of the mobile terminal.
Description
Background
With the development of mobile communication technology and the popularization of intelligent terminals, various network services and applications are endless, which puts new demands on service quality and communication delay. Although the central processing of mobile devices is more and more powerful, it is still unable to handle computationally intensive tasks in a short time. In addition, the local processing of these applications faces another problem, namely the rapid drain on battery power. Particularly for applications requiring real-time responses, such as online multiplayer VR games. In conventional cloud computing architectures, cloud servers are typically geographically concentrated in one or more data centers, which can create large transmission delays. These problems seriously affect the efficiency and user experience of the application. In order to solve the above-described problem, fog calculation (also referred to as edge calculation) has been proposed in the industry. In short, mobile edge computing is offloading computing tasks to local fog devices (e.g., routers, base stations, etc. with some computing power) over a wireless network rather than sending the computing tasks to a remote cloud server. The purpose of edge computing is to completely replace cloud computing, and to remedy some of its shortcomings. Edge computing extends cloud computing only to network edges. In summary, "fog" is cloud service that is closer to the end user. Edge computation can significantly improve the user experience compared to traditional methods. When tasks which cannot be processed or have excessive processing cost (time delay and energy consumption) of the mobile device are encountered, the computing tasks can be unloaded onto a fog server or a cloud server, and after the computing is completed, the result is returned to the mobile terminal. Today, the 5G technology is about to be put into commercial use, and the mobile edge computing technology will exert its power to the greatest extent. It not only can promote new mobile application, but also can greatly raise quality of existent application.
In such a context, how to make the offloading decision becomes critical. That is to say which tasks the mobile terminal needs to offload to the "cloud"? Which are unloaded onto "fog? Which are the least costly to process locally? Improper offloading decisions not only do not reduce overhead, but instead increase power consumption and latency. According to the cloud computing system, a mathematical model of a cloud computing architecture is established to convert the cloud computing architecture into a mixed integer programming problem, the mixed integer programming problem is designed and solved through an algorithm, only a scene with a cloud server is considered in the existing similar technology, and the cloud computing system solves the problem that the cloud server and the cloud server exist simultaneously.
Disclosure of Invention
The invention aims to provide a method for obtaining an optimal task unloading decision in a cloud computing environment, and meanwhile, the time delay and the energy consumption are considered to minimize the system overhead. The method for obtaining the optimal task unloading decision in the cloud computing environment mainly comprises the following three steps:
A. Mathematical modeling of the system: assuming that n tasks need to be offloaded in one cell, in a cloud and fog computing system architecture, three execution modes of the computing tasks are respectively local execution, and the tasks are offloaded to a fog node for execution and cloud node for execution. We use Respectively represents the energy consumption of the nth task under three conditionsRepresenting the nth task as written and used in three casesThe weighted sum of experiment and energy consumption under the three conditions of the nth task is respectively expressed, so that the total cost of the selected unloading decision is expressed. The subscript n of the symbol in this patent collectively indicates the nth computational task, and is not specifically indicated below. The energy consumption and the time delay in the three cases are respectively modeled first
(1) Overhead of native execution
(2) Offloading overhead to foggy node computation
When the terminal equipment selects to offload the task to the fog node, the time delay is composed of two parts, namelyTask transmission delayFog node processing delay (the calculation result downloading delay is negligible). The energy consumption also comprises two parts, namelyTask transmission energy consumptionAnd the idle state energy consumption of the terminal equipment.
(3) Offloading overhead to cloud node execution
Similar to the mathematical model offloaded to the foggy node, both energy consumption and time delay are two more components. But a transmission delay t is introduced when the cloud node is unloaded to calculate.
D n represents the CPU cycles required to complete the computational tasks. B n denotes the size of the computational task that needs to be transferred. P d,Pt,Pi represents the power of the terminal device when performing the calculation task, uploading the calculation task and being idle, respectively.F e,fc represents the CPU frequencies of the terminal device, the cloud node and the cloud node, respectively.Is the size of the upload bandwidth of the terminal device.
B. formulating the problem as a mixed integer programming problem:
Subject to:
C1:
C2:
C3:
C1 represents that the total upload bandwidth of n devices is max C. C2 indicates that the upload bandwidth allocated to each device cannot be negative. C3 indicates that the terminal device can only execute locally, and the terminal device can execute from one of three strategies of offloading to the cloud node and offloading to the cloud node, which are denoted by 0,1 and 2 respectively.
C. Solving using a branch-and-bound algorithm: and using programming languages such as python, matlab and the like to design and realize a branch-and-bound algorithm function, and entering the obtained mixed integer programming model through modeling and the sizes of n tasks into the function to obtain the minimum cost and corresponding unloading decision.
Compared with the prior art, the invention has the following remarkable advantages:
1. consider a scenario where cloud nodes and cloud nodes coexist.
2. The branch-and-bound method is used for solving, so that the method has a faster solving speed compared with the traditional exhaustion method.
3. The parameter adjustment is flexible, and different environments can be simulated by setting different parameter sizes.
For example, in an application scenario where delay sensitivity is low and power consumption is required, β may be increased and α may be decreased.
Drawings
Fig. 1 is a general flow chart of the present invention.
Fig. 2 is a time-consuming comparison of a conventional exhaustive process.
Fig. 3 is a ratio of overhead corresponding to a real optimal decision system to overhead corresponding to an optimal offloading decision derived by an algorithm.
Fig. 4 is a diagram of the system overhead corresponding to the computing tasks within different size ranges obtained by the method in this patent.
Detailed Description
Embodiments of the present invention will be described below with reference to the accompanying drawings. FIG. 1 is a general flow chart of the invention, and the specific implementation mode is as follows:
A. mathematical modeling is performed on a cloud computing architecture: obtaining constant parameters required in mathematical models by prior art sensing and incorporating into mathematical models of build systems
B. Formulating a mathematical model obtained by modeling into a mixed integer programming problem: and formulating a mathematical model obtained by modeling into a mixed integer programming problem according to the problem to be solved.
C. Implementing a branch-and-bound algorithm with a programming language design: the algorithm is implemented using a high-level programming language such as java, python. Or a third party open source matlab library function BNB20_new ().
D. and (3) carrying out algorithm solution on the mixed integer programming problem and the calculation task vector: according to the function interface, the mixed integer programming function model and the calculation task vector are expressed in the form of the adopted high-level language, and then are brought into algorithm solution.
Claims (1)
1. The method for obtaining the optimal task unloading decision in the cloud computing environment comprises the following three steps:
A. Mathematical modeling of the system: assuming that n tasks in a cell need to be offloaded in a cloud-to-fog computing system architecture, three execution modes of computing tasks are respectively local execution, fog node execution and cloud node execution; we use Respectively represents the energy consumption of the nth task under three conditionsRepresenting the time consumption in the three cases of the nth task and usingRespectively representing weighted sums of time consumption and energy consumption under the three conditions of the nth task, so as to represent the total cost of the selected unloading decision; the subscript n uniformly represents the nth computing task; the energy consumption and the time delay in the three cases are respectively modeled first
(1) Overhead of native execution
(2) Offloading overhead to foggy node computation
When the terminal equipment selects to offload the task to the fog node, the time delay is composed of two parts, namelyTask transmission delayThe fog node processes the time delay, and the time delay for downloading the calculation result is negligible; the energy consumption also comprises two parts, namelyTask transmission energy consumptionThe idle state energy consumption of the terminal equipment;
(3) Offloading overhead to cloud node execution
Similar to the mathematical model unloaded to the fog node, the energy consumption and the time delay are both two parts; however, a transmission delay t is introduced when the cloud node is unloaded to calculate;
D n denotes the CPU cycles required to complete the computational tasks; b n represents the size of the computational task to be transferred; p d,Pt,Pi represents the power of the terminal device when executing the calculation task, uploading the calculation task and being idle; f e,fc represents CPU frequencies of the terminal equipment, the fog node and the cloud node respectively; the size of the uploading bandwidth of the terminal equipment;
B. formulating the problem as a mixed integer programming problem:
Subject to:
C1:
C2:
C3:
c1 represents that the total uploading bandwidth of n devices is maximum C; c2 indicates that the upload bandwidth allocated to each device cannot be negative; c3 represents that the terminal equipment can only execute from local, and one of three strategies of unloading to the fog node and unloading the cloud node is selected to execute, wherein the strategies are respectively represented by 0,1 and 2;
C. Solving using a branch-and-bound algorithm: and using programming languages such as python, matlab and the like to design and realize a branch-and-bound algorithm function, and entering the obtained mixed integer programming model through modeling and the sizes of n tasks into the function to obtain the minimum cost and corresponding unloading decision.
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| CN111124531B (en) * | 2019-11-25 | 2023-07-28 | 哈尔滨工业大学 | Method for dynamically unloading calculation tasks based on energy consumption and delay balance in vehicle fog calculation |
| WO2021217401A1 (en) * | 2020-04-28 | 2021-11-04 | 重庆邮电大学 | Traffic management method and management apparatus |
| CN112004239B (en) * | 2020-08-11 | 2023-11-21 | 中国科学院计算机网络信息中心 | Cloud edge collaboration-based computing and unloading method and system |
| CN112416603B (en) * | 2020-12-09 | 2023-04-07 | 北方工业大学 | Combined optimization system and method based on fog calculation |
| CN112787920B (en) * | 2021-03-03 | 2021-11-19 | 厦门大学 | Underwater acoustic communication edge calculation time delay and energy consumption optimization method for ocean Internet of things |
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| CN107493334A (en) * | 2017-08-18 | 2017-12-19 | 西安电子科技大学 | A kind of cloud and mist calculating network framework and the method for strengthening cloud and mist network architecture reliability |
| CN108540406A (en) * | 2018-07-13 | 2018-09-14 | 大连理工大学 | A kind of network discharging method based on mixing cloud computing |
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| CN107493334A (en) * | 2017-08-18 | 2017-12-19 | 西安电子科技大学 | A kind of cloud and mist calculating network framework and the method for strengthening cloud and mist network architecture reliability |
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