CN107087019A - A kind of end cloud cooperated computing framework and task scheduling apparatus and method - Google Patents

A kind of end cloud cooperated computing framework and task scheduling apparatus and method Download PDF

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CN107087019A
CN107087019A CN201710148795.4A CN201710148795A CN107087019A CN 107087019 A CN107087019 A CN 107087019A CN 201710148795 A CN201710148795 A CN 201710148795A CN 107087019 A CN107087019 A CN 107087019A
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task
cloud
terminal
cluster
scheduling
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CN107087019B (en
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沈玉龙
王思怡
司旭
保积元
张宇恒
阮金清
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Xidian University
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
    • H04L67/61—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources taking into account QoS or priority requirements
    • 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
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533—Hypervisors; Virtual machine monitors
    • G06F9/45558—Hypervisor-specific management and integration aspects
    • 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
    • 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
    • G06F9/5038—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 considering the execution order of a plurality of tasks, e.g. taking priority or time dependency constraints into consideration
    • 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]
    • G06F9/5083—Techniques for rebalancing the load in a distributed system
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
    • H04L67/1004—Server selection for load balancing
    • H04L67/1008—Server selection for load balancing based on parameters of servers, e.g. available memory or workload
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/56—Provisioning of proxy services
    • 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
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533—Hypervisors; Virtual machine monitors
    • G06F9/45558—Hypervisor-specific management and integration aspects
    • G06F2009/45595—Network integration; Enabling network access in virtual machine instances
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2209/00—Indexing scheme relating to G06F9/00
    • G06F2209/48—Indexing scheme relating to G06F9/48
    • G06F2209/484—Precedence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2209/00—Indexing scheme relating to G06F9/00
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Abstract

The invention discloses a kind of end cloud cooperated computing framework and task scheduling apparatus and method, the method for scheduling task under end cloud cooperated computing framework and the framework under edge cloud computing platform proposed by the present invention, aim to solve the problem that the cloud application epoch of fast development, existing edge cloud platform load is big, and task is committed to the problem of remote communication expense is big.It is different from traditional cloud computing platform, the present invention will be also added in computing resource pond in safety certification access system and with the terminal of certain computing capability, compositing terminal cluster, terminal cluster has stronger autgmentability, add the autgmentability of cloud, terminal cluster and being calculated for the task of single cloud platform before the service node cluster in static cloud jointly completion, optimize traditional cloud platform, effectively reduce the load in high in the clouds.The framework have concurrently edge cloud it is real-time the characteristics of, can provide the application of the big datas such as Internet of Things more powerful and stable service again.

Description

A kind of end cloud cooperated computing framework and task scheduling apparatus and method
Technical field
The present invention relates to field of computer technology, more particularly to a kind of end cloud cooperated computing framework and task scheduling apparatus and Method.
Background technology
Cloud era make it that various services are fast-developing, and various intelligent uses are also following.In the presence of all conglomerate is to digitlization Stride forward, store, calculate and be all increasingly dependent on high in the clouds, high in the clouds turns into the hinge for constructing information-intensive society.In the big data epoch, if All data depend on cloud data center and are uniformly processed, and too many load is not only added to cloud, and task is committed to distal end There is certain communication overhead in cloud center, traditional cloud platform will face many problems, therefore generate data-centered Edge cloud.In the epoch of Kuai Sufazhan cloud application, the load of edge cloud computing platform can be gradually excessive, it is necessary to add some nearby Intelligent terminal and equipment carry out the expansion of resource in real time, it is ensured that the stabilization and high efficiency of edge cloud, it is ensured that Internet of Things etc. is big Data application normally provides service, so as to generate the end cloud cooperated computing framework under edge cloud computing platform.
Cloud computing is based on network technology and virtualization technology, to improve the resource utilizations such as calculating, storage as mesh A kind of resource use pattern that developed.Task scheduling directly determines effective utilization of resource, is that cloud computing is most important One of and the problem of most critical.
End cloud collaboration is different from traditional single cloud computing platform, it will thus provide the terminal of certain computing capability also adds resource Chi Zhong, completes calculating task jointly.Now due to the isomerism of resource, the otherness of terminal resource computing capability and cloud, significantly Add the complexity of task scheduling.
The research on the task scheduling algorithm of cloud computing is more built upon on the basis of grid computing at present, is passed through Traditional classical algorithm is improved to adapt to cloud computing platform, but is due to the isomerism of resource, terminal resource computing capability With the otherness of cloud, the end cloud cooperated computing under edge cloud computing platform is not appropriate for.
The content of the invention
In view of the drawbacks described above of prior art, the technical problems to be solved by the invention are to provide a kind of end cloud collaboration meter Framework and task scheduling apparatus and method are calculated, to reduce the useless expense of traditional virtualization, the utilization rate and distribution for improving resource are imitated Rate.
To achieve the above object, the invention provides a kind of end cloud cooperated computing framework and task scheduling apparatus, its feature It is, including：
Static state under service node in static cloud and virtual machine node under high in the clouds cluster, including edge cloud, edge cloud Service node in cloud is typically made up of virtual machine calculate node, and virtual machine calculate node is dynamically given birth in the management server of high in the clouds Into virtual machine, virtual machine node can by high in the clouds management server dynamic generation, management and destroy；
Cloud collaboration middleware is held, is on the one hand responsible for the credible and secure certification access of terminal resource, is on the other hand end cloud association Rational task distribution and scheduling strategy are provided with computing architecture, it is ensured that task is rationally efficiently cooperateed between terminal and high in the clouds Calculate；
Terminal cluster, is made up of various hardware terminals, and cloud platform is accessed in a secured manner, and there is provided certain calculating money Source.
A kind of above-mentioned end cloud cooperated computing framework and task scheduling apparatus, it is characterised in that in the middle of the end cloud collaboration Part includes automatic find and the monitoring of expansion module, terminal resource and synchronization module, end cloud task scheduling modules.
A kind of above-mentioned end cloud cooperated computing framework and task scheduling apparatus, it is characterised in that the terminal cluster it is each Kind hardware terminal is divided into the PC of wired connection, the PC of wireless connection, the various terminals using Android as major operating system and set It is standby.
A kind of end cloud cooperated computing framework and method for scheduling task, it is characterised in that comprise the following steps：
Step 1: user submits task to task queue, and the priority offered the challenge；
Step 2: the task that user submits is joined the team according to the real-time of the priority of task and task, pass through queue plan Slightly control selections go out the task of team, if the going out team of the task is real-time, task is directly sent to the service section into static cloud Point, if task is non real-time, the task is sent to end cloud Collaboration Server；
Step 3: end cloud Collaboration Server by the un-real time job that task queue is sent be divided into can point task with inseparable Business, then carries out task scheduling, inseparable task is distributed to the single service node processing in static cloud, pair can divide task according to Service node cluster and terminal cluster processing in static cloud are distributed to according to end cloud cooperated computing task allocation algorithms；
Step 4: the service node cluster and terminal cluster in static cloud set up heartbeat mechanism with end cloud Collaboration Server, Service node cluster and terminal cluster in static cloud report task performance, CPU speed, node to end cloud Collaboration Server Load, idling-resource, network transmission speed, to carry out the distribution of resource according to current resource status when task is distributed And scheduling.
A kind of above-mentioned end cloud cooperated computing framework and method for scheduling task, it is characterised in that the step 3 is inseparable Task is there is dependence between not subdivisible or subtask, it is necessary to which serial perform.
A kind of above-mentioned end cloud cooperated computing framework and method for scheduling task, it is characterised in that the step 3 is for hair The task of terminal cluster is given, is first backed up beyond the clouds, because the network and resource situation of terminal are complex, if terminal is calculated Existing problem, the service node processing that the task can be in static cloud.
The beneficial effects of the invention are as follows：
A kind of end cloud cooperated computing framework and task scheduling apparatus and side under edge cloud computing platform proposed by the present invention Method, it is intended to solve the fast-developing cloud application epoch, existing edge cloud platform load is big, and task is committed to remote communication expense Big the problem of.Traditional cloud computing platform is different from, the present invention will calculate energy in safety certification access system and with certain The terminal of power is also added in computing resource pond, compositing terminal cluster, and terminal cluster has stronger autgmentability, adds the expansion of cloud Malleability, terminal cluster and being calculated for the task of single cloud platform before the service node cluster in static cloud jointly completion, Traditional cloud platform is optimized, the load in high in the clouds is effectively reduced.The framework have concurrently edge cloud it is real-time the characteristics of, can be again The application of the big datas such as Internet of Things provides more powerful and stable service.
The technique effect of the design of the present invention, concrete structure and generation is described further below with reference to accompanying drawing, with It is fully understood from the purpose of the present invention, feature and effect.
Brief description of the drawings
Fig. 1 is end cloud cooperated computing Organization Chart
Fig. 2 is end cloud cooperated computing flow chart
Fig. 3 is the end cloud cooperated computing method for scheduling task flow chart of the present invention
Embodiment
As shown in figure 1, a kind of end cloud cooperated computing framework and task scheduling apparatus, it is characterised in that including：
High in the clouds cluster：Including the service node and virtual machine node in the static cloud under edge cloud, the static state under edge cloud Service node in cloud is typically made up of virtual machine calculate node, virtual machine node can by high in the clouds management server dynamic generation, Management and destruction.
Hold cloud collaboration middleware：End cloud collaboration middleware includes automatic discovery and expansion module, and terminal resource is monitored and same Module is walked, cloud task scheduling modules are held.Cloud collaboration middleware is held on the one hand to be responsible for the credible and secure certification access of terminal resource, separately On the one hand rational task distribution and scheduling strategy are provided for end cloud cooperated computing framework, it is ensured that task is rationally efficiently in terminal The cooperated computing between high in the clouds.
Terminal cluster：Terminal cluster is made up of various hardware terminals, and these terminals can be divided into the PC of wired connection, wirelessly connect The PC connect, the various terminal equipment using Android as major operating system is such as mobile phone and flat board, and these terminal quantities are many It is many, it can be extended whenever and wherever possible, while these terminals access cloud platform in a secured manner, there is provided certain calculating money Source.
Cloud collaboration middleware is held mainly to include following several big functional modules：
1st, it is automatic to find and expansion module
Cloud cooperated computing framework is held to set up on the basis of this destination edge cloud, the problems such as in the absence of cross-domain outgoing access.Place It can be interacted in the terminal in the region by way of wired, wireless network connection with whole cloud.
The automatic basic function for being the discovery that end cloud Collaboration Server, is a kind of dynamic side of cloud platform terminal extension resource Method.The method for holding cloud Collaboration Server to use active probe, for finding available terminal resource in time.Hold cloud Collaboration Server Corresponding packet is regularly sent in the network actively accessed to terminal, if the terminal that can now provide computing resource receives this Packet, then using corresponding safety authentication protocol, reuse certain format packet send to end cloud Collaboration Server enter Row resource authentic authentication.End cloud synergist is received after packet to be parsed according to regulation data format, judge whether certification into Work(.If the terminal is considered as believable computing resource node by certification success, Collaboration Server immediately, real-time computing resource pond is added In, and in record data bag MacAddr fields as trusted terminal unique mark, if CMT wireless network is unstable to cause IP Address changes and disconnected, and system can carry out ARP request by the terminal MacAddr of record, obtain the network of terminal Address, is reconnected.
2nd, terminal resource monitoring and synchronization module
Resource status and task attribute of the cloud Collaboration Server when task is distributed in current system is held to carry out The distribution and scheduling of resource, therefore end cloud Collaboration Server needs the resource pool that maintenance one is calculated in real time, record is current to be added Enter each high in the clouds of cloud platform or the computing resource information of terminal node.
Terminal resource monitoring modular calls respective service after terminal is successfully accessed cloud platform, and computing resource is matched somebody with somebody into confidence Breath is sent to end cloud Collaboration Server.Hold cloud Collaboration Server can be regularly to terminal down distributing resource information request, terminal receives request Continue to call monitoring resource module to feed back to end cloud Collaboration Server in time, ensure as far as possible and the note in real-time computing resource pond Record is synchronous.
3rd, cloud task scheduling modules are held
Cloud task scheduling modules are held, are completed with task queue and end cloud Collaboration Server.Task queue first will be by Classified according to task attribute or priority, then holding cloud Collaboration Server to take out needs corresponding task to be processed, according to working as Preceding real time resources information combines certain task allocation algorithms, carries out the scheduling of task and resource.It is final to ensure that task is rational It is assigned to high in the clouds and terminal is calculated, holding task directly calculates the load balancing of collaboration progress and whole system in end cloud.
A kind of end cloud cooperated computing framework and method for scheduling task, it is characterised in that comprise the following steps：
Step 1: user submits task to task queue, and the priority offered the challenge.
Step 2: the task that user submits is joined the team according to the real-time of the priority of task and task, pass through queue plan Slightly control selections go out the task of team, if the going out team of the task is real-time, task is directly sent to the service section into static cloud Point, if task is non real-time, the task is sent to end cloud Collaboration Server.
Step 3: end cloud Collaboration Server by the un-real time job that task queue is sent be divided into can point task with inseparable Business, then carries out task scheduling, and inseparable task is there is dependence, it is necessary to serially hold between not subdivisible or subtask OK, inseparable task is distributed to the single service node processing in static cloud, task pair can be divided to appoint according to end cloud cooperated computing Business allocation algorithm is distributed to service node cluster and terminal cluster processing in static cloud, for being sent to appointing for terminal cluster Business, is first backed up beyond the clouds, because the network and resource situation of terminal are complex, is gone wrong if terminal is calculated, the task can With the service node processing in static cloud.
Step 4: the service node cluster and terminal cluster in static cloud set up heartbeat mechanism with end cloud Collaboration Server, Service node cluster and terminal cluster in static cloud report task performance, CPU speed, node to end cloud Collaboration Server Load, idling-resource, network transmission speed etc., to carry out point of resource according to current resource status when task is distributed Match somebody with somebody and dispatch.
As shown in Figure 2,3, it is the end cloud cooperated computing method for scheduling task flow chart of the present invention, the following institute of whole process Show：
1st, the priority of being proposed according to the real-time of task and user for task is joined the team to task, then passes through queue Task goes out the order of team in policy control decision queue.
2nd, real-time judgement is carried out to going out group task.
If the service node into static cloud a. in real time, is sent immediately.
If b. non real-time, the un-real time job for going out team is sent to end cloud Collaboration Server.
3rd, separability judgement is carried out to un-real time job.
If a. task is inseparable, task is sent to the service node in static cloud.
If b. task dividable, calculates the task amount that each node should be distributed according to task allocation algorithms, is then distributed to static state Service node and terminal cluster processing in cloud, if task is sent to the node of terminal cluster, while carrying out safety backup beyond the clouds, with It is standby to occur being recovered during mistake.
Task allocation algorithms are as follows：
Define 1：Task scheduling is by scheduling node, network, end or the part of cloud computing node three composition.By end and cloud computing section From the point of view of make an entirety and be uniformly processed, the collection of all calculate nodes is combined into M, and the calculate node of resource is d, wherein M= {d1,d2,…,dn, n is the total number of calculate node.Calculate node diTransmission time and calculating time difference to unit task For biAnd ci。
Define 2：The task size total amount for being currently needed for user's submission of processing is A, and the purpose of this algorithm is total amount Task for A is divided into n subtask (being the number of all calculate nodes) and is assigned to operation in each calculate node.This A little task is independent task, and does not have dependence between other tasks.
Define 3：Calculate node diHandle subtask aiDeadline be Ti.The shortest time that task is completed is all The time that subtask is completed simultaneously is T1=T2=...=TnIf the shortest time, completion was run in not all subtask simultaneously, that Task in the calculate node of also unfinished operation, which gives the calculate node for having completed to run, can obtain shorter operation Time.
Step 1: determining the optimal scheduling order of all calculate nodes, optimal calculate node dispatching sequence is that bandwidth is passed Subtract order, because first can just allow task to reach calculate node operation as early as possible to the high calculate node scheduler task of bandwidth. Because user submits task to be directly to be submitted to this destination edge cloud, cloud network bandwidth is relatively excellent, and high in the clouds calculate can It is higher by property, the problems such as integrated communication expense and security, i.e., cloud task is generally first distributed to, then be distributed to end task.If d1,d2,…,dnSorted according to dispatching sequence.
Step 2: calculate node diHandle subtask aiDeadline be Ti, equal to task transmission time plus calculate Time is Ti=aibi+aici, and because the time that the shortest time that task is completed is the completion simultaneously of all subtasks is T1=T2 =...=Tn, each calculate node is calculated from the equations above assigns to relation a between task sizei=βia1, wherein
Step 3: assigning to the relation a between task size according to each calculate nodei=βia1With task total amount A=a1+a2 +…+an, obtain first calculate node and assign to task size
Step 4: assigning to the relation a between task size according to each calculate nodei=βia1With first calculate node Assign to task sizeCalculate each calculate node and assign to task sizePress The high in the clouds of each in system is distributed to according to solving result or terminal calculate node is calculated.
Preferred embodiment of the invention described in detail above.It should be appreciated that one of ordinary skill in the art without Need creative work just can make many modifications and variations according to the design of the present invention.Therefore, all technologies in the art Personnel are available by logical analysis, reasoning, or a limited experiment on the basis of existing technology under this invention's idea Technical scheme, all should be in the protection domain being defined in the patent claims.

Claims (6)

1. a kind of end cloud cooperated computing framework and task scheduling apparatus, it is characterised in that including：
In static cloud under service node in static cloud and virtual machine node under high in the clouds cluster, including edge cloud, edge cloud Service node be typically made up of virtual machine calculate node, virtual machine calculate node is dynamic generation in the management server of high in the clouds Virtual machine, virtual machine node can be by high in the clouds management server dynamic generation, management and destruction；
Cloud collaboration middleware is held, is on the one hand responsible for the credible and secure certification access of terminal resource, is on the other hand end cloud collaboration meter Calculate framework and rational task distribution and scheduling strategy are provided, it is ensured that task rationally efficiently cooperates with meter between terminal and high in the clouds Calculate；
Terminal cluster, is made up of various hardware terminals, and cloud platform is accessed in a secured manner, and there is provided certain computing resource.
2. a kind of end cloud cooperated computing framework and task scheduling apparatus as claimed in claim 1, it is characterised in that the end cloud Middleware is cooperateed with to include automatic find and the monitoring of expansion module, terminal resource and synchronization module, end cloud task scheduling modules.
3. a kind of end cloud cooperated computing framework and task scheduling apparatus as claimed in claim 1, it is characterised in that the terminal The various hardware terminals of cluster are divided into the PC of wired connection, the PC of wireless connection, using Android as the various of major operating system Terminal device.
4. a kind of end cloud cooperated computing framework and method for scheduling task, it is characterised in that comprise the following steps：
Step 1: user submits task to task queue, and the priority offered the challenge；
Step 2: the task that user submits is joined the team according to the real-time of the priority of task and task, pass through queue policies control System selects the task of team, if the going out team of the task is real-time, task directly is sent into the service node into static cloud, such as Fruit task is non real-time, and the task is sent to end cloud Collaboration Server；
Step 3: end cloud Collaboration Server by the un-real time job that task queue is sent be divided into can point task and inseparable task, Then carry out task scheduling, inseparable task be distributed to the single service node processing in static cloud, pair can divide task according to End cloud cooperated computing task allocation algorithms are distributed to service node cluster and terminal cluster processing in static cloud；
Step 4: the service node cluster and terminal cluster in static cloud set up heartbeat mechanism with end cloud Collaboration Server, it is static Service node cluster and terminal cluster in cloud are reported task performance, CPU speed, node to end cloud Collaboration Server and born Load, idling-resource, network transmission speed, so as to when task is distributed according to current resource status carry out resource distribution and Scheduling.
5. a kind of end cloud cooperated computing framework and method for scheduling task as claimed in claim 4, it is characterised in that the step Three inseparable tasks are there is dependence between not subdivisible or subtask, it is necessary to which serial perform.
6. a kind of end cloud cooperated computing framework and method for scheduling task as claimed in claim 4, it is characterised in that the step Three, for being sent to the task of terminal cluster, first back up beyond the clouds, because the network and resource situation of terminal are complex, if eventually End calculating goes wrong, the service node processing that the task can be in static cloud.
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