Scheduling for Grid Computing
龚 斌
山东大学计算机科学与技术学院
山东省高性能计算中心
Reference
• Fangpeng Dong and Selim G.Akl ：Scheduling
Algorithms for Grid Computing : State of the Art
and Open Problems
• Yanmin ZHU : A Survey on Grid Scheduling
Systems
• Peter Gradwell : Overview of Grid Scheduling
Systems
• Alain Andrieux et al : Open Issues in Grid
Scheduling
• Jia yu and Rajkumar Buyya : A Taxonomy of
Workflow Systems for Grid Computing
什么是网格？
网格（Grid）是构筑在Internet上的一组新兴技术，
它将高速互联网、高性能计算机、大型数据库、传感
器、远程设备等融为一体，为科技人员和普通老百姓
提供更多的资源。Internet主要为人们提供E-mail、网
页浏览等通信功能，而网格功能更多更强，能让人们
透明地使用计算、存储、信息处理等其他资源。
1998, The Grid: Blueprint for a New Computing
Infrastructure.
Ian Foster :美国阿岗国家实验室资深
科学家、美国计算网格项目负责人
The Definition of Grid
• A type of parallel and distributed system
that enables the sharing, selection, and
aggregation of geographically distributed
autonomous and heterogeneous resources
dynamically at runtime depending on their
availability, capability, performance, cost
and users’ quality-of-service requirements
Characteristics of Grid Computing
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•
•
•
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Exploiting underutilized resources
Distributed supercomputing capability
Virtual organization for collaboration
Resource balancing
Reliability
Class of Grid Computing
• Function:
– Computing Grid
– Data Grid
– Service Grid
• Size:
– IntraGrid
– ExtraGrid
– InterGrid
Traditional Parallel Scheduling Systems
• System:
– SMP : 对称多处理，共享内存
– Cluster：机群
– CC-NUMA: SGI
• Scheduling Systems:
– OpenPBS, LSF, SGE，Loadlevel,
Condor,etc…
Cluster Scheduling
The Assumption Underlying Tradition
Systems
• All resources reside within a single administrative domain.
• To provide a single system image, the scheduler controls
all of the resources.
• The resource pool is invariant.
• Contention caused by incoming application can be
managed by the scheduler according to some policies, so
that its impact on the performance that the site can provide
to each application can be well predicted.
• Computation and their data reside in the same site or data
staging is a highly predictable process, usually from a
predetermined source to a predetermined destination,
which can be viewed as constant overhead.
Characteristics of Cluster Scheduling
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Homogeneity of resource and application
Dedicated resource
Centralized scheduling architecture
High-speed interconnection network
Monotonic performance goal
年代
The Terms of Grid Scheduling
• A task is an atomic unit to be scheduled by the scheduler
and assigned to a resource.
• The properties of a task are parameters like CPU/memory
requirement, deadline, priority, etc.
• A job (or metatask, or application) is a set of atomic tasks
that will be carried out on a set of resources. Job can have
a recursive structure, meaning that jobs are composed of
sub-jobs and /or tasks, and sub-jobs can themselves be
decomposed further into atomic tasks.
• A resource is something that is required to carry out an
operation, for example: a processor for data processing, a
data storage device, or a network link for data transporting.
• A site (or node) is an autonomous entity composed of one
or multiple resources.
• A task scheduling is the mapping of tasks to a selected
group of resources which may be distributed in
administrative domains.
Three Stages of Scheduling Process
• Resource discovering and filtering
• Resource selecting and scheduling
according to certain objectives
• Job submission
Stages of SuperScheduling
• Resource Discovery
– Authorization Filtering
– Application requirement definition
– Minimal requirement filtering
• System Selection
– Gathering information (query)
– Select the system (s) to run on
• Run Job
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(optional) Make an advance reservation
Submit job to resources
Preparation Tasks
Monitor progress (maybe go back to System Selection)
Find out J is done
Completion tasks
Grid Scheduling framework
• Application Model
– Extracts the characteristics of applications to be scheduled.
• Resource Model
– Describes the characteristics of the underlying resources in Grid
systems.
• Performance Model
– Responsible for behavior of a specific job on a specific
computation resource.
• Scheduling Policy
– Responsible for deciding how applications should be executed and
how resources should be utilized.
Applications Classification
• Batch vs. Interactive
• Real-time vs. Non real-time
• Priority
Resources Classification
• Time-shared vs. Non time-shared
• Dedicated vs. Non-dedicated
• Preemptive vs. Non-preemptive
Performance Estimation
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Simulation
Analytical Modeling
Historical Data
On-line Learning
Hybrid
Scheduling Policy
• Application-centric
– Execution Time : the time duration spent executing the job
– Wait Time : the time duration spent waiting in the ready queue
– Speedup : the ratio of time spent executing the job on the original
platform to time spent executing the job on the Grid.
– Turnaround Time : also called response time. It is defined as the sum of
waiting time and executing time.
– Job Slowdown : it is defined as the ratio of the response time of a job to
its actual run time.
• System-centric
– Throughput : the number of jobs completed in one unit of time, such as
per hour or per day.
– Utilization : the percent of time a resource is busy.
– Flow Time : the flow time of a set of jobs is the sum of completion time
of all jobs.
– Average Application performance.
Scheduling Strategy
• Performance-driven
• Market-driven
• Trust-driven
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Security policy
Accumulated reputation
Self-defense capability
Attack history
Site vulnerability（弱点、攻击）
A logical Grid scheduling architecture
Broken lines : resource or application information flows
Real lines : task or task scheduling command flows
Grid Scheduler
• Grid Scheduler (GS) receives application from Grid users,
select feasible resources for these application according to
acquired information from the Grid Information Service
module, and finally generates application-to-resource
mappings based on certain objective functions and
predicted resource performance.
– GS usually cannot control Grid resources directly, but work like
broker or agents
– Metascheduler, SuperScheduler
– Is not an indispensable component in the Grid infrastructure. Not
included in the Globus Tookit
– In reality multiple such schedulers might be deployed, and
organized to form different structures (centralized, hierarchical and
decentralized) according to different concerns, such as
performance or scalability.
Grid Information Service (GIS)
• To provide such information to Grid schedulers
• GIS is responsible for collecting and predicting the
resource state information, such as CPU capacities,
memory size, network bandwidth, software availabilities
and load of a site in a particular period.
• GIS can answer queries for resource information or push
information subscribers
• Globus : Monitoring and Discovery System (MDS)
• Application profiling (AP) is used to extract properties of
applications
• Analogical Benchmarking (AB) provides a measure of
how well a resource can perform a given type of job.
Launching and Monitoring (LM)
• Binder
• Implements a finally-determined schedule
by submitting applications to selected
resources, staging input data and
executables if necessary, and monitoring the
execution of the applications
• Globus :Grid Resource Allocation and
Management, GRAM
Local Resource Manager (LRM)
• Is mainly responsible for two jobs: local
scheduling inside a resource domain, where not
only jobs from exterior Grid users, but also jobs
from the domain’s local users are executed, and
reporting resource information to GIS.
• Open PBS, Condor，LSF，SGE，etc
• NWS : Network Weather Service, Hawkeye,
Ganglia
Evaluation Criteria for Grid
Scheduling Systems
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Application Performance Promotion
System Performance Promotion
Scheduling Efficiency
Reliability
Scalability
Applicability to Application and Grid
Environment
Scheduler Organization
• Centralized
• Decentralized
• Hierarchical
Centralized Scheduling
Decentralized Scheduling
Hierarchical Scheduling
Existing Grid Scheduling Systems
• Information Collection Systems
– MDS (Meta Directory Service)
– NWS (Network Weather Service)
•
•
•
•
•
•
Condor
Condor-G
AppLeS
Nimrod-G
GRaDS
Etc…
Characteristics of scheduling for
Grid Computing
• Heterogeneity and Autonomy
– Does not have full control of the resources
– Hard to estimate the exact cost of executing a task on different sites.
– Is required to be adaptive to different local policies
• Performance Dynamism
– Grid resources are not dedicated to a Grid application
– Performance fluctuation, compared with traditional system
– Some methods: QoS negotiation, resource reservation, rescheduling
• Resource Selection and Computation-Data Separation
– In tradition systems, executable codes of application and input/output data
are usually in the same site, or the input sources and output destinations
determined before the application is submitted, The cost of data staging
can be neglected.
• Application Diversity
Grid Scheduling Algorithms
• The Complexity of a general scheduling
problem is NP-Complete.
• The scheduling problem becomes more
challenging because of some unique
characteristics belonging to Grid computing.
A Hierarchical taxonomy for scheduling algorithm
A Taxonomy of Grid Scheduling
Algorithm
• Local vs. Global
– Grid scheduling falls into the global scheduling.
• Static vs. Dynamic
– Both static and dynamic scheduling are widely
adopted in Grid computing
Static Scheduling
• Every task comprising the job is assigned once to
a resource, the placement of an application is static,
and a firm estimate of the cost of the computation
can be made in advance of the actual execution.
• Easier to program from a scheduler’s point of
view
• Rescheduling mechanism are introduced for task
migration.
• Another side-effect is that the gap between static
scheduling and dynamic scheduling becomes less
important.
Dynamic Scheduling
• Online Scheduling
• Two Major components : system state estimation , decision
making.
• Advantage : the system need not be aware of the run-time
behavior of the application before execution
• The primary performance : maximizing resource utilization,
rather than minimizing runtime for individual jobs.
• Four basic approaches:
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Unconstrained FIFO
Balance-constrained techniques
Cost-constrained techniques
Hybrid of static and dynamic techniques
Unconstrained FIFO
• The resource with the currently shortest
waiting queue or the smallest waiting queue
time is selected for the incoming task
• Opportunistic Load Balancing (OLB), or
myopic ( 近视) algorithm
• Simplicity, but far from optimal
Balance-constrained
• Attempts to rebalance the loads on all resources by
periodically shifting waiting tasks from one waiting queue
to another.
• The rebalance only happens inside a “neighborhood”
where all resources are better interconnected.
• Advantages:
– The initial loads can be quickly distributed to all resources and
started quickly
– The rebalancing process is distributed and scalable
– The communication delay of rebalancing can be reduced since task
shifting only happens among the resources that are “close” to each
other
Cost-constrained
• Not only considers the balance among resources but also
the communication cost between tasks
• Instead of doing a task exchange periodically, tasks will be
checked before their move.
• This approach is more efficient than the previous one when
the communication costs among resources are
heterogeneous and communication cost to execute the
application is the main consideration
• It is also flexible, and can be used with other cost factors
such as seeking lowest memory size or lowest disc drive
activity, and so on.
Hybrid
• A further improvement is the static-dynamic
hybrid scheduling
• Is to take the advantages of static schedule and at
the same time capture uncertain behaviors of
applications and resources.
• For example, in those cases where there are
special QoS requirements in some tasks, the static
phase can be used to map those task with QoS
requirements, and dynamic scheduling can be used
for the remaining tasks.
A Taxonomy of Grid Scheduling
Algorithm (cont.)
• Optimal vs. Suboptimal
– Some criterion : minimum makespan , maximum
resource utilization
– Makespan : is the time spent from the beginning of the
first task in a job to the end of the last task of the job.
– The NP-Complete nature of scheduling algorithms
– Current research tries to find suboptimal solutions
A Taxonomy of Grid Scheduling
Algorithm (cont.)
• Approximate vs. Heuristic
– Approximate
• Use formal computational models, but instead of searching the entire
solution space for an optimal solution
• The factor:
– Availability of a function to evaluate a solution
– The time required to evaluate a solution
– The ability to judge the value of an optimal solution according to some
metric.
– Availability of a mechanism for intelligently pruning the solution space.
– Heuristic
• Represents the class of algorithms which make the most realistic
assumptions about a priori knowledge concerning process and system
loading characteristics.
• are more adaptive to the Grid scenarios where both resources and
applications are highly diverse and dynamic
A Taxonomy of Grid Scheduling
Algorithm (cont.)
• Distributed vs. Centralized
– The centralized strategy has the advantage of
ease of implementation, but suffers from the
lack of scalability, fault tolerance and the
possibility of becoming a performance
bottleneck.
A Taxonomy of Grid Scheduling
Algorithm (cont.)
• Cooperative vs. Non-cooperative
– In the non-cooperative case, individual schedulers act
alone as autonomous entities and arrive at decisions
regarding their own optimum objects independent of
the effects of the decision on the rest of system.
– In the cooperative case, each Grid scheduler has the
responsibility to carry out its own portion of the
scheduling task, but all schedulers are working toward a
common system-wide goal.
Objective Functions
• The two major parties in Grid computing
– Resource consumers who submit various application
– Resources Providers who share their resources
• Application Centric
– Makespan
– Economic Cost
• Resource Centric
– Resource Utilization
– Economic Profit
Application-Centric
• Aim to optimize the performance of each
individual application, as application-level
schedulers do.
• time : makespan
• Grid economic model : economic cost
• QoS : Quality of Services
Resource-Centric
• Aim to optimize the performance of the resources
• Throughput : which is the ability of a resource to process a
certain number of jobs in a given period.
• Utilization : which is the percentage of time a resource is
busy
• Grid economic Model : Economic Profit
• TPCC : Total Processor Cycle Consumption, which is the
total number of instructions the grid could compute from
the starting time of executing the schedule to the
completion.
– Represents the total computing power consumed by an application
– Advantage : it can be little affected by the variance of resource
performance, yet still related to the makespan.
Adaptive Scheduling
• The demand for scheduling adaptation
comes from:
– The heterogeneity of candidate resources
– The dynamism of resource performance
– The diversity of applications
• Resource Adaptation
• Dynamic Performance Adaptation
• Application Adaptation
Resource Adaptation
• Su et al : show how the selection of a data storage site affects the
network transmission delay.
• Dail et al : proposed a resource selection algorithm
– Available resources are grouped first into disjoint subsets according to the
network delays between the subsets
– Inside each subset, resources are ranked according to their memory size
and computation power
– An appropriately-size resource group is selected from the sorted lists
• Subhlok et al : show algorithms to jointly analyze computational and
communication resource for different application demands and a
framework for automatic node selection
– The algorithm are adaptive to demands like selecting a set of nodes to
maximize minimum available bandwidth between any pair of nodes and
selecting a set of nodes to maximize the minimum available fractional
compute and communication capacities.
Dynamic Performance Adaptation
• The adaptation of the dynamic performance of resources is :
– Changing scheduling policies or rescheduling
– Workload distributing according to application-specific
performance models
– Finding a proper number of resources to be used
• Usually adopt some kind of divide-and conquer approach
– Parameter sweep applications
– Data stripe processing
• Cluster-aware Random Stealing (CRS)
– Allows an idle resource steal jobs not only from the local cluster
but also from remote ones with a very limited amount of wide-area
communication
Application Adaptation
• Dial et al : explicitly decouple the scheduler core from
application-specific and platform-specific components
used by the core.
• Aggarwal et al : resource reservation
• Wu et al : give a very good example of how a self-adaptive
scheduling algorithm cooperates with long-term resource
performance prediction.
– The algorithm is adaptive to indivisible single sequential jobs, jobs
that can be partitioned into independent parallel tasks, and jobs that
have a set of indivisible tasks.
– When prediction error of the system utilization id reaching a
threshold, the scheduler will try to reallocate tasks.
Task Dependency of an Application
• Independent
– Static
– Dynamic
• Dependent
– Static
• List Algorithm
• Cluster Algorithm
• Duplication-based Algorithm
– Dynamic
– Static Enhanced by Dynamic Rescheduling
Independent Task Scheduling
• Algorithms with Performance Estimate
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MET
MCT
Min-min
Max-min
Xsuffrage
Task Grouping
• Algorithms without Performance Estimate
MET Algorithm
• Minimum Execution Time
• Assigns each task to the resource with the best
expected execution time for that task, no matter
whether this resource is available or not at the
present time.
• The motivation behind MET is to give each task
its best machine
• This can cause a severe load imbalance among
machine
MET Algorithm
For each arrived task S[k]
for each host H[j] in Heterogeneous Machines Set H
查找出最小的E[k,j]以及获得该最小值的机器H[t]
endfor
更新机器就绪时间：r[t]=r[t]+E[k,t]
endfor
S[k]：任务集合
H[j]：机器集合
E[k,j]：任务S[k]在机器H[j]上的期望执行时间
R[t]：机器H[j]的期望就绪时间
MCT Algorithm
• Minimum Completion Time
• Assigns each task, in an arbitrary order, to the
resource with the minimum expected completion
time for that task.
• This cause some tasks to be assigned to machines
that do not have the minimum execution time for
them.
• The intuition behind MCT is to combine the
benefits of opportunistic load balance (OLB) and
MET, while avoiding the circumstances in which
OLB and MET perform poorly.
MCT Algorithm
For each arrived task S[k]
for each host H[j] in heterogeneous Machines Set H
计算预测完成时间：C[k,j]=E[k,j]+r[j]
查找出最小的C[k,j]以及获得该最小值的机器H[t]
endfor
更新机器就绪时间：r[t]=r[t]+E[k,t]
endfor
C[k,j]：任务S[k]在机器H[j]上的期望完成时间
Min-min Algorithm
• Algorithm:
– Begins with the set U of all unmapped tasks
– The set of minimum completion time from M for each task in U is
found
– The task with the overall minimum completion time from M is
selected and assigned to the corresponding machine
– The newly mapped task is removed from U, and the process
repeats until all tasks are mapped (i.e., U is empty).
• Based on the minimum completion time, as is MCT.
Min-min Algorithm
For all task S[k] in scheduling-set SS
for all machines H[j] in heterogeneous host set H
C[k,j]=E[k,j]+r[j]//计算全部任务在每一个机器上的期望完成时间
Do until all tasks in SS are mapped
for each task in SS//找出全部未映射任务在每个机器上的最小完成时间及
其机器
find the earliest (minimum) completion time and the host that obtains it
endfor
//在所有未映射任务的最小完成时间中找出最小值机器获得该值的机器H[j]
find task S[k] with the minimum earliest completion time
assign task S[k] to the host H[j] that gives the earliest completion time
delete task S[k] from SS and update r[j]
Update C[k,j] for all host H[j]
enddo
Max-min Algorithm
• Algorithm : is very similar to Min-min.
– Begins the set U of all unmapped tasks
– The set of minimum completion time M is found
– The task with the overall maximum from M is selected
and assigned to the corresponding machine
– The newly mapped task is removed from U, and the
process repeats until all tasks are mapped
• Min-min and Max-min algorithms are
simple and can be easily amended to adapt
to different scenarios
– X. He et al : is presents a QoS Guided Minmin heuristic, can guarantee the QoS
requirement of particular tasks and minimum
the makespan at the same time.
– Wu, Shu and Zhang : gave a Segmented Minmin algorithm.
Max-Int Algorithm
(最大时间跨度算法)
For all task S[k] in scheduling-set SS
for all machines H[j] in heterogeneous host set H
C[k,j]=E[k,j]+r[j]//计算全部任务在每一个机器上的期望完成时间
Do until all tasks in SS are mapped
for each task in SS
find the earliest (minimum) completion time C[k,m] and the host H[m] that
obtains it
find second earliest completion time C[k,n] and the host H[n] that obtains it
计算Interval：I[k]=C[k,n]-C[k,m]，并将I[k]作为向量I的一个元素
endfor
//从全部任务的时间间隔I中，找出具有最小时间间隔的任务S[t]
for all task S[k] in SS find the task S[t] with the maximum Interval
assign task S[t] to the host H[m] that gives the earliest completion time
delete task S[t] from SS and update r[j]
Update C[t,m] for all host H
enddo
Max-Int Algorithm
• 吸取Min-min和Max-min算法的优点，除
利用历史调度信息，还利用预测信息减
少调度任务时间
• 未来调度总是趋向最佳
Suffrage algorithm
• 一个资源将被分配给这样的一个作业，
如果作业不分配到该节点上，将会蒙受
最大的损失。
• 每个作业有一个sufferage值，定义在该任
务的最好完成时间和它的次好完成时间
之间，sufferage值高的作业有优先权。
Algorithm
for 作业集合T中所有的作业
for 所有网格节点mj
ckj=ekj+rj
do until T中所有任务映射
for 作业集合T中所有的作业tk
寻找具有最早完成时间的mj
sufferage value= 次好完成时间-最好完成时间
if mj 没有指派
指派tk给mj，从T中删除tk，标记mj为已经指派
else
if 已经指派给mj的tk的sufferage小于tk的sufferage value
取消ti的指派，把ti放回T中，指派tk给mj，从T中删除tk
endfor
基于指派给机器的作业更新向量r，
更新c矩阵
enddo
Task Grouping
• Some cases in which applications with a large number of
lightweight jobs. The overall processing of these
applications involves a high overhead cost in terms of
scheduling and transmission to or from Grid resources.
• Muthuvelu et al : propose a dynamic task grouping
scheduling algorithm to deal with these cases.
– Once a set of fine grained tasks are received, the scheduler groups
them according to their requirements for computation and the
processing that a Grid resource can provide in a certain time period.
– All tasks in the same group are submitted to the same resource
which can finish them all in the given time.
– The overhead for scheduling and job launching is reduced and
resource utilization is increased.
Algorithms without Performance
Estimate
• Do not use performance estimate but adopt the idea of
duplication, which is feasible in the Grid environment
where computational resources are usually abundant but
mutable.
• Subramani et al : a simple duplication scheme
– Distributes each job to the K least load sites
– Each of these K sites schedules the job locally
– When a job is able to start at any of the sites, the site informs the
scheduler at the job-originating site, which in turn contacts the
other K-1 sites to cancel the jobs from their respective queue.
• Silva et al : Workqueue with Replication (WQR)
Dependent Task Scheduling
• Directed Acyclic Graph (DAG)
– Node represents a task.
– Directed edge denotes the precedence orders
between its two vertices.
– In some cases, weights can be added to nodes
and edges to express computational costs and
communication costs respectively
• Condor DAGMan, CoG, Pegasus, GridFlow,
ASKALON
DAG
Grid Systems Supporting Dependent
Task Scheduling
• To run a workflow in a Grid :
– How the tasks in the workflow are scheduled.
Grid workflow generators
– How to submit the scheduled tasks to Grid
resources without violating the structure of the
original workflow. Grid workflow engines
Taxonomy of Algorithms for
Dependent Task Scheduling
• List Heuristics
– Heterogeneous Earliest-Finish-Time, HEFT
– Fast Critical Path, FCP
• Duplication Based Algorithms
– Task Duplication-based Scheduling, TDS
– Task duplication-based scheduling Algorithm for
Network of Heterogeneous systems, TANH
• Clustering Heuristics
– Dominant Sequence Clustering, DSC
– CASS-II
Data Scheduling
• In high energy physics, bioinformatics, and
other disciplines, there are application
involving numerous, parallel tasks that both
access and generate large data sets,
sometime in the petabyte range.
• Remote data storage, access management,
replication services, and data transfer
protocol.
Park et al’s model of cost measured
in makespan
•
•
•
•
•
Local Data and Local Execution
Local Data and Remote Execution
Remote Data and Local Execution
Remote Data and Same Remote Execution
Remote Data and Different Remote
Execution
On Data Replication
• When the scheduling problem with data
movement is considered, there are two situation :
whether data replication is allowed or not.
• In Pegasus, the CWG assumes that accessing an
existing dataset is always more preferable than a
new one, when it maps an abstract workflow to a
concrete one.
• Ranganthan et al : view data sets in the Grid as a
tiered system and use dynamic replication
strategies to improve data access.
On Computation and Data Scheduling
• When the interaction of computation
scheduling and data scheduling is
considered, we can also find two different
of approaches
– Decoupling computation from data scheduling
– Conducting a combination scheduling
Non-traditional Approaches for Grid
Task Scheduling
• Grid Economy
– Economic Cost/Profit Considered
– None Economic Cost/Profit Considered
• Nature’s Heuristics
– Genetic Algorithms
– Simulated Annealing
– Tabu Serarch
Scheduling under QoS Constains
• Ina distributed heterogeneous non-dedicated
environment, quality of services (QoS) is a big
concern of many application. The meaning of QoS
can be varied according to the concerns of
different users. It could be a requirement on the
CPU speed, memory size, bandwidth, software
version or deadline.
• In general, QoS is not the ultimate objective of an
application, but a set of conditions to run
application successfully.
Strategies Treating Dynamic
Resource Performance
• On-Time-Information from GIS
• Performance Prediction Based on GIS,
Historical Record and Workload Modeling
– On Prediction Accuracy
– Prediction Based on Historical Record
– Prediction Based on Workload Modeling
• Rescheduling
Open Issues On the Grid Scheduling
• Application and Enhancement of Classic Heterogeneous
Scheduling Algorithms in Grid Environment
• New Algorithms Utilizing Dynamic Performance
Prediction
• New Rescheduling Algorithms Adaptive Performance
Variation
• New Algorithms under QoS Constraints
• New Algorithms Considering Combined Computation and
Data Scheduling
• New Problems Introduced by New Models
• New Algorithm Utilizing the Grid Resource Overlay
Structure
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• 由点到面，由杂到精，由量到质
• 好记性不如烂笔头
• 善于总结，提出自己的想法
– 我应该如何利用该论文？
– 真的像作者宣称的那样么？
– 如果……会发生什么？