Fault-Tolerant Scheduling for Scientific Workflow
with Task Replication Method in Cloud
Zhongjin Li
1
, Jiacheng Yu
1
, Haiyang Hu
1
, Jie Chen
1
, Hua Hu
1
, Jidong Ge
2
and Victor Chang
3
1
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
2
State Key Laboratory for Novel Software Technology, Software Institute, Nanjing University, China
3
International Business School Suzhou, Xi'an Jiaotong Liverpool University, Suzhou, China
Keywords: Cloud Computing, Fault-tolerant, Scientific Workflow Scheduling.
Abstract: Cloud computing has become a revolutionary paradigm by provisioning on-demand and low cost computing
resources for customers. As a result, scientific workflow, which is the big data application, is increasingly
prone to adopt cloud computing resources. However, internal failure (host fault) is inevitable in such large
distributed computing environment. It is also well studied that cloud data center will experience malicious
attacks frequently. Hence, external failure (failure by malicious attack) should also be considered when
executing scientific workflows in cloud. In this paper, a fault-tolerant scheduling (FTS) algorithm is
proposed for scientific workflow in cloud computing environment, the aim of which is to minimize the
workflow cost with the deadline constraint even in the presence of internal and external failures. The FTS
algorithm, based on tasks replication method, is one of the widely used fault tolerant mechanisms. The
experimental results in terms of real-world scientific workflow applications demonstrate the effectiveness
and practicality of our proposed algorithm.
1 INTRODUCTION
Cloud computing is the popular and promising
computing platforms for users or customers, and its
on-demand computational resources can be obtained
easily in the form of virtual machine (VM) (Foster et
al., 2008; Mell and Grance, 2009; Sun et al., 2016).
Workflow is common formed by a number of tasks
and the control structures, which typically modeled
as a directed acyclic graph (DAG) (Kyriazis et al.,
2008). It is used to model scientific computing
applications, such as physics, bioinformatics,
astronomy, numerical weather forecast and so on (Li
et al., 2017). With the growth complexity of these
applications, scientific workflows are become big
data applications and require large-scale
infrastructures to conduct in a reasonable time (Li et
al., 2016; Rodriguez and Buyya, 2014].
Accordingly, scientific workflows are prone to
exploit the cloud computing resources (Kashlev and
Lu, 2014; Zhao et al., 2011).
Although executing scientific workflows on
cloud platform bring many advantages, cloud
computing, similar to other distributed computing
system, is also easily to emerge resource failures.
Some of them result from internal failure (i.e., host
fault) (Jeannot et al., 2012; Zhu et al., 2016; Yao et
al., 2017, Qiu et al., 2017). According to the report
that a system consists of 10 thousand physical
servers, one will fail once a day (Dean, 2009).
Moreover, about 1-5 percentage of disk drives die
and hosts crash at least twice with roughly 2-4
percentage every year (Zhu et al., 2016). Thus, the
workflow application is likely to delay even if only
one server fails during the task executing process.
Moreover, various security threats (such as spoofing
and alteration) are the great concern for cloud users
and providers (Zeng et al., 2015; Li et al., 2016;
Chen et al., 2017). For example, alteration is one of
the malicious attacks that can lead to serious task
faults by changing the execution data. Hence,
external failure (i.e., failure by malicious attack)
should also be considered when executing scientific
workflow applications. Fortunately, integrity
service, a security check method, can be utilized to
ensure that no one modify or tamper with the data
without being detected during the process of task
executing (Xie and Qin, 2006, 2008). Hence, it is
Li, Z., Yu, J., Hu, H., Chen, J., Hu, H., Ge, J. and Chang, V.
Fault-Tolerant Scheduling for Scientific Workflow with Task Replication Method in Cloud.
DOI: 10.5220/0006687300950104
In Proceedings of the 3rd International Conference on Internet of Things, Big Data and Security (IoTBDS 2018), pages 95-104
ISBN: 978-989-758-296-7
Copyright
c
 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
95

also necessary to deploy security service to check
the integrity of running data for workflow tasks.
The task resubmission and replication methods
are two extensively utilized fault tolerance methods
(Chen et al., 2016; Vinay and Dilip Kumar, 2017).
As for the resubmission, it resubmits a task
execution after a failure happens. The resubmission
mechanism is generally used during the course of
task execution and can enhance the resource
utilization of computing system. Nevertheless,
resubmission method will result in much late finish
time for tasks and may fail to meet the deadline
constraint of workflow (Vinay and Dilip Kumar,
2017). Alternatively, tasks can also be duplicated to
avoid failures, and the replications of a task can be
executed simultaneously (Chen et al., 2016). The
replication is also realized in a primary-backup
mode, where the backup starts executing when the
primary fails (Ghosh et al., 1997; Manimaran and
Murthy, 1998; Zhu et al., 2011, 2016; Sun et al.,
2017). Therefore, the replication method is
applicable for the task scheduling phase and is good
for saving execution time of task.
In the cloud computing environment, task
failures may result from internal failure or external
failures. Moreover, workflow scheduling in cloud
usually takes the deadline into consideration. So,
resubmission method is not applicable for cloud
workflow scheduling. In this paper, we propose a
fault-tolerant scheduling (FTS) algorithm for
scientific workflow in cloud computing
environment, the aim of which is to optimize the
workflow cost with the deadline constraint even in
the presence of various failures. The task replication
scheme is integrated into the FTS algorithm, and the
number of replications depends on the internal and
external failures probabilities. The experimental
results, on the basis of real-world scientific
workflow applications, demonstrate the effective-
ness and practicality of our proposed algorithm. The
main contributions of this work are given as follows:
• We propose a fault-tolerant scheduling
algorithm for scientific workflow in cloud to
optimize the workflow execution cost while
meeting the deadline constraint.
• The proposed FTS algorithm, which is based
on task replication, can ensure the successful
execution of task in the presence of internal
failure (i.e., host failure) or external failure
(i.e., failure by malicious attack).
• In terms of real-world scientific workflow
applications, our experiments demonstrate the
effectiveness and practicality of our proposed
FTS algorithm.
The remainder of this paper is organized as
follows. Section 2 summarizes the related work.
Section 3 describes the models and problem
formulation. Section 4 introduces the algorithm
implementation. Section 5 analyses the experimental
results. Finally, the conclusions and future work are
given in Section 6.
2 RELATED WORK
The problems of workflow scheduling in cloud have
been well studied recently. Zhao et al. (2011)
present the key challenges and research opportuni-
ties in running scientific workflow on cloud. Then, a
cloud scientific workflow management system is
proposed, which integrates Swift system with the
OpenNebula cloud computing platform (Zhao et al.,
2012). In commercial multi-cloud environment, Fard
et al. (2013) introduce a pricing and truthful model
for workflow scheduling to minimize the workflow
makespan and monetary cost simultaneously.
Rodriguez and Buyya (2014) propose a scientific
workflow scheduling mechanism according to
Infrastructure as a Service (IaaS) that optimizes the
entire workflow scheduling cost with the deadline
constraint. Li et al. (2017) present a cost and energy
aware workflow scheduling algorithm, which is
based on four optimization steps, to minimize the
workflow cost and reduce the energy consumption
under the constraint of workflow deadline.
Furthermore, many studies have concentrated on
multiple objective workflow scheduling problems in
cloud. Durillo et al. (2012) propose a multi-objective
list scheduling heuristic for workflow in cloud
computing environment. As an alternative, Zhu et al.
(2016) develop an evolutionary multi-objective
optimization (EMO) algorithm for scientific work-
flow scheduling in the same scenario. However, the
aforementioned workflow management system,
single objective and multi-objective scheduling
algorithms neglect the tasks failure problem that will
influence the quality of service (QoS) of workflow.
Since the occurrences of internal faults are
usually unpredictable in computer systems, fault
tolerance must be considered when devising
workflow scheduling algorithms. Ghosh et al. (1997)
provide a fault-tolerance technique in dynamic
systems that can help system designers determine
how many processors should be needed. Manimaran
and Murthy (1998) propose a fault-tolerant
algorithm to dynamically schedule real-time tasks in
the multiprocessor system. Zhu et al. (2011) present
a fault-tolerant scheduling algorithm that can
IoTBDS 2018 - 3rd International Conference on Internet of Things, Big Data and Security
96

Table 1: Notations.
Symbol Semantics


Task 

of workflow
(

)
The size of input data of task 

(

)
Workload of task 

(

)
Predecessor set of task 

(

)
Successor set of task 


The number of tasks of workflow

(

)
The thVM type
()
Processing capacity of ()
()
The cost per unit time of ()

The bandwidth between VMs
λ
Failure coefficient

Weight parameter


(

)
Fault probability of task 



(


)
The number of copies of task 



(


)
Transmission time of task 



(

,())
Execution time of task 



(


)
Start time of task 



(


)
End time of task 



(

,
(

)
VM rent time of task 

on ()


,
(

)

VM rent cost of task 

on ()

The cost of workflow

The makespan of workflow
tolerate one node failures for real-time tasks in
heterogeneous cluster environment. However, these
fault-tolerant
scheduling algorithms cannot be directly
applied to cloud computing environment or workflow
scheduling problem.
In (Plankensteiner and Prodan,
2012), a resubmission heuristic strategy is proposed
to support fault tolerant execution of scientific
workflows. Wang et al. (2015) present a fault-
tolerant mechanism which extends the primary-
backup model to cloud computing system. Chen et
al. (2016) propose three clustering strategies of fault
tolerant to improve the QoS of workflow. Zhu et al.
(2016) construct a real-time workflow fault-tolerant
model that extends the traditional primary-backup
model based on many cloud computing
characteristics, and the task allocation and message
transmission mechanism are developed to ensure
task faults can be done in the process of workflow
executing. However, the fault-tolerant methods
mentioned above only consider the internal faults,
but they ignore the external faults.
The security problem in workflow scheduling
has been studied to deal with external malicious
attacks in cloud. Chen et al. (2017) investigate the
problems of workflow scheduling with security-
sensitive intermediate data. Li et al. (2016) propose
a security and cost aware scheduling algorithm for
scientific workflow, the aim of which is to optimize
the workflow cost under the deadline and risk rate
constraints. Zeng et al. (2015) propose a security-
aware and budget-aware (SABA) workflow
scheduling scheme to minimize makespan within
both the security and budget constraints. However,
existing algorithms only consider the security
constraints for workflow scheduling and are
incapable to solve the failure problem by malicious
attack.
Unlike the aforementioned approaches, in this
study, we task both internal and external failures into
count simultaneously and propose a fault-tolerant
scheduling (FTS) algorithm for scientific workflow
in cloud computing environment. The FTS algorithm
is based on tasks replication method (one of the
widely used fault tolerant mechanisms), and the aim
of which is to minimize the workflow cost with the
deadline constraint even in the presence of various
failures. The experimental results in terms of four
real-world scientific workflow applications
demonstrate the effectiveness and practicality of our
proposed algorithm.
3 MODELS AND PROBLEM
FORMULATION
In this section, first we describe some models used
in this paper, including workflow model, cloud
model and fault model. Then, the problem
formulation of fault-tolerant scientific workflow
scheduling is introduced. The major notations and
their semantics in this paper are summarized in
Table 1.
3.1 Workflow Model
The model of workflow is usually represented by the
DAG (directed acyclic graph) model, that is =
(, ) , where ={

,

,…,

,…,

} is the
workflow tasks set, ={(

,

)|

,

∈} is the set
of edges between tasks. Let (

) and (

)
represent the set of predecessor and set of successor
of task 

respectively. Then, suppose a DAG has
exactly one entry task and one exit task, and a task is
called entry task 

, if and only if 

=
∅; a task is called exit task 

, if and only if
(

)=∅. Moreover, symbol (

) is the
workload of task 

, which is quantified in unit of
compute unit, and (

) represents the size of the
input data of tasks 

. In addition, each workflow has
a deadline 

which is defined as the
constraint of execution time.
Fault-Tolerant Scheduling for Scientific Workflow with Task Replication Method in Cloud
97

Table 2: m4 series of VMs in Amazon EC2.
VM number VM type Compute Unit Cost per Hour ($)
1 m4.large 2 0.1
2 m4.xlarge 4 0.2
3 m4.2large 8 0.4
4 m4.4large 16 0.8
5 m4.10large 40 2
6 m4.16large 64 3.2
3.2 Cloud Model
Through virtualization technology, each server in
cloud systems can be virtualized to a set of
heterogeneous VMs. Hence, the VM is the basic
processor unit in cloud instead of server. Suppose
the cloud systems offer a set of VM resources in the
form of  =
{

(
1
)
,…,
(

)
,…,
(

)}
to
users in the pay-per-use model. For example,
Amazon EC2 provides six types of m4 series VMs
which is shown in Table 2 (Amazon EC2, 2017).
Specially, a VM instance () is mainly specified
by processing capacity () (in compute unit) and
cost per hour(). Without loss of the generality,
Amazon EC2 charge users by the hourly-based
pricing model that means users have to pay for the
whole leased hour even if the VM leased just one
minute (Rodriguez and Buyya, 2014; Li et al.,
2017). Empowered by the virtualization technology,
an infinite amount of VMs can be accessed in cloud
computing platform, and so users can rent the
arbitrary number of VMs. Moreover, all VMs
located in the one cloud data center so that the
bandwidth between VMs is supposed to be equal (Li
et al., 2016; Yao et al., 2017).
3.3 Fault Model
We take the internal and external faults
simultaneously into account in cloud workflow
scheduling problem. As for internal failure, host
failure is focused, which can bring about failures
including VMs and workflow tasks. So, a fault-
detection mechanism is used to detect host failure
(Ghosh et al., 1997; Manimaran and Murthy, 1998).
Furthermore, failures on hosts may be transient or
permanent, independent, which means that a fault
occurred on one host will not affect other hosts.
Since the probability that two hosts fail
simultaneously is small, we assume that at most one
host fails at a time (Zhu et al., 2011, 2016).
Security threats (Snooping, spoofing and
alteration) are a big concern in cloud computing
system. As far as we known, snooping and spoofing
attacks only incur significant data losses of
workflow. However, only alteration is an
unauthorized attack that can lead to invalid tasks
execution, which is termed the external failure.
Then, we can apply integrity service to check
whether the task executing successfully (Xie and
Qin, 2006; Li et al., 2016). There are many hash
functions for integrity services such as TIGER,
RIFDMD-160, SHA-1, RIFDMD-128, MD5, etc
(Xie and Qin, 2006, Li et al., 2016; Chen et al.,
2017). Each hash function is assigned a security
level in the range 0 to 1. However, adding the
security services to applications inevitably produces
time overhead, which will increase the makespan
and cost of applications. Among the above hash
functions, the TIGER method, with the highest
security level, has the most security overhead.
Moreover, the time overhead of security service is in
direct proportion to the size of data (Li et al., 2016;
Chen et al., 2017). In order to check the executing
data whether is altered by malicious users during the
task executing, we use the TIGER method as the
integrity service. Then, the time of task 

using the
TIGER method to check the execution data is
computed by Eq. (1).


(


)
=∙(

) (1)
where  is the weight parameter of TIGER security
service and (

) is the size of execution data of
task 

. As for scientific workflow, e.g., NCFS
workflow, the size of input data may range from
0.5GB to 8.7GB (Zeng et al., 2015). Hence, the
security time overhead cannot be overlooked when
devising the workflow scheduling algorithm.
3.4 Problem Formulation
The aim of this paper is to minimize the workflow
cost with the deadline constraint even in the
presence of failures. Then, an efficient scheduling
scheme of mapping workflow tasks onto VMs
should be found. Then, Let the start time and end
time of a task 

as 

(

) and 

(

), and the
start time of 

is represented by Eq. (2).
IoTBDS 2018 - 3rd International Conference on Internet of Things, Big Data and Security
98



(


)
=max


∈(

)
{



} (2)
Note that if 

=

, then 

(


)
=0. A task


can start its execution if and only if it receives
input data from all its predecessors. Then, the
transmission time is computed by


(


)
=(

)/ (3)
where  is the bandwidth between two VMs in
cloud computing platform. Then, the task 

begins
to execute, and the execution time is given by


(


,()
)
=(

)/() (4)
Thus, the end time of task 

is computed as follows.


(


)
=

(


)
+

(


)
+

(


,()
)
+

(

) (5)
We know that the end time of task 

is the
makespan of workflow, then
 = 

(


)
(6)
Based on the above definitions, the VM rent time of
task 

executed on 
(

)
is given by




,
(

)
=

(


)
+



,
(

)
+

(


)
(7)
However, Amazon EC2 typically charges the
users by an hourly-based pricing model. Then, the
cost of one copy executed on 
(

)
is represented
as follows.


,
(

)
=



(

,
(

)
)

∙() (8)
In the cloud computing environment, task failures
are inevitable, and we use the replication method to
ensure the fault-tolerant. Hence, suppose 

(

)
is the number of replications of task 

. Then, the
cost of task 

is calculated by

(


)
=

(

)∙

,
(

)
 (9)
Thus, he cost of workflow can be computed by
 =
∑

(


)


∈
(10)
Finally, the workflow scheduling problem can be
formally defined as follows: find a schedule scheme
to minimize, and the  is equal or
less than

, which is described as follows.
Minimize: c (11)
Subject to:  ≤ 

(12)
4 ALGORITHM
IMPLEMENTATION
In this section, we propose a fault-tolerant
scheduling algorithm for scientific workflow to
address the faults happened in internal and external
cloud computing environment. The FTS algorithm is
capable of reducing workflow cost while meeting
the deadline. In this section, we present the
implementation of proposed algorithms in detail as
follows.
With the aim of meeting the deadline constraint
of workflow, we first introduce a concept of sub-
makespan. The sub-makespan stands for the
assigned execution time of a task, which is similar to
the makespan of the workflow. Obviously, if the
execution time of each task is no more than its sub-
makespan, then the makespan of workflow will not
exceed the deadline.
First, we map each task to the maximum
compute unit (). Then, the minimum execution
time of task is calculated by


(


,()
)
=(

)/() (13)
In this case, we can derive the minimum makespan
of workflow 

when all tasks of
workflow execute on () . Without loss of
generality, we assume that the specified deadline


will no less than the minimum makespan,
that is 

≥

. We define the
sub-makespan of task 

as 

(

) which is
represented as follows (Li et al., 2017).


(


)
=(

(


)
+



,
(

)

+

(


)
)∙

/

(14)
FTS Algorithm
BEGIN
01. for each task 

∈
02. Calculate the minimum 

(


,()
)
;
03. end for
04. Calculate the minimum makespan 

;
05. for each task 

∈
06. Calculate the sub-makespan based on Eq. (14);
07. Find the feasible set 

(

);
08. for each () ∈ 

(

)
09. Find an optimal 

() that satisfy Eq. (20);
10. end for
11. end for
12. Compute the  according to Eq. (6);
13. Compute the  according to Eq. (10);
END
Figure 1: The pseudo code of FTS algorithm.
Fault-Tolerant Scheduling for Scientific Workflow with Task Replication Method in Cloud
99

Workflow executing in cloud computing
environment is not risk-free. As for external failure,
the failure occurrence is approximated by a Poisson
distribution (Qiu et al., 2017). Then, the fault
probability of task 

executed on cloud computing
platform is modeled by an exponential distribution
given as follows (Fard et al., 2012).


(

)=1−exp(−∙

(

, ())) (15)
where  is the failure coefficient of cloud computing
environment.
To guarantee the successful execution of all
tasks, we utilize the replication method to duplicate
multiple copies of tasks to execute simultaneously.
Then, we assume that parameter  is a small positive
integer that is approximate to zero. Then, we have
(

(

))



(

)
≤ (16)
where 


(

) is number of task 

copies which is
used to against the external failure. Also the task
may be fault due to internal failure, i.e. host or
server fault. Since the probability that two hosts fail
simultaneously is small, hence suppose at most one
host fails at a time. Then, we add another one copy
to solve this problem. Thus, the total copies of task


calculated by Eq. (17).


(


)
=


(


)
+1 (17)
Eq. (17) can be explained as follows: cloud
computing environment may suffer from internal
and external failures, and a task replication method
is used to ensure the task successful execution. First,



(

) copies of tasks are aim at the external
malicious attack. In additional, one extra copy is to
guarantee the internal host fault. Hence, 

(


)
copies of tasks execute on VMs simultaneously with
one copy of task will be successful at least.
Moreover, note that if 

(


)
=2, then our
multiple replication method is similar to well-known
primary-backup model (Ghosh et al., 1997;
Manimaran and Murthy, 1998; Zhu et al., 2016).
Then, we transfer the optimization problem as
follows.
Minimize: 
(


)
(18)
Subject to: 

(

,())≤

(

) (19)
Eq. (16) (20)
The new problem means that as for each task, we
find a () for task 

to satisfy Eqs. (19) and
(20), and minimize the 
(


)
. Eqs. (19) and (20)
represent the sub-makespan constraint and the
conditions for successfully executing respectively.
First, we find a VM set 

(

) of task 

which can satisfy the Eq. (19). Then, from the set


(

) , the optimal 

() is selected
that the Eq. (20) is met and the cost of task 

is
minimized. Here, the enumeration is used to find the
optimal VM type effectively. The time complexity
of enumeration depends on the number of VMs.
Overall, the pseudo code of our proposed FTS
algorithm is described in Fig. 1. We can see that the
time complexity of computing minimum execution
time is () (lines 1-3). Then, the worst time
complexity of calculating the optimal VM type is
(

) (lines 5-11). As a result, the time complexity
of FTS algorithm is (

).
(a) Montage (b) LIGO (c) SIPHT (d) CyberShake
Figure 2: Structures of real-world scientific workflows.
IoTBDS 2018 - 3rd International Conference on Internet of Things, Big Data and Security
100

(a) Montage (b) LIGO
(c) SIPHT (d) CyberShake
Figure 3: Workflow cost with different deadlines.
5 SIMULATION EXPERIMENTS
In this section, we simulate a series of experiments
to evaluate the performance of our FTS approach.
First, we introduce the experiment setup. Then, the
simulation results on the basis of four real-world
workflows are discussed.
5.1 Experiment Setup
The simulation is run on a machine with an Intel
Core i7 4 cores and 4 GB of RAM. All algorithms
are implemented in Python 3.5. Four scientific
workflow models from different areas, such as
Montage, LIGO, SIPHT and CyberShake, are used
in our experiments are shown in Fig. 2 (Zhu et al.,
2016; Li et al., 2016, 2017). For each task in a
workflow, the size of input/output data and the
workload are all according to the uniform
distribution in the range [10, 100] GB and [1, 64]
CU respectively. The bandwidth between two VMs
is 0.1GB/s. Let  = /

is
the rate of predefined deadline to minimum
makespan. In order to set the proper deadline for
each workflow, we must have  ≥ 1.
We also consider other three workflow
scheduling scenario to compare with FTS approach.
The comparison algorithms are presented as follows.
No Fault: That means no internal and external
failures exist in the cloud computing environment,
and users need not to employ any fault-tolerant
strategies.
Internal Fault: In this case, only host fault will
happen during the tasks executing.
External Fault: Users only consider the
malicious attack failure in this assumption. Then, the
security time overhead should be integrated into the
makespan and cost computation.
5.2 Simulation Results
The experiment results of four scientific workflows
on cost under different deadlines are shown in Fig.
3, where the ratio is from 1 to 5 with the increment
of 0.5,  = 0.005 and =10

. We can see from
the four figures that the workflow cost become
lower as the ratio increases. This is because that a
larger workflow deadline will generate larger sub-
makespan for tasks, then a task will map to the low
performance VM with low cost. Moreover, No Fault
algorithm has the lowest cost. The cost of Internal
Fault algorithm is double of the No Fault algorithm,
for it always have two copies tasks to execute. The
cost of External Fault algorithm is roughly same to
Internal Fault algorithm. Our proposed FTS
algorithm performs worst in scheduling cost. This is
due to the fact that internal and external faults are
45
95
145
195
245
1 1,5 2 2,5 3 3,5 4 4,5 5
Cost ($)
Ratio
FTS
No Fault
Internal Fault
External Fault
30
70
110
150
190
230
11,522,533,544,55
Cost ($)
Ratio
FTS
No Fault
Internal Fault
External Fault
50
100
150
200
250
300
1 1,5 2 2,5 3 3,5 4 4,5 5
Cost ($)
Ratio
FTS
No Fault
Internal Fault
External Fault
30
60
90
120
150
180
11,522,533,544,55
Cost ($)
Ratio
FTS
No Fault
Internal Fault
External Fault
Fault-Tolerant Scheduling for Scientific Workflow with Task Replication Method in Cloud
101

(a) Montage (b) LIGO
(c) SIPHT (d) CyberShake
Figure 4: Workflow cost under different .
(a) Montage (b) LIGO
(c) SIPHT (d) CyberShake
Figure 5: Workflow cost under various ε.
considered at the same time in FTS algorithm. In
order to meet the reliability execution of workflow,
multiple copies of a task, up to the failure
coefficient, are needed to execute. Although FTS
algorithm has the most workflow cost, only it can
ensure the successful execution of workflow even in
the presence of host fault and malicious attack
simultaneously in the cloud computing environment.
We plot the workflow cost under different  for
FTS algorithm and the peer algorithms are presented
50
100
150
200
250
300
0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1
Cost ($)
λ (×10
-2
)
FTS
No Fault
Internal Fault
External Fault
40
80
120
160
200
240
0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1
Cost ($)
λ (×10
-2
)
FTS
No Fault
Internal Fault
External Fault
50
100
150
200
250
300
350
0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1
Cost ($)
λ (×10
-2
)
FTS
No Fault
Internal Fault
External Fault
30
60
90
120
150
180
210
0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1
Cost ($)
λ (×10
-2
)
FTS
No Fault
Internal Fault
External Fault
50
100
150
200
250
1 10 100 1000 10000
Cost ($)
ε (×10
-5
)
FTS
No Fault
Internal Fault
External Fault
30
60
90
120
150
180
1 10 100 1000 10000
Cost ($)
ε (×10
-5
)
FTS
No Fault
Internal Fault
External Fault
50
100
150
200
250
300
1 10 100 1000 10000
Cost ($)
ε (×10
-5
)
FTS
No Fault
Internal Fault
External Fault
40
80
120
160
200
1 10 100 1000 10000
Cost ($)
ε (×10
-5
)
FTS
No Fault
Internal Fault
External Fault
IoTBDS 2018 - 3rd International Conference on Internet of Things, Big Data and Security
102

in Fig. 4, where  = 2.5 and =10

. As we
can see from the figures, No Fault algorithm and
Internal Fault algorithm are independent of . With
the larger λ, the cost of FTS algorithm and External
Fault algorithm increase with the same pace. This
results from the fact that a larger value of  means
the cloud computing platform will suffer from more
external malicious attacks. Then, a user should
schedule more copies of tasks to VMs that induce
more cost. Similarly, the FTS algorithm performs
the worst on execution cost, the reason of which is
that FTS can tackle the internal and external faults
more efficiently than any other comparison
algorithms.
Fig. 5 illustrates the workflow cost of four
algorithms under different self-defined  , where
 = 2.5 and  = 0.005 . From the four
workflow structures, it can be seen that the curves of
No Fault algorithm and Internal Fault algorithm are
flat. The rationale is that No Fault algorithm and
Internal Fault algorithm are independent with the
parameter . The cost curves of FTS algorithm and
External Fault algorithm are decreasing when
parameter  become large. The reason is that less
task copies will demand when parameter  is large
according to Eq. (16), which introduces less VM
cost. Moreover, the cost of FTS algorithm and
External Fault algorithm perform equal with the cost
of Internal Fault algorithm and No Fault algorithm
respectively when  = 0.01 and =0.1. As for
External Fault algorithm and No Fault algorithm,
when the  is large enough, the failure probability
produced by the current failure coefficient  is
already less than  , hence no additional copy is
needed. Thus, External Fault algorithm and No Fault
algorithm have the same workflow cost. Also, the
same explanation can be used for FTS algorithm and
internal algorithm.
6 CONCLUSION AND FUTURE
WORK
This paper we proposed a fault-tolerant scheduling
for scientific workflow in cloud computing
environment. The purpose of FTS algorithm is to
minimize the workflow cost with the deadline
constraint, which is based on tasks replication
method (one of the widely used fault tolerant
mechanisms). As far as we known, it is the first time
that both failure models (i.e. internal fault and
external fault) are considered in cloud workflow
scheduling problem. The simulation results with
real-world scientific workflow models show that
only FTS algorithm can ensure the successful
execution of workflow, although it has the most
workflow cost.
For the future work, we will extend our fault-
tolerant workflow scheduling strategy considering
unstable computing environments. Another direction
of our future work is to design an energy efficient
fault-tolerant task scheduling algorithm from the
perspective of cloud providers.
ACKNOWLEDGEMENTS
This work was supported by the National Science
Foundation of China (No. 61572162, 61572251,
61702144), the Zhejiang Provincial National
Science Foundation of China (No. LQ17F020003),
the Zhejiang Provincial Key Science and
Technology Project Foundation (NO.2018C01012),
the National Key R&D Program of China
(2016YFC0800803), the Fundamental Research
Funds for the Central Universities. Hua Hu is the
corresponding author.
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