Secure Scheduling of Scientific Workflows in Cloud
Shubhro Roy
1
, Arun Ramamurthy
1
, Anand Pawar
2,∗
, Mangesh Gharote
1
and Sachin Lodha
1
1
TCS Research, Tata Consultancy Services, India
2
Indian Institute of Information Technology, Pune, India
Keywords:
Scientific Workflow Scheduling, Cloud Computing, Security in Cloud, Task Scheduling, Evolutionary
Optimization Algorithms.
Abstract:
Scheduling of tasks in scientific workflow has been challenging due to heterogeneous and interdependent tasks
in workflow. The scheduling involves selection of different types of virtual machines (VM) belonging to differ-
ent instance series (computing, memory, storage) to minimize the overall execution cost and time (makespan).
Apart from VM selection, selection of security services (such as authentication, integrity, confidentiality) is
critical. In this paper, we propose OptReUse - a workflow schedule generation algorithm for efficient reuse of
VMs. Our approach of OptReUse algorithm along with combinatorial optimization approach gives lower cost
of scheduling compared to the prior art without incurring delay in the makespan. Further, we enhance the se-
curity model by accurate estimation of risks. Our experiments using standard scientific workflows demonstrate
that the proposed method gives lower costs compared to the prior VM resource utilization methods.
1 INTRODUCTION
Scientific workflow consists of several heterogeneous
and interdependent computational tasks. Such work-
flows could occur in various research areas, such as in
physics, bioinformatics, seismology, industrial con-
trol systems, etc. Large-scale computational infras-
tructure is needed to schedule them. Scheduling in-
volves processing the tasks on selected computational
resources considering the dependency order. Since
Cloud Service Providers (CSPs) offer highly scalable
computational resources, such as Virtual Machines
(VMs) at pay-per-use model, cloud has emerged as a
cost- and time-effective platform for solving scientific
workflow problems.
CSPs offer different series of VMs such as com-
putation intensive, memory intensive and data inten-
sive, for processing heterogeneous tasks. Each series
of VMs contains different VM types which vary in
renting cost and processing capacity. Higher process-
ing capacity VMs help in lowering the workflow exe-
cution time (makespan) but may lead to higher rental
cost. While lower processing capacity VMs could be
cheaper, processing all tasks on them might lead to vi-
olation of deadlines. So, selecting the right VM com-
∗
Intern, TCS Research
bination for heterogeneous tasks becomes a challeng-
ing combinatorial optimization problem.
2 RELATED WORK
In the study (Liu et al., 2020) present a detailed
survey on scientific workflow scheduling algorithms.
Finding the right combination of VMs is a challeng-
ing combinatorial optimization problem (Zhou et al.,
2019; Mboula et al., 2020). As this problem is NP-
Hard (Hilman et al., 2018), researchers have pro-
posed different evolutionary optimization algorithms
such as Genetic Algorithm (GA) (Shishido et al.,
2018), Particle Swarm Optimization (PSO) (Li et al.,
2016; Shishido et al., 2018), Improved PSO (Peng and
Wolter, 2019), Frog Leaping Algorithm (Kaur and
Mehta, 2017), Firefly based approach (Adhikari et al.,
2020), and deadline constrained co-evolutionary GA-
based algorithm (Liu et al., 2017).
Evolutionary optimization algorithms are used to
determine the optimal VM combinations for process-
ing the tasks in the workflow. A separate Workflow
Schedule Generation (WSG) algorithm is required to
efficiently schedule each of the tasks on the selected
VM combination. WSG algorithm would compute
the: workflow execution cost and makespan based on
170
Roy, S., Ramamurthy, A., Pawar, A., Gharote, M. and Lodha, S.
Secure Scheduling of Scientific Workflows in Cloud.
DOI: 10.5220/0011038600003200
In Proceedings of the 12th International Conference on Cloud Computing and Services Science (CLOSER 2022), pages 170-177
ISBN: 978-989-758-570-8; ISSN: 2184-5042
Copyright
c
 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

the selected VM combinations. Further, researchers
proposed different VM resource utilization strate-
gies to reduce the workflow execution cost. In the
study (Lee et al., 2015) proposed a resource utiliza-
tion strategy by delaying a combination of few tasks.
This strategy resulted in increase in makespan while
achieving the cost benefits. In other study (Malawski
et al., 2015) used a similar strategy of delaying tasks
to improve resource utilization. They proposed a de-
cision algorithm for scheduling workflows, consider-
ing budget and deadline constraints. However, if in-
crease in makespan resulted in violation of deadline,
then new VMs with higher processing capacity were
selected, which resulted in higher rental cost.
Further, (Li et al., 2016) and (Shishido et al.,
2018) proposed a WSG algorithm where VMs were
reused among different tasks and cost benefits were
achieved without delay in makespan. Thus in sum-
mary, prior art has addressed different heuristics and
evolutionary algorithms (Challita et al., 2017) for bet-
ter resource utilization but most have not included
the concept of VM reuse or do not explore all pos-
sible VM reuse options. In this paper, we propose
OptReUse algorithm that overcomes the above stated
limitations, and obtain higher cost savings compared
to WSG. Specifically, we reduce the cost by explor-
ing all possible VM reuse options, and by not includ-
ing any compulsory delay in task. Other VM utiliza-
tion strategies proposed by (Weng et al., 2016) are by
monitoring overheads. Further, (Ramamurthy et al.,
2021) addresses the problem of VM allocation for sci-
entific workflow considering multi-objectives and un-
certainties.
Another critical factor in scheduling of scientific
workflows in cloud is security. The security in cloud
is a shared responsibility (Kumar et al., 2018). CSPs
provide security services to mitigate different attacks.
We focus on three crucial security attacks alteration,
snooping, and spoofing attacks. Alteration affects
the data integrity, snooping affects confidentiality and
spoofing provides illicit access to sensitive data. To
mitigate these attacks, different cryptographic algo-
rithms are used for task security (Li et al., 2016;
Shishido et al., 2018). However, cryptographic algo-
rithms that provide stronger security have higher over-
heads in terms of computation time, which increase
the makespan and operational cost. Further, all the
tasks in workflow, might not require the same level of
security services. Thus, the problem of scheduling of
scientific workflow involves selecting the right com-
binations of VMs and security model (i.e. different
security services and level) for keeping the risk rate
below permissible limit.
The security model proposed in the prior art (Li
et al., 2016) considers that each task would require
cryptographic security services for a limited period,
more specifically, for at most one hour. But tasks can
continue to use a VM for several hours till the pro-
cessing is complete. Hence, risk is underestimated in
the security model in prior art (Li et al., 2016). We
propose an enhanced security model that keeps the
risk rate of the workflow below a permissible limit
while accurately estimating the risk. Thus, the main
contributions of the paper are:
• OptReUse algorithm for efficient reuse of VMs to
obtain cost benefits, and
• An enhanced security model, and more accurate
estimation of risks.
The rest of the paper is organized as follows: Section
3 describes the problem and the system model. Sec-
tion 4 compares the WSG and OptReU se algorithms.
Section 5 presents coding strategies for GA and PSO
and contains the experimentation results. Conclusion
and future works are laid out in Section 6.
3 WORKFLOW MODEL
A scientific workflow is modeled as a Directed
Acyclic Graph (DAG) and represented as DAG(T,E),
where T represents the set of tasks {t
0
,t
1
,t
2
...,t
n
}
modeled as vertices and E represents the set of edges
{e
0
,e
1
,. . . , e
k
} modeled as dependencies between the
tasks. The predecessor and successor of a task t
i
is
represented as pre(t
i
) and suc(t
i
). A task t
i
cannot
start until all its predecessor tasks have completed
their execution. A sample workflow is shown in Fig-
ure 2.
CSPs provide different series of VMs for process-
ing heterogenous tasks. We consider three series of
VMs namely Computing Optimized, Memory Opti-
mized and Storage Optimized (refer Tables 2, 3 and 4
from (Li et al., 2016)). Each VM is rented based on
an hourly pricing model. For example, if a task has
a processing time of 1 hour and 10 minutes on a VM
vm
k
s
, the user must pay for 2 hours. We assume there
is no bound on the number of VMs that can be rented
from a CSP.
The scientific workflows running on cloud are vul-
nerable to different types of security attacks. In this
work, we have considered three types of attacks: 1)
Snooping attack (theft of information), 2) Alteration
attack (modification of information), 3) Spoofing at-
tack (deceitful access to information). To protect the
VMs against these attacks, security services such as
authentication, integrity and confidentiality are used.
Secure Scheduling of Scientific Workflows in Cloud
171

Different security algorithms under these security ser-
vices are listed in (Xie and Qin, 2006) (Tables 1, 2 and
3), (Li et al., 2016) (Table 5). As discussed in these
works, the algorithms differ in the offered security
level and the associated overhead. In general, an al-
gorithm with a higher security level has a higher over-
head than the one with a lower security level. Hence,
using lower levels of security services can reduce cost
and makespan but increases the attack probability and
vise-a-versa. The security overheads for the three ser-
vices (a,g,c) are computed as given in (Li et al., 2016)
and (Xie and Qin, 2006). The overall security over-
head for task t
i
is the sum of individual security over-
heads as below:
T SC(t
i
) = SC
a
(t
i
) + SC
g
(t
i
) + SC
c
(t
i
) (1)
The security overhead significantly increases the task
processing time and leads to higher task execution
cost. Hence, optimal selection of security levels
for each security service is essential for minimizing
workflow execution cost and makepsan.
The pictorial representation of task processing on
VMs is obtained from (Li et al., 2016) (Figure 2) and
the equations for task execution process analysis are
modified in our paper. For processing a task (say, t
i
),
the output data (GB) from the VM of its predecessor
task (say, t
j
) needs to be transferred to the VM of t
i
.
The total input data transfer time for task is given as
T T (t
i
) =
∑
t
j
∈pre(t
i
)
d
o
j
/B (2)
Note that d
o
j
= 0 if t
i
reuses the same VM instance
type allocated to t
j
since data transfer is not required
when a VM is reused. The transfer time for start tasks
is assumed to be zero.
The execution time and total processing time (in-
cluding all security overheads) for a task is given by
ExT (t
i
,vm
k
s
) = W
i
/p
k
s
(3)
PT (t
i
,vm
k
s
) = T T (t
i
) + ExT (t
i
,vm
k
s
) + T SC(t
i
) (4)
The start time and end time for a task is related as
ET (t
i
) = ST (t
i
) + PT (t
i
,vm
k
s
) (5)
Total Execution Cost for the entire workflow can be
also computed as
T EC =
n−1
∑
i=0
d(ET (t
i
) − ST (t
i
) − IT
a
(t
j
,vm
k
s
))/60ec
k
s
(6)
. Since VMs are borrowed on hourly basis, the idle
time exists between the completion of the task and
the end of that hour slot. The Total Execution Time
(T ET ), that is, the makespan of a workflow, is given
by the equation as
T ET = max{ET (t
i
)|t
i
∈ T } (7)
Table 1: Notation.
Symbol Definition and units
t
i
An individual task of a workflow
vm
k
s
VM of instance series s and type k
p
k
s
Processing Capacity (MFLOPS)
c
k
s
Renting cost ($/hr)
q = {a, g,c} Set of security services
a,g Authentication, Integrity
c Confidentiality
sr
q
i
Required security levels
sl
q
i
Provided security level
SC
q
(t
i
) Individual security overhead
T SC(t
i
) Total security overhead
d
o
j
Output data from predecessor task
B Bandwidth of data transfer (GB/s)
Tt(t
i
) Total input data transfer time (min)
W
i
Task workload (MFLOP)
ExT (t
i
,vm
k
s
) Task execution time on vm
k
s
PT (t
i
,vm
k
s
) Total task processing time on vm
k
s
ST (t
i
), ET (t
i
) Start and End time of task (hr)
T EC Total Execution Cost ($)
IT
a
(t
j
,vm
k
s
) Available idle time on vm
k
s
(min)
T ET Total Execution Time (hr)
3.1 Risk Analysis
Different security services are provided to mitigate
risk of attacks. It could happen that, there is differ-
ence between the security services required and ser-
vices provided for a particular workflow. Hence, the
risk of attack exists.
The security model considered in the prior art (Li
et al., 2016) assumed the risk rate over a unit time
interval. While the workflow execution could occur
over several time intervals (hours). Hence, security
model needs to be enhanced. The risk probability of
attack for a given task P(t
i
,sl
l
i
) is given as
P(t
i
,sl
l
i
) = 1 − exp(−λ
l
(sr
l
i
− sl
l
i
)N(t
i
)) (8)
where, N(t
i
) is the number of time intervals (each
time interval could be an hour) for which t
i
is exe-
cuted on the VM. For a given task t
i
, the arrival rate
(λ
l
) of snooping, alteration and spoofing attacks is as-
sumed to follow a Poisson distribution. Consequently,
the risk probability P(t
i
) for task t
i
, involving all three
security services is computed as
P(t
i
) = 1 −
∏
l∈{a,g,c}
(1 − P(t
i
,sl
l
i
)) (9)
For a given a workflow, consisting of a set of T tasks,
risk probability P(T ) can be computed as
P(T ) = 1 −
∏
t
i
∈T
(1 − P(t
i
)) (10)
This value of P(T ) must be lower than the risk rate
threshold P
c
(P
c
∈ [0, 1]), which is the permissible risk
CLOSER 2022 - 12th International Conference on Cloud Computing and Services Science
172

rate of the workflow. The constraint P(T) ≤ P
c
, can
be also written as 1 − P(T ) ≥ 1 − P
c
.
∏
t
i
∈T
(1 − P(t
i
)) ≥ 1 − P
c
(11)
On further expanding the LHS
∏
t
i
∈T
∏
l∈{a,g,c}
(1 − P(t
i
,sl
l
i
)) ≥ 1 − P
c
(12)
or
∏
t
i
∈T
∏
l∈{a,g,c}
exp(−λ
l
(sr
l
i
− sl
l
i
)N(t
i
)) ≥ 1 − P
c
(13)
Taking log on both sides the inequality becomes:
∑
t
i
∈T
∑
l∈{a,g,c}
−λ
l
(sr
l
i
− sl
l
i
)N(t
i
) ≥ log(1 − P
c
) (14)
By introducing the correction factor N(t
i
), the secu-
rity model is enhanced and it provides a better es-
timation of risk. However, providing security levels
to tasks lower than required could result in violation
of risk rate constraint whereas higher security levels
could result in high cost and makespan. This makes
the workflow scheduling problem challenging.
4 OUR APPROACH
The flow chart in Figure 1 shows our methodology
for scientific workflow scheduling. As stated earlier,
we use evolutionary optimization algorithm (GA or
PSO) to obtain a best combination of VM types and
security levels for tasks in a workflow. Further, an ef-
ficient workflow schedule generation is required to al-
locate the selected VMs for lowering the cost and the
makespan. The efficient workflow schedule is gener-
ated using OptReUse algorithm, which does the VM
reuse along with the schedule generation. The process
stops, when converge criteria is met (i.e. best solution
is obtained).
Problem Statement: Given a scientific workflow
with n tasks, the problem is to determine the optimal
combination of VM types and security levels for each
task such that the overall execution cost is minimal,
and the workflow is processed within a given dead-
line (T
d
) and permissible risk limit.
Objective:
Minimize T EC (15)
Subject to:
T ET ≤ T
d
(16)
P(T ) ≤ P
c
(17)
Since scientific workflow scheduling problem is a
combinatorial optimization problem and NP-Hard (Li
Input: Workflow
task parameters
Generate a set of combinations using
evolutionary optimization algorithm
Workflow schedule generation
using OptReUse
Convergence
NO
Output: Best combination
(VM, security services & task order selection)
YES
Figure 1: Approach for Scientific Workflow Scheduling.
et al., 2016), we use evolutionary optimization al-
gorithm for solving the problem. We demonstrate
the results using both the GA and PSO. In the be-
low section, first we illustrate the benefits of VM
re-utilization and in the subsequent section, discuss
the intuition behind our workflow schedule genera-
tion OptReUse algorithm.
4.1 Intuition: WSG and OptReUse
If each task is allocated to a separate VM then it leads
to under utilization of the resources. For the task with
similar instance type, VM can be reused, resulting in
reduced rental costs and lower data transfer delays.
WSG proposed by (Li et al., 2016), (Shishido et al.,
2018) helps to reduce the cost and transfer time delays
by reuse of VMs among adjacent tasks. For example,
consider the workflow instance as shown in Figure
2. The tasks are traversed by a traversal order, such
as topological sort (t
0
,t
3
,t
1
,t
2
,t
4
), and simultaneously
allocated to VMs based on the VM types selected by
the evolutionary optimization algorithm. This enables
t
3
to reuse the VM allocated to t
0
and avoid extra data
t0
t1
t2
t3
t4
Data intensive
Compute intensive
Data intensive
Data intensive
Memory intensive
PT = 70 mins
ST = 0 mins
ET = 70 mins
IT = 50 mins
PT = 80 mins
ST = 70 mins
ET = 150 mins
IT = 30 mins
PT = 90 mins
ST = 70 mins
ET = 160 mins
IT = 30 mins
PT = 200 mins
ST = 285 mins
ET = 485 mins
IT = 40 mins
PT = 125 mins
ST = 160 mins
ET = 285 mins
IT = 55 mins
WSG
PT = 125 mins
ST = 160 mins
ET = 285 mins
IT = 15 mins
OptReUse
Figure 2: Sample workflow with task processing time.
Secure Scheduling of Scientific Workflows in Cloud
173

transfer delays.
For t
0
and t
3
, the total cost to be paid is for 150
minutes (i.e. 3 hours) and for the remaining tasks new
VMs need to be borrowed. Task t
2
which belongs to
the same VM instance series (Data intensive), a new
VM is borrowed for 125 mins (i.e. 3 hours). The
WSG approach proposed in prior art (Li et al., 2016),
(Shishido et al., 2018) keeps the VM reuse limited
between adjacent tasks only.
The VM on which t
3
is processed has an idle time
of 30 minutes (180−(70 +80)). Task t
2
which is non-
adjacent task to t
3
can also avail this idle time. Note,
task (t
0
, t
3
and t
2
) belongs to the same instance series.
But as task t
2
starts only after completion of task t
1
(i.e
ST = 160 mins). The available idle time on VM for t
3
is 20 minutes, which can be utilized by task t
2
. Hence,
the VM rental cost for task t
2
has to be paid only for
105 minutes (125 −20)) (i.e. 2 hours), which is lower
than the cost paid for t
2
using WSG approach. Our
proposed OptReUse algorithm is based on this VM
reuse strategy. Along with this strategy, task ordering
has significant impact of VM reuse. In the subsequent
section, we elaborate on how ordering of task belong-
ing to the same VM instance series can further reduce
the VM rental cost.
4.2 Task Order Selection and VM Reuse
Suppose, tasks t
0
, t
1
and t
2
are part of a workflow, as
displayed in Figure 3. Assume, these tasks belong to
the same VM instance series (data intensive). Con-
sider that the least expensive VM types are selected
for these tasks. The first workflow in Figure 3 dis-
plays, task t
1
reuses the VM and in second workflow
task t
2
reuses the VM of task t
0
.
• If t
1
reuses the VM of t
0
, then the total cost to be
paid for t
0
and t
1
is for 150 minutes (i.e. 3 units)
and the cost for t
2
has to be paid for 130 minutes
(i.e. 3 units). Thus, the total cost to be paid for all
the three tasks is 6 units.
• If t
2
has reused the VM of t
0
, then the total cost
to be paid for t
0
and t
2
is for 170 minutes (i.e. 3
t0
t2
t1
t0
t2
t1
Reuse
Reuse
PT = 40 mins
ST = 0 mins
ET = 40 mins
IT = 20 mins
Cost = 1 unit
PT = 40 mins
ST = 0 mins
ET = 40 mins
IT = 20 mins
Cost = 1 unit
PT = 110 mins
ST = 40 mins
ET = 150 mins
IT = 30 mins
Cost = 2 units
PT = 130 mins
ST = 40 mins
ET = 170 mins
IT = 50 mins
Cost = 3 units
PT = 130 mins
ST = 40 mins
ET = 170 mins
IT = 10 mins
Cost = 2 units
PT = 110 mins
ST = 40 mins
ET = 150 mins
IT = 10 mins
Cost = 2 units
(1)
(2)
Figure 3: Impact of order selection on VM reuse.
units) and the cost for t
1
is for 110 minutes (i.e. 2
units). Thus, the total cost for second workflow is
5 units.
Thus, task order impacts the VM reuse and cost. In
our approach, along with VM re-utilization across ad-
jacent and non-adjacent task, benefits due to task or-
dering is considered.
4.3 OptReUse Algorithm
The best combination of VM types and security levels
for workflow tasks are determined by an evolutionary
optimization algorithm (like GA or PSO). Different
combinations are explored to obtain the best combina-
tion using evolutionary optimization algorithm. Each
combination is represented as a chromosome (GA) or
particle (PSO). The OptReUse algorithm computes
the execution cost (T EC), time (T ET ) and risk rate
(P(T )) for each such combination to determine the
fitness value. OptReUse algorithm comprises of two
parts: Algorithms 1 and 2.
Using Algorithm 1, we compute the start time and
the end time of each task, for the given combination
of VM types and security levels. The other inputs to
Algorithm 1 are processing capacity, workload, out-
put data size of tasks, which assist in computing the
ST and ET . The ST of a task is same as the ET of its
predecessor task which completed last. First, we do
a general sort on the ST array in an ascending order.
Later, using the task order selection vector provided
by the evolutionary algorithm, we rearrange the task.
These task vector comprises of those task having the
same start time.
Algorithm 1: Start Time Computation and Task Ordering.
1: INPUT: VM and security services for n tasks
2: INPUT: Task order vector based on start time
3: INPUT: Processing capacity, workload, output
data size, VM rent cost
4: for i = 1,2, . .., n tasks do
5: Compute PT[i] for each task.
6: end for
7: for i = 1,2, . .., n tasks do
8: ST[i] = max{ET [ j]; j ∈ pre(i)}
9: Calculate ET [i]
10: end for
11: Sort ST based on the task order selection ST
12: OUTPUT: ST,ET
The output of Algorithm 1 is used as input to Al-
gorithm 2. Before allocating a task i to a new VM,
all possible underutilized VMs of the same instance
series and type are explored for reuse.
CLOSER 2022 - 12th International Conference on Cloud Computing and Services Science
174

• If the task reuses a VM used by an adjacent task
then reduction in cost is due to (i) re-utilization of
available idle time on the VM and (ii) data transfer
costs between the tasks is null.
• If the task reuses a VM used by a non-adjacent
task then reduction in cost is only due to re-
utilization of available idle time on the VM.
If such a reusable VM is found, the task i is allo-
cated to the VM where it gets maximum cost reduc-
tion or else a new VM is rented for that task.
Algorithm 2: Task-VM Allocation Algorithm.
1: INPUT: ST,ET
2: Initialize TEC = 0
3: Initialize IT [n] = {0,0, ...,0}
4: for i = 1,2, . .., n tasks do
5: Search for vm
k
s
where idea time is available.
6: if VM is available then
7: Allocate task i to VM, where maximum
cost reduction is available.
8: Update Idle time on vm
k
s
, if reused.
9: else
10: Allocate a new VM.
11: end if
12: Compute new idle time and update IT [i].
13: Increment TEC as per equation 6.
14: Update ST and ET .
15: Sort ST.
16: end for
17: Calculate TET = max(ET ) as per equation 7.
18: Calculate P(T) as per equation 10
19: OUTPUT: T EC, T ET , P(T ).
Since OptReUse does not compute T EC while
traversing the workflow DAG by a traversal order, but
sorts tasks based on their start time and allocate re-
sources, OptReU se can find VM reuse between tasks
of different workflows running simultaneously. The
implementation details and experimentation results
on various scenario is explained in the subsequent
section.
5 IMPLEMENTATION DETAILS
The individuals in the population-based evolutionary
algorithms represent a combination of VM types, se-
curity levels and task order. Workflow schedule gen-
eration approaches like WSG or OptReU se helps in
efficient resource utilization for a given combination.
Each combination is used as an input to the WSG and
OptReUse algorithms. The total cost, makespan and
risk rate for that combination is computed. The fitness
of a chromosome or a particle is measured in terms of
total cost, makespan and risk rate. In the subsequent
section, we discuss the individual coding strategy and
the implementation details for both the evolutionary
algorithms.
Parameters and Coding Strategy: In GA we used
random sampling with Tournament Selection. Sim-
ulated binary crossover (prob = 0.9) operation and
polynomial mutation operation were adapted. The
chromosome coding strategy is shown in Figure 4.
The first chromosome consists of set of n values for
VM type, authentication, integrity and confidentiality
security service level for each task. The second chro-
mosome is for task order selection. These tasks are
those having the same starting time. The second chro-
mosome represents the task ordering between tasks
having same starting time. The PSO particle coding
VM1 .... VMn
VM
types
a1 .... an
Authentication
g1 .... gn
Integrity
c1 .... cn
Confidentiality
0 1 .... 1
Order
Selection
Figure 4: Chromosome structure.
strategy is similar to GA chromosome coding strat-
egy. Both GA and PSO is run for 1000 generations
and the number of particles in each generation is 150.
For PSO, the initial velocity is kept 0. Initially param-
eters have values like w = 0.64, c
1
= 2.0 and c
2
= 2.0.
However, the parameters are adaptive which means
they keep changing at each iteration.
5.1 Results and Discussion
Our approach is tested on three standard scientific
workflows: LIGO, SIPHT, CyberShake (refer Figure
7 from (Li et al., 2016)). The experimentation set-
tings are considered from the paper (Li et al., 2016).
The workload and output data for each task is ran-
domly generated from a uniform distribution in the
range [5000, 50000] GFLOP and [10, 100] GB, re-
spectively. The bandwidth is considered as 0.1 GB/s.
The two evolutionary optimization algorithms GA
and PSO are used for selecting the optimal combi-
nation of VM types, security levels and task order
for tasks. The cost performance of our proposed
OptReUse algorithm is compared with the workflow
scheduling generation (WSG) proposed in (Li et al.,
2016).
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175

Figure 5: LIGO Workflow Cost Comparison.
Figure 6: CyberShake Workflow Cost Comparison.
5.2 Comparison: WSG and OptReUse
We compare the performance between WSG and
OptReUse for different risk rates. The results are
shown in Figures 5 and 6. We consider an instance
of each standard workflow. For a given evolutionary
algorithm (GA or PSO) and for a given value of (P
c
),
OptReUse always results in lower cost compared to
WSG.
1. For LIGO, the average percentage reduction in
cost obtained by OptReUse over WSG is 8.07%.
2. CyberShake is a data intensive workflow requiring
mostly storage optimized VMs and they are the
costliest VMs. Therefore, in CyberShake work-
flow more VMs are reused compared to LIGO.
Hence, the average percentage reduction in cost
is 11.2%.
3. SIPHT workflow is a computation intensive work-
flow requiring mostly the cheapest compute opti-
mized VMs. But it has the highest number of tasks
which makes it possible for OptReUse to search
for more reuse cases. We observed for SIPHT the
average percentage reduction in cost as 13.15%.
With increase in value of P
c
, T EC deceases be-
cause of the selection of lower security levels. The
makespan in all the three benchmark test cases for all
the P
c
values in the range [0.1,..., 0.9] does not change
Figure 7: Average performance of OptReUse: Mean cost
and Margin of Error.
significantly {LiGO = 45.47, SIPHT = 53.81, Cyber-
Shake = 102.2} minutes. Note, as time is not an ob-
jective function in our problem statement but it is a
constraint by a deadline.
5.3 OptReUse: Average Performance
To demonstrate the average performance of our ap-
proach, we experimented over number of instances.
We generated 75 workflow instances of CyberShake
work. Each instance having different task workload
and output data size. We chose three (high, medium,
low) permissible risk rates (P
c
) to observe the im-
pact of security levels on workflow execution cost and
makespan. The results are demonstrated in Figure 7.
Each bar in the figure gives the mean cost and margin
of error for confidence interval of 95%. It is observed
that OptReUse gives lower mean cost and margin of
error compared to W SG. When P
c
is increased, the
difference between the required and the provided se-
curity levels, increases. Hence, with the selection of
lower security levels (with lower overheads), overall
cost is reduced (refer Figure 7). Note, the average
makespan for both WSG and OptReUse algorithm is
46.66 minutes. Therefore, we can conclude that using
OptReUSe approach, there is reduction in cost with-
out any delay.
5.4 OptReUse: Multiple Workflows
Consider the case of scheduling more than one work-
flow instance simultaneously. We demonstrate using
two instances of CyberShake workflow. Suppose, all
the tasks are assigned with the least expensive VM
types. Scheduling multiple workflows simultaneously
by OptReUse results in lower cost compared to the
combined cost of scheduling workflows separately
(both by WSG or OptReUse). Ths is due to reuse of
VMs between tasks of different workflow instances as
shown in Table 2.
CLOSER 2022 - 12th International Conference on Cloud Computing and Services Science
176

Table 2: Workflow scheduling results.
Instance Approach Cost ($) Makespan (hrs)
1 WSG 73.56 46.72
2 WSG 67.66 51.125
1 OptReU se 72.876 46.72
2 OptReU se 67.35 51.125
1&2 OptReuse 137.45 51.125
6 CONCLUSION
Scientific workflow scheduling is about selecting the
right VMs, security levels and schedule generation
such that the overall cost and makespan is minimal.
We demonstrate, how VM reuse can reduce the over-
all cost without any delay. In particular, we demon-
strate the benefits of VM reuse across adjacent and
non-adjacent task, and further due to ordering of tasks
with the same start time. We design an enhanced se-
curity model for accurate estimation of risk. The re-
sults are shown by using two evolutionary algorithms
(GA and PSO) and the approach is tested on three
benchmark datasets. Our approach provides signifi-
cant cost reduction via VM reutilization.
REFERENCES
Adhikari, M., Amgoth, T., and Srirama, S. N. (2020). Multi-
objective scheduling strategy for scientific workflows
in cloud environment: A firefly-based approach. Ap-
plied Soft Computing, 93:106411.
Challita, S., Paraiso, F., and Merle, P. (2017). A study of vir-
tual machine placement optimization in data centers.
In 7th International Conference on Cloud Computing
and Services Science (CLOSER), pages 343–350.
Hilman, M. H., Rodriguez, M. A., and Buyya, R. (2018).
Task runtime prediction in scientific workflows us-
ing an online incremental learning approach. In 2018
IEEE/ACM 11th International Conference on Utility
and Cloud Computing (UCC), pages 93–102. IEEE.
Kaur, P. and Mehta, S. (2017). Resource provisioning and
work flow scheduling in clouds using augmented shuf-
fled frog leaping algorithm. Journal of Parallel and
Distributed Computing, 101:41–50.
Kumar, P. R., Raj, P. H., and Jelciana, P. (2018). Exploring
data security issues and solutions in cloud computing.
Procedia Computer Science, 125:691–697.
Lee, Y. C., Han, H., Zomaya, A. Y., and Yousif, M.
(2015). Resource-efficient workflow scheduling in
clouds. Knowledge-Based Systems, 80:153–162.
Li, Z., Ge, J., Yang, H., Huang, L., Hu, H., Hu, H., and
Luo, B. (2016). A security and cost aware scheduling
algorithm for heterogeneous tasks of scientific work-
flow in clouds. Future Generation Computer Systems,
65:140–152.
Liu, J., Lu, S., and Che, D. (2020). A survey of modern sci-
entific workflow scheduling algorithms and systems in
the era of big data. In 2020 IEEE International Con-
ference on Services Computing (SCC), pages 132–
141. IEEE.
Liu, L., Zhang, M., Buyya, R., and Fan, Q. (2017).
Deadline-constrained coevolutionary genetic algo-
rithm for scientific workflow scheduling in cloud com-
puting. Concurrency and Computation: Practice and
Experience, 29(5):e3942.
Malawski, M., Juve, G., Deelman, E., and Nabrzyski, J.
(2015). Algorithms for cost-and deadline-constrained
provisioning for scientific workflow ensembles in iaas
clouds. Future Generation Computer Systems, 48:1–
18.
Mboula, J. E. N., Kamla, V. C., and Djamegni, C. T. (2020).
Cost-time trade-off efficient workflow scheduling in
cloud. Simulation Modelling Practice and Theory,
103:102107.
Peng, G. and Wolter, K. (2019). Efficient task scheduling
in cloud computing using an improved particle swarm
optimization algorithm. In CLOSER, pages 58–67.
Ramamurthy, A., Pantula, P. D., Gharote, M. S., Mitra, K.,
and Lodha, S. (2021). Multi-objective optimization
for virtual machine allocation in computational scien-
tific workflow under uncertainty. In CLOSER, pages
240–247.
Shishido, H. Y., Estrella, J. C., Toledo, C. F. M., and
Arantes, M. S. (2018). Genetic-based algorithms ap-
plied to a workflow scheduling algorithm with secu-
rity and deadline constraints in clouds. Computers &
Electrical Engineering, 69:378–394.
Weng, C., Liu, Q., Li, K., and Zou, D. (2016). Cloudmon:
monitoring virtual machines in clouds. IEEE Trans-
actions on Computers, 65(12):3787–3793.
Xie, T. and Qin, X. (2006). Scheduling security-critical
real-time applications on clusters. IEEE transactions
on computers, 55(7):864–879.
Zhou, J., Wang, T., Cong, P., Lu, P., Wei, T., and Chen, M.
(2019). Cost and makespan-aware workflow schedul-
ing in hybrid clouds. Journal of Systems Architecture,
100:101631.
Secure Scheduling of Scientific Workflows in Cloud
177