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Performance Parameters Review of Workflow
Scheduling in Public Cloud 1M. Shyamala Devi,
2Aparna S. Joshi and
3V. Usha
1Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and
Technology,
Chennai.
shyamalapmr@gmail.com 2Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and
Technology,
Chennai.
aparna.joshi82@gmail.com 3Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and
Technology,
Chennai.
husha88@gmail.com
Abstract Workflow scheduling algorithm is used for load balancing in a cloud
computing environment. It performs efficient load balancing. This paper
takes a review of research carried out to improve performance of workflow
scheduling algorithm. The review is broadly analyzed considering
important performance parameters such as makespan and cost, energy
consumption, execution time and resource utilization. It was found that
most algorithms perform scheduling based on one or two parameter. A
better scheduling algorithm can be developed from the existing method by
considering more number of evaluation parameters.
Key Words:Cloud computing, workflow scheduling algorithm, load
balancing, performance parameters.
International Journal of Pure and Applied MathematicsVolume 119 No. 16 2018, 5005-5017ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/
5005
1. Introduction
Cloud computing technology (1) is gaining immense popularity in the last few
years due to its ability to utilize software, hardware, infrastructure and
computational resources as per user requirements on rent basis. However, cloud
computing faces many challenges which include security, load balancing,
resource scheduling, Quality of Service(QoS) management, energy
consumption, data lock-in and performance monitoring (2) (3). Main challenge
in cloud computing environment is load balancing (4). It is the process of
assigning and reassigning the load among available resource. (5) (6) (7).
Workflow scheduling algorithm is one of the scheduling algorithm in load
balancing. It is used to reduce makespan, cost, energy consumption, execution
time and maximizes resource utilization. It decompose bigger task into several
smaller subtask. This generated work flow must be scheduled to realize the
application execution. This benefits both cloud user and service provider as well
as minimizes throughput, cost and response time, improves performance and
resource utilization as well as energy saving.
This paper takes a review of research related to workflow specific scheduling
algorithm. The review is broadly analyzed considering important performance
parameters such as makespan and cost, energy consumption, execution time and
resource utilization. The paper is structured as follows: the introduction to
workflow scheduling algorithm is presented in section 2. Analysis of workflow
scheduling algorithm is presented in section 3 and section 4 concluded the work
carried out.
2. Introduction to Workflow Scheduling
Workflow scheduling is one of the prominent issue in cloud computing. It aims
at execution of workflows by considering their Quality of Service (QoS)
requirements such as deadline and budget constraints. In workflow scheduling,
bigger task is divided into different sub task. Resources are allocated to sub task
in such a way that predefined criteria’s are met. In a workflow application,
series of steps are executed. These steps have parent child relationship. This is
important particularly in problems of Bioinformatics, astronomy and business
enterprise (8) where to carry out bigger task, a set of sub task is executed in a
particular sequence. The next section describes workflow structure and various
parameters involved.
A. Structure of Workflow Scheduling
Workflow structure describes relationship among the tasks of the workflow.
Workflow structure is represented by Directed Acyclic Graph (DAG) and Non-
Directed Acyclic Graph (N-DAG) (9) as shown in figure1. Further it is
subdivided by a sequence of task execution, see Figure 1. First is Sequence
Structure where tasks are executed in series and new task is executed only after
completion of previous task. Second is Parallelism Structure where tasks are
International Journal of Pure and Applied Mathematics Special Issue
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executed concurrently. Third is a Choice Structure where tasks are executed in
series or concurrently.
Figure 1: Classification of Workflow Structure
A workflow application is generally represented as Directed Acyclic Graph
(DAG) such as 𝐺 (𝑉,𝐸)where 𝑉 is number of task and 𝐸 is the information
among the task. In Directed Acyclic Graph (DAG), there are two tasks: entry
task and exit task. Entry task is a root task and exit task is a task which does not
have any further task. It is also called child task. This is further illustrated using
figure 2. Parent task A called as entry task which executes before child task B,
C and D. Task E which is called as exit task executes after execution of task B,
C and D.
Figure 2: A Workflow Representation in the form of DAG
B. Performance Parameters in Workflow Scheduling
In Workflow scheduling algorithms, various parameters are used to evaluate
system performance (9). These parameters are described below:
Execution Time: This is exact time taken to execute the given task
Makespan: Maximum time required for completion of job i.e. from job
submission to job completion
Energy Consumption: Amount of energy consumed by all nodes
Cost: Cost for usages of resources
Resource Utilization: Maximum utilization of computing resources
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Fault Tolerance: It determine the capability of algorithm even after the failure of
one or more component pieces in any layer
Throughput: Number of process completed per unit time
Migration time: Time required to transfer one task from one node to another
anode
Performance: Overall efficiency of load balancing algorithm
Carbon emission: Amount of carbon produce by all resources
The research in workflow scheduling algorithm was studied considering the
above parameters. Next section describes the analysis of existing work flow
scheduling algorithm considering above parameters.
3. Analysis of Existing Workflow Scheduling Algorithm
Number of authors has done work in the area of workflow scheduling
algorithm. This survey work is presented in three parts. First subsection
describes research work that is mainly focused to minimize makespan and cost.
Second subsection describes research work that is mainly focused to reduce
energy consumption. Third subsection describes the research work which is
focused to minimize execution time and efficient resource utilization.
A. Makespan and Cost
Time period of job execution along with cost of execution are very
important performance parameters. Therefore, researchers concentrated
their efforts to reduce it.
Rizos Sakellarious et al. (10) introduced hybrid heuristic Directed Acyclic
Graph(DAG) algorithm to minimize cost. In this algorithm, one heuristic is used
for DAG scheduling, and another one for scheduling independent tasks. Both
heuristics improved performance behavior in the forms of Dynamic List
Scheduling, Heterogeneous Earliest Finish Time, Critical path on Processor,
Fastest Critical path and leveled Min Time.
Zhangjun Wu et al. (11) introduced Particle Swarm Optimization for Cloud
Workflow Scheduling algorithm to minimize makespan and optimize cost. In
this algorithm, the candidate solution is presented by the set of task-service
pairs, each particle not only learns from different exemplars, but also learns the
other feasible pairs for different dimensions.
This algorithm yields outstanding performance on scheduling workflow
applications in cloud environment.
Marek Wieczorek et al. (12) introduced Genetic, HEFT, and Myopic algorithm
to minimize makespan. This algorithm was compared using balanced and
unbalanced workflows on the basis of execution time. HEFT performs better as
compared with Myopic and Genetic.
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Z. Wu et al. (13)introduced market-oriented hierarchical scheduling strategy for
workflow scheduling to minimize makespan and optimize cost. Market-oriented
hierarchical scheduling strategy is divided into service-level scheduling and task
level scheduling. The service-level scheduling deals with the Task-to-Service
assignment and the task-level scheduling deals with the optimization of the
Task-to VM assignment in local cloud data centers.
Bogdan Simion et al. (14)introduced Improved Critical Path using Descendant
Prediction(ICPDP) to minimize makespan and optimize resource utilization.
This algorithm has quadratic polynomial time complexity. It finds the schedule
that results in makespan minimization and improve the utilization of resources.
Kianpisheh, S., Charkari et al. (15)introduced Ant Colony based constrained
workflow scheduling to minimize makespan and optimize cost. Probability of
Violation (POV) of constraint is proposed as a criterion for the schedule
robustness. Simulation result shows that it reduces the probability of violation
of workflow constraint, reducing makespan and cost.
Moschakisa et al. (16)introduced Bag-of-Task application on heterogeneous
interlinked clouds to minimize makespan and optimize cost. Simulated
annealing and thermodynamic simulated annealing is evaluated with virtual
machines of heterogeneous performance serving Bag-of-Tasks applications. A
state-of-the-art algorithm for Bag-of-Task scheduling and Fastest Processor
Largest Task was used for comparison.
B. Energy Management
To reduce the amount of energy consumed by node is very important.
Therefore, researchers concentrated their efforts to minimized energy
consumption.
Sonia Yassa et al. (17) introduced Multi-Objective Approach for Energy-Aware
Workflow Scheduling to minimize makespan, optimize cost and minimize
energy consumption. This algorithm is used to optimize the scheduling
performance. Method is based on the Dynamic Voltage and Frequency Scaling
(DVFS) technique to minimize energy consumption which allows processors to
operate in different voltage supply levels.
F. Coutinho et al. (18) introduced Workflow Scheduling Algorithm for
Optimizing Energy-Efficient Grid Resources usage to minimize energy
consumption.
This algorithm schedules the heavier task on maximum green resources.
Simulation results have proved that algorithm can significantly reduce the
power consumption in global grids.
Eugen Feller et al. (19) introduced Ant Colony Based Workload Placement in
Clouds, Grid Computing to minimize energy consumption. Novel dynamic
workload placement algorithm based on the Ant Colony Optimization (ACO) is
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introduced. The simulation results prove Ant Colony Optimization algorithm
provides superior energy gains than greedy algorithm.
Rajarathinam Jeyarani et al. (20) introduced Self Adaptive Particle Swarm
Optimization(SAPSO) to minimize energy consumption.
A novel approach for optimal placement of virtual machine in cloud is
proposed. The experimental results show that SAPSO was compared with
Multi-Strategy Ensemble Particle Swarm Optimization (MEPSO) outperforms
the power aware adaptive VM provisioning in a large scale, heterogeneous and
dynamic cloud environment.
Jiandun Li et al (21) introduced energy efficient workflow scheduling algorithm
to minimize energy consumption, optimize resource utilization and cost, and
minimize execution time.
An Energy Efficient Scheduling Approach was used to schedule workflow on
Private Clouds. The Simulation result shows that it can save more time for users
conserve more energy and achieve higher level of load balancing.
Jiachen Yang et al. (22) introduced a task scheduling algorithm considering
game theory to minimize energy consumption. Proposed a task scheduling
algorithm for energy management in cloud computing for big data by
considering game theory. The Simulation result shows that the proposed method
can perform better scheduling result over the task scheduling algorithm such as
Ant Colony Optimization, Genetic Algorithm, and Multi stage algorithm.
Mohamed Abu Sharkh et al. (23) introduced mathematical optimization mode
for workflow scheduling in cloud environment to minimize energy
consumption.
Problem of energy efficiency in a cloud data center is handled by a proposed
method. Smart VM Over Provision (SVOP), offers a major advantage to cloud
providers when live migration of VMs is not preferred.
C. Execution Time and Resource Utilization
To minimize the time taken to execute the given task and to maximum
utilization of computing resources are very important issues in load
balancing. Therefore, researchers concentrated their efforts to optimize it.
C. Lin et al. (24) introduced Scheduling Scientific Workflows in cloud
environment to optimize execution time. To schedule a workflow elastically on
a Cloud computing environment Scalable-Heterogeneous-Earliest-Finish-Time
algorithm (SHEFT) is proposed.
Experimental result shows that SHEFT outperforms several representative
workflow scheduling algorithms in optimizing workflow execution time.
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Soumi Ghosh et al. (25) introduced Priority Based Modified Throttled
Algorithm to minimize execution time, optimize cost and resource utilization. A
new enhanced algorithm is proposed and implemented in cloud computing
environment which adds a new feature like priority basis service of each
request.
Firstly determine the priorities of a request and allocate request to Virtual
Machines. A switching queue has proposed to hold the low priority requests due
to the arrival of high priority request. Simulation results show that response
time is improved in comparison with Throttled and Round Robin algorithm.
G.Suryadevi et al. (26) introduced Distributed Dynamic Load Balancing
Algorithm to minimize execution time and optimize cost and resource
utilization. For the scheduling of virtual machines on cloud environment
efficient distributed dynamic Load balancing Algorithm in Eucalyptus platform
has proposed.
All the incoming requests from the clients have been automatically redirected to
virtual machines based on priority and also the new virtual machines are created
and redirected when the requests are overloaded. Simulation results show that
allocating the resources on virtual machines based on priority achieves the
better response time and processing time.
Mainak Adhikari et al. (27) introduced workflow scheduling in cloud
environment to minimize execution time and optimize resource utilization and
cost.
To handle large number of application simultaneously, an Efficient Workflow
Scheduling Algorithm (EWSA) is proposed. The objective of the algorithm is to
estimate the execution time of all the tasks dynamically. The algorithm also
creates suitable VMs with minimum resources such that the entire application
can be executed within its deadline. Simulation results show that the proposed
algorithm maximize the resource utilization and execute the workflow within its
deadline.
Harmandeep Singh Brar et al. (28) introduced priority based load balancing
algorithm to minimize execution time. Proposed a priority based load balancing
algorithm mainly focuses on assigning tasks to virtual machines in such a way
that high priority tasks can be completed earlier which is done based on
execution length.
Simulation results show that it is good to use this algorithm for real time
applications as it shows a great improvement in execution time of the cloudlets.
Work done in various workflows scheduling algorithm is summarized in
TABLE 1 in terms of parameter used, reference, scheduling algorithm,
algorithm type and tools used to carry out experiment.
International Journal of Pure and Applied Mathematics Special Issue
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Table 1: Workflow Scheduling Algorithms used for Load Balancing
Parameters Authors & Year Scheduling Algorithms Algorithm
Types
Tools Used
Makespan
and
Cost
Rizos Sakellarious
et al. 2004(10)
Hybrid Heuristic DAG
scheduling
Heuristics Real world workflow
Zhangjun Wu et al.
2010 (11)
Particle Swarm
Optimization for Cloud
Workflow Scheduling
Meta-
Heuristics
Amazon EC2
Marek Wieczorek
et al.
2005 (12)
Genetic,HEFT,Myopic
algorithm
Heuristics Real world workflow
Z. Wu et al.
2011 (13)
market-oriented
hierarchical scheduling
strategy
Meta-
Heuristics
SwinDeW-C
Bogdan Simion et
al
2007. (14)
Improved Critical Path
using Descendant
Prediction
Heuristics Real world workflow
Kianpisheh S. et
al.
2016 (15)
Ant Colony based
constrained workflow
scheduling
Meta-
Heuristics
Real world workflow
Moschakisa et al.
2015 (16)
Bag-of-Task application on
heterogeneous interlinked
clouds
Meta-
Heuristics
Scientific Workflow
Luiz Fernando
Bittencourt et al.
2011 (29)
Optimized Cost Algorithm Heuristics CloudSim
Energy
Management
Sonia Yassa et al.
2013 (17)
Multi-Objective Approach
for Energy-Aware
Workflow Scheduling
Meta-
Heuristics
Scientific Workflow
F. Coutinho et al.
2011 (18)
Workflow Scheduling
Algorithm for Optimizing
Energy-Efficient Grid
Resources usage
Heuristics GridSim
Eugen Feller et al.
2011 (19)
Ant Colony Based
Workload Placement in
Clouds, Grid Computing
Meta-
Heuristics
Java based toolkit
Rajarathinam
Jeyarani et al.
2011 (20)
Self-Adaptive Particle
Swarm Optimization
Meta-
Heuristics
CloudSIM
Jiandun Li et al.
2011 (21)
An Energy Efficient
Scheduling
Meta-
Heuristics
Real world workflow
Jiachen Yang et al.
2017 (22)
A task scheduling
algorithm considering
game theory
Heuristics Real world workflow
Mohamed Abu
Sharkh et al. 2017
(23)
mathematical optimization
mode
Meta-
Heuristics
Discrete simulator
Execution
Time
and
Resource
Utilization
C. Lin et al.
2011 (24)
Scheduling Scientific
Workflows
Heuristics CloudSim
Soumi Ghosh et al.
2016 (25)
Priority Based Modified
Throttled Algorithm
Heuristics CloudSim3.0
G.Suryadevi et al.
2014 (26)
Distributed Dynamic Load
Balancing Algorithm
Meta-
Heuristics
Eucalyptus
Mainak Adhikari
et al.
2016 (27)
Workflow scheduling Meta-
Heuristics
Montag,LIGO,CyberShake
Harmandeep Singh
Brar et al. 2014
(28)
Priority Based Load
balancing algorithm
Heuristics CloudSim
International Journal of Pure and Applied Mathematics Special Issue
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TABLE 2 describes parameters considered by researchers in evaluating system
performance. It has been observed that only few of all the parameters were
considered at a time for evaluation.
Table 2: Parameters to Evaluate the Performance of Workflow Scheduling
Algorithm
Parameters
Scheduling
Algorithms
Makespan Budget
or
Cost
Energy
Management
Execution
Time
Resource
Utilization
Hybrid Heuristic DAG
scheduling (10)
Yes No No No No
Particle Swarm
Optimization for Cloud
Workflow Scheduling (11)
Yes Yes No No No
Genetic, HEFT, Myopic
algorithm (12)
Yes No No No No
Market-oriented hierarchical
scheduling strategy (13)
Yes Yes No No No
Improved Critical Path
using Descendant Prediction
(14)
Yes No No No Yes
Ant Colony based
constrained workflow
scheduling (15)
Yes Yes No No No
Bag-of-Task application on
heterogeneous interlinked
clouds [16]
Yes Yes No No No
Optimized Cost Algorithm
(29)
No Yes No Yes No
Multi-Objective Approach
for Energy-Aware
Workflow Scheduling (17)
Yes Yes Yes No No
Workflow Scheduling
Algorithm for Optimizing
Energy-Efficient Grid
Resources usage (18)
No No Yes No No
Ant Colony Based
Workload Placement in
Clouds, Computing(19)
No No Yes No No
Self-Adaptive Particle
Swarm Optimization (20)
No No Yes No No
An Energy Efficient
Scheduling (21)
No Yes Yes Yes Yes
A task scheduling algorithm
considering game theory
(22)
No No Yes No No
mathematical optimization
mode (23)
No No Yes No No
Scheduling Scientific
Workflows (24)
No No No Yes Yes
Priority Based Modified
Throttled Algorithm (25)
No Yes No Yes Yes
Distributed Dynamic Load
Balancing Algorithm (26)
No Yes No Yes Yes
Workflow scheduling (27) No Yes No Yes Yes
Priority Based Load
balancing algorithm (28)
No No No Yes No
International Journal of Pure and Applied Mathematics Special Issue
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4. Conclusions
This paper analyzes various existing workflow scheduling algorithm with their
objectives to optimize solution. The present research is summarized based on
performance parameters. It was found that most algorithms perform scheduling
based on one or two parameter. A better scheduling algorithm can be developed
from the existing method by adding more number of metrics. This will help to
enhance the performance of system.
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