A Survey on Task Scheduling and Load Balanced Algorithms in Cloud Computing

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A Survey on Task Scheduling and Load Balanced Algorithms in Cloud
Computing
T. Kannadasan1,Dr. R. Pragaladan2
1Associate Professor and Head, Department of Computer Science, Thanthai Periyar Government Arts and
Science College (Autonomous), Tiruchirappalli
2Assistant Professor and Head, Department of Computer Science, Sri Vasavi College, Erode
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Abstract:- Cloud Computing is a recent developmental paradigm in the field of computing offering huge power to
next-generation computers. The dynamic provisioning acts as a base for cloud computing facilitating and
supporting the network services. It focuses on making the vision of utility computing a reality with pay-as-you-go.
It offers immense potential to bloom the world with applications and products focusing on greater resource
utilization and scalability. This paper presents the survey on the basics of cloud computing, the concepts of load
balancing, and the scheduling of tasks in the cloud. It elaborates on the existing load scheduling algorithms with
their merits and demerits, suitability in the cloud, heterogeneous computing environment, and proposes a new
perspective for better results as per desired parameters.
Keywords: Load balancing, Cloud Optimization, Response time, Scheduling Algorithms.
1. INTRODUCTION
1. 1. Load balancing
Load balancing is the reapportion of the workload equally across all processors, so that no processor is overloaded. A load
balancer is a physical device, running on a specialized hardware or software process and it accepts multiple requests from
users and distributes them evenly across servers [20]. Load balancing increases throughput and thereby reduces response
time. Load balancing in clouds, distributes the excess dynamic local workload evenly across all the nodes. It ensures better
service provisioning and resource utilization ratio, and in turn, improves the overall performance of the system. Incoming
tasks, which are received from different locations, are received by the load balancer and are then distributed to the data
center for the proper load distribution. Figure (1) shows the typical load balancing mechanism.
Fig. 1. The Load balancing Mechanism
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 04 | Apr 2022 www.irjet.net p-ISSN: 2395-0072

1. 2. Challenges of Load Balancing Algorithm
1. 2. 1. Overhead: Any load balancing system should consider the amount of overhead involved in the implementation of a
load balancing system [1]. It consists of overhead due to VM migration cost or the cost of communication. A well-designed
load balancing algorithm should reduce overhead.
1. 2. 2. Performance: It denotes the efficiency of the system. Performance can be ascertained from users experience and
satisfaction [1]. Ensuring optimum performance is the foremost challenge for VM load balancing algorithms. The
performance includes the following perspectives:
1. 2. 2. 1. Resource utilization – It is a measure of whether a host is overloaded or underutilized [1]. Many VM load
balancing algorithms prefer offloading of overloaded hosts with higher resource utilization.
1. 2. 2. 2. Scalability – The quality of service delivered is kept smooth and without lag, even when the number of users
increases irrespective of the algorithm management approach, like centralized or distributed.
1. 2. 2. 3. Response time – The quantity of time to react by a load balancing algorithm upon input parameters in a cloud
system. The response time should be reduced for better performance.
1. 2. 2. 4. The point of failure – One of the concerns in the system design is that a single point failure does not affect the
provisioning of services. This issue prevails in centralized systems, when the central node fails, then the whole system
conk out. So, load balancing algorithms should be designed with high availability to overcome this problem.
1. 2. 3. Load balancing in cloud – The cloud is referred to as a subscription based service where computing resources and
storage space in the network can easily be accessed. The clouds act as virtualized data centers [1, 22]. The platforms used
in cloud computing are dynamically built using virtualized software, hardware, data, and networks. Thus, cloud computing
is a new and upcoming computing paradigm that supports data and computational outsourcing [27]. Figure (2) shows the
typical architecture of load balancing in cloud environment.
Fig. 2. The Architecture of Load Balancing in Cloud environment
Cloud computing is a scalable and distributed system requiring the allocation of resources to several users. This could lead
to congestion or imbalanced allocation of the system [3]. So, a load balancing strategy is needed to deal with the imbalance
in the network. Load balancing is the mechanism used for distributing the load for optimal resource utilization on the
system processes or virtual machines. The load refers to the task needed to be done on the system and balancing refers to
the handling or the management of the load. Load balancing is applied to the resources in the system which can be disks,
drivers, memory buffers, processors, network simulators, etc. [4]. The load balancing is performed and applied to achieve
minimum response time, maximum throughput, and the reduction of the overheads produced by the system. The main aim
is to process the user’s request by the efficient, and effective utilization of the resources present in the system [23].
3. LOAD SCHEDULING
Load scheduling thereby the load balancing is attained, is a computer networking technique to distribute workload across
multiple computers or computer clusters, disk drives, networks, [28] central processing units (CPUs) and other resources
incited due to scalability for achieving higher throughput, optimal resource utilization, lesser response time, and avoiding

overloading of the system[1]. Using multiple components with load balancing [14] instead of a single component, increases
the reliability with the help of redundancy. The task of providing the load balancing service is done by dedicated software
and hardware, such as a Domain Name System server. Load balancing plays an important role in the distributed computing
system. The load distribution in a network is performed among servers. Load distribution is the method of reallocating the
total available load to the specific and single node available on the network for efficient utilization of resources, improving
the response time of the task, and removing the situation of node overloading as well underloading in the system. The load
balancer is a software service that listens on the port to avail services by connecting with external users. The load balancer
intern forwards requests to the servers located at the backend and the feedback data is passed to the load balancer and
then back to the user. The user receives the reply from the load balancer, but the user is unaware of the internal separation
or abstraction of functions [12, 15]. It also prevents the user’s direct contact with the backend servers, thereby increasing
the benefits obtained from security by the inner network’s structural abstraction, and preventing attacks on the service
ports and network stack of the kernel.
3. 1. Major goals of the load balancing:
a) To enhance the system performance.
b) To have a backup plan ready if any failure occurs in the system.
c) To uphold the system stability.
d) To allow further modifications in the system.
e) To distribute the load effectively and obtain cost-effectiveness.
4. LOAD SCHEDULING ALGORITHMS
The load scheduling, as well as the task scheduling algorithms that are currently available in the cloud environment, are
discussed below and tabularized in Table (1).
4. 1. Max-Min Scheduling Algorithm
In this algorithm, firstly a set of minimum execution time M is computed. The task having a total maximum completion
time amongst elements of M is selected and allocated to the corresponding machine is called the Max-min strategy. The
recently mapped task, T is removed from the set M and the procedure is repeated till the system maps the remaining tasks
[1, 16, 19]. The minimizing algorithm attempts to minimize the degree of shortcomings incurred for performing tasks with
longer execution times. It offers better mapping than the shorter tasks that get executed first, followed by the execution of
long-running tasks while several machines remain idle waiting for resources. Thus, the Max-min heuristic provides
mapping with a more nicely balanced load across machines and a better makespan (completion time).
4. 2. Min-Min Scheduling Algorithm
It is the simplest algorithm enduring as a base for existing [1, 16, 21, 30] cloud scheduling algorithms. It is fast and
provides higher and better performance than others by scheduling by considering the best case first. It begins using a set
of completely unmapped tasks, S. Then, the resource R having a minimum completion time for all tasks is found and the
task T with the minimum size is selected and assigned to the corresponding resource R. Lastly, from the set of unmapped
tasks, S executed task, T is removed and the process goes on repeating using Min-min algorithm till all tasked are finished.
Assuming [29] we have a set of n tasks (Tn) that need to be scheduled onto m available resources (Rm). We denote the
Expected Completion Time for task i starting from 1 to n on resources j from 1 to m as CTij and RTj represents the Ready
Time of resource Rj. ETij represents the Execution Time of the task Ti on resource Rj. So, the completion time is CTij = ETij +
RTj.
4. 3. Segmented Min-Min Scheduling Algorithm
This algorithm sorts the tasks based on ETCs (Estimated Time to Complete) [17]. The tasks are sorted based on the sorting
key using an ordered list by the trade-off factor N in average ETC, minimum ETC, or the maximum ETC. After this,
segments with equal size using a trade-off factor N are created using the task list-partitioning scheme. The larger task
segments are scheduled and executed first followed by the smaller tasks i.e. in decreasing order. Min-min is applied to

assign tasks to machines for every segment. The task sorting is done before scheduling so that the larger tasks are
scheduled earlier.
4. 4. A* Scheduling Algorithm
It is a search technique based on a binary tree beginning at the root node (null solution). As the tree propagates, nodes
represent partial mappings where a subset of the tasks is assigned to machines [1]. The partial mapping (solution) of a
child node has one more task mapped than the parent node. The children are generated by the parent node with the
possible additional task’s mapping (ta) and then the parent node becomes inactive. Pruning is applied to keep track of the
execution time and at a time restriction on the maximum number of tree’s active nodes. Each node contains a cost function.
Its children replace the node having a minimum cost function. This process continues until a complete mapping is done or
a leaf node is located.
4. 5. Heterogeneous Earliest Finish Time Algorithm (HEFT)
This algorithm determines the average execution time of the tasks and average communication time among resources of
the consecutive tasks [24]. Then, the algorithm orders the tasks, based on the rank function in the workflow i.e. higher
rank value task is awarded higher priority. The scheduling of tasks occurs depending on the priority order and resources
are assigned to tasks to complete them at the earliest time in the allocation phase.
4. 6. Multiple QoS Constrained Scheduling Strategy of Multi-Workflows ((MQMW)
This algorithm works on multiple workflows and many Quality of Service parameters [6]. This strategy involves
minimizing the makespan (the time difference between the beginning and end of a job or task sequence) and the cost of
workflows for a cloud computing platform. This algorithm also leads to an increase in the scheduling access rate in the
system.
4. 7. A Double Min-Min Algorithm for Task Scheduling
In this algorithm, a priori is used in a heterogeneous environment, which is defined, by the meta-tasks size and the number
of machines [4, 5]. These are static heuristics, therefore on each machine, the task expected execution time for accurate
estimation is known to be priorly and stored in an ETC matrix where ETC (ti, mj) denotes the estimated execution time of
the task i on machine j. The scheduling is accomplished using the Min-min approach. In this, we allocate a number of
independent jobs to the available resources, there is a sufficient number of machines available for allocating tasks and
workload and processing capability of each job and resources (in millions of instructions per second MIPS) are taken into
account.
4. 8. QoS Guided Min-Min Algorithm
This algorithm adds QoS (Quality of Service) constraints like bandwidth, time, and memory [2, 10] to the basic Min-min
heuristic. In this, some tasks require high network bandwidth, whereas others could be contented with low network
bandwidth. It assigns tasks having high QoS request parameters first similar to the Min-min heuristic. In the worst case, all
tasks generally need either low QoS or high QoS.
4. 9. Improved Cost-Based Algorithm for Task Scheduling
This algorithm is used for efficient mapping of tasks to resources in the cloud [8, 10]. It measures the computational
performance and the resource cost incurred in the system, thereby helping in the improvement of the computational or
communication ratio.
4. 10. Optimized Resource scheduling algorithm
This algorithm aims to obtain complete optimization or sub-optimization in the cloud. It supports multiple instances in the
cloud for processing the user requests, bewaring the cost and performance. It supports an automated scheduling policy
and uses an Improved Genetic Algorithm (IGA) [14, 31] to increase the resource utilization rate and speed of execution in
the system.

4. 11. A Particle Swarm Optimization-based Heuristic for Scheduling Workflow Applications
This algorithm uses particle swarm optimization-based heuristics to schedule applications to the resources present in
cloud by taking into consideration the cost of computation and the data transmission [7, 18]. It is used for a workflow
application by decreasing the applications’ computational as well as communication costs with significant cost savings and
optimal workload distribution of the resources in the system.
4. 12. Resource Aware Scheduling Algorithm (RASA)
This algorithm is based on Max-min and Min-min task scheduling algorithms and is referred to as Resource Aware
Scheduling Algorithm (RASA) [8]. It utilizes the benefits of both Max-min and Min-min algorithms and hides their
disadvantages by outperforming the existing scheduling algorithms in large-scale distributed systems and then using the
better solution [16]. It is adjusted to exploit the conditions, with minimal overhead and higher performance either by Max-
min or Min-min.
4. 13. Round Robin Algorithm
It is a static and decentralized algorithm [29] used on web servers. The processes are divided among the processors in a
round-robin fashion using a particular timestamp. The allocation order of the resources to the processors is locally
maintained irrespective of remote ones. They possess equal distribution of workload among the processors and different
processing times of tasks for processors. But the pay-off is that nodes become overloaded and times become underloaded
[13].
4. 14. Weighted Round Robin Algorithm
In this algorithm, the weights are assigned in a particular order to every task in the system to allocate resources for
optimal utilization of resources [13, 29]. After assigning the weights, the allocation of the data centers occurs depending on
the time quantum or time slot in the round-robin fashion.
4. 15. Scalable Heterogeneous Earliest Finish Time Algorithm (SHEFT)
This algorithm schedules the workflow elastically on a cloud computing environment with the benefits of the
Heterogeneous Earliest Finish Time algorithm (HEFT) which also induces scalability [6, 25]. The parameters act as a
means for scheduling the resources and tasks. The scalability is generally determined by adding a large number of systems
for resources. It outpaces other workflow scheduling algorithms to obtain an optimized execution time and provides the
ability to elastically scale resources at runtime.
4. 16. A Compromised Time-Cost Scheduling Algorithm
This algorithm [20] considers the characteristics of cloud computing to provide multiple instances for performing
computation on the system and implementing intensive cost constraint workflows by compromising or lessening the
execution time and cost with the help of the user input enabled on the fly.
4. 17. Optimal Workflow based Scheduling (OWS) Algorithm
This algorithm schedules the workflow in a cloud environment by finding a solution by meeting the quality of service like
bandwidth, memory constraints used by the user and performs optimal workflow execution [9, 15]. Thereby, achieving an
important improvement and better results in terms of CPU utilization and the costs incurred.

EXISTING LOAD BALANCING (SCHEDULING) ALGORITHMS
Table 1. The observations and strategies handled by the existing scheduling algorithms
Algorithm Method Used Allocation Strategy Observations
Min-Min Scheduling
Algorithm [1, 30]
Many cases used,
Minimum
completion time
Tasks allocated resources
for execution
1. Resource cost and computational
performance is calculated.
2. Higher utilization rate of resources
Max-Min Scheduling
Algorithm [1, 19]
Multiple cases,
Speed, Maximum
completion time
Resources allocated to
tasks
1. Computational performance and resource
cost are evaluated.
2. Greater resource utilization.
Segmented Min-Min
Scheduling Algorithm
[17]
Multiple cases,
Speed, Resource
Utilization
Request provisioning,
Segmentation
1. Measures both resource cost and
computation performance
2. The utilization rate of resources is high.
A* Scheduling
Algorithm [1]
Group approach,
Resource Utilization
Unscheduled task groups,
Pruning is done
1. Complete the task at the earliest time.
2. Obtaining lower makespan and cost.
3. Better allocation.
Multiple QoS
Constrained
Scheduling Strategy
of Multi Workflows
[6, 15]
Segments are made,
Quality of Service,
Constraints are
considered
1. Optimizes execution time.
2. Allows elastic scaling of resources in
workflow execution.
A Double Min-Min
Algorithm for Task
Scheduling [4]
Various Cases,
Speed, Utilization
Request allocation
similar to Min-min
computation performance.
QoS Guided Min-Min
Algorithm [2]
Segments are made,
Quality of Service,
Constraints considered
1. Similar to Min-min algorithm with sub-
policies.
2. Resources are scaled elastically in
execution with a higher utilization rate.
Optimized Resource
[31]
Multiple instances,
Speed, Resource
Utilization
Allocation of resources as
requested by the user
1. Speed is higher.
2. The Genetic Algorithm improves resource
utilization rate.
Improved cost-based
algorithm for task
scheduling [10]
Multiple cases, Cost,
Performance
Task groups are
unscheduled
2. Improves the
computation/communication ratio.
A compromised Time
Cost Scheduling
Algorithm [20]
Many cases, Cost
and time,
Constraints of cost
applied
Job cases are created by
considering service level
scheduling
1. Optimizes and reduces cost and time.
2. Reduces the execution time.
Heterogeneous
Earliest Finish Time
algorithm (HEFT)
[25]
Dependency
Method, Execution
time, Scalability
Group of tasks ordered
by rank function
2. Resources are scaled elastically during
execution.

5. CONCLUSION AND FUTURE WORK
In this appraisal paper, we briefly discoursed the load and task scheduling algorithms implemented in various
heterogeneous cloud servers. These algorithms are constrained by assorted scheduling parameters and strategies. For
example, a higher utilization rate was achieved by using min-min, segmented min-min, double min-min, and max-min
algorithms. The ETC factor completes a task at the earliest time and the weighted round-robin reduces computation cost.
The exploration was served for greater resource utilization, reduced cost and debt to achieve maximum throughput, and
higher performance. Even though the existing load balancing techniques doing well but not enough to fulfill the current
demand from the different cloud environments. So, in future work, we have to concentrate on the existing problems to
suss out these in the cloud computing environment with better acumination subjective to resource constraints. The future
proposed paper must be designed to improve the optimized load balancing in cloud computing environment for efficient
utilization of resources, the improved response time of tasks and refining the situation of node congestion, overloading,
and underloading in the distributed cloud environment.
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1Mr. T. Kannadasan, designated as Associate Professor in Computer Science in the Tamil Nadu State
Government Collegiate Education Service from October 1998. Pursuing the Ph.D. Degree in Computer
Science (Part-Time) in Bharathiar University, Coimbatore under the guidance and supervision of
Dr.R.Pragaladan in Cloud Computing.
2Dr. R. Pragaladan conferred the Ph.D. Degree in Computer Science by Bharathiar University, Coimbatore
through Erode Arts and Science College under Faculty Development fellowship Programme (FDP) for
Teacher by UGC, Erode Tamilnadu, India in the year 2018. Currently, he is designated as Assistant
Professor in Computer Science and Head of the Department of Computer Science, Sri Vasavi College, Erode
affiliated with Bharathiar University. He has been a supervisor and mentor for several students in B.Sc.
(CS), M.Phil., and Ph.D. programs all around for the past 14 years. He has vast experience and published 5
papers at Conferences and more than 15 papers in reputed Journals. His principal domain of fascination
includes Cloud Computing, Internet of Things, Bio-Informatics, Cyber Security, and Computer Networks.
BIOGRAPHIES

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