A Review on Scheduling in Cloud Computing

International Journal of UbiComp (IJU), Vol.7, No.3, July 2016
DOI:10.5121/iju.2016.7302 9
A Review on Scheduling in Cloud Computing
Sujitha.A1
, Gunasekar.K2
1
M.E.Scholar, Department of Computer Science & Engineering, Nandha Engineering
College, Erode-638052, Tamil Nadu, India
2
Associate Professor, Department of Computer Science & Engineering, Nandha
Engineering College, Erode-638052, Tamil Nadu, India
ABSTRACT
Cloud computing is the requirement based on clients that this computing which provides software,
infrastructure and platform as a service as per pay for use norm. The scheduling main goal is to achieve
the accuracy and correctness on task completion. The scheduling in cloud environment which enables the
various cloud services to help framework implementation. Thus the far reaching way of different type of
scheduling algorithms in cloud computing environment surveyed which includes the workflow scheduling
and grid scheduling. The survey gives an elaborate idea about grid, cloud, workflow scheduling to
minimize the energy cost, efficiency and throughput of the system.
KEYWORDS
Cloud Computing, Scheduling, Virtualization
1. INTRODUCTION
Cloud computing is also referred as on-demand computing, is a kind of Internet-based computing
that provides resources sharing and data to computers and other devices on demand. It relies on
sharing of resources to achieve integrity and scale of economy, resemble to a utility computing.
Cloud computing has become a highly demanded service due to the benefit of high computing
power, monetary cost, high performance, availability ,scalability along with accessibility. There
was several cloud vendors are experiencing growth rates of 50% per year, but being still in a
stage of inception, it has risk that have to be focused to make cloud computing services more
reliable and user friendly. The figure 1 is about the cloud computing services.
Figure 1 Cloud Computing Services

10
Virtualized resources with an under utilization rate which consume an unsatisfactory amount of
energy compared to the energy consumption of a completely utilized cloud computing. According
to [13], energy consumption of an idle resource is about as 60% or peak power. In cloud
computing, there is a connection between energy consumption and resource utilization. An
effective technique is Task consolidation which is incredibly enabled by virtualization
technologies, which aid the concurrent execution of several tasks, harness resource utilization and
in turn reduce the energy consumption [1].
2. OVERVIEW
2.1 Scheduling
The cloud computing is effective based on the scheduling. Based on various parameters the task
is to be scheduled such as arrival time, system load, execution time and deadline. It makes the
task to finish on time and which guarantee the clients to improve flexibility in cloud and
reliability of systems in cloud. The tasks are uncertain, so scheduling is deployed to overcome the
uncertainty [13].
2.2 Virtualization
Virtualization technology is responsible for the creation, migration and cancellation of virtual
machines [8]. When the task needs space in excess, fluctuation in utilization of resources leads to
migration. Virtualization carries out the load balancing, consolidation, and hot spot mitigation
[11]. It allocates data center resources dynamically based on the demands given by the user and
number of servers is to be reduced to bloom out the green computing. The figure 2 describes the
work of virtualization technology.
Figure 2 the Work of Virtualization Technology
3. LITERATURE SURVEY
A Cloud Gaming System Based on User-Level Virtualization
Author focused on the cloud gaming. Cloud gaming renders an interactive gaming application in
the cloud and streams the scenes as a video sequence to the player over Internet. Author proposed
GCloud, a GPU/CPU hybrid cluster for cloud gaming based on the user-level virtualization
technology. Deployed a performance model to analyze the server-capacity and games’ resource-
consumptions, which sort by type into two games: CPU-critical and memory-io-critical.
hardware
virtualization
virtualized host os
service models
Cloud users

11
Simulation tests evident that both of the First-Fit-like and the Best-Fit-like strategies outrun the
other(s). Test results indicate that GCloud is efficient.
ANGEL: Agent-Based Scheduling for Real-Time Tasks
Author devised a novel agent-based scheduling mechanism [7] in cloud computing environment
to assign real-time tasks and dynamically provision resources. A bidirectional announcement-
bidding mechanism and the collaborative processes employed consist of three phases. The three
phases are basic matching phase, forward announcement-bidding phase and backward
announcement-bidding phase. In a meanwhile, author designs both forward and backward
announcement-bidding phases for calculation rule of the bidding values and two heuristics for
selecting contractors. The bidirectional announcement-bidding mechanism is used to propose an
agent-based dynamic scheduling algorithm named ANGEL for real-time, separate and aperiodic
tasks in clouds. Extensive experiments are conducted on cloudsim platform.
An Energy-Saving Task Scheduling Strategy Based on Vacation Queuing Theory
High energy consumption [15] is one of the major issues of cloud computing systems. Requested
jobs in cloud computing environments have the nature of changeability, and gauge nodes have to
be powered on all the time to await requested tasks. This results in an incredible wastage of
energy. Task scheduling algorithm of an energy-saving based on the vacation queuing model for
cloud computing systems is proposed here. No of computer nodes, total idle energy, the number
of tasks arriving at the system, heuristic task scheduling algorithms, the meta-heuristic task
scheduling algorithms, the queuing theory-based algorithms used here. Simulation results evident
that the proposed algorithm can ensure task performance, while reducing the energy cost of a
cloud computing system effectively.
Exploring Blind Online Scheduling for Mobile cloud
Mobile cloud is a technology through the enabled users will enjoy abundant multimedia
applications in a computing environment. An important issue for the mobile cloud is the
scheduling of massive multimedia flows with heterogeneous QoS guarantee. Multimedia servers
based on the slot information of the users’ requests that occurs on last time, and route all the
multimedia flows according to the first-come first-served rule. Operating time of schedule, User
waiting time, Performance improvement time is used to measure the performance of the system.
Blind online scheduling algorithm (BOSA) [2] is used here. Mobile Cloud Multimedia Services
(MCMS) are the environmental tool. Simulation results shows that the proposed scheme can
efficiently schedule heterogeneous multimedia flows to satisfy dynamic QoS requirements in a
practical mobile cloud.
Temporal Load Balancing with Energy Cost Optimization
A cloud computing service plays a vital role in people’s daily life. These services are encouraged
by infrastructure known as Internet data center (IDC) [1]. As demand for cloud computing
services sounds, energy consumed by IDCs is blown forward. Workload intensity, Queuing delay,
Energy costs are used to measure the performance. Eco-IDC Minimize energy cost algorithm
used here. Eco-IDC Minimize energy cost Service delay avoidance as a result of this technique.
A Hyper-Heuristic Scheduling Algorithm for Cloud

12
Rule-based scheduling algorithms have been used on vast cloud computing systems because they
are simple and easy to implement. There are a lot of areas to improve these algorithms
performance, notably by using heuristic scheduling. A novel heuristic scheduling algorithm is
called hyper-heuristic scheduling algorithm (HHSA) [5] which is used to find better scheduling
solutions for cloud computing systems. Result shows that it reduce the make span of task
scheduling compared with the other scheduling algorithms.
FESTAL: Fault-Tolerant Elastic Scheduling Algorithm for Real-Time Tasks in Virtualized
Clouds
Fault tolerance in clouds receives an attention in both industry and academia mainly for real-time
applications due to their safety critical nature. Researches on the fault-tolerant scheduling [16]
study the virtualization and the elasticity is the two key features of clouds. To address this issue,
author presents a fault-tolerant mechanism which extends the primary-backup model to
incorporate the features of clouds. Host, Time, Task count, Interval time are used to measure the
performance of the system. An efficient fault-tolerant elastic scheduling algorithm FESTAL Non-
Migration-FESTAL (NMFESTAL), Non-Overlapping-FESTAL (NOFESTAL),Elastic First Fit
(EFF). FESTAL is able to achieve both fault tolerance and high performance in terms of resource
utilization.
Virtual Machine Scheduling for Improving Energy Efficiency in IAAS Cloud
Author leveraged a VM scheduling scheme encounter resource constraints, like the physical
server size (CPU, memory, storage, bandwidth, etc.) and capacity of network link to minimize
both the numbers of active PMs and network elements so as to finally reduce energy
consumption. Numbers of VM, Total energy consumption, changing traffic between VM are used
to measure the performance of the system. VM-Mig algorithms [19] are used here.
Evolutionary Multi-Objective Workflow Scheduling in Cloud
An already established workflow scheduling algorithms in classic distributed or heterogeneous
computing environments, it arise some issues in being directly applied to the Cloud
environments. To solve this workflow scheduling problem on an infrastructure as a service
(IAAS) platform, they used an evolutionary multi-objective optimization (EMO)-based algorithm
[6]. Time, Cost, Runtime ratio are used to measure the performance of the system. An
evolutionary multi-objective optimization (EMO)-based algorithm is used. The result of this
paper solves the multi-objective Cloud scheduling problem which minimizes both make span and
cost simultaneously.
Scheduling in Compute Cloud with Multiple Data Banks Using Divisible Load Paradigm
Author leveraged that to design a scheduling strategy for heterogeneous computing resources
with shared data banks is challengeable one [9]. The compute cloud environment is used to
reduce the total processing time. No of processors, processing time, No of workers role are used
to measure the performance. In order to divisible load theory, the scheduling challenge is
formulated as relevant recursive equations and constraints. In that are derived from the continuity
of processing time because retrieval from multiple data banks. The scheduling problem in a
compute cloud is equated as a linear programming problem. One is to utilize the best possible use
of available productive resources, and the other is to solve complex problems by splitting them
into solvable parts.

13
4. comparisons on different scheduling techniques
TITLE ALGORITHM PARAMETER CONCLUSION
A Cloud Gaming System
Based on User-Level
Virtualization
Gcloud, a
GPU/CPU hybrid
cluster
Server number
Total game requests
Balance the gaming-
responsiveness, costs
Temporal Load Balancing
with Energy Cost
Optimization
Eco-IDC Workload intensity
Queuing delay
Energy cost
Energy cost reduction for
IDC -Alleviates
workload drop.
ANGEL: Agent-Based
Scheduling for Real-Time
Tasks
Dynamic
scheduling
Algorithm—
ANGEL
Task count
Task Guarantee ratio
It addresses the issue of
schedulability,
Priority,scalability, real-
time in virtualized cloud
environment .
An Energy-Saving Task
Scheduling Strategy
Based on Vacation
Queuing Theory
 Heuristic-task
scheduling
 Meta-heuristic-
task scheduling
 The queuing
theory
Algorithms
No of computer
nodes
Total idle energy
The number of tasks
arriving at the system
It ensure task performance,
reducing
The energy cost of a cloud
computing system
effectively.
Scheduling in Compute
Cloud with Multiple Data
Banks Using Divisible
Load Paradigm
Scheduling
Algorithm
No of processors
Processing time
No of workers role
It solve
Complex problems by
breaking them into
solvable parts.
Exploring Blind Online
Scheduling for Mobile
cloud
Blind online
scheduling
Algorithm (BOSA)
Operating time of
schedule
User waiting time
Performance
improvement time
It reduce the delay and
Energy among the servers.
A Hyper-Heuristic
Scheduling
Algorithm for Cloud
Hyper-heuristic
scheduling
algorithm (HHSA)
Interaction
Best so far make span
Reduce the make span of
task
Evolutionary Multi-
Objective Workflow
Scheduling in Cloud
Evolutionary multi-
objective
optimization
(EMO)-based
algorithm
Time
Cost
Runtime ratio
Minimizes make span and
cost simultaneously
FESTAL: Fault-Tolerant
Elastic Scheduling
Algorithm for Real-Time
Tasks in Virtualized
Clouds
 FESTAL.
 Non-
Migration-
FESTAL,
 Non-
Overlapping-
FESTAL
Host
Time
Task count
Interval time
Tools:
Cloudsim
FESTAL is able to
Achieve both fault
tolerance and high
performance in terms
Of resource utilization.
Virtual Machine
Scheduling for Improving
Energy Efficiency in
IAAS Cloud
VM-Mig algorithm Number of VM
Total energy
consumption
Changing traffic
between VM
This paper reduces the
Quantity of physical
resources to save energy
Consumption

14
5. CONCLUSION
In cloud computing, there were numerous services are providing on demand service provisioning
is the main feature in IAAS. Scheduling is to provide the service and to reach the end user on
time. Various techniques for efficiently schedule the task are discussed in this paper. So in this
paper, our focus is basically on how effectively schedule the task to finish it off with accuracy
and correctness. We have also discussed process of scheduling and the algorithms. In this paper
various problems are surveyed and their solutions are discussed.
REFERENCES
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Data Center Energy Cost Optimization” , IEEE transactions on parallel and distributed systems,Vol.
25, No. 3, March 2014.
[2] Liang Zhou and Zhen Yang, “Exploring blind online scheduling for mobile cloud multimedia
services”, IEEE Wireless Communications, June 2013.
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Tasks Oriented Energy-Aware Scheduling In Virtualized Clouds”, IEEE Transactions On Cloud
Computing, Vol. 2/April-June 2014.
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IEEE Transactions On Parallel And Distributed Systems, Vol. 27, No. 5, May 2016.
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15
[14] Maria Alejandra Rodriguez and Rajkumar Buyya “Deadline Based Resource Provisioning and
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