Load Balancing in Cloud Computing Environment: A Comparative Study of Service...Eswar Publications
Load balancing is a computer networking method to distribute workload across multiple computers or a computer cluster, network links, central processing units, disk drives, or other resources, to achieve optimal resource utilization, maximize throughput, minimize response time, and avoid overload. Using multiple components with load balancing, instead of a single component, may increase reliability through redundancy. The
load balancing service is usually provided by dedicated software or hardware, such as a multilayer switch or a Domain Name System server. In this paper, the existing static algorithms used for simple cloud load balancing have been identified and also a hybrid algorithm for developments in the future is suggested.
Using Grid Technologies in the Cloud for High Scalabilitymabuhr
An unstated assumption is that clouds are scalable. But are they? Stick thousands upon thousands of machines together and there are a lot of potential bottlenecks just waiting to choke off your scalability supply. And if the cloud is scalable what are the chances that your application is really linearly scalable? At 10 machines all may be well. Even at 50 machines the seas look calm. But at 100, 200, or 500 machines all hell might break loose. How do you know?
You know through real life testing. These kinds of tests are brutally hard and complicated. who wants to do all the incredibly precise and difficult work of producing cloud scalability tests? GridDynamics has stepped up to the challenge and has just released their Cloud Performance Reports.
Load Balancing In Cloud Computing:A ReviewIOSR Journals
Abstract: As the IT industry is growing day by day, the need of computing and storage is increasing
rapidly. The amount of data exchanged over the network is constantly increasing. Thus the process of this
increasing mass of data requires more computer equipment to meet the various needs of the organizations.
To better capitalize their investment, the over-equipped organizations open their infrastructures to others by
exploiting the Internet and other important technologies such as virtualization by creating a new computing
model: the cloud computing. Cloud computing is one of the significant milestones in recent times in the
history of computers. The basic concept of cloud computing is to provide a platform for sharing of resources
which includes software and infrastructure with the help of virtualization. This paper presents a brief review
of cloud computing. The main emphasize of this paper is on the load balancing technique in cloud
computing.
Keywords: Cloud Computing, Load Balancing, Dynamic Load Balancing, Virtualization, Data Center.
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
Load Balancing in Cloud Computing Environment: A Comparative Study of Service...Eswar Publications
Load balancing is a computer networking method to distribute workload across multiple computers or a computer cluster, network links, central processing units, disk drives, or other resources, to achieve optimal resource utilization, maximize throughput, minimize response time, and avoid overload. Using multiple components with load balancing, instead of a single component, may increase reliability through redundancy. The
load balancing service is usually provided by dedicated software or hardware, such as a multilayer switch or a Domain Name System server. In this paper, the existing static algorithms used for simple cloud load balancing have been identified and also a hybrid algorithm for developments in the future is suggested.
Using Grid Technologies in the Cloud for High Scalabilitymabuhr
An unstated assumption is that clouds are scalable. But are they? Stick thousands upon thousands of machines together and there are a lot of potential bottlenecks just waiting to choke off your scalability supply. And if the cloud is scalable what are the chances that your application is really linearly scalable? At 10 machines all may be well. Even at 50 machines the seas look calm. But at 100, 200, or 500 machines all hell might break loose. How do you know?
You know through real life testing. These kinds of tests are brutally hard and complicated. who wants to do all the incredibly precise and difficult work of producing cloud scalability tests? GridDynamics has stepped up to the challenge and has just released their Cloud Performance Reports.
Load Balancing In Cloud Computing:A ReviewIOSR Journals
Abstract: As the IT industry is growing day by day, the need of computing and storage is increasing
rapidly. The amount of data exchanged over the network is constantly increasing. Thus the process of this
increasing mass of data requires more computer equipment to meet the various needs of the organizations.
To better capitalize their investment, the over-equipped organizations open their infrastructures to others by
exploiting the Internet and other important technologies such as virtualization by creating a new computing
model: the cloud computing. Cloud computing is one of the significant milestones in recent times in the
history of computers. The basic concept of cloud computing is to provide a platform for sharing of resources
which includes software and infrastructure with the help of virtualization. This paper presents a brief review
of cloud computing. The main emphasize of this paper is on the load balancing technique in cloud
computing.
Keywords: Cloud Computing, Load Balancing, Dynamic Load Balancing, Virtualization, Data Center.
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
Efficient Resource Allocation to Virtual Machine in Cloud Computing Using an ...ijceronline
The focus of the paper is to generate an advance algorithm of resource allocation and load balancing that can deduced and avoid the dead lock while allocating the processes to virtual machine. In VM while processes are allocate they executes in queue , the first process get resources , other remains in waiting state .As rest of VM remains idle . To utilize the resources, we have analyze the algorithm with the help of First-Come, First-Served (FCFS) Scheduling, Shortest-Job-First (SJR) Scheduling, Priority Scheduling, Round Robin (RR) and CloudSIM Simulator.
LOAD BALANCING ALGORITHM TO IMPROVE RESPONSE TIME ON CLOUD COMPUTINGijccsa
Load balancing techniques in cloud computing can be applied at different levels. There are two main
levels: load balancing on physical server and load balancing on virtual servers. Load balancing on a
physical server is policy of allocating physical servers to virtual machines. And load balancing on virtual
machines is a policy of allocating resources from physical server to virtual machines for tasks or
applications running on them. Depending on the requests of the user on cloud computing is SaaS (Software
as a Service), PaaS (Platform as a Service) or IaaS (Infrastructure as a Service) that has a proper load
balancing policy. When receiving the task, the cloud data center will have to allocate these tasks efficiently
so that the response time is minimized to avoid congestion. Load balancing should also be performed
between different datacenters in the cloud to ensure minimum transfer time. In this paper, we propose a
virtual machine-level load balancing algorithm that aims to improve the average response time and
average processing time of the system in the cloud environment. The proposed algorithm is compared to the
algorithms of Avoid Deadlocks [5], Maxmin [6], Throttled [8] and the results show that our algorithms
have optimized response times.
dynamic resource allocation using virtual machines for cloud computing enviro...Kumar Goud
Abstract—Cloud computing allows business customers to scale up and down their resource usage based on needs., we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of “skewness” to measure the unevenness in the multidimensional resource utilization of a server. By minimizing imbalance, we will mix completely different of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.
Index Terms—Cloud computing, resource management, virtualization, green computing.
Dynamic resource allocation using virtual machines for cloud computing enviro...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
A Comparative Study of Load Balancing Algorithms for Cloud ComputingIJERA Editor
Cloud Computing is fast growing technology in both industry research and academy. User can access the cloud
service and pay based on the usage of resource. Balancing the load is major task of cloud service provider with
minimum response time, maximum throughput and better resource utilization. There are many load balancing
algorithms proposed to assign a user request to cloud resource in efficient manner. In this paper three load balancing
algorithms are simulated in Cloud Analyst and results are compared.
Cloud computing Review over various scheduling algorithmsIJEEE
Cloud computing has taken an importantposition in the field of research as well as in thegovernment organisations. Cloud computing uses virtualnetwork technology to provide computer resources tothe end users as well as to the customer’s. Due tocomplex computing environment the use of high logicsand task scheduler algorithms are increase which resultsin costly operation of cloud network. Researchers areattempting to build such kind of job scheduling algorithms that are compatible and applicable in cloud computing environment.In this paper, we review research work which is recently proposed by researchers on the base of energy saving scheduling techniques. We also studying various scheduling algorithms and issues related to them in cloud computing.
Dynamic resource allocation using virtual machines for cloud computing enviro...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Load Balancing in Auto Scaling Enabled Cloud Environmentsneirew J
Cloud computing is growing in popularity and it has been continuously updated with more improvements.
Auto scaling is one of such improvements that help to maintain the availability of customer’s subscribed
cloud system. The appearance of an auto scaling mechanism in the cloud system with many existing system
mechanisms is an issue that needs to be considered. Because, normally, there is no free drawbacks
whenever a new part is added to a certain stable system. In this paper, we consider how existing load
balancing and auto scaling impact on each other. For the purpose, we have modeled a cloud system with
an auto scaler and a load balancer and implementing simulations based on the constructed model. Also
based on the results from the computer simulations we proposed about choosing load balancers for
subscribed cloud system with auto scaling service.
Efficient Resource Allocation to Virtual Machine in Cloud Computing Using an ...ijceronline
The focus of the paper is to generate an advance algorithm of resource allocation and load balancing that can deduced and avoid the dead lock while allocating the processes to virtual machine. In VM while processes are allocate they executes in queue , the first process get resources , other remains in waiting state .As rest of VM remains idle . To utilize the resources, we have analyze the algorithm with the help of First-Come, First-Served (FCFS) Scheduling, Shortest-Job-First (SJR) Scheduling, Priority Scheduling, Round Robin (RR) and CloudSIM Simulator.
LOAD BALANCING ALGORITHM TO IMPROVE RESPONSE TIME ON CLOUD COMPUTINGijccsa
Load balancing techniques in cloud computing can be applied at different levels. There are two main
levels: load balancing on physical server and load balancing on virtual servers. Load balancing on a
physical server is policy of allocating physical servers to virtual machines. And load balancing on virtual
machines is a policy of allocating resources from physical server to virtual machines for tasks or
applications running on them. Depending on the requests of the user on cloud computing is SaaS (Software
as a Service), PaaS (Platform as a Service) or IaaS (Infrastructure as a Service) that has a proper load
balancing policy. When receiving the task, the cloud data center will have to allocate these tasks efficiently
so that the response time is minimized to avoid congestion. Load balancing should also be performed
between different datacenters in the cloud to ensure minimum transfer time. In this paper, we propose a
virtual machine-level load balancing algorithm that aims to improve the average response time and
average processing time of the system in the cloud environment. The proposed algorithm is compared to the
algorithms of Avoid Deadlocks [5], Maxmin [6], Throttled [8] and the results show that our algorithms
have optimized response times.
dynamic resource allocation using virtual machines for cloud computing enviro...Kumar Goud
Abstract—Cloud computing allows business customers to scale up and down their resource usage based on needs., we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of “skewness” to measure the unevenness in the multidimensional resource utilization of a server. By minimizing imbalance, we will mix completely different of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.
Index Terms—Cloud computing, resource management, virtualization, green computing.
Dynamic resource allocation using virtual machines for cloud computing enviro...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
A Comparative Study of Load Balancing Algorithms for Cloud ComputingIJERA Editor
Cloud Computing is fast growing technology in both industry research and academy. User can access the cloud
service and pay based on the usage of resource. Balancing the load is major task of cloud service provider with
minimum response time, maximum throughput and better resource utilization. There are many load balancing
algorithms proposed to assign a user request to cloud resource in efficient manner. In this paper three load balancing
algorithms are simulated in Cloud Analyst and results are compared.
Cloud computing Review over various scheduling algorithmsIJEEE
Cloud computing has taken an importantposition in the field of research as well as in thegovernment organisations. Cloud computing uses virtualnetwork technology to provide computer resources tothe end users as well as to the customer’s. Due tocomplex computing environment the use of high logicsand task scheduler algorithms are increase which resultsin costly operation of cloud network. Researchers areattempting to build such kind of job scheduling algorithms that are compatible and applicable in cloud computing environment.In this paper, we review research work which is recently proposed by researchers on the base of energy saving scheduling techniques. We also studying various scheduling algorithms and issues related to them in cloud computing.
Dynamic resource allocation using virtual machines for cloud computing enviro...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Load Balancing in Auto Scaling Enabled Cloud Environmentsneirew J
Cloud computing is growing in popularity and it has been continuously updated with more improvements.
Auto scaling is one of such improvements that help to maintain the availability of customer’s subscribed
cloud system. The appearance of an auto scaling mechanism in the cloud system with many existing system
mechanisms is an issue that needs to be considered. Because, normally, there is no free drawbacks
whenever a new part is added to a certain stable system. In this paper, we consider how existing load
balancing and auto scaling impact on each other. For the purpose, we have modeled a cloud system with
an auto scaler and a load balancer and implementing simulations based on the constructed model. Also
based on the results from the computer simulations we proposed about choosing load balancers for
subscribed cloud system with auto scaling service.
DYNAMIC ALLOCATION METHOD FOR EFFICIENT LOAD BALANCING IN VIRTUAL MACHINES FO...acijjournal
This paper proposes a Dynamic resource allocation method for Cloud computing. Cloud computing is a model for delivering information technology services in which resources are retrieved from the internet through web-based tools and applications, rather than a direct connection to a server. Users can set up
and boot the required resources and they have to pay only for the required resources. Thus, in the future providing a mechanism for efficient resource management and assignment will be an important objective of Cloud computing. In this project we propose a method, dynamic scheduling and consolidation mechanism that allocate resources based on the load of Virtual Machines (VMs) on Infrastructure as a service (IaaS). This method enables users to dynamically add and/or delete one or more instances on the basis of the load and the conditions specified by the user. Our objective is to develop an effective load balancing algorithm using Virtual Machine Monitoring to
maximize or minimize different performance parameters(throughput for example) for the Clouds of
different sizes (virtual topology de-pending on the application requirement).
Intel IT Open Cloud - What's under the Hood and How do we Drive it?Odinot Stanislas
L'IT d'Intel fait sa révolution et s'impose d'agir comme un "Cloud Service Provider". La transformation est initiée avec au programme la mise en place d'un Cloud Fédéré, Interopérable et Open mais aussi d'un framework de maturité, du DevOps et de la prise de risque. Bref, vraiment intéressant
Cloud computing
Definition of Cloud Computing
History and origins of Cloud Computing
Cloud Computing services and model
cloud service engineering life cycle
TEST AND DEVELOPMENT PLATFORM
Cloud migration
Dynamic Cloud Partitioning and Load Balancing in Cloud Shyam Hajare
Cloud computing is the emerging and transformational paradigm in the field of information technology. It mostly focuses in providing various services on demand and resource allocation and secure data storage are some of them. To store huge amount of data and accessing data from such metadata is new challenge. Distributing and balancing of the load over a cloud using cloud partitioning can ease the situation. Implementing load balancing by considering static as well as dynamic parameters can improve the performance cloud service provider and can improve the user satisfaction. Implementation the model can provide dynamic way of resource selection de-pending upon different situation of cloud environment at the time of accessing cloud provisions based on cloud partitioning. This model can provide effective load balancing algorithm over the cloud environment, better refresh time methods and better load status evaluation methods.
ITA: THE IMPROVED THROTTLED ALGORITHM OF LOAD BALANCING ON CLOUD COMPUTINGIJCNCJournal
Cloud computing makes the information technology industry boom. It is a great solution for businesses who want to save costs while ensuring the quality of service. One of the key issues that make cloud computing successful is the load balancing technique used in the load balancer to minimize time costs and optimize costs economically. This paper proposes an algorithm to enhance the processing time of tasks so that it can help improve the load balancing capacity on cloud computing. This algorithm, named as Improved Throttled Algorithm (ITA), is an improvement of Throttled Algorithm. The paper uses the Cloud Analyst tool to simulate. The selected algorithms are used to compare: Equally Load, Round Robin, Throttled and TMA. The simulation results show that the proposed algorithm ITA has improved the processing time of tasks, time spent processing requests and reduced the cost of Datacenters compared to the selected popular algorithms as above. The improvement of ITA is because of selecting virtual machines in an index table that is available but in order of priority. It helps response times and processing times remain stable, limits the idling resources, and cloud costs are minimized compared to selected algorithms.
ITA: The Improved Throttled Algorithm of Load Balancing on Cloud ComputingIJCNCJournal
Cloud computing makes the information technology industry boom. It is a great solution for businesses who want to save costs while ensuring the quality of service. One of the key issues that make cloud computing successful is the load balancing technique used in the load balancer to minimize time costs and optimize costs economically. This paper proposes an algorithm to enhance the processing time of tasks so that it can help improve the load balancing capacity on cloud computing. This algorithm, named as Improved Throttled Algorithm (ITA), is an improvement of Throttled Algorithm. The paper uses the Cloud Analyst tool to simulate. The selected algorithms are used to compare: Equally Load, Round Robin, Throttled and TMA. The simulation results show that the proposed algorithm ITA has improved the processing time of tasks, time spent processing requests and reduced the cost of Datacenters compared to the selected popular algorithms as above. The improvement of ITA is because of selecting virtual machines in an index table that is available but in order of priority. It helps response times and processing times remain stable, limits the idling resources, and cloud costs are minimized compared to selected algorithms.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals
VIRTUAL MACHINE SCHEDULING IN CLOUD COMPUTING ENVIRONMENTijmpict
Cloud computing is an upcoming technology in dispersed computing facilitating paying for each model as
for each user demand and need. Cloud incorporates a set of virtual machine which comprises both storage
and computational facility. The fundamental goal of cloud computing is to offer effective access to isolated
and geographically circulated resources. Cloud is growing every day and experiences numerous problems
such as scheduling. Scheduling means a collection of policies to regulate the order of task to be executed
by a computer system. An excellent scheduler derives its scheduling plan in accordance with the type of
work and the varying environment. This research paper demonstrates a generalized precedence algorithm
for effective performance of work and contrast with Round Robin and FCFS Scheduling. Algorithm needs
to be tested within CloudSim toolkit and outcome illustrates that it provide good presentation compared
some customary scheduling algorithm.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
TOP 10 B TECH COLLEGES IN JAIPUR 2024.pptxnikitacareer3
Looking for the best engineering colleges in Jaipur for 2024?
Check out our list of the top 10 B.Tech colleges to help you make the right choice for your future career!
1) MNIT
2) MANIPAL UNIV
3) LNMIIT
4) NIMS UNIV
5) JECRC
6) VIVEKANANDA GLOBAL UNIV
7) BIT JAIPUR
8) APEX UNIV
9) AMITY UNIV.
10) JNU
TO KNOW MORE ABOUT COLLEGES, FEES AND PLACEMENT, WATCH THE FULL VIDEO GIVEN BELOW ON "TOP 10 B TECH COLLEGES IN JAIPUR"
https://www.youtube.com/watch?v=vSNje0MBh7g
VISIT CAREER MANTRA PORTAL TO KNOW MORE ABOUT COLLEGES/UNIVERSITITES in Jaipur:
https://careermantra.net/colleges/3378/Jaipur/b-tech
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ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
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6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
2. 6/2/2018Cloud Computing And Load Balancing
2
Under the Guidance of –
Prof Mrs. Sukhada Bhingarkar
Presented By-
Komal Shete
Cloud Computing And Load
Balancing
5. 6/2/2018Cloud Computing And Load Balancing
5
-when IT’S Smarter to rent than buy...
The Advancement Of the Technology Encompassing Networks
Storage
Processing power
Led To Epitome Of Computing
Cloud Computing
6. 6/2/2018Cloud Computing And Load Balancing
6
Cloud Computing Is A Paradigm That Allow On-Demand Network Access To
Shared Computing Resources
In simple Cloud computing is using the internet to access someone else's
software running on someone else's hardware in someone else's data centre.
A model for Managing, Storing & Processing Data Online via Internet
What is Cloud Computing?
7. 6/2/2018Cloud Computing And Load Balancing
7
Some cloud computing characteristics
include:
What is Cloud Computing?
On Demand
Service
8. 6/2/2018Cloud Computing And Load Balancing
8
Some cloud computing characteristics
include:
What is Cloud Computing?
On Demand
Service
You Use It
When You
Need It
9. 6/2/2018Cloud Computing And Load Balancing
9
Some cloud computing characteristics
include:
What is Cloud Computing?
Network
Access
10. 6/2/2018Cloud Computing And Load Balancing
10
Some cloud computing characteristics
include:
What is Cloud Computing?
Network
Access
Uses
Internet
As A
Medium
11. 6/2/2018Cloud Computing And Load Balancing
11
Some cloud computing characteristics
include:
What is Cloud Computing?
Shared
Resources
12. 6/2/2018Cloud Computing And Load Balancing
12
Some cloud computing characteristics
include:
What is Cloud Computing?
Shared
Resources
Resources Are
Pooled Together
Used By Multiple
Clients
13. 6/2/2018Cloud Computing And Load Balancing
13
Some cloud computing characteristics
include:
What is Cloud Computing?
Scalability
14. 6/2/2018Cloud Computing And Load Balancing
14
Some cloud computing characteristics
include:
What is Cloud Computing?
Scalability
Allows
Elasticity Of
Resources
16. 6/2/2018Cloud Computing And Load Balancing
16SAAS SOFTWARE AS A SERVICE
SAAS
PASS
IAAS
Just run it for me!
• On-demand Service.
• Independent Platform
Don’t need to install the software on your PC
• Runs A Single Instance of the software
Available for multiple users
• Cloud computing cheap
Computing resources managed by vendors
• It is an application that can be accessed from anywhere on the
world as long as you can have an computer with an Internet
Connection.
17. 6/2/2018Cloud Computing And Load Balancing
17• Who uses SAAS ?
End Customers i.e. Frequent users of SAAS
Popular SAAS Providers
• Pros
• Cons
18. 6/2/2018Cloud Computing And Load Balancing
18PAAS PLATFORM AS A SERVICE
SAAS
PASS
IAAS
• Give us nice API (Application Programming Interface) and takes care of the
implementation.
• In the PaaS model, cloud providers deliver a computing platform and/or solution
stack typically including operating system, programming language execution
environment, database, and web server.
• It is a platform for developers to write and create their own SaaS i.e. applications
,which means rapid development at low cost.
19. 6/2/2018Cloud Computing And Load Balancing
19• Who uses PAAS ?
Developers
Popular PAAS Providers
• Pros
• Cons
20. 6/2/2018Cloud Computing And Load Balancing
20IAAS
INFRASTRUCTURE AS A SERVICE
SAAS
PASS
IAAS
• Also known as hardware as a service.
• Is a computing power that you can rent for a limited period of time.
• Allows existing applications to be run on a cloud suppliers hardware.
• cloud providers offer computers – as physical or more often as virtual machines –
raw (block) storage, firewalls, load balancers, and networks
21. 6/2/2018Cloud Computing And Load Balancing
21• Who uses IAAS ?
Sysadmins
Popular IAAS Providers
• Pros
• Cons
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Modes Of Clouds
Private
Cloud
Hybrid
Cloud
Public
Cloud
Public Cloud is hosted by cloud vendor at the vendors premises
and shared by various organizations.
E.g. : Amazon, Google, Microsoft, Sales force
Private Cloud is dedicated to a particular organization and not
shared with other organizations.
E.g. : HP data centre, IBM, Sun, Oracle, 3tera
Hybrid Cloud is relatively less security concerns on public
cloud. Usage of both public and private together is called hybrid
cloud.
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Author Methodology Advantages Limitations
Dynamic resource
allocation using
virtual machines [1]
Honey bee
behaviour
Average execution time
and reduction in waiting
time of tasks on queue
were improved
Inefficient while working in
homogeneous type of
System
Honey bee
behaviour inspired
load balancing of
tasks[2]
Dynamic resource
allocation using
virtual machines
Improved the overall
utilization of server
resources
QoS parameters such as
response time or
completion time of tasks are
not discussed
An enhanced
scheduling in
weighted round
robin[3]
Enhanced
scheduling in
weighted round
robin
Minimized the response
time of the jobs by
optimally utilizing the
participating VMs
Load balancing in the
heavily loaded scenarios for
the task migrations has not
been considered.
Literature Survey
25. Problem Statement
Load Balancing in Cloud Computing Environment Using
Improved Weighted Round Robin Algorithm for
Non pre-emptive Dependent Tasks
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26. Objectives
To study the performance of some of the existing load balancing algorithms
To study the scheduling and load balancing design.
To study the Improved Round Robin Algorithm for Non pre-emptive Dependent
Task
To evaluate the performance of the proposed approach .
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User
Job queue
Dependency task Queue
Independent task Queue
Interface
Task Manager Scheduler
Load balancer
Resource Manager
Resources
Scheduling and Load balancing Design
28. Algorithms
The two most frequently used scheduling principles in a non pre-emptive system are :
Round Robin
Weighted Round Robin
Improved weighted round robin is the proposed algorithm.
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Client
Data centre broker
Scheduling controller
and load balancer Dynamic scheduler Resource proberStatic scheduler
Multitask and task
dependent scheduler
Data centre-1
Host-1
VM1 VM2
Host-2 Host-3 Host-4
VM3 VM4
Data centre-2
VM6 VM7 VM8VM5
System Architecture
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Start
Identify the child tasks of the
arrived tasks
If child tasks
size > 0
Place the parent task into the
dependent queue
Select a child task from the collection
of Child to a parent and run it in loop
Run the task by using the
static/dynamic scheduler
Update the parent task of
completed status in dependency
queue
If parent task
size > 0
End
Yes
No
No
Flow chart of multilevel interdependency tasks.
31. Mathematical Models Used
(a) Set pendingJobsTotLength = JobsRemainingLengthInExecList +
JobsRemainingLengthInWaitList + JobsRemainingLengthInPauseList
(b) 𝐶V𝑚 is the processing capacity of the VM.
(c) Set pendingETime = pendingJobsTotLength/𝐶V𝑚
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(1) Identify the Pending Execution Time in each of the VMs by collecting the Pending
Execution length from executing, waiting & paused list.
(2) Arrange the VMs based on the least pending execution time to the highest pending
execution time and group it, in case two VMs fall in the same pending length. This Map
should contain pending execution time as key and it’s associated VMs as a value.
(a) Sort the VMMap by the Pending Execution Time of each VM
(3) Re-arrange the incoming Jobs based on the length & priority of the Jobs.
(a) Sort the JobSubmittedList based on length & priority.
(4) Initiate the vmIndex, jobIndex variable & totalJobs
Set vmIndex = 0
Set totalJobs = length of JobSubmittedList
Set totalVMsCount = size of VMMap
Set jobIndex = 0
Set jobToVMratio = totalJobs/totalVMsCount
(5) Assign the incoming jobs to the VMs based on the least Pending Execution
Time in the VMs & its processing capacity
Algorithm :IWRR dynamic scheduler.
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(a) While (true)
Set job = JobSubmittedList.get(jobIndex)
Set jobLength = lengthOf(job)
Set newCompletiontimeMap = EmptyMap
For startNumber from 0 by 1 to totalVMsCount do {
Set vm = VMMap.getValue(startNumber)
Set probableNewCompTime = jobLength/𝐶V𝑚 + VMMap.getKey(startNumber)
newCompletiontimeMap.add(probableNewCompTime, vm)
}
SortByCompletionTime(newCompletionta)
Set selectedVM = newCompletiontimeMap.getValue(0) selectedVM.assign(job)
For startNumber from 0 by 1 to totalVMsCount do {
Set vm = VMMap.getValue(startNumber)
If (vm equals selectedVM)
Set currentLength = VMMap.getKey(startNumber)
Set newCurrentLength = currentLength + newCompletiontimeMap.getKey(0)
VMMap.removeItem(startNumber)
VMMap.add(newCurrentLength, vm)
EndIf
}
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sortByCompletionTime(VMMap)
Increase the jobIndex by 1
If (jobIndex equals totalJobs)
Break
(b) End While
(6) Remove all the assigned Jobs from the JobSubmittedList
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(1) Identify the number of executing/pending tasks in each VM and arrange it in increasing order on a
Queue.
(a) Set numTaskInQueue = Number of Executing/Waiting Tasks in each VM and arrange it in increasing
order
(2) If the number of tasks in the first item of the queue is greater than or equal to “1”, then terminate the
Load Balancing logic execution else proceed to the 3rd step.
(a) If (numTaskInQueue.first() ≥1) then
Return;
(3) If the number of tasks in the last item of the queue is less than or equal to “1”, then terminate the
Load Balancing logic execution else proceed to the 4th step.
(a) If (numTaskInQueue.last()≤1) then Return;
(4) Identify the Pending Execution Time in each of the VMs by adding the Pending Execution length
from executing, waiting & paused list and then divided the value by the processing capacity of the VM.
Algorithm : IWRR load balancer..
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case two VMs fall in the same pending time.
(a) Sort the VMMap by the Pending Execution time of each VM
(6) Remove a task from the higher pending time VM, which contains more than one task and
assign this task to the lower pending time VM, which has no task to process.
While (true)
Set OverLoadedVM = VMMap.get(VMMap.size())
Set LowLoadedVM = VMMap.get(0)
Varlowerposition = 1;
Varupperposition = 1;
While(true)
If (OverLoadedVM.taskSize() > 1 &&LowLoadedVM.taskSize() < 1) Break;
Else if (OverLoadedVM.taskSize() > 1)
LowLoadedVM = VMMap.get(lowerposition) Lowerposition++
Else if (LowLoadedVM.taskSize() < 1)
OverLoadedVM = VMMap.get(VMMap.size() - upperposition) Upperposition++
Else
Break The Outer While Loop
End While
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Set migratableTask = OverLoadedVM.getMigratableTask()
LowLoadedVM.assign(migratableTask) Break
End While
(7) Re execute from the step 1
(8) Then the steps 2 and 3 will decide the load balancing further.
(9) This load balancing will be called after every task completion irrespective of any VMs.
38. Conclusion
The improved weighted round robin algorithm considers the capabilities of each VM and the task length
of each requested job to assign the jobs into the most appropriate VMs
The load balancer in the improved weighted round robin runs at the end of each task’s completion. This
always makes the loads evenly distributed across all the VMs at the end of each task’s completion and
thus eliminates any idle time in the participating resources(VMs).
The performance analysis and experiment results of this algorithm proved that the improved weighted
round robin algorithm is most suitable to the heterogeneous/homogenous jobs with heterogeneous
resources (VMs) compared to the other round robin and weighted round robin algorithms. This
algorithm considers the response time as the main QoS parameter.
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39. Future Scope
Cloud Computing is a vast concept and load balancing plays a very important role in case
of Clouds.
There is a huge scope of improvement in this area. We have discussed only two divisible
load scheduling algorithms that can be applied to clouds, but there are still other
approaches that can be applied to balance the load in clouds.
The performance of the given algorithms can also be increased by varying different QoS
parameters.
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40. References
1) Z. Xiao, W. Song, and Q. Chen, “Dynamic resource allocation using virtual machines for cloud computing environment,”
IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 6, pp. 1107–1117, 2013.
2) L. D. Dhinesh Babu and P. Venkata Krishna, “Honey bee behaviour inspired load balancing of tasks in cloud computing
environments,” Applied Soft Computing Journal, vol. 13, no. 5, pp. 2292–2303, 2013.
3) R. Basker, V. Rhymend Uthariaraj, and D. Chitra Devi, “An enhanced scheduling in weighted round robin for the cloud
infrastructure services,” International Journal of Recent Advance in Engineering & Technology, vol. 2, no. 3, pp. 81–86,
2014.
4) Hindawi Publishing Corporation e Scientific World Journal Volume 2016, Article ID 3896065, 14 pages
http://dx.doi.org/10.1155/2016/3896065
5) https://www.youtube.com/watch?v=36zducUX16w&index=1&list=LLVnBa5vCutTSTutoelnCrIg&t=0s
[online,available,01/04/2018]
6) https://www.google.co.in/search?q=speaker+in+hand+of+guy [online,available,01/04/2018]
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