This document outlines a proposed approach for efficient load balancing using a dynamic Ant-Bee algorithm in cloud computing. It discusses limitations of existing ant colony and bee colony algorithms for load balancing. The author aims to develop a new AB algorithm approach that combines aspects of ant colony optimization and bee colony algorithms to improve load balancing optimization and overcome issues like slow convergence and tendency to stagnate in ant colony algorithms. The proposed approach would leverage both the dynamic path finding of ants and pheromone updating of bees for more effective load balancing in cloud environments.
A load balancing model based on cloud partitioning for the public cloud. ppt Lavanya Vigrahala
Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
Base paper ppt-. A load balancing model based on cloud partitioning for the ...Lavanya Vigrahala
A load balancing model based on cloud partitioning for the public cloud. -Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
Performance Comparision of Dynamic Load Balancing Algorithm in Cloud ComputingEswar Publications
Cloud computing as a distributed paradigm, it has the latent to make over a large part of the Cooperative industry. In cloud computing it’s automatically describe more technologies like distributed computing, virtualization, software, web services and networking. We review the new cloud computing technologies, and indicate the main challenges for their development in future, among which load balancing problem stands out and attracts our attention Concept of load balancing in networking and in cloud environment both are widely different. Load balancing in networking its complete concern to avoid the problem of overloading and under loading in any sever networking cloud computing its complete different its involves different elements metrics such as security, reliability, throughput, tolerance, on demand services, cost etc. Through these elements we avoiding various node problem of distributing system where many services waiting for request and others are heavily loaded and through these its increase response time and degraded performance optimization. In this paper first we classify algorithms in static and dynamic. Then we analyzed the dynamic algorithms applied in dynamics environments in cloud. Through this paper we have been show compression of various dynamics algorithm in which we include honey bee algorithm, throttled algorithm, Biased random algorithm with different elements and describe how and which is best in cloud environment with different metrics mainly used elements are performance, resource utilization and minimum cost. Our main focus of paper is in the analyze various load
balancing algorithms and their applicability in cloud environment.
A load balancing model based on cloud partitioning for the public cloud. ppt Lavanya Vigrahala
Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
Base paper ppt-. A load balancing model based on cloud partitioning for the ...Lavanya Vigrahala
A load balancing model based on cloud partitioning for the public cloud. -Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
Performance Comparision of Dynamic Load Balancing Algorithm in Cloud ComputingEswar Publications
Cloud computing as a distributed paradigm, it has the latent to make over a large part of the Cooperative industry. In cloud computing it’s automatically describe more technologies like distributed computing, virtualization, software, web services and networking. We review the new cloud computing technologies, and indicate the main challenges for their development in future, among which load balancing problem stands out and attracts our attention Concept of load balancing in networking and in cloud environment both are widely different. Load balancing in networking its complete concern to avoid the problem of overloading and under loading in any sever networking cloud computing its complete different its involves different elements metrics such as security, reliability, throughput, tolerance, on demand services, cost etc. Through these elements we avoiding various node problem of distributing system where many services waiting for request and others are heavily loaded and through these its increase response time and degraded performance optimization. In this paper first we classify algorithms in static and dynamic. Then we analyzed the dynamic algorithms applied in dynamics environments in cloud. Through this paper we have been show compression of various dynamics algorithm in which we include honey bee algorithm, throttled algorithm, Biased random algorithm with different elements and describe how and which is best in cloud environment with different metrics mainly used elements are performance, resource utilization and minimum cost. Our main focus of paper is in the analyze various load
balancing algorithms and their applicability in cloud environment.
ieee standard base paper.-Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
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.
Modified Active Monitoring Load Balancing with Cloud Computingijsrd.com
Cloud computing is internet-based computing in which large groups of remote servers are networked to allow the centralized data storage, and online access to computer services or resources. Load Balancing is essential for efficient operations in distributed environments. As Cloud Computing is growing rapidly and clients are demanding more services and better results, load balancing for the Cloud has become a very interesting and important research area. In the absence of proper load balancing strategy/technique the growth of CC will never go as per predictions. The main focus of this paper is to verify the approach that has been proposed in the model paper [3]. An efficient load balancing algorithm has the ability to reduce the data center processing time, overall response time and to cope with the dynamic changes of cloud computing environments. The traditional load balancing Active Monitoring algorithm has been modified to achieve better data center processing time and overall response time. The algorithm presented in this paper efficiently distributes the requests to all the VMs for their execution, considering the CPU utilization of all VMs.
In the FACTS-based transmission line, if the fault does not include FACTS device, then the impedance calculation is like an ordinary transmission line, and when the fault includes FACTS, then the impedance calculation accounts for the impedances introduced by FACTS device.
(Slides) Task scheduling algorithm for multicore processor system for minimiz...Naoki Shibata
Shohei Gotoda, Naoki Shibata and Minoru Ito : "Task scheduling algorithm for multicore processor system for minimizing recovery time in case of single node fault," Proceedings of IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2012), pp.260-267, DOI:10.1109/CCGrid.2012.23, May 15, 2012.
In this paper, we propose a task scheduling al-gorithm for a multicore processor system which reduces the
recovery time in case of a single fail-stop failure of a multicore
processor. Many of the recently developed processors have
multiple cores on a single die, so that one failure of a computing
node results in failure of many processors. In the case of a failure
of a multicore processor, all tasks which have been executed
on the failed multicore processor have to be recovered at once.
The proposed algorithm is based on an existing checkpointing
technique, and we assume that the state is saved when nodes
send results to the next node. If a series of computations that
depends on former results is executed on a single die, we need
to execute all parts of the series of computations again in
the case of failure of the processor. The proposed scheduling
algorithm tries not to concentrate tasks to processors on a die.
We designed our algorithm as a parallel algorithm that achieves
O(n) speedup where n is the number of processors. We evaluated
our method using simulations and experiments with four PCs.
We compared our method with existing scheduling method, and
in the simulation, the execution time including recovery time in
the case of a node failure is reduced by up to 50% while the
overhead in the case of no failure was a few percent in typical
scenarios.
Hybrid Scheduling Algorithm for Efficient Load Balancing In Cloud ComputingEswar Publications
In cloud computing environment, various users send requests for the transmission of data for different demands. The access to different number of users increase load on the cloud servers. Due to this, the cloud server does not provide best efficiency. To provide best efficiency, load has to be balanced. The highlight of this work is the division of different jobs into tasks. The job dependency checking is done on the basis of directed acyclic graph. The dependency checking the make span has to be created on the basis of first come first serve and priority based scheduling algorithms. In this paper, each scheduling algorithm has been implemented sequentially and the hybrid algorithm (round robin and priority based) has also been compared with other scheduling algorithms.
Cloud Computing Load Balancing Algorithms Comparison Based SurveyINFOGAIN PUBLICATION
Cloud computing is an online primarily based computing. This computing paradigm has increased the employment of network wherever the potential of 1 node may be used by alternative node. Cloud provides services on demand to distributive resources like info, servers, software, infrastructure etc. in pay as you go basis. Load reconciliation is one amongst the vexing problems in distributed atmosphere. Resources of service supplier have to be compelled to balance the load of shopper request. Totally different load reconciliation algorithms are planned so as to manage the resources of service supplier with efficiency and effectively. This paper presents a comparison of assorted policies used for load reconciliation.
ieee standard base paper.-Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
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.
Modified Active Monitoring Load Balancing with Cloud Computingijsrd.com
Cloud computing is internet-based computing in which large groups of remote servers are networked to allow the centralized data storage, and online access to computer services or resources. Load Balancing is essential for efficient operations in distributed environments. As Cloud Computing is growing rapidly and clients are demanding more services and better results, load balancing for the Cloud has become a very interesting and important research area. In the absence of proper load balancing strategy/technique the growth of CC will never go as per predictions. The main focus of this paper is to verify the approach that has been proposed in the model paper [3]. An efficient load balancing algorithm has the ability to reduce the data center processing time, overall response time and to cope with the dynamic changes of cloud computing environments. The traditional load balancing Active Monitoring algorithm has been modified to achieve better data center processing time and overall response time. The algorithm presented in this paper efficiently distributes the requests to all the VMs for their execution, considering the CPU utilization of all VMs.
In the FACTS-based transmission line, if the fault does not include FACTS device, then the impedance calculation is like an ordinary transmission line, and when the fault includes FACTS, then the impedance calculation accounts for the impedances introduced by FACTS device.
(Slides) Task scheduling algorithm for multicore processor system for minimiz...Naoki Shibata
Shohei Gotoda, Naoki Shibata and Minoru Ito : "Task scheduling algorithm for multicore processor system for minimizing recovery time in case of single node fault," Proceedings of IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2012), pp.260-267, DOI:10.1109/CCGrid.2012.23, May 15, 2012.
In this paper, we propose a task scheduling al-gorithm for a multicore processor system which reduces the
recovery time in case of a single fail-stop failure of a multicore
processor. Many of the recently developed processors have
multiple cores on a single die, so that one failure of a computing
node results in failure of many processors. In the case of a failure
of a multicore processor, all tasks which have been executed
on the failed multicore processor have to be recovered at once.
The proposed algorithm is based on an existing checkpointing
technique, and we assume that the state is saved when nodes
send results to the next node. If a series of computations that
depends on former results is executed on a single die, we need
to execute all parts of the series of computations again in
the case of failure of the processor. The proposed scheduling
algorithm tries not to concentrate tasks to processors on a die.
We designed our algorithm as a parallel algorithm that achieves
O(n) speedup where n is the number of processors. We evaluated
our method using simulations and experiments with four PCs.
We compared our method with existing scheduling method, and
in the simulation, the execution time including recovery time in
the case of a node failure is reduced by up to 50% while the
overhead in the case of no failure was a few percent in typical
scenarios.
Hybrid Scheduling Algorithm for Efficient Load Balancing In Cloud ComputingEswar Publications
In cloud computing environment, various users send requests for the transmission of data for different demands. The access to different number of users increase load on the cloud servers. Due to this, the cloud server does not provide best efficiency. To provide best efficiency, load has to be balanced. The highlight of this work is the division of different jobs into tasks. The job dependency checking is done on the basis of directed acyclic graph. The dependency checking the make span has to be created on the basis of first come first serve and priority based scheduling algorithms. In this paper, each scheduling algorithm has been implemented sequentially and the hybrid algorithm (round robin and priority based) has also been compared with other scheduling algorithms.
Cloud Computing Load Balancing Algorithms Comparison Based SurveyINFOGAIN PUBLICATION
Cloud computing is an online primarily based computing. This computing paradigm has increased the employment of network wherever the potential of 1 node may be used by alternative node. Cloud provides services on demand to distributive resources like info, servers, software, infrastructure etc. in pay as you go basis. Load reconciliation is one amongst the vexing problems in distributed atmosphere. Resources of service supplier have to be compelled to balance the load of shopper request. Totally different load reconciliation algorithms are planned so as to manage the resources of service supplier with efficiency and effectively. This paper presents a comparison of assorted policies used for load reconciliation.
Load Balancing from the Cloud - Layer 7 Aware SolutionImperva Incapsula
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Green cloud computing using heuristic algorithmsIliad Mnd
Green computing is defined as the study and practice of designing , manufacturing, using, and disposing of computers, servers, and associated sub systems such as
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Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progres...AtakanAral
In addition to software delivered as services over the Internet, hardware and software systems that make the delivery possible are referred as cloud computing. In cloud computing, resources such as CPU, memory, bandwidth and storage are treated as utilities that can scale up and down on demand. It also allows per-usage metering and billing of these resources. By means of cloud computing, cloud users can handle unexpected high demands without over-provisioning and they do not need to invest for abundant hardware resources initially. Cloud providers, on the other hand, have the opportunity of reallocating idle resources for other cloud users.
One general research challenge in cloud computing is the efficient allocation of cloud resources to users since cloud providers should satisfy quality of service (QoS) objectives while minimizing their operational cost. Up to 85 percent of computing capacity remains idle in distributed computing environments and this wastage is mainly due to poor optimization of job placement, parallelization and scheduling. We aim to model resource allocation problem in cloud systems, analyze and optimize it using graphs and formal behavioral models (e.g. finite automata).
The problem that we are currently interested, is to distribute virtual machines (VMs) to the datacenters (DCs) with geographical locations in such a way that network latency and infrastructure cost is minimized while WAN bandwidth and DC capacity limits are respected. We model latency as a function of DC load, inter-DC communication and proximity to user. Both VM requests and cloud infrastructure are represented by graphs where vertices correspond to machines (either physical or virtual) and edges correspond to network connections between them. Our approach employs weighted graph similarity and subgraph matching to suggest an efficient placement or the list of migrations to reach an efficient placement.
In the first six months period of the thesis project, we collected evaluation data, developed the simulation environment and defined the experimental setup which contains metrics and baseline methods. In addition, we developed the preliminary version of the resource selection algorithm. While, in the second period, we completed the algorithm design and tuning, carried out detailed, progressive evaluation on the suggested algorithm and documented our work.
Energy efficient utilization of data center resources can be carried out by optimization of the resources allocated in virtual machine placement through live migration. This paper proposes a method to optimize virtual machine placement in Banker algorithm for energy efficient cloud computing to tackle the issue of load balancing for hotspot mitigation and proposed method is named as Optimized Virtual Machine Placement in Banker algorithm (OVMPBA). By determining the state of host overload through dynamic thresholds technique and minimization migration policy for VM selection from the overloaded host an attempt is made to efficiently utilize the available computing resources and thus minimize the energy consumption in the cloud environment. The above research work is experimentally simulated on CloudSim Simulator and the experimental result shows that proposed OVMPBA method provides better energy efficiency and lesser number of migrations against existing methods of host overload detection-virtual machine selection and therefore maximizes the cloud energy efficiency.
Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progres...AtakanAral
As the volume and velocity of data in the cloud is increasing, the geographical distribution of where it is produced, processed and consumed is also gaining more significance. It is getting less feasible to move data to a distant data center for processing and move output again to the consumer location. Several promising approaches including Fog Computing, Mobile Cloud Computing, Cloudlets and Nano Data Centers are instead suggesting to bring processing entities to the edge of the cloud network to reduce latency. One issue we have identified in this scenario regarding resource management is the optimal selection of processing entity count and location. Since processing entities may also be communicating among themselves and possibly with a centralized data storage, we suggest that a hierarchical caching mechanism for the distant data will increase computation performance. We formulated this problem in the third period of the thesis and planned to further study during the fourth period.
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An efficient load balancing using Bee foraging technique with Random stealingiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Tremendous usage of internet has made huge data on the network, without compromising on the
performance of network the end-users must obtain best service. As cloud provides different services on
leasing basis many companies are migrating from their own Infrastructure to cloud,This migration should
not compromise on performance of the cloud, The performance of the cloud can be improved by having
excellent load balancing strategy such that the end user is satisfied. The paper reveals the method by which
a cloud can be partitioned and a study of different algorithm with comparative study to balance the
dynamic load. The comparative study between Ant Colony and Honey Bee algorithm gives the result which
algorithm is optimal in normal load condition also the simplest round robin algorithm is applied when the
partition are in Idle state
Cloud computing is a mix of distributed, grid and parallel processing. It is as of late in pattern on account of the
benefits it gives. It gives a pool of resources which are shared among different clients. Alongside its expanding request, it endures
with a few issues. A standout amongst the most vital and testing issue of cloud computing is load balancing. Load balancing
essentially intends to adjust the load similarly among a few hubs so hub is over-burden, under loaded or sitting inactive. Till date
there are numerous calculations proposed to deal with load balancing yet none of them has been demonstrated as productive one.
In this paper a load balancing algorithm is proposed utilizing rule of genetic algorithm. Fitness of assignments is ascertained and
on the premise of fitness load balancing is done. In this algorithm priority is appointed to the wellness computed in like manner
the chromosome with most noteworthy fitness is doled out least priority. Fitness here stands for the aggregate cost needs to
actualize an errand. Increasingly the cost more is the fitness. The entire simulation is performed on cloudsim 3.0 toolbox which is
JAVA based simulator.
CLOUD COMPUTING – PARTITIONING ALGORITHM AND LOAD BALANCING ALGORITHMijcseit
Tremendous usage of internet has made huge data on the network, without compromising on the
performance of network the end-users must obtain best service. As cloud provides different services on
leasing basis many companies are migrating from their own Infrastructure to cloud,This migration should
not compromise on performance of the cloud, The performance of the cloud can be improved by having
excellent load balancing strategy such that the end user is satisfied. The paper reveals the method by which
a cloud can be partitioned and a study of different algorithm with comparative study to balance the
dynamic load. The comparative study between Ant Colony and Honey Bee algorithm gives the result which
algorithm is optimal in normal load condition also the simplest round robin algorithm is applied when the
partition are in Idle state
CLOUD COMPUTING – PARTITIONING ALGORITHM AND LOAD BALANCING ALGORITHMijcseit
Tremendous usage of internet has made huge data on the network, without compromising on the
performance of network the end-users must obtain best service. As cloud provides different services on
leasing basis many companies are migrating from their own Infrastructure to cloud,This migration should
not compromise on performance of the cloud, The performance of the cloud can be improved by having
excellent load balancing strategy such that the end user is satisfied. The paper reveals the method by which
a cloud can be partitioned and a study of different algorithm with comparative study to balance the
dynamic load. The comparative study between Ant Colony and Honey Bee algorithm gives the result which
algorithm is optimal in normal load condition also the simplest round robin algorithm is applied when the
partition are in Idle state
A Novel Switch Mechanism for Load Balancing in Public CloudIJMER
In cloud computing environment, one of the core design principles is dynamic scalability,
which guarantees cloud storage service to handle the growing amounts of application data in a flexible
manner or to be readily enlarged. By integrating several private and public cloud services, the hybrid
clouds can effectively provide dynamic scalability of service and data migration. A load balancing is a
method of dividing computing loads among numerous hardware resources. Due to unpredictable job
arrival pattern and the capacities of the nodes in cloud differ for the load balancing problem. In this load
control is very crucial to improve system performance and maintenance. This paper presents a switch
mechanism for load balancing in cloud computing. The load balancing model given in this work is aimed
at the public cloud which has numerous nodes with distributed computing resources in many different
geographical areas. Thus, this model divides the public cloud environment into several cloud partitions.
When the cloud environment is very large and complex, these divisions simplify the load balancing. The
cloud environment has a main controller that chooses the suitable partitions for arriving jobs while the
balancer for each cloud partition chooses the best load balancing strategy
Continental division of load and balanced antIJCI JOURNAL
Increasing usability of internet is creating huge data which should be managed by the industries, but this
should not affect the processing time which may create inconvenience to end user. As cloud is emerging as
back bone of IT industry there are much enhancement needed in it Many companies are switching their
data from small storage location to Cloud, The migration is done such that the companies do not have a
burden to purchase the Infrastructure they merely have to rent out the Infrastructure but the migration
should not cost on the speed of storage or retrieval of the data from the server. Load balancing is one of the
major issue in cloud computing, but these problems are Tractable There are many algorithm for load
balancing which has advantage over the other in this paper Ant colony algorithm is studied .
A HYBRID K-HARMONIC MEANS WITH ABCCLUSTERING ALGORITHM USING AN OPTIMAL K VAL...IJCI JOURNAL
Large quantities of data are emerging every year and an accurate clustering algorithm is needed to derive
information from these data. K-means clustering algorithm is popular and simple, but has many limitations
like its sensitivity to initialization, provides local optimum solutions. K-harmonic means clustering is an
improved variant of K-means which is insensitive to the initialization of centroids, but still in some cases it
ends up with local optimum solutions. Clustering using Artificial Bee Colony (ABC) algorithm always gives
global optimum solutions. In this paper a new hybrid clustering algorithm (KHM-ABC) is presented by
combining both K-harmonic means and ABC algorithm to perform accurate clustering. Experimental
results indicate that the performance of the proposed algorithm is superior to the available algorithms in
terms of the quality of clusters.
Hybrid aco iwd optimization algorithm for minimizing weighted flowtime in clo...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Hybrid aco iwd optimization algorithm for minimizing weighted flowtime in clo...eSAT Journals
Abstract Scientists and engineers conduct several experiments by executing the same coding against the various input data, which is achieved by the Parameter Sweep Experiments (PSEs). This may finally results in too many jobs with high computational requirements. Therefore the distributed environments, particularly clouds, are used in-order to fulfill these demands. Since it is an NP-complete problem the job scheduling is much changeling. Now the proposed work is determined by the Cloud scheduler based on the bio-inspired techniques, since it works well in approximating problems with little input. But in existing proposals the job priority is ignored; which in turn it is the important aspect in PSEs because it accelerates the result of the PSE and visualization of scientific clouds. The weighted flow time is minimized with the help of the cloud scheduler based on Ant Colony Optimization (ACO). All matching recourses of the job requirements and the routing information are defined by the Intelligent Water Drops (IWDs) in order to reach the recourses. Among all matching resources of the job the Ant colony optimization is determined as the best resources. The main aim of this approach is to converge to the optimal scheduler faster, minimize the make span of the job, improve load balancing. Keywords: Ant Colony Optimization, Intelligent Water Drops, Parameter Sweep Experiments, Weighted Flowtime.
Task Scheduling using Hybrid Algorithm in Cloud Computing Environmentsiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Virtual machine placement in cloud using artificial bee colony and imperiali...IJECEIAES
Increasing resource efficiency and reducing energy consumption are significant challenges in cloud environments. Placing virtual machines is essential in improving cloud systems’ performance. This paper presents a hybrid method using the artificial bee colony and imperialist competitive algorithm to reduce provider costs and decrease client expenditure. Implementation of the proposed plan in the CloudSim simulation environment indicates the proposed method performs better than the Monarch butterfly optimization and salp swarm algorithms regarding energy consumption and resource usage. Moreover, average central processing unit (CPU) and random-access memory (RAM) usage and the number of host shutdowns show better results for the proposed model.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
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Acetabularia Information For Class 9 .docxvaibhavrinwa19
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An efficient approach for load balancing using dynamic ab algorithm in cloud computing.
1. Prepared By:
Pooja Gothi
En No.-140350702014
M.E. Computer Engg.
N.G.I.
Junagadh
Guided By:
Prof. Piyush Gohel
Asst. Professor
C.E. Department
N.G.I.
Junagadh
An Efficient approach for Load
balancing using Dynamic AB
Algorithm in cloud computing.
2. OutlineOutline
Introduction
Problem Statements and Motivation
Objectives
Introduction of Approach
Existing System
Literature Review
Proposed Work
Implementation Methodology
Conclusion and Future Work
References
3. IntroductionIntroduction
Load balancing is a methodology 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.[1]
Grid computing is aggregation of autonomous resources that are
geographically distributed. The nodes in grid permit sharing and
selection dynamically at runtime.[1]
Cloud computing refers to a parallel and distributed computing
system consisting of a collection of inter-connected and
virtualized computers that are dynamically provisioned and
presented as one or more unified computing resources based on
service-level agreements (SLA) established through negotiation
between the service provider and consumers. [1,2]
4. Conti…Conti…
Ant colony algorithm:
The ants work together in search of new
sources of food and simultaneously use the existing food sources to
shift the food back to the nest.
It is a random search algorithm. It takes the behavior of real ant
colonies in nature to search the food and connect to each other by
pheromone laid down on path optimization algorithm. [2,6]
The Artificial bee colony algorithm (ABC) is an optimization
algorithm based on the intelligent foraging behavior of honey bee
swarm and was proposed by Karaboga in 2005 [15].
This both algorithm is completely inspired by natural foraging
behavior .
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5. MotivationMotivation
Cloud Computing is the fast growing technology, which shares the
resource to achieve consistency and economies of scale similar to a
utility over a network. Resource sharing requires more optimized
algorithm.
In ACO there exist limitations like slow convergence, tendency to
stagnancy.
Bee colony algorithm obtain the solution only particular distance,
because bee provides optimal solution based on small path.
So I wish to explain both algorithm approaches combine and get
more feasible and optimization for dynamic algorithm.
6. Problem StatementProblem Statement
There are certain limitation of ant colony and bee colony
algorithm.
In ACO ant’s pheromone is a locally not globally works
dynamic in everywhere. After initialization the pheromone of
ants by moving through neighbor node of the construction in
path . Bee colony is not properly work for the allocating the path
in collective some wrong information.
So, to overcome above problems I need to develop new
approach for better optimization in load balancing and
scheduling to using AB algorithm in effective load balancing.
7. ObjectivesObjectives
Ant-Bee algorithm is in fact an optimization on Ant-Net
algorithm and tries to improve its performance .
This algorithm, at the beginning uses forward ants to find a
suitable solution from one node to another and then these are the
bees who update the pheromone on ants collected data.
It work like as routing table value in pheromone updating , so the
we rollback ant pheromone to starting towards bee.
8. Cloud environmentCloud environment
Cloud is a pool of heterogeneous resources. It is a mesh of
huge infrastructure and has no relevance with its name
“Cloud”.
In order to make efficient use of these resources and ensure
their availability to the end users “Computing” is done based
on certain criteria specified in SLA. Infrastructure in the
Cloud is made available to the user’s On-Demand basis in
pay-as-you-say-manner. [1]
Computation in cloud is done with the aim to achieve
maximum resource utilization with higher availability at
minimized cost.
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10. IntroductionIntroduction
What is load balancing ? why these need of
cloud computing?
Load Balancing is essential for efficient operations in distributed
environments. As Cloud Computing is a greatest platform which
provides storage of data in very lower cost and available for all
time over the internet, that’s why load balancing for the cloud
has become a very interesting and important research area.
Load balancing helps to attain a high user satisfaction and
resource utilization ratio by ensuring an efficient and fair
allocation of every computing resource.
Load balancing makes sure that all the processor in the system or
every node in the network does more or less the equal amount of
work at any moment of time.
11. Conti…Conti…
The goals of load balancing are to:
• Improve the performance
• Maintain system stability
• Build fault tolerance system
• Increase the availability
• Increase the user satisfaction
• Improve the resource utilization ratio
12. Effective load balancingEffective load balancing
ApproachesApproaches
There are tow main approach for load balancing named static
load balancing and dynamic load balancing. [2]
1. Static load balancing.
In this approach of load balancing, we consider static
information of system to choose the least loaded node.
It performs better in terms of complexity issue but
compromises with the result as decision is made on statically
gathered data.
It is further classified as Distributed and Centralized
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13. Conti…Conti…
2. dynamic load balancing
This approach provides QOS aware load balancing.
In this strategy, current system state plays major role while
making decisions. Despite the fact that dynamic load
balancing has higher run rime complexity then static one,
dynamic has better performance report as it considers current
load of system for choosing next datacenter to serve the
request.
This will surely provide an optimal choice from available
ones for that state of system.
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14. Ant Colony optimizationAnt Colony optimization
Ant colony optimization (ACO) is a population-based
metaheuristic that can be used to find approximate solutions
to difficult optimization problems.[7]
In ACO, ant find the food and follows the path and put some
chemical substance which called pheromone.
And remaining Ant follows the one kind of instruction on the
base of pheromone.
The solution construction process is biased by a pheromone
table as like routing table.
ACO algorithm can be used to schedule large-scale work
flows with various QOS parameters .
15. Here in fig we show the naturalHere in fig we show the natural
behavior of antbehavior of ant
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16. Dynamic Ant colonyDynamic Ant colony
OptimizationOptimization
ACO can be used in many ways , but limitation like slow
conversion and poor performance.
There is only one kind of pheromone in ACO and the path
and path weight is stable, and cannot fit for dynamic load
balancing.
Dynamically here Ant follows the instruction of Pheromone
but not finding the best laid path for searching thus dynamic
ACO works behind on the find best path or shortest path to
finding the source.
17. Bee Colony optimizationBee Colony optimization
Bee colony optimization (BCO) is a swarm used meta- heuristic
algorithm.[13]
This algorithm simulates the foraging behavior of honey bees. This
algorithm has three phases. There are employee bees, onlooker bees
and scout bees.
Scout bee: it is responsible for finding new food. The new nectar
source.
Onlooker bee : It gets the information of food sources from the
employed bees in the hive and select one of the food source to gathers
the nectar.
Employee bee: It stay on a food source and provides the neighborhood
of the source in its memory
The algorithm has a well-balanced exploration and development
ability.
18. Conti…
Bees life algorithm is an optimization algorithm used for job scheduling. Bees
colony contains single breeding female bee called queen and male known as
Drones.
Bees start with scout bees with initial population first the bees choose
randomly in space. Then, fitness is calculated for bees. The highest fitness is
chosen as “selected bees” and remaining bees are workers. The selected bees
alone visit the site by choosing neighbour search.
19. Why this combined ?
In traditional many static algorithms are proposed, but all
these algorithms do not produce optimal job scheduling and
load balancing.
Ant-Bee algorithm is in fact an optimization on Ant-Net
algorithm and tries to improve its performance .
The AB algorithm is a combination of two dynamic
algorithms, Ant Colony Optimization and Bees Life algorithm.
here in path finding the ant pheromone is not rollback then we
started bee here to continuing the path finding by the
pheromone updating.
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ANT COLONY
ALGO.
BEE COLONY
ALGO.
AB
ALGORITHM
20. Existing system
In existing system researchers have work on Ant colony
algo and Bee colony algorithms used for load balancing
in cloud computing.
Ant algo is give better work in cloud using load
balancing but it’s not attempt dynamic job to pheromone
value and bee is not used load balancing to another
approach.
So , now more effective load balancing technique to
consider both Ant & Bee is AB algo to improve load
balancing strategy.
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21. Paper Method / Algorithm Advantage Drawbacks
Ant colony optimization
for effective load
balancing in cloud
computing.[2]
(IJETTCS)
Publication year:2014
In here different kinds of
load balancing technique
is used.
Provide dynamic
and effective load
balancing
Ant can move only one
direction ,it can’t rollback.
Workflow scheduling in
grids : An Ant colony
optimization approaches[6]
Ieee paper
Publication year :2007
Aco algorithm used for
scheduling to balanced
load in grid.
Give dynamic
solution using QOS
parameters
In shortest path ant’s choice
is ignoring it work for
priority.
Grid resource
management by means
of Ant colony
optimization.[8]
Ieee paper
Publication Year :
2006
A simple ant-based
technique is proposed in
for resource management
and task scheduling and
its scalability is validated
Established optical
network for
important
requirement in load
balancing
It is not scalable and can’t
extended process to
incorporate information
about job requirement.
Literature Survey
22. Conti..Conti..
Paper Method / Algorithm Advantage Drawbacks
A bee colony based
multi-objective load
balancing technique
for cloud computing
environment.[7]
(IJCA)
publication year :
2014
Bee colony algo Bee colony and
genetic combination
based algorithm
provides better
efficiency and
effectively utilize the
resource
Runtime fault
tolerance may become
unavailable and
during heavy load
condition improve
QOS parameters.
Improve performance
of load balancing
using artificial Bee
colony.[9]
(IJCA)
Publication year :
march 2015
Load balancing
technique
And some threshold
aspect.
Improve time
efficiency and reduce
makespan time and
minimize the node of
failure
They do not share the
load among the
available resources.
fail in produce load
balance schedule.
23. ContiConti……
Paper Method/ Algo Advantages Drawback
Artificial Bee colony
algorithm and it’s
application to
generalized assignment
problem.[20]
Source: Swarm
Intelligence: Focus on
Ant and Particle Swarm
Optimization
Publication year : jan
2014
PSO
NP-hard problem is
presented in detail along
with some comparisons.
Effectively solve
small to medium
size task and also
solve complex
optimize problem
Not work on large
size and tightly
constrained
generalised
assignment problem.
An Ant Colony Based
Load Balancing
Strategy in Cloud
Computing.[13]
Publication year : 2014
Springer paper
load balancing strategy
traditional approaches like
(FCFS), local search
algorithm like Stochastic
Hill Climbing
(SHC),another soft
computing approach
Genetic Algorithm (GA)
Give surety for
QOS approach
Requirement is
fulfill here.
fault tolerance and
different function
variation to calculate
the pheromone value
can be used for
further research
work.
24. Conti…Conti…
Paper Method/Algo Advantage Drawback
Comparison of Ant
Colony and Bee
Colony Optimization
for Spam Host
Detection.[10]
Ijerd journal
Publication year :Nov-
2012
Compare both algo….. Here give
performance by the set
of classification rule.
Cloud result not give
better exact through
put in any critical
condition
An efficient load
balancing using Bee
foraging technique
with Random stealing.
[11]
(IOSR-JCE)
Publication year :
Mar-Apr
2014
Use random stealing
method.
Virtual machine is
idle. It thus saves the
idle time of the
processing element in
a Virtual machine
It provide energy
aware scheduling
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25. Proposed systemProposed system
In proposed system, the ant colony and bees life algorithm are
combined to improve the effectiveness of load balancing .
The ants in our proposed algorithm will continuously
originate from the Head node. These ants traverse the width
and length of the network in such a way that they know about
the location of underloaded or overloaded nodes in the
network.
These Ants along with their traversal will be updating a
pheromone table, which will keep a tab on the resources
utilization by each node.
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27. Conti…Conti…
Ant-Bee algorithm
In this section we are briefly explaining Ant-Bee algorithm:[24]
A forward ant starts traveling through the network. Whenever this
forward ant reaches a node, if this node is not the destination node, it is
directed toward the destination. These forward ants have the same
priority as data packets.
When a forward ant reaches the destination, based on the provided
information by her, a backward bee is created then the forward ant is
killed and the new born bee continues the journey.
This backward bee traverses the forward ant’s travelled route in
reversed direction and on its way updates the pheromone tables and is
finally killed at the starting node (the node which had initiated the
forward ant). It has to be mentioned that backward bees have more
priority than data packets to be able to apply the emergency changes as
rapidly as possible.
29. Work of Ant Bee colonyWork of Ant Bee colony
The pheromone evaporation at time t be ρt , where the value
of ρt lies on [0,1] now evaporation of pheromone at time t+1
is suggest one natural and traditional equation is
ρt+1 = α ρt + β(1- ρt) = k ρt + β
Where α , β = constant
k= α – β
0 ≤ α , β ≤ 1
f(ρt ) = ρt +1
30. Conti…Conti…
Here for effective load balancing in our existing system ant
optimization use updated pheromone is as follows equation.
Modified ACO Pheromone Updated Strategy:
Pheromone is a chemical substance which the ants release on
path while traversing the cloud network.
The probability of traversing a particular path by ants
depends on the pheromone concentration on the path, which
can be retrieved from foraging, trailing pheromone
31. Conti…Conti…
The main aim of the two types of pheromone updating
according to the types of nodes they are currently searching
for.
The ants after originating from the head node, by default
follow the Foraging pheromone, and in the process, they
update the FP trails according to the formula.
After coming upon an overloaded node they follow the
Trailing Pheromones and simultaneously update the TP trails
of the path.
After reaching an underloaded node of the same type they
update the data structure so as to move a particular amount of
data from the overloaded node to under loaded node.
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32. Conti…Conti…
We also proposed the movement of ants in two ways similar
to the classical ACO, which are as follows:
1) Forward movement-
The ants continuously move in the forward direction in the
cloud encounter in overloaded node or under loaded node.
2) Backward movement-
If an ant encounters an overloaded node in its movement
when it has previously encountered an under loaded node
then it will go backward to the under loaded node to check if
the node is still under loaded or not and if it finds it still under
loaded then it will redistribute the work to the under loaded
node. The vice-versa is also feasible and possible.
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33. Conti…Conti…
Foraging Pheromone (FP) :
While moving from underloaded node to overloaded node, ant
will update FP. Equation for updating FP pheromone is
FP( t+1 ) = ( 1 - βeva )FP(t) + ∆FP.
Where, β eva = Pheromone evaporation rate
FP = Foraging pheromone of the edge before the move
FP( t+1 ) = Foraging pheromone of the edge after the move
∆FP = Change in FP
34. Conti…Conti…
Trailing Pheromone (TP):
While moving from overloaded node to underloaded node,
ant will update TP. Equation for updating TP pheromone is
TP( t+1) = ( 1 –βeva )TP(t) + ∆TP
Where, β eva = Pheromone evaporation rate
TP = Trailing pheromone of the edge before the move
TP( t+1 ) = Trailing pheromone of the edge after the move
∆TP = Change in TP
35. Bee optimization in systemBee optimization in system
The distinguishing element between Ant-Bee and Ant-Net
algorithms is Ant-Bee’s use of backward bees , different kinds
of bees which are in use in this algorithm are:
1. Dancer bee: whenever this bee reaches a node, after updating
the node’s pheromone table, sets down its traverse time in
related field. The pheromone updating strategy is the same as
backward ants in Ant-Net algorithm.
2. Follower bee: These are considered as naive bees that should
collect information based on dancer bee’s dancing parameters.
They use equation for doing so. Assume that the follower bee
has come from node j to i on its way to destination d.
36. Conti…Conti…
T= β T_foll+(1- β) T_dan 0 ≤ β ≤ 1
Where T_foll= follower bee’s trip time.
T_dan = selected trip time among dance bee based
β : An impact factor which determines the two first factor’s
effect. If β = 1 , it means the follower bee doesn’t pay any
attention to dancer bees and this algorithm will work the same
as Ant-Net algorithm. In contrast, if the follower bee is
entirely following the dancer bee.
37. Process…Process…
Ants attract to pheromone laid from ants in some colony, the
so called ” repulsion ” and “attraction” strategy prevents ants
from different colony in same path.
This strategy is introduced by varela & sinclire. it used in
multiple way network for virtually.
General structure of a pheromone table for Multiple Ant-Bee
colony is shown in Table .
38. Pheromone tablePheromone table
To
From
1 2 ….
…….
N
1
.
.
P11 , M11
.
.
P12 , M12
.
.
……………..
P1n , M1n
.
.
.
.
.
.
.
.
.
.
.
……………
.
.
.
L Pl1 , Ml1 P12 ,
M12 ……………
Pln , Mln
38
L= All the outgoing links
N =number of nodes minus one (the node itself).
Each cell contains values M ij and Pij .
Pij= chance of node i to be selected as the next node for colony K
Mij =represents some of the recorded trip times of dancer bees form
K th colony which have travelled from i to j
39. Conti…Conti…
Here the proposed algorithm considers some pheromone value for
selecting optimal VM for load balancing.
Here VMs are defined based on their loads
OVM – Overloaded node.
UVM – Underloaded node
Here some information to when random node is overloaded then we
put it in by underloaded to balanced the node.
When load fairly distributing on the server node but finding a load
is imbalance then to achieving this, define a THRESHOLD
variable which tells how much load should exchange between
nodes.
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40. Selection process of nodeSelection process of node
Now updating the pheromone value then selecting a next
node whenever ant is killed here.
We used two strategy
1) Attraction strategy
2) Repulsion strategy
Attraction strategy: we use α parameter to indicate attraction
of a forward ant from colony K to node I on its way to
destination.
Repulsion strategy: In the proposed algorithm we use β
parameter to show repulsion of forward ants of colony K to j
on its way to destination d.
41. Conti…Conti…
hbijn
: quantity of pheromone K in the edge
linked to node j on its way to destination d(jd
element in pheromone table of K th colony)
Ni= A set containing all possible outgoing edge
for forward ant.
: Repulsion of forward ants of colony K
i to j on its way to destination d.
: Amount of pheromone from all colonies
(except K itself) in edge linked to node j on its
way toward destination d.
Ni= sum of all possible edges for the forward ant.
42. AB Algo steps.AB Algo steps.
AB algo Programming Steps:-
1. Configure cloud analyst tool in develop environment.
2. Create data center and host servers into it.
3. Create Virtual Machines (VM) and allocate resources (HDD,
RAM, CPU, and bandwidth) to it.
4. Run AB algorithm.
5. Initialize the threshold value per Virtual Machine (VM) in terms
of resources.
6. Run modified ACO and Artificial Bee algorithm simultaneously
and output optimal solutions. It is done by initializing pheromone
and bee's population. Then pheromone concentration and bees
fitness is checked simultaneously to get the optimal node.
7. Select common best optimal solution nodes.
8. Distribute load on them and achieve load balancing.
9. Move to step 5.
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44. start
Initialize pheromone
table for ants value
Declare to node
Load move to node
overloaded
Traverse to
maximum
Tp
Update pheromone
Load
is >
Traverse to
minimum FP
Update pheromone
Move on
next strategy
46. Implementation MethodologyImplementation Methodology
In cloud environment different users, resources , scheduler
implement by Cloudsim.
Cloudsim is a well known simulation for cloud computing and
designed to support various simulation tests across the IAAS
,PAAS and SAAS.
Cloud Sim provides support for modeling and simulation of
virtualized Cloud-based data center environments such as dedicated
management interfaces for VMs, memory, storage, and bandwidth.
So AB algorithm used Cloudsim as a developing tool .
47. Conclusion and future workConclusion and future work
We have represented survey of existing Ant and Bee colony
work different dynamic strategy in load balancing.
It can be extended with scenario having more data center
scattered around different location and trying to improve load
balancing approach according to some different way like used
AB algorithm approaches. In future AB algorithm work
establishing to task scheduling and node allocate on load to
random VM.
In future work I will show whole system in brief that provide
good solution for effective load balancing.
48. Paper publication detail.Paper publication detail.
Title of paper: ” An Effective Load Balancing
Approach in Cloud Using Dynamic AB Algorithm ”
Journal name: International Journal of Innovative Research
in Science, Engineering and Technology
Impact Factor : 5.45
DOI:10.15680/IJIRSET.2015.0408129
Date of publication: 24/8/2015
Issue: Vol. 4, Issue 8, August 2015
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