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Cloud computing,
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Project title 2015
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ENERGY-AWARE LOAD BALANCING AND APPLICATION SCALING FOR THE CLOUD ECOSYSTEMNexgen Technology
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Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Energy aware load balancing and application scaling for the cloud ecosystemKamal Spring
In this paper we introduce an energy-aware operation model used for load balancing and application scaling on a cloud. The basic philosophy of our approach is defining an energy-optimal operation regime and attempting to maximize the number of servers operating in this regime. Idle and lightly-loaded servers are switched to one of the sleep states to save energy. The load balancing and scaling algorithms also exploit some of the most desirable features of server consolidation mechanisms discussed in the literature.
Energy aware load balancing and application scaling for the cloud ecosystemLeMeniz Infotech
Energy aware load balancing and application scaling for the cloud ecosystem
Do Your Projects With Technology Experts
To Get this projects Call : 9566355386 / 99625 88976
Visit : www.lemenizinfotech.com / www.ieeemaster.com
Mail : projects@lemenizinfotech.com
Energy-aware Load Balancing and Application Scaling for the Cloud Ecosystem1crore projects
IEEE PROJECTS 2015
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It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
Load Rebalancing for Distributed Hash Tables in Cloud Computingiosrjce
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.
ENERGY-AWARE LOAD BALANCING AND APPLICATION SCALING FOR THE CLOUD ECOSYSTEMNexgen Technology
bulk ieee projects in pondicherry,ieee projects in pondicherry,final year ieee projects in pondicherry
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Energy aware load balancing and application scaling for the cloud ecosystemKamal Spring
In this paper we introduce an energy-aware operation model used for load balancing and application scaling on a cloud. The basic philosophy of our approach is defining an energy-optimal operation regime and attempting to maximize the number of servers operating in this regime. Idle and lightly-loaded servers are switched to one of the sleep states to save energy. The load balancing and scaling algorithms also exploit some of the most desirable features of server consolidation mechanisms discussed in the literature.
Energy aware load balancing and application scaling for the cloud ecosystemLeMeniz Infotech
Energy aware load balancing and application scaling for the cloud ecosystem
Do Your Projects With Technology Experts
To Get this projects Call : 9566355386 / 99625 88976
Visit : www.lemenizinfotech.com / www.ieeemaster.com
Mail : projects@lemenizinfotech.com
Energy-aware Load Balancing and Application Scaling for the Cloud Ecosystem1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
Load Rebalancing for Distributed Hash Tables in Cloud Computingiosrjce
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.
Energy aware load balancing and application scaling for the cloud ecosystemCloudTechnologies
We are the company providing Complete Solution for all Academic Final Year/Semester Student Projects. Our projects are
suitable for B.E (CSE,IT,ECE,EEE), B.Tech (CSE,IT,ECE,EEE),M.Tech (CSE,IT,ECE,EEE) B.sc (IT & CSE), M.sc (IT & CSE),
MCA, and many more..... We are specialized on Java,Dot Net ,PHP & Andirod technologies. Each Project listed comes with
the following deliverable: 1. Project Abstract 2. Complete functional code 3. Complete Project report with diagrams 4.
Database 5. Screen-shots 6. Video File
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IEEE, WEB, WINDOWS PROJECTS ON DOT NET, JAVA& ANDROID TECHNOLOGIES,EMBEDDED SYSTEMS,MAT LAB,VLSI DESIGN.
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cloudtechnologies
# 304, Siri Towers,Behind Prime Hospitals
Maitrivanam, Ameerpet.
Contact:-8121953811,8522991105.040-65511811
cloudtechnologiesprojects@gmail.com
http://cloudstechnologies.in/
Cloud computing offers to users worldwide a low cost on-demand services, according to their requirements. In the recent years, the rapid growth and service quality of cloud computing has made it an attractive technology for different Tech Companies. However with the growing number of data centers resources, high levels of energy cost are being consumed with more carbon emissions in the air. For instance, the Google data center estimation of electric power consumption is equivalent to the energy requirement of a small sized city. Also, even if the virtualization of resources in cloud computing datacenters may reduce the number of physical machines and hardware equipments cost, it is still restrained by energy consumption issue. Energy efficiency has become a major concern for today’s cloud datacenter researchers, with a simultaneous improvement of the cloud service quality and reducing operation cost. This paper analyses and discusses the literature review of works related to the contribution of energy efficiency enhancement in cloud computing datacenters. The main objective is to have the best management of the involved physical machines which host the virtual ones in the cloud datacenters.
Small Signal Stability Improvement and Congestion Management Using PSO Based ...IDES Editor
In this paper an attempt has been made to study the
application of Thyristor Controlled Series Capacitor (TCSC)
to mitigate small signal stability problem in addition to
congestion management of a heavily loaded line in a
multimachine power system. The Flexible AC Transmission
System (FACTS) devices such as TCSC can be used to control
the power flows in the network and can help in improvement
of small signal stability aspect. It can also provide relief to
congestion in the heavily loaded line. However, the
performance of any FACTS device highly depends upon its
parameters and placement at suitable locations in the power
network. In this paper, Particle Swarm Optimization (PSO)
method has been used for determining the optimal locations
and parameters of the TCSC controller in order to damp small
signal oscillations. Transmission Line Flow (TLF) Sensitivity
method has been used for curtailment of non-firm load to
limit power flow congestion. The results of simulation reveals
that TCSC controllers, placed optimally, not only mitigate
small signal oscillations but they can also alleviate line flow
congestion effectively.
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.
A LOW-ENERGY DATA AGGREGATION PROTOCOL USING AN EMERGENCY EFFICIENT HYBRID ME...IJCNCJournal
Recent wireless sensor network focused on developing communication networks with minimal power and cost. To achieve this, several techniques have been developed to monitor a completely wireless sensor network. Generally, in the WSN network, communication is established between the source nodes and the destination node with an abundant number of hops, an activity which consumes much energy. The node existing between source and destination nodes consumes energy for transmission of data and maximize network lifetime. To overcome this issue, a new Emergency Efficient Hybrid Medium Access Control (EEHMAC) protocol is presented to reduce consumption of energy among a specific group of WSNs which will increase the network lifetime. The proposed model makes a residual battery is utilized for effective transmission of data with minimal power consumption. Compared with other models, the experimental results strongly showed that our model is not only able to reduce network lifetime but also to increase the overall network performance.
Achieving Energy Proportionality In Server ClustersCSCJournals
a great amount of interests in the past few years. Energy proportionality is a principal to ensure that energy consumption is proportional to the system workload. Energy proportional design can effectively improve energy efficiency of computing systems. In this paper, an energy proportional model is proposed based on queuing theory and service differentiation in server clusters, which can provide controllable and predictable quantitative control over power consumption with theoretically guaranteed service performance. Futher study for the transition overhead is carried out corresponding strategy is proposed to compensate the performance degradation caused by transition overhead. The model is evaluated via extensive simulations and is justified by the real workload data trace. The results show that our model can achieve satisfied service performance while still preserving energy efficiency in the system.
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.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
Public Cloud Partition Using Load Status Evaluation and Cloud Division RulesIJSRD
with growth of cloud computing load balancing is important impact on performance. Cloud computing efficiency depends on good load balancer. Many type of situation occur that time cloud partitioning is done by load balancer. Different type of situation needed different type of strategies for public cloud portioning using load balancer.in this paper we work on, partition of public cloud using two type of situation first is load status evaluation and second is cloud division rules. Load status evaluation is measure in number of cloudlets arrives at datacenter and cloud divisions rules are based on cloudlet come from which geographical location. On the basis of geographical location we partition public cloud and improve performance of load balancing in cloud computing. We implement proposed system with help of cloudsim3.0 simulator.
Energy aware load balancing and application scaling for the cloud ecosystemCloudTechnologies
We are the company providing Complete Solution for all Academic Final Year/Semester Student Projects. Our projects are
suitable for B.E (CSE,IT,ECE,EEE), B.Tech (CSE,IT,ECE,EEE),M.Tech (CSE,IT,ECE,EEE) B.sc (IT & CSE), M.sc (IT & CSE),
MCA, and many more..... We are specialized on Java,Dot Net ,PHP & Andirod technologies. Each Project listed comes with
the following deliverable: 1. Project Abstract 2. Complete functional code 3. Complete Project report with diagrams 4.
Database 5. Screen-shots 6. Video File
SERVICE AT CLOUDTECHNOLOGIES
IEEE, WEB, WINDOWS PROJECTS ON DOT NET, JAVA& ANDROID TECHNOLOGIES,EMBEDDED SYSTEMS,MAT LAB,VLSI DESIGN.
ME, M-TECH PAPER PUBLISHING
COLLEGE TRAINING
Thanks&Regards
cloudtechnologies
# 304, Siri Towers,Behind Prime Hospitals
Maitrivanam, Ameerpet.
Contact:-8121953811,8522991105.040-65511811
cloudtechnologiesprojects@gmail.com
http://cloudstechnologies.in/
Cloud computing offers to users worldwide a low cost on-demand services, according to their requirements. In the recent years, the rapid growth and service quality of cloud computing has made it an attractive technology for different Tech Companies. However with the growing number of data centers resources, high levels of energy cost are being consumed with more carbon emissions in the air. For instance, the Google data center estimation of electric power consumption is equivalent to the energy requirement of a small sized city. Also, even if the virtualization of resources in cloud computing datacenters may reduce the number of physical machines and hardware equipments cost, it is still restrained by energy consumption issue. Energy efficiency has become a major concern for today’s cloud datacenter researchers, with a simultaneous improvement of the cloud service quality and reducing operation cost. This paper analyses and discusses the literature review of works related to the contribution of energy efficiency enhancement in cloud computing datacenters. The main objective is to have the best management of the involved physical machines which host the virtual ones in the cloud datacenters.
Small Signal Stability Improvement and Congestion Management Using PSO Based ...IDES Editor
In this paper an attempt has been made to study the
application of Thyristor Controlled Series Capacitor (TCSC)
to mitigate small signal stability problem in addition to
congestion management of a heavily loaded line in a
multimachine power system. The Flexible AC Transmission
System (FACTS) devices such as TCSC can be used to control
the power flows in the network and can help in improvement
of small signal stability aspect. It can also provide relief to
congestion in the heavily loaded line. However, the
performance of any FACTS device highly depends upon its
parameters and placement at suitable locations in the power
network. In this paper, Particle Swarm Optimization (PSO)
method has been used for determining the optimal locations
and parameters of the TCSC controller in order to damp small
signal oscillations. Transmission Line Flow (TLF) Sensitivity
method has been used for curtailment of non-firm load to
limit power flow congestion. The results of simulation reveals
that TCSC controllers, placed optimally, not only mitigate
small signal oscillations but they can also alleviate line flow
congestion effectively.
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.
A LOW-ENERGY DATA AGGREGATION PROTOCOL USING AN EMERGENCY EFFICIENT HYBRID ME...IJCNCJournal
Recent wireless sensor network focused on developing communication networks with minimal power and cost. To achieve this, several techniques have been developed to monitor a completely wireless sensor network. Generally, in the WSN network, communication is established between the source nodes and the destination node with an abundant number of hops, an activity which consumes much energy. The node existing between source and destination nodes consumes energy for transmission of data and maximize network lifetime. To overcome this issue, a new Emergency Efficient Hybrid Medium Access Control (EEHMAC) protocol is presented to reduce consumption of energy among a specific group of WSNs which will increase the network lifetime. The proposed model makes a residual battery is utilized for effective transmission of data with minimal power consumption. Compared with other models, the experimental results strongly showed that our model is not only able to reduce network lifetime but also to increase the overall network performance.
Achieving Energy Proportionality In Server ClustersCSCJournals
a great amount of interests in the past few years. Energy proportionality is a principal to ensure that energy consumption is proportional to the system workload. Energy proportional design can effectively improve energy efficiency of computing systems. In this paper, an energy proportional model is proposed based on queuing theory and service differentiation in server clusters, which can provide controllable and predictable quantitative control over power consumption with theoretically guaranteed service performance. Futher study for the transition overhead is carried out corresponding strategy is proposed to compensate the performance degradation caused by transition overhead. The model is evaluated via extensive simulations and is justified by the real workload data trace. The results show that our model can achieve satisfied service performance while still preserving energy efficiency in the system.
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.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
Public Cloud Partition Using Load Status Evaluation and Cloud Division RulesIJSRD
with growth of cloud computing load balancing is important impact on performance. Cloud computing efficiency depends on good load balancer. Many type of situation occur that time cloud partitioning is done by load balancer. Different type of situation needed different type of strategies for public cloud portioning using load balancer.in this paper we work on, partition of public cloud using two type of situation first is load status evaluation and second is cloud division rules. Load status evaluation is measure in number of cloudlets arrives at datacenter and cloud divisions rules are based on cloudlet come from which geographical location. On the basis of geographical location we partition public cloud and improve performance of load balancing in cloud computing. We implement proposed system with help of cloudsim3.0 simulator.
A hybrid algorithm to reduce energy consumption management in cloud data centersIJECEIAES
There are several physical data centers in cloud environment with hundreds or thousands of computers. Virtualization is the key technology to make cloud computing feasible. It separates virtual machines in a way that each of these so-called virtualized machines can be configured on a number of hosts according to the type of user application. It is also possible to dynamically alter the allocated resources of a virtual machine. Different methods of energy saving in data centers can be divided into three general categories: 1) methods based on load balancing of resources; 2) using hardware facilities for scheduling; 3) considering thermal characteristics of the environment. This paper focuses on load balancing methods as they act dynamically because of their dependence on the current behavior of system. By taking a detailed look on previous methods, we provide a hybrid method which enables us to save energy through finding a suitable configuration for virtual machines placement and considering special features of virtual environments for scheduling and balancing dynamic loads by live migration method.
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.
A Prolific Scheme for Load Balancing Relying on Task Completion Time IJECEIAES
In networks with lot of computation, load balancing gains increasing significance. To offer various resources, services and applications, the ultimate aim is to facilitate the sharing of services and resources on the network over the Internet. A key issue to be focused and addressed in networks with large amount of computation is load balancing. Load is the number of tasks„t‟ performed by a computation system. The load can be categorized as network load and CPU load. For an efficient load balancing strategy, the process of assigning the load between the nodes should enhance the resource utilization and minimize the computation time. This can be accomplished by a uniform distribution of load of to all the nodes. A Load balancing method should guarantee that, each node in a network performs almost equal amount of work pertinent to their capacity and availability of resources. Relying on task subtraction, this work has presented a pioneering algorithm termed as E-TS (Efficient-Task Subtraction). This algorithm has selected appropriate nodes for each task. The proposed algorithm has improved the utilization of computing resources and has preserved the neutrality in assigning the load to the nodes in the network.
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
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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Development of a Suitable Load Balancing Strategy In Case Of a Cloud Computi...IJMER
Cloud computing is an attracting technology in the field of computer science. In
Gartner’s report, it says that the cloud will bring changes to the IT industry. The cloud is changing
our life by providing users with new types of services. Users get service from a cloud without paying
attention to the details. NIST gave a definition of cloud computing as a model for enabling
ubiquitous, convenient, on-demand network access to a shared pool of configurable computing
resources (e.g., networks, servers, storage, applications, and services) that can be rapidly
provisioned and released with minimal management effort or service provider interaction. More
and more people pay attention to cloud computing. Cloud computing is efficient and scalable but
maintaining the stability of processing so many jobs in the cloud computing environment is a very
complex problem with load balancing receiving much attention for researchers. Since the job
arrival pattern is not predictable and the capacities of each node in the cloud differ, for load
balancing problem, workload control is crucial to improve system performance and maintain
stability. Load balancing schemes depending on whether the system dynamics are important can be
either static or dynamic. Static schemes do not use the system information and are less complex
while dynamic schemes will bring additional costs for the system but can change as the system
status changes. A dynamic scheme is used here for its flexibility. The model has a main controller
and balancers to gather and analyze the information. Thus, the dynamic control has little influence
on the other working nodes. The system status then provides a basis for choosing the right load
balancing strategy. The load balancing model given in this research article is aimed at the public
cloud which has numerous nodes with distributed computing resources in many different
geographic locations. Thus, this model divides the public cloud into several cloud partitions. When
the environment is very large and complex, these divisions simplify the load balancing. The cloud
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.
Cloud computing is that ensuing generation of computation. In all probability folks can have everything they need on the cloud. Cloud computing provides resources to shopper on demand. The resources also are code package resources or hardware resources. Cloud computing architectures unit distributed, parallel and serves the requirements of multiple purchasers in various things. This distributed style deploys resources distributive to deliver services with efficiency to users in various geographical channels. Purchasers in a very distributed setting generate request haphazardly in any processor. So the most important disadvantage of this randomness is expounded to task assignment. The unequal task assignment to the processor creates imbalance i.e., variety of the processors sq. measure over laden and many of them unit of measurement to a lower place loaded. The target of load equalisation is to transfer the load from over laden technique to a lower place loaded technique transparently. Load equalisation is one altogether the central issues in cloud computing. To comprehend high performance, minimum interval and high resource utilization relation we want to transfer the tasks between nodes in cloud network. Load equalisation technique is utilized to distribute tasks from over loaded nodes to a lower place loaded or idle nodes. In following sections we have a tendency to tend to stand live discuss concerning cloud computing, load equalisation techniques and additionally the planned work of our load equalisation system. Proposed load equalisation rule is simulated on Cloud Analyst toolkit. Performance is analyzed on the parameters of overall interval, knowledge transfer, average knowledge center mating time and total value of usage. Results area unit compared with 3 existing load equalisation algorithms specifically spherical Robin, Equally unfold Current Execution Load, and Throttled. Results on the premise of case studies performed shows additional knowledge transfer with minimum interval.
Cloud computing is that ensuing generation of computation. In all probability folks can have everything they need on the cloud. Cloud computing provides resources to shopper on demand. The resources also are code package resources or hardware resources. Cloud computing architectures unit distributed, parallel and serves the requirements of multiple purchasers in various things. This distributed style deploys resources distributive to deliver services with efficiency to users in various geographical channels. Purchasers in a very distributed setting generate request haphazardly in any processor. So the most important disadvantage of this randomness is expounded to task assignment. The unequal task assignment to the processor creates imbalance i.e., variety of the processors sq. measure over laden and many of them unit of measurement to a lower place loaded. The target of load equalisation is to transfer the load from over laden technique to a lower place loaded technique transparently. Load equalisation is one altogether the central issues in cloud computing. To comprehend high performance, minimum interval and high resource utilization relation we want to transfer the tasks between nodes in cloud network. Load equalisation technique is utilized to distribute tasks from over loaded nodes to a lower place loaded or idle nodes. In following sections we have a tendency to tend to stand live discuss concerning cloud computing, load equalisation techniques and additionally the planned work of our load equalisation system. Proposed load equalisation rule is simulated on Cloud Analyst toolkit. Performance is analyzed on the parameters of overall interval, knowledge transfer, average knowledge center mating time and total value of usage. Results area unit compared with 3 existing load equalisation algorithms specifically spherical Robin, Equally unfold Current Execution Load, and Throttled. Results on the premise of case studies performed shows additional knowledge transfer with minimum interval.
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTINGijdpsjournal
Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The increased availability of the cloud models and allied developing models creates easier computing cloud environment. Energy consumption and effective energy management are the two important challenges in virtualized computing platforms. Energy consumption can be minimized by allocating computationally
intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling (DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the required QoS. However, they do not control the internal and external switching to server frequencies,
which causes the degradation of performance. In this paper, we propose the Real Time Adaptive EnergyScheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud ComputingVirtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm
minimizes consumption of energy and time during computation, reconfiguration and communication. Our proposed model confirms the effectiveness of its implementation, scalability, power consumption and execution time with respect to other existing approaches.
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTINGijdpsjournal
Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The
increased availability of the cloud models and allied developing models creates easier computing cloud
environment. Energy consumption and effective energy management are the two important challenges in
virtualized computing platforms. Energy consumption can be minimized by allocating computationally
intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling
(DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the
required QoS. However, they do not control the internal and external switching to server frequencies,
which causes the degradation of performance. In this paper, we propose the Real Time Adaptive EnergyScheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud ComputingVirtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm
minimizes consumption of energy and time during computation, reconfiguration and communication. Our
proposed model confirms the effectiveness of its implementation, scalability, power consumption and
execution time with respect to other existing approaches.
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTINGijdpsjournal
Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The increased availability of the cloud models and allied developing models creates easier computing cloud environment. Energy consumption and effective energy management are the two important challenges in virtualized computing platforms. Energy consumption can be minimized by allocating computationally intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling (DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the required QoS. However, they do not control the internal and external switching to server frequencies, which causes the degradation of performance. In this paper, we propose the Real Time Adaptive EnergyScheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud ComputingVirtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm minimizes consumption of energy and time during computation, reconfiguration and communication. Our proposed model confirms the effectiveness of its implementation, scalability, power consumption and execution time with respect to other existing approaches.
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ENERGY-AWARE LOAD BALANCING AND APPLICATION SCALING FOR THE CLOUD ECOSYSTEM
1. ENERGY-AWARE LOAD BALANCING AND APPLICATION SCALING
FOR THE CLOUD ECOSYSTEM
ABSTRACT
In this paper we introduce an energy-aware operation model used for load balancing and
application scaling on a cloud. The basic philosophy of our approach is deffning an energy-
optimal operation regime and attempting to maximize the number of servers operating in this
regime. Idle and lightly-loaded servers are switched to one of the sleep states to save energy. The
load balancing and scaling algorithms also exploit some of the most desirable features of server
consolidation mechanisms discussed in the literature.
EXISTING SYSTEM:
In the last few years packaging computing cycles and storage and offering them as a
metered service became a reality. Large farms of computing and storage platforms have been
assembled and a fair number of Cloud Service Providers (CSPs) offer computing services based
on three cloud delivery models SeaS (Software as a Service), PeaS (Platform as a Service), and
IaaS (Infrastructure as a Service). Warehouse-scale computers (WSCs) are the building blocks of
a cloud infrastructure. A hierarchy of networks connect 50; 000 to 100; 000 servers in a WSC.
The servers are housed in racks; typically, the 48 servers in a rack are connected by a 48-port
Gigabit Ethernet switch. The switch has two to eight up-links which go to the higher level
switches in the network hierarchy. Cloud elasticity, the ability to use as many resources as
needed at any given time, and low cost, a user is charged only for the resources it consumes,
represents solid incentives for many organizations to migrate their computational activities to a
public cloud. The number of CSPs, the spectrum of services offered by the CSPs, and the
number of cloud users have increased dramatically during the last few years. For example, in
2007 the EC2 (Elastic Computing Cloud) was the first service provided by AWS (Amazon Web
Services); five years later, in 2012, AWS was used by businesses in 200 countries. Amazon's S3
2. (Simple Storage Service) has surpassed two trillion objects and routinely runs more than 1.1
million peak requests per second.
PROPOSED SYSTEM:
There are three primary contributions of this paper: (1) a new model of cloud servers that
is based on different operating regimes with various degrees of energy effciency" (processing
power versus energy consumption); (2) a novel algorithm that performs load balancing and
application scaling to maximize the number of servers operating in the energy-optimal regime;
and (3) analysis and comparison of techniques for load balancing and application scaling using
three differently-sized clusters and two different average load profiles. Models for energy-aware
resource management and application placement policies and the mechanisms to enforce these
policies such as the ones introduced in this paper can be evaluated theoretically, experimentally,
through simulation, based on published data, or through a combination of these techniques.
Analytical models can be used to derive high-level insight on the behavior of the system in a
very short time but the biggest challenge is in determining the values of the parameters; while the
results from an analytical model can give a good approximation of the relative trends to expect,
there may be significant errors in the absolute predictions. Experimental data is collected on
small-scale systems; such experiments provide useful performance data for individual system
components but no insights on the interaction between the system and applications and the
scalability of the policies. Trace-based workload analysis such as the ones are very useful though
they provide information for a particular experimental set-up, hardware configuration, and
applications. Typically trace based simulation need more time to produce results. Traces can also
be very large and it is hard to generate representative traces from one class of machines that will
be valid for all the classes of simulated machines. To evaluate the energy aware load balancing
and application scaling policies and mechanisms introduced in this paper we chose simulation
using data published in the literature.
Module 1
3. Load Balancing in Cloud Computing
Cloud computing" is a term, which involves virtualization, distributed computing,
networking, software and web services. A cloud consists of several elements such as clients,
datacenter and distributed servers. It includes fault tolerance, high availability, scalability,
flexibility, reduced overhead for users, reduced cost of ownership, on demand services etc.
Central to these issues lies the establishment of an effective load balancing algorithm. The load
can be CPU load, memory capacity, delay or network load. Load balancing is the process of
distributing the load among various nodes of a distributed system to improve both resource
utilization and job response time while also avoiding a situation where some of the nodes are
heavily loaded while other nodes are idle or doing very little work. Load balancing ensures that
all the processor in the system or every node in the network does approximately the equal
amount of work at any instant of time. This technique can be sender initiated, receiver initiated
or symmetric type (combination of sender initiated and receiver initiated types). Our objective is
to develop an effective load balancing algorithm using Divisible load scheduling theorem to
maximize or minimize different performance parameters (throughput, latency for example) for
the clouds of different sizes (virtual topology depending on the application requirement).
Module 2
Energy Efficiency of a System
The energy efficiency of a system is captured by the ratio performance per Watt of
power." During the last two decades the performance of computing systems has increased much
faster than their energy efficiency. Energy proportional systems. In an ideal world, the energy
consumed by an idle system should be near zero and grow linearly with the system load. In real
life, even systems whose energy requirements scale linearly, when idle, use more than half the
energy they use at full load. Data collected over a long period of time shows that the typical
operating regime for data center servers is far from an optimal energy consumption regime. An
energy-proportional system consumes no energy when idle, very little energy under a light load,
4. and gradually, more energy as the load increases. An ideal energy-proportional system is always
operating at 100% efficiency. Energy efficiency of a data center; the dynamic range of
subsystems. The energy efficiency of a data center is measured by the power usage effectiveness
(PUE), the ratio of total energy used to power a data center to the energy used to power
computational servers, storage servers, and other IT equipment. The PUE has improved from
around 1:93 in 2003 to 1:63 in 2005; recently, Google reported a PUE ratio as low as 1.15. The
improvement in PUE forces us to concentrate on energy efficiency of computational resources.
The dynamic range is the difference between the upper and the lower limits of the energy
consumption of a system as a function of the load placed on the system. A large dynamic range
means that a system is able to operate at a lower fraction of its peak energy when its load is low.
Different subsystems of a computing system behave differently in terms of energy efficiency;
while many processors have reasonably good energy-proportional profiles, significant
improvements in memory and disk subsystems are necessary. The largest consumer of energy in
a server is the processor, followed by memory, and storage systems. Estimated distribution of the
peak power of different hardware systems in one of the Google's datacenters is: CPU 33%,
DRAM 30%, Disks 10%, Network 5%, and others 22% . The power consumption can vary from
45W to 200W per multi-core CPU. The power consumption of servers has increased over time;
during the period 2001 - 2005 the estimated average power use has increased from 193 to 225 W
for volume servers, from 457 to 675 for mid range servers, and from 5,832 to 8,163 W for high
end ones. Volume servers have a price less than $25 K, mid-range servers have a price between
$25 K and $499 K, and high-end servers have a price tag larger than $500 K. Newer processors
include power saving technologies. The processors used in servers consume less than one-third
of their peak power at very-low load and have a dynamic range of more than 70% of peak power;
the processors used in mobile and/or embedded applications are better in this respect. According
to, the dynamic power range of other components of a system is much narrower: less than 50%
for DRAM, 25% for disk drives, and 15% for networking switches. Large servers often use 32 �
64 dual in-line memory modules (DIMMs); the power consumption of one DIMM is in the 5 to
21 W range.
Module 3
5. Resource management policies for large-scale data centers.
These policies can be loosely grouped into five classes: (a) Admission control; (b)
Capacity allocation; (c) Load balancing; (d) Energy optimization; and (e) Quality of service
(QoS) guarantees. The explicit goal of an admission control policy is to prevent the system from
accepting workload in violation of high-level system policies; a system should not accept
additional workload preventing it from completing work already in progress or contracted.
Limiting the workload requires some knowledge of the global state of the system; in a dynamic
system such knowledge, when available, is at best obsolete. Capacity allocation means allocating
resources for individual instances; an instance is an activation of a service. Some of the
mechanisms for capacity allocation are based on either static or dynamic thresholds. Economy of
scale aspects the energy efficiency of data processing. For example, Google reports that the
annual energy consumption for an Email service varies significantly depending on the business
size and can be 15 times larger for a small business than for a large one. Cloud computing can be
more energy efficient than on-premise computing for many organizations.
Module 4
Server Consolidation
The term server consolidation is used to describe: (1) switching idle and lightly loaded
systems to a sleep state; (2) workload migration to prevent overloading of systems ; or (3) any
optimization of cloud performance and energy efficiency by redistributing the workload. Server
consolidation policies. Several policies have been proposed to decide when to switch a server to
a sleep state. The reactive policy responds to the current load; it switches the servers to a sleep
state when the load decreases and switches them to the running state when the load increases.
Generally, this policy leads to SLA violations and could work only for slow-varying, predictable
loads. To reduce SLA violations one can envision a reactive with extra capacity policy when one
attempts to have a safety margin and keep running a fraction of the total number of servers, e.g.,
20% above those needed for the current load. The AutoScale policy is a very conservative
6. reactive policy in switching servers to sleep state to avoid the power consumption and the delay
in switching them back to running state. This can be advantageous for unpredictable spiky loads.
A very different approach is taken by two versions of pre-deactive policies. The moving window
policy estimates the workload by measuring the average request rate in a window of size _
seconds uses this average to predict the load during the next second (second (_+1)) and then
slides the window one second to predict the load for second (_ + 2), and so on. The predictive
linear regression policy uses a linear regression to predict the future load.
Module 4
Energy-aware Scaling Algorithms
The objective of the algorithms is to ensure that the largest possible number of active
servers operate within the boundaries of their respective optimal operating regime. The actions
implementing this policy are: (a) migrate VMs from a server operating in the undesirable-low
regime and then switch the server to a sleep state; (b) switch an idle server to a sleep state and
reactivate servers in a sleep state when the cluster load increases; (c) migrate the VMs from an
overloaded server, a server operating in the undesirable-high regime with applications predicted
to increase their demands for computing in the next reallocation cycles. For example, when
deciding to migrate some of the VMs running on a server or to switch a server to a sleep state,
we can adopt a conservative policy similar to the one advocated by auto scaling to save energy.
Predictive policies, such as the ones discussed will be used to allow a server to operate in a
suboptimal regime when historical data regarding its workload indicates that it is likely to return
to the optimal regime in the near future. The cluster leader has relatively accurate information
about the cluster load and its trends. The leader could use predictive algorithms to initiate a
gradual wake-up process for servers in a deeper sleep state, C4 � C6, when the workload is
above a high water mark" and the workload is continually increasing. We set up the high water
mark at 80% of the capacity of active servers; a threshold of 85% is used for deciding that a
server is overloaded, based on an analysis of workload traces. The leader could also choose to
keep a number of servers in C1 or C2 states because it takes less energy and time to return to the
7. C0 state from these states. The energy management component of the hypervisor can use only
local information to determine the regime of a server.
CONCLUSION:
The realization that power consumption of cloud computing centers is significant and is
expected to increase substantially in the future motivates the interest of the research community
in energy-aware resource management and application placement policies and the mechanisms to
enforce these policies. Low average server utilization and its impact on the environment make it
imperative to devise new energy-aware policies which identify optimal regimes for the cloud
servers and, at the same time, prevent SLA violations. A quantitative evaluation of an
optimization algorithm or an architectural enhancement is a rather intricate and time consuming
process; several benchmarks and system configurations are used to gather the data necessary to
guide future developments. For example, to evaluate the effects of architectural enhancements
supporting Instruction-level or Data-level Parallelism on the processor performance and their
power consumption several benchmarks are used . The results show numerical outcomes for the
individual applications in each benchmark. Similarly, the effects of an energy-aware algorithm
depend on the system configuration and on the application and cannot be expressed by a single
numerical value. Research on energy-aware resource management in large scale systems often
use simulation for a quasi-quantitative and, more often, a qualitative evaluation of optimization
algorithms or procedures. As stated First, they (WSCs) are a new class of large-scale machines
driven by a new and rapidly evolving set of workloads. Their size alone makes them difficult to
experiment with, or to simulate efficiently." It is rather difficult to experiment with the systems
discussed in this paper and this is precisely the reason why we choose simulation.
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