Submit Search
Upload
IRJET- A Statistical Approach Towards Energy Saving in Cloud Computing
ā¢
0 likes
ā¢
21 views
IRJET Journal
Follow
https://www.irjet.net/archives/V6/i4/IRJET-V6I41018.pdf
Read less
Read more
Engineering
Report
Share
Report
Share
1 of 7
Download now
Download to read offline
Recommended
IRJET- An Energy-Saving Task Scheduling Strategy based on Vacation Queuing & ...
IRJET- An Energy-Saving Task Scheduling Strategy based on Vacation Queuing & ...
IRJET Journal
Ā
Optimization of energy consumption in cloud computing datacenters
Optimization of energy consumption in cloud computing datacenters
IJECEIAES
Ā
An Approach to Reduce Energy Consumption in Cloud data centers using Harmony ...
An Approach to Reduce Energy Consumption in Cloud data centers using Harmony ...
ijccsa
Ā
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTING
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTING
ijdpsjournal
Ā
An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Ne...
An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Ne...
IJECEIAES
Ā
Hybrid Task Scheduling Approach using Gravitational and ACO Search Algorithm
Hybrid Task Scheduling Approach using Gravitational and ACO Search Algorithm
IRJET Journal
Ā
A Review on Scheduling in Cloud Computing
A Review on Scheduling in Cloud Computing
ijujournal
Ā
Energy efficient-resource-allocation-in-distributed-computing-systems
Energy efficient-resource-allocation-in-distributed-computing-systems
Cemal Ardil
Ā
Recommended
IRJET- An Energy-Saving Task Scheduling Strategy based on Vacation Queuing & ...
IRJET- An Energy-Saving Task Scheduling Strategy based on Vacation Queuing & ...
IRJET Journal
Ā
Optimization of energy consumption in cloud computing datacenters
Optimization of energy consumption in cloud computing datacenters
IJECEIAES
Ā
An Approach to Reduce Energy Consumption in Cloud data centers using Harmony ...
An Approach to Reduce Energy Consumption in Cloud data centers using Harmony ...
ijccsa
Ā
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTING
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTING
ijdpsjournal
Ā
An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Ne...
An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Ne...
IJECEIAES
Ā
Hybrid Task Scheduling Approach using Gravitational and ACO Search Algorithm
Hybrid Task Scheduling Approach using Gravitational and ACO Search Algorithm
IRJET Journal
Ā
A Review on Scheduling in Cloud Computing
A Review on Scheduling in Cloud Computing
ijujournal
Ā
Energy efficient-resource-allocation-in-distributed-computing-systems
Energy efficient-resource-allocation-in-distributed-computing-systems
Cemal Ardil
Ā
Stochastic Scheduling Algorithm for Distributed Cloud Networks using Heuristi...
Stochastic Scheduling Algorithm for Distributed Cloud Networks using Heuristi...
Eswar Publications
Ā
A survey on energy efficient with task consolidation in the virtualized cloud...
A survey on energy efficient with task consolidation in the virtualized cloud...
eSAT Journals
Ā
A survey on energy efficient with task consolidation in the virtualized cloud...
A survey on energy efficient with task consolidation in the virtualized cloud...
eSAT Publishing House
Ā
BGPC: Energy-Efficient Parallel Computing Considering Both Computational and ...
BGPC: Energy-Efficient Parallel Computing Considering Both Computational and ...
Tarik Reza Toha
Ā
IRJET- Advance Approach for Load Balancing in Cloud Computing using (HMSO) Hy...
IRJET- Advance Approach for Load Balancing in Cloud Computing using (HMSO) Hy...
IRJET Journal
Ā
Load Balancing in Cloud Computing Through Virtual Machine Placement
Load Balancing in Cloud Computing Through Virtual Machine Placement
IRJET Journal
Ā
Improved Max-Min Scheduling Algorithm
Improved Max-Min Scheduling Algorithm
iosrjce
Ā
Energy-Aware Adaptive Four Thresholds Technique for Optimal Virtual Machine P...
Energy-Aware Adaptive Four Thresholds Technique for Optimal Virtual Machine P...
IJECEIAES
Ā
Energy Efficient Change Management in a Cloud Computing Environment
Energy Efficient Change Management in a Cloud Computing Environment
IRJET Journal
Ā
A Dynamic Programming Approach to Energy-Efficient Scheduling on Multi-FPGA b...
A Dynamic Programming Approach to Energy-Efficient Scheduling on Multi-FPGA b...
TELKOMNIKA JOURNAL
Ā
Survey: An Optimized Energy Consumption of Resources in Cloud Data Centers
Survey: An Optimized Energy Consumption of Resources in Cloud Data Centers
IJCSIS Research Publications
Ā
Intelligent Workload Management in Virtualized Cloud Environment
Intelligent Workload Management in Virtualized Cloud Environment
IJTET Journal
Ā
Scheduling Algorithm Based Simulator for Resource Allocation Task in Cloud Co...
Scheduling Algorithm Based Simulator for Resource Allocation Task in Cloud Co...
IRJET Journal
Ā
IRJET- Reducing electricity usage in Internet using transactional data
IRJET- Reducing electricity usage in Internet using transactional data
IRJET Journal
Ā
Energy efficient VM placement - OpenStack Summit Vancouver May 2015
Energy efficient VM placement - OpenStack Summit Vancouver May 2015
Kurt Garloff
Ā
A novel load balancing model for overloaded cloud
A novel load balancing model for overloaded cloud
eSAT Publishing House
Ā
Energy and carbon efficient placement of virtual machines in distributed clou...
Energy and carbon efficient placement of virtual machines in distributed clou...
Pradeeban Kathiravelu, Ph.D.
Ā
Energy efficient task scheduling algorithms for cloud data centers
Energy efficient task scheduling algorithms for cloud data centers
eSAT Publishing House
Ā
Energy-Efficient Task Scheduling in Cloud Environment
Energy-Efficient Task Scheduling in Cloud Environment
IRJET Journal
Ā
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTING
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTING
ijdpsjournal
Ā
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTING
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTING
ijdpsjournal
Ā
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTING
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTING
ijdpsjournal
Ā
More Related Content
What's hot
Stochastic Scheduling Algorithm for Distributed Cloud Networks using Heuristi...
Stochastic Scheduling Algorithm for Distributed Cloud Networks using Heuristi...
Eswar Publications
Ā
A survey on energy efficient with task consolidation in the virtualized cloud...
A survey on energy efficient with task consolidation in the virtualized cloud...
eSAT Journals
Ā
A survey on energy efficient with task consolidation in the virtualized cloud...
A survey on energy efficient with task consolidation in the virtualized cloud...
eSAT Publishing House
Ā
BGPC: Energy-Efficient Parallel Computing Considering Both Computational and ...
BGPC: Energy-Efficient Parallel Computing Considering Both Computational and ...
Tarik Reza Toha
Ā
IRJET- Advance Approach for Load Balancing in Cloud Computing using (HMSO) Hy...
IRJET- Advance Approach for Load Balancing in Cloud Computing using (HMSO) Hy...
IRJET Journal
Ā
Load Balancing in Cloud Computing Through Virtual Machine Placement
Load Balancing in Cloud Computing Through Virtual Machine Placement
IRJET Journal
Ā
Improved Max-Min Scheduling Algorithm
Improved Max-Min Scheduling Algorithm
iosrjce
Ā
Energy-Aware Adaptive Four Thresholds Technique for Optimal Virtual Machine P...
Energy-Aware Adaptive Four Thresholds Technique for Optimal Virtual Machine P...
IJECEIAES
Ā
Energy Efficient Change Management in a Cloud Computing Environment
Energy Efficient Change Management in a Cloud Computing Environment
IRJET Journal
Ā
A Dynamic Programming Approach to Energy-Efficient Scheduling on Multi-FPGA b...
A Dynamic Programming Approach to Energy-Efficient Scheduling on Multi-FPGA b...
TELKOMNIKA JOURNAL
Ā
Survey: An Optimized Energy Consumption of Resources in Cloud Data Centers
Survey: An Optimized Energy Consumption of Resources in Cloud Data Centers
IJCSIS Research Publications
Ā
Intelligent Workload Management in Virtualized Cloud Environment
Intelligent Workload Management in Virtualized Cloud Environment
IJTET Journal
Ā
Scheduling Algorithm Based Simulator for Resource Allocation Task in Cloud Co...
Scheduling Algorithm Based Simulator for Resource Allocation Task in Cloud Co...
IRJET Journal
Ā
IRJET- Reducing electricity usage in Internet using transactional data
IRJET- Reducing electricity usage in Internet using transactional data
IRJET Journal
Ā
Energy efficient VM placement - OpenStack Summit Vancouver May 2015
Energy efficient VM placement - OpenStack Summit Vancouver May 2015
Kurt Garloff
Ā
A novel load balancing model for overloaded cloud
A novel load balancing model for overloaded cloud
eSAT Publishing House
Ā
Energy and carbon efficient placement of virtual machines in distributed clou...
Energy and carbon efficient placement of virtual machines in distributed clou...
Pradeeban Kathiravelu, Ph.D.
Ā
Energy efficient task scheduling algorithms for cloud data centers
Energy efficient task scheduling algorithms for cloud data centers
eSAT Publishing House
Ā
What's hot
(18)
Stochastic Scheduling Algorithm for Distributed Cloud Networks using Heuristi...
Stochastic Scheduling Algorithm for Distributed Cloud Networks using Heuristi...
Ā
A survey on energy efficient with task consolidation in the virtualized cloud...
A survey on energy efficient with task consolidation in the virtualized cloud...
Ā
A survey on energy efficient with task consolidation in the virtualized cloud...
A survey on energy efficient with task consolidation in the virtualized cloud...
Ā
BGPC: Energy-Efficient Parallel Computing Considering Both Computational and ...
BGPC: Energy-Efficient Parallel Computing Considering Both Computational and ...
Ā
IRJET- Advance Approach for Load Balancing in Cloud Computing using (HMSO) Hy...
IRJET- Advance Approach for Load Balancing in Cloud Computing using (HMSO) Hy...
Ā
Load Balancing in Cloud Computing Through Virtual Machine Placement
Load Balancing in Cloud Computing Through Virtual Machine Placement
Ā
Improved Max-Min Scheduling Algorithm
Improved Max-Min Scheduling Algorithm
Ā
Energy-Aware Adaptive Four Thresholds Technique for Optimal Virtual Machine P...
Energy-Aware Adaptive Four Thresholds Technique for Optimal Virtual Machine P...
Ā
Energy Efficient Change Management in a Cloud Computing Environment
Energy Efficient Change Management in a Cloud Computing Environment
Ā
A Dynamic Programming Approach to Energy-Efficient Scheduling on Multi-FPGA b...
A Dynamic Programming Approach to Energy-Efficient Scheduling on Multi-FPGA b...
Ā
Survey: An Optimized Energy Consumption of Resources in Cloud Data Centers
Survey: An Optimized Energy Consumption of Resources in Cloud Data Centers
Ā
Intelligent Workload Management in Virtualized Cloud Environment
Intelligent Workload Management in Virtualized Cloud Environment
Ā
Scheduling Algorithm Based Simulator for Resource Allocation Task in Cloud Co...
Scheduling Algorithm Based Simulator for Resource Allocation Task in Cloud Co...
Ā
IRJET- Reducing electricity usage in Internet using transactional data
IRJET- Reducing electricity usage in Internet using transactional data
Ā
Energy efficient VM placement - OpenStack Summit Vancouver May 2015
Energy efficient VM placement - OpenStack Summit Vancouver May 2015
Ā
A novel load balancing model for overloaded cloud
A novel load balancing model for overloaded cloud
Ā
Energy and carbon efficient placement of virtual machines in distributed clou...
Energy and carbon efficient placement of virtual machines in distributed clou...
Ā
Energy efficient task scheduling algorithms for cloud data centers
Energy efficient task scheduling algorithms for cloud data centers
Ā
Similar to IRJET- A Statistical Approach Towards Energy Saving in Cloud Computing
Energy-Efficient Task Scheduling in Cloud Environment
Energy-Efficient Task Scheduling in Cloud Environment
IRJET Journal
Ā
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTING
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTING
ijdpsjournal
Ā
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTING
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTING
ijdpsjournal
Ā
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTING
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTING
ijdpsjournal
Ā
A Review on Scheduling in Cloud Computing
A Review on Scheduling in Cloud Computing
ijujournal
Ā
A Review on Scheduling in Cloud Computing
A Review on Scheduling in Cloud Computing
ijujournal
Ā
A Review on Scheduling in Cloud Computing
A Review on Scheduling in Cloud Computing
ijujournal
Ā
An Approach to Reduce Energy Consumption in Cloud data centers using Harmony ...
An Approach to Reduce Energy Consumption in Cloud data centers using Harmony ...
neirew J
Ā
Reliable and efficient webserver management for task scheduling in edge-cloud...
Reliable and efficient webserver management for task scheduling in edge-cloud...
IJECEIAES
Ā
IRJET- Time and Resource Efficient Task Scheduling in Cloud Computing Environ...
IRJET- Time and Resource Efficient Task Scheduling in Cloud Computing Environ...
IRJET Journal
Ā
Cost and performance aware scheduling technique for cloud computing environment
Cost and performance aware scheduling technique for cloud computing environment
International Journal of Reconfigurable and Embedded Systems
Ā
Energy efficient resource allocation007
Energy efficient resource allocation007
Divaynshu Totla
Ā
ENERGY EFFICIENT VIRTUAL MACHINE ASSIGNMENT BASED ON ENERGY CONSUMPTION AND R...
ENERGY EFFICIENT VIRTUAL MACHINE ASSIGNMENT BASED ON ENERGY CONSUMPTION AND R...
IAEME Publication
Ā
Optimized load balancing mechanism in parallel computing for workflow in clo...
Optimized load balancing mechanism in parallel computing for workflow in clo...
International Journal of Reconfigurable and Embedded Systems
Ā
IRJET- Cloud Cost Analyzer and Optimizer
IRJET- Cloud Cost Analyzer and Optimizer
IRJET Journal
Ā
Welcome to International Journal of Engineering Research and Development (IJERD)
Welcome to International Journal of Engineering Research and Development (IJERD)
IJERD Editor
Ā
Performance analysis of an energy efficient virtual machine consolidation alg...
Performance analysis of an energy efficient virtual machine consolidation alg...
IAEME Publication
Ā
Demand-driven Gaussian window optimization for executing preferred population...
Demand-driven Gaussian window optimization for executing preferred population...
IJECEIAES
Ā
An energy optimization with improved QOS approach for adaptive cloud resources
An energy optimization with improved QOS approach for adaptive cloud resources
IJECEIAES
Ā
LoadAwareDistributor: An Algorithmic Approach for Cloud Resource Allocation
LoadAwareDistributor: An Algorithmic Approach for Cloud Resource Allocation
IRJET Journal
Ā
Similar to IRJET- A Statistical Approach Towards Energy Saving in Cloud Computing
(20)
Energy-Efficient Task Scheduling in Cloud Environment
Energy-Efficient Task Scheduling in Cloud Environment
Ā
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTING
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTING
Ā
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTING
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTING
Ā
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTING
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTING
Ā
A Review on Scheduling in Cloud Computing
A Review on Scheduling in Cloud Computing
Ā
A Review on Scheduling in Cloud Computing
A Review on Scheduling in Cloud Computing
Ā
A Review on Scheduling in Cloud Computing
A Review on Scheduling in Cloud Computing
Ā
An Approach to Reduce Energy Consumption in Cloud data centers using Harmony ...
An Approach to Reduce Energy Consumption in Cloud data centers using Harmony ...
Ā
Reliable and efficient webserver management for task scheduling in edge-cloud...
Reliable and efficient webserver management for task scheduling in edge-cloud...
Ā
IRJET- Time and Resource Efficient Task Scheduling in Cloud Computing Environ...
IRJET- Time and Resource Efficient Task Scheduling in Cloud Computing Environ...
Ā
Cost and performance aware scheduling technique for cloud computing environment
Cost and performance aware scheduling technique for cloud computing environment
Ā
Energy efficient resource allocation007
Energy efficient resource allocation007
Ā
ENERGY EFFICIENT VIRTUAL MACHINE ASSIGNMENT BASED ON ENERGY CONSUMPTION AND R...
ENERGY EFFICIENT VIRTUAL MACHINE ASSIGNMENT BASED ON ENERGY CONSUMPTION AND R...
Ā
Optimized load balancing mechanism in parallel computing for workflow in clo...
Optimized load balancing mechanism in parallel computing for workflow in clo...
Ā
IRJET- Cloud Cost Analyzer and Optimizer
IRJET- Cloud Cost Analyzer and Optimizer
Ā
Welcome to International Journal of Engineering Research and Development (IJERD)
Welcome to International Journal of Engineering Research and Development (IJERD)
Ā
Performance analysis of an energy efficient virtual machine consolidation alg...
Performance analysis of an energy efficient virtual machine consolidation alg...
Ā
Demand-driven Gaussian window optimization for executing preferred population...
Demand-driven Gaussian window optimization for executing preferred population...
Ā
An energy optimization with improved QOS approach for adaptive cloud resources
An energy optimization with improved QOS approach for adaptive cloud resources
Ā
LoadAwareDistributor: An Algorithmic Approach for Cloud Resource Allocation
LoadAwareDistributor: An Algorithmic Approach for Cloud Resource Allocation
Ā
More from IRJET Journal
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
IRJET Journal
Ā
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
IRJET Journal
Ā
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...
IRJET Journal
Ā
Effect of Camber and Angles of Attack on Airfoil Characteristics
Effect of Camber and Angles of Attack on Airfoil Characteristics
IRJET Journal
Ā
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...
IRJET Journal
Ā
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...
IRJET Journal
Ā
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...
IRJET Journal
Ā
A Review of āSeismic Response of RC Structures Having Plan and Vertical Irreg...
A Review of āSeismic Response of RC Structures Having Plan and Vertical Irreg...
IRJET Journal
Ā
A REVIEW ON MACHINE LEARNING IN ADAS
A REVIEW ON MACHINE LEARNING IN ADAS
IRJET Journal
Ā
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...
IRJET Journal
Ā
P.E.B. Framed Structure Design and Analysis Using STAAD Pro
P.E.B. Framed Structure Design and Analysis Using STAAD Pro
IRJET Journal
Ā
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...
IRJET Journal
Ā
Survey Paper on Cloud-Based Secured Healthcare System
Survey Paper on Cloud-Based Secured Healthcare System
IRJET Journal
Ā
Review on studies and research on widening of existing concrete bridges
Review on studies and research on widening of existing concrete bridges
IRJET Journal
Ā
React based fullstack edtech web application
React based fullstack edtech web application
IRJET Journal
Ā
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...
IRJET Journal
Ā
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.
IRJET Journal
Ā
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...
IRJET Journal
Ā
Multistoried and Multi Bay Steel Building Frame by using Seismic Design
Multistoried and Multi Bay Steel Building Frame by using Seismic Design
IRJET Journal
Ā
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...
IRJET Journal
Ā
More from IRJET Journal
(20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
Ā
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
Ā
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...
Ā
Effect of Camber and Angles of Attack on Airfoil Characteristics
Effect of Camber and Angles of Attack on Airfoil Characteristics
Ā
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...
Ā
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...
Ā
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...
Ā
A Review of āSeismic Response of RC Structures Having Plan and Vertical Irreg...
A Review of āSeismic Response of RC Structures Having Plan and Vertical Irreg...
Ā
A REVIEW ON MACHINE LEARNING IN ADAS
A REVIEW ON MACHINE LEARNING IN ADAS
Ā
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...
Ā
P.E.B. Framed Structure Design and Analysis Using STAAD Pro
P.E.B. Framed Structure Design and Analysis Using STAAD Pro
Ā
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...
Ā
Survey Paper on Cloud-Based Secured Healthcare System
Survey Paper on Cloud-Based Secured Healthcare System
Ā
Review on studies and research on widening of existing concrete bridges
Review on studies and research on widening of existing concrete bridges
Ā
React based fullstack edtech web application
React based fullstack edtech web application
Ā
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...
Ā
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.
Ā
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...
Ā
Multistoried and Multi Bay Steel Building Frame by using Seismic Design
Multistoried and Multi Bay Steel Building Frame by using Seismic Design
Ā
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...
Ā
Recently uploaded
APPLICATIONS-AC/DC DRIVES-OPERATING CHARACTERISTICS
APPLICATIONS-AC/DC DRIVES-OPERATING CHARACTERISTICS
KurinjimalarL3
Ā
š9953056974š!!-YOUNG call girls in Rajendra Nagar Escort rvice Shot 2000 nigh...
š9953056974š!!-YOUNG call girls in Rajendra Nagar Escort rvice Shot 2000 nigh...
9953056974 Low Rate Call Girls In Saket, Delhi NCR
Ā
ZXCTN 5804 / ZTE PTN / ZTE POTN / ZTE 5804 PTN / ZTE POTN 5804 ( 100/200 GE Z...
ZXCTN 5804 / ZTE PTN / ZTE POTN / ZTE 5804 PTN / ZTE POTN 5804 ( 100/200 GE Z...
ZTE
Ā
Biology for Computer Engineers Course Handout.pptx
Biology for Computer Engineers Course Handout.pptx
DeepakSakkari2
Ā
microprocessor 8085 and its interfacing
microprocessor 8085 and its interfacing
jaychoudhary37
Ā
Decoding Kotlin - Your guide to solving the mysterious in Kotlin.pptx
Decoding Kotlin - Your guide to solving the mysterious in Kotlin.pptx
JoĆ£o Esperancinha
Ā
Internship report on mechanical engineering
Internship report on mechanical engineering
malavadedarshan25
Ā
IVE Industry Focused Event - Defence Sector 2024
IVE Industry Focused Event - Defence Sector 2024
Mark Billinghurst
Ā
Past, Present and Future of Generative AI
Past, Present and Future of Generative AI
abhishek36461
Ā
Exploring_Network_Security_with_JA3_by_Rakesh Seal.pptx
Exploring_Network_Security_with_JA3_by_Rakesh Seal.pptx
null - The Open Security Community
Ā
What are the advantages and disadvantages of membrane structures.pptx
What are the advantages and disadvantages of membrane structures.pptx
wendy cai
Ā
High Profile Call Girls Nagpur Isha Call 7001035870 Meet With Nagpur Escorts
High Profile Call Girls Nagpur Isha Call 7001035870 Meet With Nagpur Escorts
ranjana rawat
Ā
Call Girls Delhi {Jodhpur} 9711199012 high profile service
Call Girls Delhi {Jodhpur} 9711199012 high profile service
rehmti665
Ā
Call Us -/9953056974- Call Girls In Vikaspuri-/- Delhi NCR
Call Us -/9953056974- Call Girls In Vikaspuri-/- Delhi NCR
9953056974 Low Rate Call Girls In Saket, Delhi NCR
Ā
Call Girls Narol 7397865700 Independent Call Girls
Call Girls Narol 7397865700 Independent Call Girls
ssuser7cb4ff
Ā
SPICE PARK APR2024 ( 6,793 SPICE Models )
SPICE PARK APR2024 ( 6,793 SPICE Models )
Tsuyoshi Horigome
Ā
ā CALL US 9953330565 ( HOT Young Call Girls In Badarpur delhi NCR
ā CALL US 9953330565 ( HOT Young Call Girls In Badarpur delhi NCR
9953056974 Low Rate Call Girls In Saket, Delhi NCR
Ā
Architect Hassan Khalil Portfolio for 2024
Architect Hassan Khalil Portfolio for 2024
hassan khalil
Ā
(MEERA) Dapodi Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Escorts
(MEERA) Dapodi Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Escorts
ranjana rawat
Ā
GDSC ASEB Gen AI study jams presentation
GDSC ASEB Gen AI study jams presentation
GDSCAESB
Ā
Recently uploaded
(20)
APPLICATIONS-AC/DC DRIVES-OPERATING CHARACTERISTICS
APPLICATIONS-AC/DC DRIVES-OPERATING CHARACTERISTICS
Ā
š9953056974š!!-YOUNG call girls in Rajendra Nagar Escort rvice Shot 2000 nigh...
š9953056974š!!-YOUNG call girls in Rajendra Nagar Escort rvice Shot 2000 nigh...
Ā
ZXCTN 5804 / ZTE PTN / ZTE POTN / ZTE 5804 PTN / ZTE POTN 5804 ( 100/200 GE Z...
ZXCTN 5804 / ZTE PTN / ZTE POTN / ZTE 5804 PTN / ZTE POTN 5804 ( 100/200 GE Z...
Ā
Biology for Computer Engineers Course Handout.pptx
Biology for Computer Engineers Course Handout.pptx
Ā
microprocessor 8085 and its interfacing
microprocessor 8085 and its interfacing
Ā
Decoding Kotlin - Your guide to solving the mysterious in Kotlin.pptx
Decoding Kotlin - Your guide to solving the mysterious in Kotlin.pptx
Ā
Internship report on mechanical engineering
Internship report on mechanical engineering
Ā
IVE Industry Focused Event - Defence Sector 2024
IVE Industry Focused Event - Defence Sector 2024
Ā
Past, Present and Future of Generative AI
Past, Present and Future of Generative AI
Ā
Exploring_Network_Security_with_JA3_by_Rakesh Seal.pptx
Exploring_Network_Security_with_JA3_by_Rakesh Seal.pptx
Ā
What are the advantages and disadvantages of membrane structures.pptx
What are the advantages and disadvantages of membrane structures.pptx
Ā
High Profile Call Girls Nagpur Isha Call 7001035870 Meet With Nagpur Escorts
High Profile Call Girls Nagpur Isha Call 7001035870 Meet With Nagpur Escorts
Ā
Call Girls Delhi {Jodhpur} 9711199012 high profile service
Call Girls Delhi {Jodhpur} 9711199012 high profile service
Ā
Call Us -/9953056974- Call Girls In Vikaspuri-/- Delhi NCR
Call Us -/9953056974- Call Girls In Vikaspuri-/- Delhi NCR
Ā
Call Girls Narol 7397865700 Independent Call Girls
Call Girls Narol 7397865700 Independent Call Girls
Ā
SPICE PARK APR2024 ( 6,793 SPICE Models )
SPICE PARK APR2024 ( 6,793 SPICE Models )
Ā
ā CALL US 9953330565 ( HOT Young Call Girls In Badarpur delhi NCR
ā CALL US 9953330565 ( HOT Young Call Girls In Badarpur delhi NCR
Ā
Architect Hassan Khalil Portfolio for 2024
Architect Hassan Khalil Portfolio for 2024
Ā
(MEERA) Dapodi Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Escorts
(MEERA) Dapodi Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Escorts
Ā
GDSC ASEB Gen AI study jams presentation
GDSC ASEB Gen AI study jams presentation
Ā
IRJET- A Statistical Approach Towards Energy Saving in Cloud Computing
1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072 Ā© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 4687 A statistical approach towards energy saving in cloud computing Anushree chausalkar Assistant Professor Computer Science Engineering. , ITMGOI Gwalior, India ---------------------------------------------------------------------------------------------------------------------------------------- Abstract : Due to the budget and the environmental issues, achieving energy efficiency gradually receives a lot of attentions these days. In our previous research, a prediction technique has been developed to improve the monitoring statistics. In this research, by adopting the predictive monitoring information, our new proposal can per-form the optimization to solve the energy issue of cloud computing. Actually, the optimization technique, which is convex optimization, is coupled with the proposed prediction method to produce a near-optimal set of hosting physical machines. After that, a corresponding migrating instruction can be created eventually. Based on this instruction, the cloud orchestrator can suitably relocate virtual machines to a designed subset of infrastructure. Subsequently, the idle physical servers can be turned off in an appropriate manner to save the power as well as maintain the system performance. For the purpose of evaluation, an experiment is conducted based on 29-day period of Google traces. By utilizing this evaluation, the proposed approach shows the potential to significantly reduce the power consumption without affecting the quality of services. CCS CONCEPTS ā¢ Computing methodologies ā Supervised learning by regression; ā¢ Computer systems organization ā Cloud computing; ā¢ Mathematics of computing ā Stochastic processes; KEYWORDS IaaS, Cloud Computing, Predictive Analysis, Convex Optimization, Energy Efficiency, VMs, Gaussian process regression 1 INTRODUCTION In recent years, a number of data centre recognized cloud computing as a popular platform to manage most of the operations. Naturally, cloud computing improves utilization and scalability of underlying physical infrastructure. As a substitution for independently allocating the computing facilities when being requested, cloud computing is able to deliver the ordered resource as a virtual package conveniently via internet connection. Besides, it is worth noting that cloud computing can be used to enhance the utilization of the infrastructure by virtualizing the service composition in a higher level. Hence, the capacity of the physical facilities can be unified to provide better quality of services. Finally, implementing cloud computing can lessen the management cost to consequently save the money. In order to achieve the reduction of power consumption in cloud computing, it would be a must to understand the sources that consume the energy and how to efficiently reduce the corresponding consumption. Obviously, when a computing system is online, most of the internal components burn the power to do the assigned jobs. Because of this reason, any inefficiently running devices, which are in the idle state, actually waste the power for very limited value. Critically, this kind of facilities should be minimized to save the energy. Regularly, the conventional approach is to decline the number of working physical machines to an optimal quantity. By using the virtualization, cloud computing has a chance to implement this approach through stacking the virtual machines (VMs). In order to do that, the VMs can be migrated to an optimal designated physical machines (PMs). Subsequently, the remaining idle PMs are turned off to fulfil the requirement of mitigating the power burning. In the recent research, we have developed an enhanced prediction technique based on Gaussian process regression to improve the monitoring statistics. In this research, we would like to propose an optimization scheme to reduce the power consumption in cloud computing. The remaining parts of the paper are organized as follows. In section 2, we included some related works of energy efficiency in cloud computing area. Section 3 shows the description of our proposed architecture. In this section, the summary of our preceding research, including the prediction, is also briefly intro-duced. Section 4 includes the proposal of optimization technique to do the energy optimization. Section 5 presents how we conduct the performance evaluation to show the usefulness of the solution. In the end, section 6 attaches the conclusion of paper and outlines our future work.
2.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072 Ā© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 4688 2 RELATED WORKS Energy efficiency in cloud computing is mostly related to VM consolidation philosophy. It means that the problem of interest focuses on choosing the suitable placement for VMs with regards to the utilization of PMs [3]. Basically, we can model the VMs and PMs as a regular object-bin problem. Hence, the VM consolidation can be simplified to bin-packing problem, which is NP-hard [1]. Consequently, the heuristics- oriented techniques might be the promising solutions. Whereby, some well-known approaches popularly adopt this methodology, namely best fit decreasing [1] and first fit decreasing [5]. By engaging these techniques, the cloud orchestrator has a tendency to assign VMs to minimize the number of hosting PMs. Due to this attractive feature, the mentioned bin-packing model is widely used to generate the solution to deal with the energy efficiency. However, heuristics approaches have a critical drawback when implementing in action. In order to produce good solution, this family requires the fixed number of objects and bins at the beginning of time. In other words, the quantity of VMs and PMs must be recognized in advance. Apparently, this requirement is unfeasible since it breaks the principles that make cloud computing, which are the elasticity and the multi-tenancy. In addition, the rapid changes in infrastructureās utilization clearly degrade bin-packing approaches in term of accuracy. Because of that, this issue eventually casts bad effects on system performance. In order to breakthrough the mentioned obstacle, other approaches utilize the prediction techniques as a preprocessing step to enhance the input data. By predicting the infrastructureās workload, the cloud orchestrator can produce more reasonable decision to lessen only unexpected effect of utilization fluctuation. There would be a number of research take into account this method to their proposals. The candidates for prediction algorithms are various from hidden Markov model [8] to polynomial fitting [16]. Unfortunately, these authors do not pay enough attention to the designed philosophy of versatile resource provision in cloud computing. Therefore, these techniques, might not provide good prospect of underlying system to the orchestrator. Besides, there is another research trying to utilize the Wiener filter [7] to predict the workload. However, to the best of our knowledge, Wiener filter performs properly only with the stationary signal and noise spectrum. Bringing signal processing technique to the cloud computing domain without a rigorous analysis might not be a good idea. Due to this reason, Wiener filter might be inapplicable for prediction purpose in the domain of interest. Furthermore, one different kind of approaches that should be included is the modified specific schedulers in [6], [14] and [2]. These schedulers are the efforts to solve other aspects of energy efficiency in network traffic, resource reconfiguration and communication rates. By proposing these schedulers, the authors claim that they can optimize the network throughput as well as balance the resource utilization, eventually save the energy. However, these research do not consider the importance of system performance preservation. Therefore, the referred schedulers are unable to implemented in service providing systems. By investigating the research area, a conclusion can be made that even though the energy efficiency is a hot topic in computer engineering these days, not enough research has comprehensively been successful in equating the energy savings with an acceptable performance, especially in a predictive and optimized manner. Be-cause of that, we would like to propose a solution which engages our previous prediction method [4] and convex optimization tech-nique to reduce the energy consumption in cloud computing. The rest of the proposal is described in the next sections. 3 PROPOSED ARCHITECTURE 3.1 System description Assume that the infrastructure of interest is homogeneous system. That means all the physical computing facilities are identical. This assumption is only to make the equation derivatives more convenient. In fact, this configuration does not degrade the generality because the heterogeneous system can be transformed to homogeneous system just by adding some weighted arguments. As stated previously, the target of the research is to reduce the power consumption in cloud computing. In order to do that, we follow the VMs stacking philosophy. In the other words, VMs consolidation is chosen to compact the size of running PMs. This choice relies on the fact that an idle PM actually burns an amount of power up to 60% [9][11][10] of the peak power, which is used to maintain the same PM in peak performance. It is worth mentioning that booting up a PM just burns 23.9% [13] of the same power. Furthermore, reducing the number of running PMs delivers additional reduction of the extra power for maintaining the cooling system as well as the networking devices. Due to these reasons, stopping idle PMs can help to save more power than leaving them serving no specific purpose, even an extra expense is needed to re- activate the offline computing facilities subsequently. Relying on this reasoning, we design an architecture, namely the energy efficiency management (E2M) system shown in
3.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072 Ā© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 4689 Figure 1. The main target of this architecture is to optimally create VMs consolidation strategy and send it to the orchestrator periodically. Finally, the idle PMs are temporarily deactivated to reduce power consumption. Following is the functionality of each component in the architecture: ā¢ Ganglia: this component collects most of operation statistics for both PMs and VMs. The collected information is actually used as the input for the prediction step in the next stage. Note that Ganglia is known to be trusted platform for years for monitoring purposes. This component is light-weight but powerful and versatile enough to integrate to any solution. ā¢ Predictor: this component is the data sink for Gangliaās statistics. After receiving the aforementioned data, the enhanced Gaussian process regression is activated to do the prediction step. The output of this step is the predictive monitoring statistics. In other words, the predictor provides the futuristic perspective of the working status of infrastructure. This kind of anticipated system utilization is more valuable for the optimization step than the original data. ā¢ Energy optimizer: the predictive monitoring statistics, that are retrieved from the predictor, can be engaged as the precious input for creating near-optimal consolidating instruction. The strategy, if possible, has to save as much power as possible without deteriorating the quality of services. In fact, the responsibility of this component is to decide the mini- mum but feasible set of PMs to normally host the increasing
4.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072 Ā© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 4690 VM Migrating Instruction Energy Optimizer Predictive Physical Physical Statistics Server Server Predictor Cloud Monitoring Ganglia Statistics Orchestrator Physical Physical Server Server Energy Efficiency Management Physical Physical Server Server Figure 1: Architecture of energy efficiency management (E2M) system. VMs. Finally, the complete package of VM consolidation is delivered to the cloud orchestrator for implementation. 3.2 Prediction model As mentioned above, the migrating instruction would be created in the energy optimizer component. Before the optimization procedure can be issued, it is mandatory to enhance the monitoring data in advance. The reason for the need of this enhancement is twofold. Firstly, it is known to be true that the monitoring statistics is always the delayed information. It means that the data we has received at the time t actually reflects the system status at the time t ā Ļ , in which, Ļ is the monitoring window that triggers the data collecting process. Any decision making based on this obsolete data might not be reasonable at the time the reaction is executed. Considering this fact, there is obviously a requirement for data prediction. The second reason is that sometimes it is better to apply proactive reaction rather than reactive model. In that case, there would be higher chance for the orchestrator to reduce the violation to quality of services in advance. Regularly, the target of the predictor is to provide the futuristic utilization of resources to the optimizer. In order to do that, the Bayesian learning and the Gaussian process regression are chosen to make the regression. The guidance on how to build this prediction model is provided in detail in our preceding research [4]. 4 ENERGY OPTIMIZATION Coming to this step, we assume that the energy optimizer receives enough information from the predictor, it is right time to conduct the optimization for power consumption. As said previously, a minimum-but-feasible number of PMs is required as the output of this stage. Note that the output is subsequently used to con-struct the instruction for VM migration. Primarily, there are two sub-components in the energy optimizer, namely power manage-ment and cluster optimizer. The power management observes the resource pool and incorporates the energy decision that has been made from the cluster optimizer. The final decision can be referred to as the instruction for VM migration. This instruction is sent to the cloud orchestrator to actuate. 4.1 Performance modeling Since CPU is one of the most sensitive parameters, this factor should be chosen to model the performance. Denote the global utilization as Umfi ā R+ and the individual utilization as Imfi ā R+ regarding the resource fi (For instance, fc stands for CPU). The number of active PMs, which is denoted by am at the monitoring window m, are the target to calculate. It is crucial to mention that consolidating the VMs into a number am of PMs might result the infrastructure to its peak performance. Hence, this procedure needs to be controlled. Otherwise, the whole sys-tem might suffer very high latency [15] and violate the quality of services, which is described in the service level agreement (SLA) document. Therefore, the utilization of CPU resource should be formulated as follows: Im = max{Imfc } = max{ Ufc m }. (1) amC fc fc fc Observing (1), Im is known to be a decreasing function of am . In other words, decreasing the number of PMs might cast high latency to entire system. Denote the average latency of task processing in CPUs as lm . This parameter can be computed by engaging the expectation waiting time E(fc ) of the exhausted CPU: l I = fc ) = Ī»m 1/Āµ2 , (2) m ( m ) E( 2(1 ā Ī 1 Āµ m / ) in which, Ī»m is the arrival rate of the tasks, Āµ is the service rate of homogeneous CPU. By comparing lm to the threshold l (which is depicted in the SLA document), the quality of services can be estimated to be violated or not. If the violation occurs, the penalty
5.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072 Ā© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 4691 cost Cmp needs to be calculated as follows: Cmp = wmsp (lm (Im ) ā l)+, (3) in which, wm and sp stand for the weight factor that reflects the magnitude of violation, and the fine that needs to be paid for the penalty, respectively. The weight factor wm is also supposed to extinguish the trend of the average latency increment. In other words, this parameter flexibly allows a controlled number of under- performance PMs as a preventive method for reducing system overhead. Essentially, this weight plays the role of preserving the SLA execution. 4.2 Energy modeling As we all know that the power consumption in running clusters can be broken down to two periods: the period of processing the assigned tasks and the period of maintaining the idle state. This fact can be modeled by using following equation: em = Pidl e + Pr unninŠ“ . (4) Assume that sm stands for the fine of electricity at monitoring window m, the power expense, denoted by Cme , is represented as follows: Cme (am ) = smamem = smam (Pidl e + Pr unninŠ“ ). (5) In (5), the energy, which is used for processing the tasks, is un- touchable. As a result, the represented parameter Pr unninŠ“ should not be considered in the optimization procedure. So on, (5) is re-duced to: Cme (am ) = smam Pidl e . (6) 4.3 Cluster optimizer The heart of energy optimizer is represented in this section. As a brief recapitulation, our objective is to reduce the power consump-tion but still preserve the quality of services. This objective can be achieved via minimizing the number am of active PMs. Mathe-matically, the variable am needs to be found optimally. Firstly, we model the problem by engaging convex optimization as follows: 0 min (wmsp (lm (Imz ) ā l)+ + smam Pidl e ). (7) ā¤am ā¤Pm As stated before, the function lm (Im ) is a decreasing function of am . Therefore, the condition of am can be depicted as below: a Ī“m . (8) m ā¤ lā1 l In case a and m( ) + = 0,ā„ Ī“m lmā1 l lm Iz l ) = lm Ī“m am ) ā lm / ( ) ( ( m) ā ( ( / ) then any reduction of a to Ī“m lmā1 l ) can also reduce the burning m / ( power. Due to this reason, (7) can be re-formulated as shown below: minĪ“m (wmsp (lm (Im z ) ā l )+ + smam Pidl e ). (9) 0ā¤am ā¤ l ā1 l m ( ) The Lagrangian function of this problem can be expressed as below: L(am , Ī³ ) = wmsp (lm (Ī“m ) ā l)++ am (10) Ī“m sm am Pidl e +Ī³(am ā lmā1(l) ) +Ī±(0āam ). Table 1: Summary of Google Tracesā Characteristics Time span # of PMs #VM requests # of users 29 days 12583 >25M 925 This function can be solved by applying KarushāKuhnāTucker (KKT) conditions to find the near-optimal value am . 5 PERFORMANCE EVALUATION 5.1 Experiment design The testbed is a cluster of 16 homogeneous servers. For the detail configuration, an Intel Xeon E7-2870 2.4Ghz and 12GB of RAM are geared towards the purposed of hosting upto 8 VMs in each serves. With these equipments, the infrastructure can host up to 128 VMs at maximum to conduct the experiment. For the dataset, we use Google traces as a simulation for the workload. Announced by Google, these traces actually comprise the monitoring data from more than 12,500 machines over a duration of 29 days. However, only a set of 6732 machines is chosen to satisfy the assumption of homogeneous system. In this set, we also extract randomly 2.26 GB from 39 GB of compressed data for the experiment. The chosen dataset consists of many parts. Each part represents a period of 24- hour of traces. For the convenience of presentation, we scale the maximum length of measurement to 60 seconds. This length is also adopted as the monitoring window. Moreover, the summary of Google tracesā characteristics is described in Table 1. 5.2 Implementation The experiment is conducted under four schemes for comparison as follows: ā¢ The default schemes: all of the PMs are activated all the time. No power savings is acquired at all. ā¢ The greedy first fit decreasing (FFD) scheme [12]: the VMs are sorted into queue by descending order in term of internal CPU utilization. This queue is subsequently submit to the first host that matches the resource requirement. Basically, the bin-packing approach is used to relocate VMs. ā¢ The proposed approach (E2M) scheme: the proposed method is implemented to create near-optimal energy consumption and preserve the quality of services. ā¢ The optimal energy-aware scheme: an optimal solution is pre- calculated to achieve minimum energy consumption. In this scheme, the quality of services is not taken into ac-count. In order words, the quality of services is sacrificed to significantly save the energy. 5.3 Results The Google traces is actually a set of synthesized data. Therefore, in order to measure the energy consumption, an external equiva-lent energy calculation [13] is applied to compute the result. The description of calculation and related parameters is depicted in the original paper and summarized in Table 2. As shown in Fig-ure 2 and 3, because of activating the PMs all the time, the default scheme consumes egregious amount of power. Meanwhile, in the
6.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072 Ā© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 4692 Table 2: Energy Estimation Parameters Parameter Value Unit Esleep 107 Watt Eidle 300.81 Watt Epeak 600 Watt Eactiveāsleep 1.530556 Watt-hour Esleepāactive 1.183333 Watt-hour Eactiveāoff 1.544444 Watt-hour Eoffāactive 11.95 Watt-hour 100 90 (%) 80 servers 70 ofphysical 60 50 Percentage 40 30 20 default scheme FFD 10 Optimal E2M 0 0 100 200 300 400 500 600 Time (hour) Figure 2: Percentage of active physical servers in Google traces experiment. FFD scheme, even the power utilization is less than the default scheme, a remarkable amount of power is wasted since many idle PMs are kept alive when the workload fluctuates. The reason for this issue is that, without the capability of prediction, the FFD is unable to appropriately perform the bin- packing algorithm in ma-jority of times. Another reason is the obsolete status information of underlying computing facilities. Oppositely, the proposed ap-proach, namely E2M, can save much better energy by equipping with the prediction on resource utilization and the optimization on the pool of active PMs. There is also another additional aspect of this achievement, which is the gap between E2M and the optimal scheme. Apparently, the optimal scheme has better energy savings regardless the system performance. Because the quality of service is totally not considered in this scheme, this optimal solution brings to the infrastructure too much overhead and tends to frequently violate the SLA. For more detail on quantitatively measuring the energy savings, our proposal can obtain the reduction of power consumption up to 100 90 (kWh) 80 Consumption 70 Power 60 50 default scheme FFD Optimal E2M 40 0 20 40 60 80 100 120 Sampling time (minute) Figure 3: Power consumption evaluation of the proposed method in Google traces experiment (lower is better). 100 Power Consumption (%) 90 Latency (second) 80 70 60 50 40 30 20 10 0 default scheme FFD E2M Optimal Name of approach Figure 4: Power consumption vs average latency in Google traces experiment. 34.89% compared with the default scheme. The detail evaluation can be found in Figure 4. This achievement can be taken into account as a significant improvement. As a side note, the optimal scheme can only achieve up to 37.08%. It means that the proposed method can be seen as a near-optimal solution. Also in Figure 4, our method suffers around 54.72% less than the optimal solution in term of average latency of system scheduling. Therefore, the quality of services can be preserved in an acceptable level.
7.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072 Ā© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 4693 6 CONCLUSION In this research, a near-optimal energy efficient solution is proposed based on the utilization prediction of infrastructure and the power consumption optimization. By engaging the mentioned techniques, our proposal can create suitable VM migration strategy. Based on this migration scheme, the cloud orchestrator can issue more rea-sonable VMs consolidation and condense near-optimal the pool of active PMs. As a result, a significant reduction in energy con-sumption can be achieved while still preserving the SLA. In future, we plan to integrate the heuristics algorithm to build a knowledge base that might help to reduce the overhead when performing the prediction. This integration might boost up the prediction part to even more quickly create the VM migrating instruction. (ICEAC), 2011 International Conference on. IEEE, 1ā6. [14] M. Shojafar, N. Cordeschi, and E. Baccarelli. 2016. Energy-efficient Adaptive Resource Management for Real-time Vehicular Cloud Services. IEEE Transactions on Cloud Computing PP, 99 (2016), 1ā1. https://doi.org/10.1109/TCC.2016.2551747 [15] Qi Zhang, Mohamed Faten Zhani, Shuo Zhang, Quanyan Zhu, Raouf Boutaba, and Joseph L Hellerstein. 2012. Dynamic energy-aware capacity provisioning for cloud computing environments. In Proceedings of the 9th international conference on Autonomic computing. ACM, 145ā154. [16] Yuanyuan Zhang, Wei Sun, and Yasushi Inoguchi. 2006. CPU load predictions on the computational grid*. In Cluster Computing and the Grid, 2006. CCGRID 06. Sixth IEEE International Symposium on, Vol. 1. IEEE, 321ā326. REFERENCES [1] Yasuhiro Ajiro and Atsuhiro Tanaka. 2007. Improving packing algorithms for server consolidation. In Int. CMG Conference. 399ā406. [2] Enzo Baccarelli, Nicola Cordeschi, Alessandro Mei, Massimo Panella, Mohammad Shojafar, and Julinda Stefa. 2016. Energy-efficient dynamic traffic offloading and reconfiguration of networked data centers for big data stream mobile computing: review, challenges, and a case study. IEEE Network 30, 2 (2016), 54ā61. [3] Anton Beloglazov, Jemal Abawajy, and Rajkumar Buyya. 2012. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future generation computer systems 28, 5 (2012), 755ā768. [4] Dinh-Mao Bui, Huu-Quoc Nguyen, YongIk Yoon, SungIk Jun, Muhammad Bilal Amin, and Sungyoung Lee. 2015. Gaussian process for predicting CPU utilization and its application to energy efficiency. Applied Intelligence 43, 4 (2015), 874ā891. [5] Edward G Coffman Jr, Michael R Garey, and David S Johnson. 1996. Approx- imation algorithms for bin packing: a survey. In Approximation algorithms for NP-hard problems. PWS Publishing Co., 46ā93. [6] Nicola Cordeschi, Mohammad Shojafar, and Enzo Baccarelli. 2013. Energy-saving self-configuring networked data centers. Computer Networks 57, 17 (2013), 3479ā 3491. [7] Mehiar Dabbagh, Bechir Hamdaoui, Mohsen Guizani, and Ammar Rayes. 2015. Energy-efficient resource allocation and provisioning framework for cloud data centers. IEEE Transactions on Network and Service Management 12, 3 (2015), 377ā391. [8] Christopher Dabrowski and Fern Hunt. 2009. Using markov chain analysis to study dynamic behaviour in large-scale grid systems. In Proceedings of the Seventh Australasian Symposium on Grid Computing and e-Research-Volume 99. Australian Computer Society, Inc., 29ā40. [9] Xiaobo Fan, Wolf-Dietrich Weber, and Luiz Andre Barroso. 2007. Power provi- sioning for a warehouse-sized computer. In ACM SIGARCH Computer Architecture News, Vol. 35. ACM, 13ā23. [10] Ajay Gulati, Anne Holler, Minwen Ji, Ganesha Shanmuganathan, Carl Wald- spurger, and Xiaoyun Zhu. 2012. Vmware distributed resource management: Design, implementation, and lessons learned. VMware Technical Journal 1, 1 (2012), 45ā64. [11] David Meisner, Brian T Gold, and Thomas F Wenisch. 2009. PowerNap: eliminat-ing server idle power. In ACM Sigplan Notices, Vol. 44. ACM, 205ā216. [12] Thiago Kenji Okada, Albert De La Fuente Vigliotti, Daniel MacĆŖdo Batista, and Alfredo Goldman vel Lejbman. 2015. Consolidation of VMs to improve energy efficiency in cloud computing environments. (2015), 150ā158. [13] Imad Sarji, Cesar Ghali, Ali Chehab, and Ayman Kayssi. 2011. CloudESE: Energy efficiency model for cloud computing environments. In Energy Aware Computing
Download now