Efficient Load Optimization Method Using VM Migration in Cloud Environment

2021 ◽  
pp. 83-97
Author(s):  
Sarita Negi ◽  
Man Mohan Singh Rauthan ◽  
Kunwar Singh Vaisla ◽  
Neelam Panwar
Keyword(s):  
Optimization Method ◽  
Cloud Environment ◽  
Vm Migration ◽  
Load Optimization

scholarly journals A Switch Based Resource Management Method for Energy Optimization in Cloud Data Center

2021 ◽  
pp. 85-91
Author(s):  
Shally Vats ◽  
Sanjay Kumar Sharma ◽  
Sunil Kumar
Keyword(s):  
Data Center ◽  
Data Centers ◽  
Energy Requirement ◽  
Energy Optimization ◽  
Cloud Service ◽  
Optimization Method ◽  
Level Energy ◽  
Cloud Data Center ◽  
Vm Migration ◽  
Cloud Data

Proliferation of large number of cloud users steered the exponential increase in number and size of the data centers. These data centers are energy hungry and put burden for cloud service provider in terms of electricity bills. There is environmental concern too, due to large carbon foot print. A lot of work has been done on reducing the energy requirement of data centers using optimal use of CPUs. Virtualization has been used as the core technology for optimal use of computing resources using VM migration. However, networking devices also contribute significantly to the responsible for the energy dissipation. We have proposed a two level energy optimization method for the data center to reduce energy consumption by keeping SLA. VM migration has been performed for optimal use of physical machines as well as switches used to connect physical machines in data center. Results of experiments conducted in CloudSim on PlanetLab data confirm superiority of the proposed method over existing methods using only single level optimization.

Numerical Simulation of Load Optimization in Multi-Dimension Vibration Test

2013 ◽  
Vol 307 ◽  
pp. 236-239
Author(s):  
Jie Hu ◽  
Huai Yun Zhao
Keyword(s):  
Numerical Simulation ◽  
Equivalence Principle ◽  
Optimization Method ◽  
Shaking Table ◽  
Vibration Test ◽  
Target Response ◽  
Minimum Error ◽  
Numerical Simulation Result ◽  
Load Optimization ◽  
Control Response

This paper introduced a load optimization method in multi-dimension vibration test when the number of shaking table is less than the number of target response. The response equivalence principle is considered as response approximation, the optimization purpose is set as the minimum error between control response and target response, then the load applying in multi-dimension vibration test is analyzed stand at the point of load optimization. Genetic algorithm(GA) is used as the optimization arithmetic, and the numerical simulation result verified the effectiveness of this optimization method. The research of this paper proposed an effective way of calculating the load in multi-dimension vibration test.

scholarly journals Study of Load Optimization and Performance Issues in Cloud

2018 ◽  
Vol 11 (3) ◽  
pp. 1035
Author(s):  
Madhina D Banu ◽  
Aranganathan Aranganathan
Keyword(s):  
Load Balancing ◽  
Performance Optimization ◽  
Research Study ◽  
Optimization Approach ◽  
Minimal Cost ◽  
Cloud Environment ◽  
Effective Solution ◽  
Heavy Load ◽  
Load Optimization ◽  
Cloud Application

“Nowadays, cloud computing is the latest computing platform which is feasible to the user for computation and unlimited storage and data transmission with minimal cost and time in a cloud environment during the internet. The load balancing is important criteria of cloud environment that avoid same nodes overloaded and others are idle. Ultimately load balancing can enhance the QoS parameters including make span, cost and resource utilization. To optimize the load, the existing load optimization approach is properly utilized federation mechanisms, which offers physical resources based on demand to maintain the cloud application efficiency. However, the technique failed to optimize load where part of the servers suffering from heavy load after an execution of the application. In the current scenario, several systems are facing same kinds of problem which is the biggest cause to increase the virtual machine (VM) cost. Current systems still have a time delay, request-response process error from data center side in cloud environments. To overcome these issues, the research study studies all related technique for cloud performance optimization and load balancing issues. The main framework objective is to offer an effective solution to store/search/transmit/ data with minimal cost and time without compromising the QoS constraints. The study represents different policies and cloud-specific strategies to enhance the performance of cloud application with minimal cost and time. The research study is also planning to find out an effective solution for traffic, data congestion and media streaming issues in a cloud environment.

scholarly journals A Constraint-Aware Optimization Method for Concurrency Bug Diagnosis Service in a Distributed Cloud Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Lili Bo ◽  
Shujuan Jiang
Keyword(s):  
Data Access ◽  
Optimization Method ◽  
Service Performance ◽  
Cloud Environment ◽  
Security Issue ◽  
Concurrent Software ◽  
Concurrent Program ◽  
Cloud Computation ◽  
Distributed Cloud ◽  
Concurrency Bug

The advent of cloud computation and big data applications has enabled data access concurrency to be prevalent in the distributed cloud environment. In the meantime, security issue becomes a critical problem for researchers to consider. Concurrency bug diagnosis service is to analyze concurrent software and then reason about concurrency bugs in them. However, frequent context switches in concurrent program execution traces will inevitably impact the service performance. To optimize the service performance, this paper presents a static constraint-aware method to simplify concurrent program buggy traces. First, taking the original buggy trace as the operation object, we calculate the maximal sound dependence relations based on the constraint models. Then, we iteratively check the dependent constraints and move forward current event to extend thread execution intervals. Finally, we obtain the simplified trace that is equivalent to the original buggy trace. To evaluate our approach, we conduct a set of experiments on 12 widely used Java projects. Experimental results show that our approach outperforms other state-of-the-art approaches in terms of execution time.

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