Resource management for clusters of virtual machines

2005 ◽  
Author(s):  
G. Czajkowski ◽  
M. Wegiel ◽  
L. Daynes ◽  
K. Palacz ◽  
M. Jordan ◽  
...  
Keyword(s):  
Resource Management ◽  
Virtual Machines

scholarly journals A dynamic programming approach to manage virtual machines allocation in cloud computing

2018 ◽  
Vol 7 (4.6) ◽  
pp. 128
Author(s):  
Abdellah Ouammou ◽  
Mohamed Hanini ◽  
Abdelghani Ben Tahar ◽  
Said El Kafhali
Keyword(s):  
Cloud Computing ◽  
Dynamic Programming ◽  
Resource Management ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Stochastic Modelling ◽  
Programming Approach ◽  
Conflicting Objectives ◽  
Machine Allocation ◽  
Virtual Machine Allocation

As a result of the dynamic nature of Virtual Machine allocation in cloud computing, it is not easy to manage system resources or choose the best configuration based solely on human experience.  In this work, we used stochastic modelling instead of comprehensive experiments to evaluate the best resource management of the system. In such complex systems, choosing the best decision is a challenge, for this reason we have designed a heuristic algorithm, specifically, dynamic programming as a resource management and programming tool that finds a way that attempts to satisfy the conflicting objectives of high performance and low power consumption. As a scenario for using this algorithm, we addressed the problem of virtual machine allocation, a subset of physical machines is designated as "reserve", and the reserves are actives when the number of jobs in the system is sufficiently high. The question is how to decide when to activate the reserves. The simulation results demonstrated the benefit of using our framework to identify the policy for consolidation or for a low energy consumption and in order to have a good quality of service in the system

Application of Particle Swarm Optimisation-R Factor Optimisation Algorithm for Efficient Resource Allocation and Scheduling in Cloud Environment

2020 ◽  
Vol 17 (4) ◽  
pp. 1990-1998
Author(s):  
R. Valarmathi ◽  
T. Sheela
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Resource Management ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Cost Optimization ◽  
Optimal Solution ◽  
Service Needs ◽  
Cloud Environment ◽  
Efficient Resource

Cloud computing is a powerful technology of computing which renders flexible services anywhere to the user. Resource management and task scheduling are essential perspectives of cloud computing. One of the main problems of cloud computing was task scheduling. Usually task scheduling and resource management in cloud is a tough optimization issue at the time of considering quality of service needs. Huge works under task scheduling focuses only on deadline issues and cost optimization and it avoids the significance of availability, robustness and reliability. The main purpose of this study is to develop an Optimized Algorithm for Efficient Resource Allocation and Scheduling in Cloud Environment. This study uses PSO and R factor algorithm. The main aim of PSO algorithm is that tasks are scheduled to VM (virtual machines) to reduce the time of waiting and throughput of system. PSO is a technique inspired by social and collective behavior of animal swarms in nature and wherein particles search the problem space to predict near optimal or optimal solution. A hybrid algorithm combining PSO and R-factor has been developed with the purpose of reducing the processing time, make span and cost of task execution simultaneously. The test results and simulation reveals that the proposed method offers better efficiency than the previously prevalent approaches.

scholarly journals Game Theory Application Resources Management and Distribution in Blockchain Network

2021 ◽  
Vol 13 (1) ◽  
pp. 65-78
Author(s):  
Cong Hung Tran ◽  
Dien Tam Le ◽  
Thanh Hieu Huynh
Keyword(s):  
Game Theory ◽  
Resource Management ◽  
Virtual Machines ◽  
Life Time ◽  
Test System ◽  
Resources Management ◽  
Theory Application

The paper illustrated a basic blockchain system, applying game theory to simulate resource management in blockchain transactions. By the method of illustration, simulation, our team has demonstrated the effect of game theory transactions, transactions with specific value can demonstrate the benefits of game theory in co-life. time can be used to manage resources in blockchain. Based on the proposed algorithm model, we have built a test system with the maximum number of virtual machines to demonstrate the effectiveness in applying game theory in managing and distributing resources for transactions in the blockchain network.

scholarly journals Optimized Scheduling Approach for Scientific Applications Based on Clustering in Cloud Computing Environment

2019 ◽  
Vol 20 (3) ◽  
pp. 527-540
Author(s):  
Walid Kadri ◽  
Belabbas Yagoubi
Keyword(s):  
Cloud Computing ◽  
Resource Management ◽  
Clustering Algorithm ◽  
Virtual Machines ◽  
Directed Acyclic Graphs ◽  
Resource Provisioning ◽  
Scientific Applications ◽  
Scheduling Policies ◽  
Use Of Resources ◽  
The Impact

Cloud Computing refers to the use of the computing capabilities of remote computers, where the user has considerable computing power without having powerful units. Scientific applications, usually represented as Directed Acyclic Graphs (DAGs), are an important class of applications that lead to challenging problems for resource management in distributed computing. With the advent of Cloud Computing, particularly the IaaS offers for on demand virtual machines leasing, multiple jobs execution, consisting of a large number of DAGs, needs an elaborated scheduling and resource provisioning policies, for efficient use of resources. Only few works exists that consider this problem in the context of clouds environment. In goal of optimization and fault tolerance, DAGs applications are generally partitioned into multiple parallel DAGs using clustering algorithm and assigned to VM (Virtual Machine) resources independently. In this work, we investigate through simulation, the impact of clustering for both provisioning and scheduling policies in the total makespan and financial costs for execution of user's application. We implemented four scheduling policies well-known in grid computing systems, and adapted clustering algorithm to our resource management policy that leases and destroys dynamically VMs. We show that dynamic policies can achieve equal or even better performance than static management policies.

Sandpiper: Black-box and gray-box resource management for virtual machines

2009 ◽  
Vol 53 (17) ◽  
pp. 2923-2938 ◽  
Author(s):  
Timothy Wood ◽  
Prashant Shenoy ◽  
Arun Venkataramani ◽  
Mazin Yousif
Keyword(s):  
Resource Management ◽  
Virtual Machines ◽  
Black Box
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