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Grant: Collaborative Research and Development Grants

Concordia University

$60,000.00

Cloud infrastructure resource management

EF 2017-18

Résumé
Cloud computing is a common name in IT industry. However increase in Cloud computing usage also comes with increased complexity. Efficient resource management for best utilization with minimal cost is a crucial task. The scope of this work is under IaaS service module, which handles infrastructure resources (Virtual Machines, Servers, Storage, and Network) allocation. Resource management offers the following benefits: elasticity/scalability, QoS, optimal utility, reduced overhead, improved throughput, reduced latency, cost effectiveness and simplified interface. VM migration is one of the most popular way to manage resources, and Live VM migration is the most used technique to reload or rearrange the resources in datacenter to keep the delivered services available. Statistics shows 70% of datacenters resources is not utilized. Therefore, reduction in number of servers and hardware will not only reduce its capital costs, but will also reduce its running and maintenance cost. In addition, Live VM migration will facilitate load balancing and energy management as part of resource management. VM migration consists of optimally selecting VM to be migrated, which physical server must be chosen to move this VM, when to start migration process, and duration of migration process. The goals for VM Selection techniques are to reduce power consumption, increase load balancing, and increase fault tolerance, which eventually will increase the cloud services availability and throughput/efficiency. The increased efficiency reduces the overall operation costs, pollution (green datacenters), and hardware maintenance. Migration Process itself targets moving the VM in minimum time to avoid any interruption of services. In this research proposal, in collaboration with Cistech Ltd, we consider investigating solutions to decrease the migration process time and optimally selecting VMs for live migration. Our project goal is to develop smart algorithms that will evaluate the available running resources for load balancing, server consolidation, network bandwidth, VMs shrinking or expanding and fault tolerance. Our project will drive the energy saving and the cost saving in running the IT datacenters.
Programme
Collaborative Research and Development Grants
Bénéficiaire
Emplacement
Quebec, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
486584-2015
Name nom
Agarwal, Anjali
Department département
Electrical and Computer Engineering
Organization id
70
Institution établissement
Concordia University
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2017
Competition year année de concours
2015
Award amount
60000
Program id
CRDPJ
Program name en
Collaborative Research and Development Grants
Program name fr
Subventions de recherche et développement coopérative
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
1207
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Information, computer and communication technologies
Area of application fr
Technologie de l'information, des ordinateurs et des réseaux de communications
Research subject code
2508
Research subject group en
Electrical and electronic engineering
Research subject group fr
Génie électrique et électronique
Research subject en
Communications networks
Research subject fr
Réseaux de télécommunications
Partie
2 & 3
Nb partie
3
Application title
Cloud infrastructure resource management
Keyword
NSERC
Application summary
Cloud computing is a common name in IT industry. However increase in Cloud computing usage also comes with increased complexity. Efficient resource management for best utilization with minimal cost is a crucial task. The scope of this work is under IaaS service module, which handles infrastructure resources (Virtual Machines, Servers, Storage, and Network) allocation. Resource management offers the following benefits: elasticity/scalability, QoS, optimal utility, reduced overhead, improved throughput, reduced latency, cost effectiveness and simplified interface. VM migration is one of the most popular way to manage resources, and Live VM migration is the most used technique to reload or rearrange the resources in datacenter to keep the delivered services available. Statistics shows 70% of datacenters resources is not utilized. Therefore, reduction in number of servers and hardware will not only reduce its capital costs, but will also reduce its running and maintenance cost. In addition, Live VM migration will facilitate load balancing and energy management as part of resource management. VM migration consists of optimally selecting VM to be migrated, which physical server must be chosen to move this VM, when to start migration process, and duration of migration process. The goals for VM Selection techniques are to reduce power consumption, increase load balancing, and increase fault tolerance, which eventually will increase the cloud services availability and throughput/efficiency. The increased efficiency reduces the overall operation costs, pollution (green datacenters), and hardware maintenance. Migration Process itself targets moving the VM in minimum time to avoid any interruption of services. In this research proposal, in collaboration with Cistech Ltd, we consider investigating solutions to decrease the migration process time and optimally selecting VMs for live migration. Our project goal is to develop smart algorithms that will evaluate the available running resources for load balancing, server consolidation, network bandwidth, VMs shrinking or expanding and fault tolerance. Our project will drive the energy saving and the cost saving in running the IT datacenters.