umu.sePublications
Change search
Link to record
Permanent link

Direct link
BETA
Li, Wubin
Publications (10 of 15) Show all publications
Espling, D., Larsson, L., Li, W., Tordsson, J. & Elmroth, E. (2016). Modeling and Placement of Cloud Services with Internal Structure. IEEE Transactions on Cloud Computing, 4(4), 429-439
Open this publication in new window or tab >>Modeling and Placement of Cloud Services with Internal Structure
Show others...
2016 (English)In: IEEE Transactions on Cloud Computing, ISSN 2168-7161, Vol. 4, no 4, p. 429-439Article in journal (Refereed) Published
Abstract [en]

Virtual machine placement is the process of mapping virtual machines to available physical hosts within a datacenter or on a remote datacenter in a cloud federation. Normally, service owners cannot influence the placement of service components beyond choosing datacenter provider and deployment zone at that provider. For some services, however, this lack of influence is a hindrance to cloud adoption. For example, services that require specific geographical deployment (due e.g. to legislation), or require redundancy by avoiding co-location placement of critical components. We present an approach for service owners to influence placement of their service components by explicitly specifying service structure, component relationships, and placement constraints between components. We show how the structure and constraints can be expressed and subsequently formulated as constraints that can be used in placement of virtual machines in the cloud. We use an integer linear programming scheduling approach to illustrate the approach, show the corresponding mathematical formulation of the model, and evaluate it using a large set of simulated input. Our experimental evaluation confirms the feasibility of the model and shows how varying amounts of placement constraints and data center background load affects the possibility for a solver to find a solution satisfying all constraints within a certain time-frame. Our experiments indicate that the number of constraints affects the ability of finding a solution to a higher degree than background load, and that for a high number of hosts with low capacity, component affinity is the dominating factor affecting the possibility to find a solution.

Place, publisher, year, edition, pages
IEEE Computer Society, 2016
Keywords
service management, service structure, placement, affinity, collocation, scheduling, integer linear programming, cloud computing
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-80125 (URN)10.1109/TCC.2014.2362120 (DOI)000390560200005 ()
Funder
eSSENCE - An eScience Collaboration
Available from: 2013-09-10 Created: 2013-09-10 Last updated: 2018-06-08Bibliographically approved
Svärd, P., Li, W., Wadbro, E., Tordsson, J. & Elmroth, E. (2015). Continuous Datacenter Consolidation. In: 2015 IEEE 7TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING TECHNOLOGY AND SCIENCE (CLOUDCOM): . Paper presented at IEEE 7th International Conference on Cloud Computing Techonology and science, Vancouver, Canada, Nov 30-Dec 03, 2015. (pp. 387-396).
Open this publication in new window or tab >>Continuous Datacenter Consolidation
Show others...
2015 (English)In: 2015 IEEE 7TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING TECHNOLOGY AND SCIENCE (CLOUDCOM), 2015, p. 387-396Conference paper, Published paper (Refereed)
Abstract [en]

Efficient mapping of Virtual Machines (VMs) onto physical servers is a key problem for cloud infrastructure providers as hardware utilization directly impacts profit. Today, this mapping is commonly only performed when new VMs are created, but as VM workloads fluctuate and server availability varies, any initial mapping is bound to become suboptimal over time. We introduce a set of heuristic methods for continuous optimization of the VM-to-server mapping based on combinations of fundamental management actions, namely suspending and resuming physical machines, migrating VMs, and suspending and resuming VMs. By using these methods, cloud infrastructure providers can continuously optimize their server resources regardless of the predictability of the workload. To verify that our approach is applicable in real-world scenarios, we build a proof-of-concept datacenter management system that implements the proposed algorithms. The feasibility of our approach is evaluated through a combination of simulations and real experiments where our system provisions a workload of benchmark applications. Our results indicate that the proposed algorithms are feasible, that the combined management approach achieves the best results, and that the VM suspend and resume mechanism has the largest impact on provider profit.

Keywords
Cloud Computing, Scheduling, Heuristic Methods, Consolidation, VM Migration, Power Management
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-125614 (URN)10.1109/CloudCom.2015.11 (DOI)000380458100051 ()978-1-4673-9560-1 (ISBN)
Conference
IEEE 7th International Conference on Cloud Computing Techonology and science, Vancouver, Canada, Nov 30-Dec 03, 2015.
Available from: 2016-10-05 Created: 2016-09-13 Last updated: 2018-06-09Bibliographically approved
Li, W. (2014). Algorithms and Systems for Virtual Machine Scheduling in Cloud Infrastructures. (Doctoral dissertation). Umeå: Umeå̊ Universitet
Open this publication in new window or tab >>Algorithms and Systems for Virtual Machine Scheduling in Cloud Infrastructures
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

With the emergence of cloud computing, computing resources (i.e., networks, servers, storage, applications, etc.) are provisioned as metered on-demand services over net- works, and can be rapidly allocated and released with minimal management effort. In the cloud computing paradigm, the virtual machine (VM) is one of the most com- monly used resource units in which business services are encapsulated. VM schedul- ing optimization, i.e., finding optimal placement schemes for VMs and reconfigu- rations according to the changing conditions, becomes challenging issues for cloud infrastructure providers and their customers.

The thesis investigates the VM scheduling problem in two scenarios: (i) single- cloud environments where VMs are scheduled within a cloud aiming at improving criteria such as load balancing, carbon footprint, utilization, and revenue, and (ii) multi-cloud scenarios where a cloud user (which could be the owner of the VMs or a cloud infrastructure provider) schedules VMs across multiple cloud providers, target- ing optimization for investment cost, service availability, etc. For single-cloud scenar- ios, taking load balancing as the objective, an approach to optimal VM placement for predictable and time-constrained peak loads is presented. In addition, we also present a set of heuristic methods based on fundamental management actions (namely, sus- pend and resume physical machines, VM migration, and suspend and resume VMs), continuously optimizing the profit for the cloud infrastructure provider regardless of the predictability of the workload. For multi-cloud scenarios, we identify key re- quirements for service deployment in a range of common cloud scenarios (including private clouds, bursted clouds, federated clouds, multi-clouds, and cloud brokering), and present a general architecture to meet these requirements. Based on this architec- ture, a set of placement algorithms tuned for cost optimization under dynamic pricing schemes are evaluated. By explicitly specifying service structure, component relation- ships, and placement constraints, a mechanism is introduced to enable service owners the ability to influence placement. In addition, we also study how dynamic cloud scheduling using VM migration can be modeled using a linear integer programming approach.

The primary contribution of this thesis is the development and evaluation of al- gorithms (ranging from combinatorial optimization formulations to simple heuristic algorithms) for VM scheduling in cloud infrastructures. In addition to scientific pub- lications, this work also contributes software tools (in the OPTIMIS project funded by the European Commissions Seventh Framework Programme) that demonstrate the feasibility and characteristics of the approaches presented. 

Abstract [sv]

I datormoln tillhandahålls datorresurser (dvs., nätverk, servrar, lagring, applikationer,

etc.) som tjänster åtkomliga via Internet. Resurserna, som t.ex. virtuella maskiner (VMs), kan snabbt och enkelt allokeras och frigöras alltefter behov. De potentiellt snabba förändringarna i hur många och hur stora VMs som behövs leder till utmanade schedulerings- och konfigureringsproblem. Scheduleringsproblemen uppstår både för infrastrukturleverantörer som behöver välja vilka servrar olika VMs ska placeras på inom ett moln och deras kunder som behöver välja vilka moln VMs ska placeras på.

Avhandlingen fokuserar på VM-scheduleringsproblem i dessa två scenarier, dvs (i) enskilda moln där VMs ska scheduleras för att optimera lastbalans, energiåtgång, resursnyttjande och ekonomi och (ii) situationer där en molnanvändare ska välja ett eller flera moln för att placera VMs för att optimera t.ex. kostnad, prestanda och tillgänglighet för den applikation som nyttjar resurserna. För det förstnämnda scenar- iot presenterar avhandlingen en scheduleringsmetod som utifrån förutsägbara belast- ningsvariationer optimerar lastbalansen mellan de fysiska datorresurserna. Därtill pre- senteras en uppsättning heuristiska metoder, baserade på fundamentala resurshanter- ingsåtgärder, fö att kontinuerligt optimera den ekonomiska vinsten för en molnlever- antör, utan krav på lastvariationernas förutsägbarhet.

För fallet med flera moln identifierar vi viktiga krav för hur resurshanteringstjänster ska konstrueras för att fungera väl i en rad konceptuellt olika fler-moln-scenarier. Utifrån dessa krav definierar vi också en generell arkitektur som kan anpassas till dessa scenarier. Baserat pp vår arkitektur utvecklar och utvärderar vi en uppsättning algoritmer för VM-schedulering avsedda att minimera kostnader för användning av molninfrastruktur med dynamisk prissättning. Användaren ges genom ny funktionalitet möjlighet att explicit specificera relationer mellan de VMs som allokeras och andra bivillkor för hur de ska placeras. Vi demonstrerar också hur linjär heltals- programmering kan användas för att optimera detta scheduleringsproblem.

Avhandlingens främsta bidrag är utveckling och utvärdering av nya metoder för VM-schedulering i datormoln, med lösningar som inkluderar såväl kombinatorisk op- timering som heuristiska metoder. Utöver vetenskapliga publikationer bidrar arbetet även med programvaror för VM-schedulering, utvecklade inom ramen för projektet OPTIMIS som finansierats av EU-kommissionens sjunde ramprogram.

metoder för VM-schedulering i datormoln, med lösningar som inkluderar såväl kombinatorisk op- timering som heuristiska metoder. Utöver vetenskapliga publikationer bidrar arbetet även med programvaror för VM-schedulering, utvecklade inom ramen för projektet OPTIMIS som finansierats av EU-kommissionens sjunde ramprogram.

Place, publisher, year, edition, pages
Umeå: Umeå̊ Universitet, 2014. p. 33
Series
Report / UMINF, ISSN 0348-0542 ; 2014:06
Keywords
cloud computing, virtual machine, scheduling, systems, algorithms
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-87310 (URN)978-91-7601-019-8 (ISBN)
Public defence
2014-04-25, MIT-Huset, MA121, Umeå Universitet, Umeå, 10:00 (English)
Opponent
Supervisors
Available from: 2014-04-04 Created: 2014-03-29 Last updated: 2018-06-08Bibliographically approved
Svärd, P., Li, W., Wadbro, E., Tordsson, J. & Elmroth, E. (2014). Continuous Datacenter Consolidation. Umeå: Umeå universitet
Open this publication in new window or tab >>Continuous Datacenter Consolidation
Show others...
2014 (English)Report (Refereed)
Abstract [en]

Efficient mapping of Virtual Machines (VMs) onto physical servers is a key problem for cloud infrastructure providers as hardware utilization directly im- pacts revenue. Today, this mapping is commonly only performed when new VMs are created, but as VM workloads fluctuate and server availability varies, any ini- tial mapping is bound to become suboptimal over time. We introduce a set of heuristic methods for continuous optimization of the VM-to-server mapping based on combina- tions of fundamental management actions, namely suspending and resuming physical machines, migrating VMs, and suspending and resuming VMs. Using these methods cloud infrastructure providers can continuously optimize their server resources regard- less of the predictability of the workload. To verify that our approach is applicable in real-world scenarios, we build a proof-of-concept datacenter management system that implements the proposed algorithms. The feasibility of our approach is evaluated through a combination of simulations and real experiments where our system provi- sions a workload of benchmark applications. Our results indicate that the proposed algorithms are feasible, that the combined management approach achieves the best results, and that the VM suspend and resume mechanism has the largest impact. 

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2014. p. 12
Series
Report / UMINF, ISSN 0348-0542 ; 2014:08
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-87385 (URN)
Available from: 2014-03-31 Created: 2014-03-31 Last updated: 2018-06-08Bibliographically approved
Huang, X.-Y., Xiang, X.-H., Li, W., Chen, K., Cai, W.-X. & Li, L. (2014). Matrix Factorization for Evolution Data. Mathematical problems in engineering (Print), 525398
Open this publication in new window or tab >>Matrix Factorization for Evolution Data
Show others...
2014 (English)In: Mathematical problems in engineering (Print), ISSN 1024-123X, E-ISSN 1563-5147, p. 525398-Article in journal (Refereed) Published
Abstract [en]

We study a matrix factorization problem, that is, to find two factor matrices U and V such that R approximate to U-T x V, where R is a matrix composed of the values of the objects O-1, O-2, ... , O-n at consecutive time points T-1, T-2, ... , T-t. We first present MAFED, a constrained optimization model for this problem, which straightforwardly performs factorization on R. Then based on the interplay of the data in U,V, and R, a probabilistic graphical model using the same optimization objects is constructed, in which structural dependencies of the data in these matrices are revealed. Finally, we present a fitting algorithm to solve the proposed MAFED model, which produces the desired factorization. Empirical studies on real-world datasets demonstrate that our approach outperforms the state-of-the-art comparison algorithms.

National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:umu:diva-91163 (URN)10.1155/2014/525398 (DOI)000337429800001 ()
Available from: 2014-07-16 Created: 2014-07-15 Last updated: 2018-06-07Bibliographically approved
Li, W., Svärd, P., Tordsson, J. & Elmroth, E. (2013). Cost-Optimal Cloud Service Placement under Dynamic Pricing Schemes. In: 6th IEEE/ACM International Conference on Utility and Cloud Computing: . Paper presented at the 6th IEEE/ACM International Conference on Utility and Cloud Computing (pp. 187-194). IEEE Computer Society
Open this publication in new window or tab >>Cost-Optimal Cloud Service Placement under Dynamic Pricing Schemes
2013 (English)In: 6th IEEE/ACM International Conference on Utility and Cloud Computing, IEEE Computer Society, 2013, p. 187-194Conference paper, Published paper (Refereed)
Abstract [en]

Until now, most research on cloud service placement has focused on static pricing scenarios, where cloud providers offer fixed prices for their resources. However, with the recent trend of dynamic pricing of cloud resources, where the price of a compute resource can vary depending on the free capacity and load of the provider, new placement algorithms are needed. In this paper, we investigate service placement in dynamic pricing scenarios by evaluating a set of placement algorithms, tuned for dynamic pricing. The algorithms range from simple heuristics to combinatorial optimization solutions. The studied algorithms are evaluated by deploying a set of services across multiple providers. Finally, we analyse the strengths and weaknesses of the algorithms considered. The evaluation suggests that exhaustive search based approach is good at finding optimal solutions for service placement under dynamic pricing schemes, but the execution times are usually long. In contrast, greedy approaches perform surprisingly well with fast execution times and acceptable solutions, and thus can be a suitable compromise considering the tradeoffs between quality of solution and execution time.

Place, publisher, year, edition, pages
IEEE Computer Society, 2013
Keywords
Cloud Computing, Dynamic Pricing, Service Placement, Deployment Optimization
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-80478 (URN)
Conference
the 6th IEEE/ACM International Conference on Utility and Cloud Computing
Funder
eSSENCE - An eScience CollaborationEU, FP7, Seventh Framework Programme, 257115
Available from: 2013-09-18 Created: 2013-09-18 Last updated: 2018-06-08Bibliographically approved
Huang, X.-Y., Li, W., Chen, K., Xiang, X.-H., Pan, R., Li, L. & Cai, W.-X. (2013). Multi-Matrices Factorization with Application to Missing Sensor Data Imputation. Sensors, 13(11), 15172-15186
Open this publication in new window or tab >>Multi-Matrices Factorization with Application to Missing Sensor Data Imputation
Show others...
2013 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 13, no 11, p. 15172-15186Article in journal (Refereed) Published
Abstract [en]

We formulate a multi-matrices factorization model (MMF) for the missing sensor data estimation problem. The estimation problem is adequately transformed into a matrix completion one. With MMF, an n-by-t real matrix, R, is adopted to represent the data collected by mobile sensors from n areas at the time, T-1, T-2, . . . , T-t, where the entry, R-i,R-j, is the aggregate value of the data collected in the ith area at T-j. We propose to approximate R by seeking a family of d-by-n probabilistic spatial feature matrices, U-(1), U-(2), . . . , U-(t), and a probabilistic temporal feature matrix, V epsilon R-dxt, where R-j approximate to U-(j)(T) T-j. We also present a solution algorithm to the proposed model. We evaluate MMF with synthetic data and a real-world sensor dataset extensively. Experimental results demonstrate that our approach outperforms the state-of-the-art comparison algorithms.

Keywords
matrix factorization, sensor data, probabilistic graphical model, missing estimation
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-86631 (URN)10.3390/s131115172 (DOI)000330321100049 ()
Available from: 2014-04-24 Created: 2014-03-03 Last updated: 2018-06-07Bibliographically approved
Li, W., Svärd, P., Tordsson, J. & Elmroth, E. (2012). A General Approach to Service Deployment in Cloud Environments. In: Cloud and Green Computing (CGC 2012): 2012 Second International Conference on. Paper presented at the 2nd International Conference on Cloud and Green Computing, Xiangtan, 1-3 November 2012 (pp. 17-24). IEEE Computer Society
Open this publication in new window or tab >>A General Approach to Service Deployment in Cloud Environments
2012 (English)In: Cloud and Green Computing (CGC 2012): 2012 Second International Conference on, IEEE Computer Society, 2012, p. 17-24Conference paper, Published paper (Refereed)
Abstract [en]

The cloud computing landscape has recently developed into a spectrum of cloud architectures, leading to a broad range of management tools for similar operations but specialized for certain deployment scenarios. This both hinders the efficient reuse of algorithmic innovations within cloud management operations and increases the heterogeneity between different management systems. Our overarching goal is to overcome these problems by developing tools general enough to support the full range of popular architectures. In this contribution, we analyze commonalities in recently proposed cloud models (private clouds, multi-clouds, bursted clouds, federated clouds, etc.), and demonstrate how a key management functionality - service deployment - can be uniformly performed in all of these by a carefully designed system. The design of our service deployment framework is validated through a demonstration of how it can be used to deploy services, perform bursting and brokering, as well as mediate a cloud federation in the context of the OPTIMIS Toolkit.

Place, publisher, year, edition, pages
IEEE Computer Society, 2012
Keywords
Cloud Computing, Cloud Architecture, Service Deployment
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-79784 (URN)10.1109/CGC.2012.90 (DOI)978-0-7695-4864-7 (ISBN)978-1-4673-3027-5 Print (ISBN)
Conference
the 2nd International Conference on Cloud and Green Computing, Xiangtan, 1-3 November 2012
Available from: 2013-09-02 Created: 2013-09-02 Last updated: 2018-06-08Bibliographically approved
Li, W., Tordsson, J. & Elmroth, E. (2012). Virtual machine placement for predictable and time-constrained peak loads. In: Kurt Vanmechelen, Jörn Altmann, Omer F. Rana (Ed.), Economics of Grids, Clouds, Systems, and Services: 8th International Workshop, GECON 2011, Paphos, Cyprus, December 5, 2011, Revised Selected Papers. Paper presented at GECON 2011 : 8th International Workshop on Economics of Grids, Clouds, Systems, and Services, December 5th, 2011 , Paphos, Cyprus (pp. 120-134). Springer Berlin/Heidelberg
Open this publication in new window or tab >>Virtual machine placement for predictable and time-constrained peak loads
2012 (English)In: Economics of Grids, Clouds, Systems, and Services: 8th International Workshop, GECON 2011, Paphos, Cyprus, December 5, 2011, Revised Selected Papers / [ed] Kurt Vanmechelen, Jörn Altmann, Omer F. Rana, Springer Berlin/Heidelberg, 2012, p. 120-134Conference paper, Published paper (Refereed)
Abstract [en]

We present an approach to optimal virtual machine placement within datacenters for predicable and time-constrained load peaks. A method for optimal load balancing is developed, based on binary integer programming. For tradeoffs between quality of solution and computation time, we also introduce methods to pre-process the optimization problem before solving it. Upper bound based optimizations are used to reduce the time required to compute a final solution, enabling larger problems to be solved. For further scalability, we also present three approximation algorithms, based on heuristics and/or greedy formulations. The proposed algorithms are evaluated through simulations based on synthetic data sets. The evaluation suggests that our algorithms are feasible, and that these can be combined to achieve desired tradeoffs between quality of solution and execution time.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2012
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 7150
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-51034 (URN)10.1007/978-3-642-28675-9_9 (DOI)3642286747 (ISBN)9783642286742 (ISBN)9783642286759 E-ISBN (ISBN)
Conference
GECON 2011 : 8th International Workshop on Economics of Grids, Clouds, Systems, and Services, December 5th, 2011 , Paphos, Cyprus
Available from: 2012-01-09 Created: 2012-01-09 Last updated: 2018-06-08Bibliographically approved
Li, W. (2012). Virtual Machine Placement in Cloud Environments. (Licentiate dissertation). Umeå: Umeå Universitet
Open this publication in new window or tab >>Virtual Machine Placement in Cloud Environments
2012 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

With the emergence of cloud computing, computing resources (i.e., networks, servers, storage, applications, and services) are provisioned as metered on-demand services over networks, and can be rapidly allocated and released with minimal management effort. In the cloud computing paradigm, the virtual machine is one of the most commonly used resource carriers in which business services are encapsulated. Virtual machine placement optimization, i.e., finding optimal placement schemes for virtual machines, and reconfigurations according to the changes of environments, become challenging issues.

The primary contribution of this licentiate thesis is the development and evaluation of our combinatorial optimization approaches to virtual machine placement in cloud environments. We present modeling for dynamic cloud scheduling via migration of virtual machines in multi-cloud environments, and virtual machine placement for predictable and time-constrained peak loads in single-cloud environments. The studied problems are encoded in a mathematical modeling language and solved using a linear programming solver. In addition to scientific publications, this work also contributes in the form of software tools (in EU-funded project OPTIMIS) that demonstrate the feasibility and characteristics of the approaches presented.

Place, publisher, year, edition, pages
Umeå: Umeå Universitet, 2012. p. 18
Series
UMINF / Department of Computing Science, Umeå University, ISSN ISSN 0348-0542 ; 2012:13
National Category
Computer Systems
Identifiers
urn:nbn:se:umu:diva-83385 (URN)978-91-7459-453-9 (ISBN)
Presentation
2012-06-07, MIT-huset, MA121, Umeå universitet, Umeå, 10:05 (English)
Opponent
Supervisors
Available from: 2014-03-26 Created: 2013-11-22 Last updated: 2018-06-08Bibliographically approved
Organisations

Search in DiVA

Show all publications