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  • 1.
    Elmroth, Erik
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hernandez, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Ali-Eldin, Ahmed
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Svärd, Petter
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Sedaghat, Mina
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Li, Wubin
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Self-management Challenges for Multi-cloud Architectures (Invited Paper)2011In: TOWARDS A SERVICE-BASED INTERNET, Berlin: Springer, 2011, Vol. 6994, p. 38-49Conference paper (Refereed)
    Abstract [en]

    Addressing the management challenges for a multitude of distributed cloud architectures, we focus on the three complementary cloud management problems of predictive elasticity, admission control, and placement (or scheduling) of virtual machines. As these problems are intrinsically intertwined we also propose an approach to optimize the overall system behavior by policy-tuning for the tools handling each of them. Moreover, in order to facilitate the execution of some of the management decisions, we also propose new algorithms for live migration of virtual machines with very high workload and/or over low-bandwidth networks, using techniques such as caching, compression, and prioritization of memory pages.

  • 2.
    Elmroth, Erik
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hernández, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Ali-Eldin, Ahmed
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Svärd, Petter
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Sedaghat, Mina
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Li, Wubin
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Self-management challenges for multi-cloud architectures2011In: Towards a Service-Based Internet: 4th European Conference, ServiceWave 2011, Poznan, Poland, October 26-28, 2011. Proceedings / [ed] Witold Abramowicz, Ignacio M. Llorente, Mike Surridge, Andrea Zisman and Julien Vayssière, Springer Berlin/Heidelberg, 2011, p. 38-49Conference paper (Refereed)
    Abstract [en]

    Addressing the management challenges for a multitude of distributed cloud architectures, we focus on the three complementary cloud management problems of predictive elasticity, admission control, and placement (or scheduling) of virtual machines. As these problems are intrinsically intertwined we also propose an approach to optimize the overall system behavior by policy-tuning for the tools handling each of them. Moreover, in order to facilitate the execution of some of the management decisions, we also propose new algorithms for live migration of virtual machines with very high workload and/or over low-bandwidth networks, using techniques such as caching, compression, and prioritization of memory pages.

  • 3.
    Espling, Daniel
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Larsson, Lars
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Li, Wubin
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Modeling and Placement of Cloud Services with Internal Structure2016In: IEEE Transactions on Cloud Computing, ISSN 2168-7161, Vol. 4, no 4, p. 429-439Article in journal (Refereed)
    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.

  • 4. Huang, Xiao-Yu
    et al.
    Li, Wubin
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Chen, Kang
    Xiang, Xian-Hong
    Pan, Rong
    Li, Lei
    Cai, Wen-Xue
    Multi-Matrices Factorization with Application to Missing Sensor Data Imputation2013In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 13, no 11, p. 15172-15186Article in journal (Refereed)
    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.

  • 5. Huang, Xiao-Yu
    et al.
    Xiang, Xian-Hong
    Li, Wubin
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Chen, Kang
    Cai, Wen-Xue
    Li, Lei
    Matrix Factorization for Evolution Data2014In: Mathematical problems in engineering (Print), ISSN 1024-123X, E-ISSN 1563-5147, p. 525398-Article in journal (Refereed)
    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.

  • 6.
    Li, Wubin
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Algorithms and Systems for Virtual Machine Scheduling in Cloud Infrastructures2014Doctoral 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. 

  • 7.
    Li, Wubin
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Virtual Machine Placement in Cloud Environments2012Licentiate 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.

  • 8.
    Li, Wubin
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Svärd, Petter
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    REST-Based SOA Application in the Cloud: A Text Correction Service Case Study2010In: Services (SERVICES 2010): 2010 6th World Congress on, IEEE Computer Society, 2010, p. 84-90Conference paper (Refereed)
    Abstract [en]

    In this paper, we present a REST-based SOA system, Set It Right (SIR), where people can get feedback on and help with short texts. The rapid development of the SIR system, enabled by designing it as a set of services, and also leveraging commercially offered services, illustrates the strength of the SOA paradigm. Finally, we evaluate the Cloud Computing techniques and infrastructures used to deploy the system and how cloud technology can help shorten the time to market and lower the initial costs.

  • 9.
    Li, Wubin
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Svärd, Petter
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    A General Approach to Service Deployment in Cloud Environments2012In: Cloud and Green Computing (CGC 2012): 2012 Second International Conference on, IEEE Computer Society, 2012, p. 17-24Conference 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.

  • 10.
    Li, Wubin
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Svärd, Petter
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Cost-Optimal Cloud Service Placement under Dynamic Pricing Schemes2013In: 6th IEEE/ACM International Conference on Utility and Cloud Computing, IEEE Computer Society, 2013, p. 187-194Conference 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.

  • 11.
    Li, Wubin
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    An aspect-oriented approach to consistency-preserving caching and compression of web service response messages2010In: Web Services (ICWS 2010): 2010 IEEE International Conference on, IEEE Computer Society, 2010, p. 526-533Conference paper (Refereed)
    Abstract [en]

    Web Services communicate through XMLencoded messages and suffer from substantial overhead due to verbose encoding of transferred messages and extensive (de)serialization at the end-points. We demonstrate that response caching is an effective approach to reduce Internet latency and server load. Our Tantivy middleware layer reduces the volume of data transmitted without semantic interpretation of service requests or responses and thus improves the service response time. Tantivy achieves this reduction through the combined use of caching of recent responses and data compression techniques to decrease the data representation size. These benefits do not compromise the strict consistency semantics. Tantivy also decreases the overhead of message parsing via storage of application-level data objects rather than XMLrepresentations. Furthermore, we demonstrate how the use of aspect-oriented programming techniques provides modularity and transparency in the implementation. Experimental evaluations based on the WSTest benchmark suite demonstrate that our Tantivy system gives significant performance improvements compared to non-caching techniques.

  • 12.
    Li, Wubin
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Modeling for Dynamic Cloud Scheduling via Migration of Virtual Machines2011In: Cloud Computing Technology and Science (CloudCom), IEEE Computer Society, 2011, p. 163-171Conference paper (Refereed)
    Abstract [en]

    Cloud brokerage mechanisms are fundamental to reduce the complexity of using multiple cloud infrastructures to achieve optimal placement of virtual machines and avoid the potential vendor lock-in problems. However, current approaches are restricted to static scenarios, where changes in characteristics such as pricing schemes, virtual machine types, and service performance throughout the service life-cycle are ignored. In this paper, we investigate dynamic cloud scheduling use cases where these parameters are continuously changed, and propose a linear integer programming model for dynamic cloud scheduling. Our model can be applied in various scenarios through selections of corresponding objectives and constraints, and offers the flexibility to express different levels of migration overhead when restructuring an existing infrastructure. Finally, our approach is evaluated using commercial clouds parameters in selected simulations for the studied scenarios. Experimental results demonstrate that, with proper parametrizations, our approach is feasible.

  • 13.
    Li, Wubin
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Virtual machine placement for predictable and time-constrained peak loads2012In: 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 (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.

  • 14.
    Svärd, Petter
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Li, Wubin
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Wadbro, Eddie
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Continuous Datacenter Consolidation2014Report (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. 

  • 15.
    Svärd, Petter
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Li, Wubin
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Wadbro, Eddie
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Continuous Datacenter Consolidation2015In: 2015 IEEE 7TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING TECHNOLOGY AND SCIENCE (CLOUDCOM), 2015, p. 387-396Conference 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.

1 - 15 of 15
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