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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.
    Sedaghat, Mina
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Capacity Management Approaches for Compute Clouds2013Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Cloud computing provides the illusion of a seamless, infinite resource pool with flexibleon-demand accessibility. However, behind this illusion there are thousands ofservers and peta-bytes of storage, running tens of thousands of applications accessedby millions of users. The management of such systems is non-trivial because theyface elastic demand, have heterogeneous resources, must fulfill diverse managementobjectives, and are vast in scale.Autonomic computing techniques can be used to tackle the complex problem ofresource management in cloud data centers by introducing self-managing elementsknown as autonomic managers. Each autonomic manager should be capable of managingitself while simultaneously contributing to the fulfillment of high level systemwideobjectives. A wide range of approaches and mechanisms can be used to defineand design these autonomic managers as well as to organize them and coordinate theiractions in order to achieve specific goals.This thesis investigates autonomic approaches for cloud resource management thataim to optimize the cloud infrastructure layer with respect to various high level objectives.The resource management problem is formulated as a problem of optimizationwith respect to one or more management objectives such as cost, profitability, or datacenter utilization, as well as performance concerns such as response time, quality ofservice, and rejection rates. The aim of the reported investigations is to address theproblems of cost-efficient elastic resource provisioning, unified management of cloudresources, and scalability in cloud resource management. This is achieved by introducingthree new concepts in capacity management: the Repacking, Holistic, and Peerto Peer approaches.

  • 4.
    Sedaghat, Mina
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Cluster Scheduling and Management for Large-Scale Compute Clouds2015Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Cloud computing has become a powerful enabler for many IT services and new technolo-gies. It provides access to an unprecedented amount of resources in a fine-grained andon-demand manner. To deliver such a service, cloud providers should be able to efficientlyand reliably manage their available resources. This becomes a challenge for the manage-ment system as it should handle a large number of heterogeneous resources under diverseworkloads with fluctuations. In addition, it should also satisfy multiple operational require-ments and management objectives in large scale data centers.Autonomic computing techniques can be used to tackle cloud resource managementproblems. An autonomic system comprises of a number of autonomic elements, which arecapable of automatically organizing and managing themselves rather than being managedby external controllers. Therefore, they are well suited for decentralized control, as theydo not rely on a centrally managed state. A decentralized autonomic system benefits fromparallelization of control, faster decisions and better scalability. They are also more reliableas a failure of one will not affect the operation of the others, while there is also a lower riskof having faulty behaviors on all the elements, all at once. All these features are essentialrequirements of an effective cloud resource management.This thesis investigates algorithms, models, and techniques to autonomously managejobs, services, and virtual resources in a cloud data center. We introduce a decentralizedresource management framework, that automates resource allocation optimization and ser-vice consolidation, reliably schedules jobs considering probabilistic failures, and dynam-icly scales and repacks services to achieve cost efficiency.As part of the framework, we introduce a decentralized scheduler that provides andmaintains durable allocations with low maintenance costs for data centers with dynamicworkloads. The scheduler assigns resources in response to virtual machine requests andmaintains the packing efficiency while taking into account migration costs, topologicalconstraints, and the risk of resource contention, as well as fluctuations of the backgroundload.We also introduce a scheduling algorithm that considers probabilistic failures as part ofthe planning for scheduling. The aim of the algorithm is to achieve an overall job reliabil-ity, in presence of correlated failures in a data center. To do so, we study the impacts ofstochastic and correlated failures on job reliability in a virtual data center. We specificallyfocus on correlated failures caused by power outages or failure of network components onjobs running large number of replicas of identical tasks.Additionally, we investigate the trade-offs between vertical and horizontal scaling. Theresult of the investigations is used to introduce a repacking technique to automatically man-age the capacity required by an elastic service. The repacking technique combines thebenefits of both scaling strategies to improve its cost-efficiency.

  • 5.
    Sedaghat, Mina
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hernandez, Francisco
    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 virtual machine re-packing approach to the horizontal vs. vertical elasticity trade-off for cloud autoscaling2013In: Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference, ACM Press, 2013, p. Article no. 6-Conference paper (Refereed)
    Abstract [en]

    An automated solution to horizontal vs. vertical elasticity problem is central to make cloud autoscalers truly autonomous. Today's cloud autoscalers are typically varying the capacity allocated by increasing and decreasing the number of virtual machines (VMs) of a predefined size (horizontal elasticity), not taking into account that as load varies it may be advantageous not only to vary the number but also the size of VMs (vertical elasticity). We analyze the price/performance effects achieved by different strategies for selecting VM-sizes for handling increasing load and we propose a cost-benefit based approach to determine when to (partly) replace a current set of VMs with a different set. We evaluate our repacking approach in combination with different auto-scaling strategies. Our results show a range of 7% up to 60% cost saving in total resource utilization cost of our sample applications and workloads.

  • 6.
    Sedaghat, Mina
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hernandez, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Peer to peer resource management for cloud data centersManuscript (preprint) (Other academic)
  • 7.
    Sedaghat, Mina
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hernandez, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Unifying cloud management: towards overall governance of business level objectives2011In: Cluster, Cloud and Grid Computing (CCGrid), 2011 11th IEEE/ACM International Symposium on, IEEE Computer Society , 2011, p. -597Conference paper (Refereed)
    Abstract [en]

    We address the challenge of providing unified cloud resource management towards an overall business level objective, given the multitude of managerial tasks to be performed and the complexity of any architecture to support them. Resource level management tasks include elasticity control, virtual machine and data placement, autonomous fault management, etc, which are intrinsically difficult problems since services normally have unknown lifetime and capacity demands that varies largely over time. To unify the management of these problems, (for optimization with respect to some higher level business level objective, like optimizing revenue while breaking no more than a certain percentage of service level agreements)becomes even more challenging as the resource level managerial challenges are far from independent. After providing the general problem formulation, we review recent approaches taken by the research community, including mainly general autonomic computing technology for large-scale environments and resource level management tools equipped with some business oriented or otherwise qualitative features. We propose and illustrate a policy-driven approach where a high-level management system monitors overall system and services behavior and adjusts lower level policies (e.g., thresholds for admission control, elasticity control, server consolidation level, etc) for optimization towards the measurable business level objectives.

  • 8.
    Sedaghat, Mina
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hernández-Rodriguez, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Autonomic resource allocation for cloud data centers: a peer to peer approach2014In: 2014 International Conference on Cloud and Autonomic Computing (ICCAC 2014), IEEE Computer Society, 2014, , p. 10p. 131-140Conference paper (Refereed)
    Abstract [en]

    We address the problem of resource management for large scale cloud data centers. We propose a Peer to Peer (P2P) resource management framework, comprised of a number of agents, overlayed as a scale-free network. The structural properties of the overlay, along with dividing the management responsibilities among the agents enables the management framework to be scalable in terms of both the number of physical servers and incoming Virtual Machine (VM) requests, while it is computationally feasible. While our framework is intended for use in different cloud management functionalities, e.g. admission control or fault tolerance, we focus on the problem of resource allocation in clouds. We evaluate our approach by simulating a data center with 2500 servers, striving to allocate resources to 20000 incoming VM placement requests. The simulation results indicate that by maintaining an efficient request propagation, we can achieve promising levels of performance and scalability when dealing with large number of servers and placement requests.

  • 9.
    Sedaghat, Mina
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hernández-Rodriguez, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Decentralized cloud datacenter reconsolidation through emergent and topology-aware behavior2016In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 56, p. 51-63Article in journal (Refereed)
    Abstract [en]

    Consolidation of multiple applications on a single Physical Machine (PM) within acloud data center can increase utilization, minimize energy consumption, and reduceoperational costs. However, these benefits comes at the cost of increasing the complex-ity of the scheduling problem.In this paper, we present a topology-aware resource management framework. Aspart of this framework, we introduce a Reconsolidating PlaceMent scheduler (RPM)that provides and maintains durable allocations with low maintenance costs for datacenters with dynamic workloads. We focus on workloads featuring both short-livedbatch jobs and latency-sensitive services such as interactive web applications. Thescheduler assigns resources to Virtual Machines (VMs) and maintains packing effi-ciency while taking into account migration costs, topological constraints, and the riskof resource contention, as well as the variability of the background load and its com-plementarity to the new VM.We evaluate the model by simulating a data center with over 65000 PMs, structuredas a three-level multi-rooted tree topology. We investigate trade-offs between factorsthat affect the durability and operational cost of maintaining a near-optimal packing.The results show that the proposed scheduler can scale to the number of PMs in thesimulation and maintain efficient utilization with low migration costs.

  • 10.
    Sedaghat, Mina
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hernández-Rodriguez, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Girdzijauskas, Šarūnas
    Divide the task, multiply the outcome: cooperative VM consolidation2014In: Proceedings of The 6th IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2014), IEEE, 2014, , p. 6p. 300-305Conference paper (Refereed)
    Abstract [en]

    Efficient resource utilization is one of the mainconcerns of cloud providers, as it has a direct impact onenergy costs and thus their revenue. Virtual machine (VM)consolidation is one the common techniques, used by infrastruc-ture providers to efficiently utilize their resources. However,when it comes to large-scale infrastructures, consolidationdecisions become computationally complex, since VMs aremulti-dimensional entities with changing demand and unknownlifetime, and users often overestimate their actual demand.These uncertainties urges the system to take consolidationdecisions continuously in a real time manner.In this work, we investigate a decentralized approach forVM consolidation using Peer to Peer (P2P) principles. Weinvestigate the opportunities offered by P2P systems, as scalableand robust management structures, to address VM consol-idation concerns. We present a P2P consolidation protocol,considering the dimensionality of resources and dynamicityof the environment. The protocol benefits from concurrencyand decentralization of control and it uses a dimension awaredecision function for efficient consolidation. We evaluate theprotocol through simulation of 100,000 physical machinesand 200,000 VM requests. Results demonstrate the potentialsand advantages of using a P2P structure to make resourcemanagement decisions in large scale data centers. They showthat the P2P approach is feasible and scalable and producesresource utilization of 75% when the consolidation aim is 90%.

  • 11.
    Sedaghat, Mina
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Wadbro, Eddie
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Wilkes, John
    Sjöstedt de Luna, Sara
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Seleznjev, Oleg
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    DieHard: Reliable Scheduling to Survive Correlated failures in Cloud Data Centers2016In: 2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), IEEE, 2016Conference paper (Refereed)
    Abstract [en]

    In large scale data centers, a single fault can lead to correlated failures of several physical machines and the tasks running on them, simultaneously. Such correlated failures can severely damage the reliability of a service or a job running on the failed hardware. This paper models the impact of stochastic and correlated failures on job reliability in a data center. We focus on correlated failures caused by power outages or failures of network components, on jobs running multiple replicas of identical tasks. We present a statistical reliability model and an approximation technique for computing a job’s reliability in the presence of correlated failures. In addition, we address the problem of scheduling a job with reliability constraints.We formulate the scheduling problem as an optimization problem, with the aim being to maintain the desired reliability with the minimum number of extra tasks to resist failures.We present a scheduling algorithm that approximates the minimum number of required tasks and a placement to achieve a desired job reliability. We study the efficiency of our algorithm using an analytical approach and by simulating a cluster with different failure sources and reliabilities. The results show that the algorithm can effectively approximate the minimum number of extra tasks required to achieve the job’s reliability.

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