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Cost- and Performance-Aware Resource Management in Cloud Infrastructures
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science. (Distributed Systems and Communications Research Group (DISCO))ORCID iD: 0000-0002-6221-3875
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

High availability, cost effectiveness and ease of application deployment have accelerated the adoption rate of cloud computing. This fast proliferation of cloud computing promotes the rapid development of large-scale infrastructures. However, large cloud datacenters (DCs) require infrastructure, design, deployment, scalability and reliability and need better management techniques to achieve sustainable design benefits. Resources inside cloud infrastructures often operate at low utilization, rarely exceeding 20-30%, which increases the operational cost significantly, especially due to energy consumption. To reduce operational cost without affecting quality of service (QoS) requirements, cloud applications should be allocated just enough resources to minimize their completion time or to maximize utilization. 

The focus of this thesis is to enable resource-efficient and performance-aware cloud infrastructures by addressing above mentioned cost and performance related challenges. In particular, we propose algorithms, techniques, and deployment strategies for improving the dynamic allocation of virtual machines (VMs) into physical machines (PMs). 

For minimizing the operational cost, we mainly focus on optimizing energy consumption of PMs by applying dynamic VM consolidation methods. To make VM consolidation techniques more efficient, we propose to utilize multiple paths to spread traffic and deploy recent queue management schemes which can maximize network resource utilization and reduce both downtime and migration time for live migration techniques. In addition, a dynamic resource allocation scheme is presented to distribute workloads among geographically dispersed DCs considering their location based time varying costs due to e.g. carbon emission or bandwidth provision. For optimizing performance level objectives, we focus on interference among applications contending in shared resources and propose a novel VM consolidation scheme considering sensitivity of the VMs to their demanded resources. Further, to investigate the impact of uncertain parameters on cloud resource allocation and applications’ QoS such as unpredictable variations in demand, we develop an optimization model based on the theory of robust optimization. Furthermore, in order to handle the scalability issues in the context of large scale infrastructures, a robust and fast Tabu Search algorithm is designed and evaluated.

Abstract [en]

High availability, cost effectiveness and ease of application deployment have accelerated the adoption rate of cloud computing. This fast proliferation of cloud computing promotes the rapid development of large-scale infrastructures. However, large cloud datacenters (DCs) require infrastructure, design, deployment, scalability and reliability and need better management techniques to achieve sustainable design benefits. Resources inside cloud infrastructures often operate at low utilization, rarely exceeding 20-30%, which increases the operational cost significantly, especially due to energy consumption. To reduce operational cost without affecting quality of service (QoS) requirements, cloud applications should be allocated just enough resources to minimize their completion time or to maximize utilization. 

The focus of this thesis is to enable resource-efficient and performance-aware cloud infrastructures by addressing above mentioned cost and performance related challenges. In particular, we propose algorithms, techniques, and deployment strategies for improving the dynamic allocation of virtual machines (VMs) into physical machines (PMs).

Place, publisher, year, edition, pages
Karlstad: Karlstads universitet, 2017. , 252 p.
Series
Karlstad University Studies, ISSN 1403-8099 ; 2017:21
Keyword [en]
Cloud Computing, OpenStack, Robust Optimization, Latency, Tabu Search, Resource Management, Resource Contention, QoS
National Category
Communication Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-48482ISBN: 978-91-7063-783-4 (print)ISBN: 978-91-7063-784-1 (print)OAI: oai:DiVA.org:kau-48482DiVA: diva2:1092881
Public defence
2017-06-21, 21A342 (Eva Erikssonsalen), Universitetsgatan 2, 651 88 Karlstad, Karlstad, 10:30 (English)
Opponent
Supervisors
Available from: 2017-05-19 Created: 2017-05-04 Last updated: 2017-06-01Bibliographically approved
List of papers
1. Deploying OpenStack: Virtual Infrastructure or Dedicated Hardware
Open this publication in new window or tab >>Deploying OpenStack: Virtual Infrastructure or Dedicated Hardware
2014 (English)In: Computer Software and Applications Conference Workshops (COMPSACW), 2014 IEEE 38th International, IEEE Press, 2014, 84-89 p.Conference paper, Published paper (Refereed)
Abstract [en]

Cloud computing is a computing model through which resources such as - infrastructure, applications or software are offered as services to the users. Cloud computing offers the opportunity of virtualization through deploying multiple virtual machines (VMs) on single physical machine, which increases resource utilization and reduces power consumption. The main benefit of a virtualized technology relies on its on-demand resource allocation strategy and flexible management. OpenStack is one of the promising open source solutions which offers infrastructure as a service. This paper covers how underlying infrastructure for deployment affects the performance of OpenStack. The aim is to provide a comparative view on the performance of OpenStack while deploying it over a virtual environment versus using dedicated hardware. We conduct three basic tests on both environments to check CPU performance, data transfer rate, and bandwidth. The results show that OpenStack over dedicated hardware performs much better than OpenStack over virtualized environment.

Place, publisher, year, edition, pages
IEEE Press, 2014
Keyword
OpenStack; Virtualization;Cloud Computing
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-35386 (URN)10.1109/COMPSACW.2014.18 (DOI)000352787700015 ()978-1-4799-3578-9 (ISBN)
Conference
The 38th Annual International Computers, Software and Applications Conference Workshops (COMPSAC 2014), July 21-25, 2014, Västerås, Sweden
Available from: 2015-03-13 Created: 2015-03-13 Last updated: 2017-05-10Bibliographically approved
2. OpenStackEmu - A Cloud Testbed Combining Network Emulation with OpenStack and SDN
Open this publication in new window or tab >>OpenStackEmu - A Cloud Testbed Combining Network Emulation with OpenStack and SDN
2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

OpenStack has been widely acknowledged to be one of the most important open source cloud platforms. In order to perform experimentally driven research in the area of cloud and cloud networking, there is however a big gap, because most researchers do not have access to a large cloud deployment and cannot change networking or compute infrastructure in order to test their algorithms and protocols on a large-scale. We developed OpenStackEmu, which is to the best of our knowledge the first attempt that combines OpenStack infrastructure with a Software Defined Networking (SDN) based controller such as OpenDaylight and a large-scale network emulator CORE (Common Open Research Emulator). The OpenStack compute and control nodes are connected to the CORE emulation server using TUN/TAP interfaces that inject the control (e.g. for VM migration) and data (VM-to-VM traffic) packets into a customizable network topology that is emulated using configurable Open vSwitches using CORE emulator. Experimenters can define e.g. fat-tree or distributed data center topologies and study the behavior of real VMs and services in those VMs under different background loads and SDN routing policies. We integrated the data center traffic generator DCT2Gen that allows to generate realistic background traffic based on traces from real data centers. Experimenters can study the performance impact of different VM migration strategies or different routing and load balancing schemes on real VM and application performance using different emulated topologies. We believe that OpenStackEmu is an important tool for both the SDN and OpenStack community in order to evaluate the performance of novel algorithms and protocols in the area of cloud networking.

National Category
Communication Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-48478 (URN)
Conference
The 14th Annual IEEE Consumer Communications & Networking Conference (CCNC)
Projects
HITS
Available from: 2017-05-04 Created: 2017-05-04 Last updated: 2017-08-08
3. Mobile Publish/Subscribe System for Intelligent Transport Systems over a Cloud Environment
Open this publication in new window or tab >>Mobile Publish/Subscribe System for Intelligent Transport Systems over a Cloud Environment
2014 (English)In: 2014 International Conference on Cloud and Autonomic Computing (ICCAC 2014): Proceedings of a meeting held 8-12 September 2014, London, United Kingdom, IEEE Press, 2014, 187-195 p.Conference paper, Published paper (Refereed)
Abstract [en]

With the advent of Smart Cities, public transport authorities are more and more interested in Intelligent Transport System (ITS) applications that allow to process a large amount of static and real time data in order to make public transport smarter. However, deploying such applications in a large scale distributed environment is challenging and requires an automated, scalable, flexible, elastic, loosely-coupled communication models in order to dynamically link information providers and consumers. To this end, Publish/Subscribe (Pub/Sub) systems offer an asynchronous, dynamic, decoupled interaction scheme that is perfectly suitable for developing up-to-date, large-scale distributed applications within the ITS domain. In addition, cloud computing offers computational resources as services to utility driven model regardless of considering geographical locations in a scalable, elastic, fault tolerant and cost-effective way. In this work, we build an ITS application “Real-time Public Transit Tracking” on top of a Mobile Pub/Sub System (MoPS), and deploy it over an open source cloud platform, OpenStack, in order to achieve high performance and flexible management. We conduct a set of experiments to evaluate the performance of the implemented ITS application in terms of scalability, resource usage, and efficiency of the underlying matching algorithm under automated mobility of the subscribers. Our experimental results show that the ITS application can handle a large number of subscribers and publishers with proper reliability and negligible notification delay under real-time constraints. Further, we present a measurement study to characterize the impact of different workloads on the performance of OpenStack.

Place, publisher, year, edition, pages
IEEE Press, 2014
Keyword
Publish/Subscribe System; Mobility; Cloud Computing; OpenStack; Intelligent Transport System (ITS)
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kau:diva-35387 (URN)10.1109/ICCAC.2014.15 (DOI)000370731000024 ()9781479958429 (ISBN)
Conference
The IEEE International Conference on Cloud and Autonomic Computing 8-12 sept London UK (CAC 2014)
Available from: 2015-03-13 Created: 2015-03-13 Last updated: 2017-05-04Bibliographically approved
4. Network-centric Performance Improvement for Live VM Migration
Open this publication in new window or tab >>Network-centric Performance Improvement for Live VM Migration
2015 (English)In: 2015 IEEE 8th International Conference on Cloud Computing, IEEE conference proceedings, 2015, 106-113 p.Conference paper, Published paper (Refereed)
Abstract [en]

Live Virtual Machine (VM) migrations are an important tool that is used in modern datacenters in order to e.g. consolidate server racks for maintenance or optimize VM placements across physical hosts. However, live VM migration causes a lot of network stress due to the potential large volume of data that is transmitted between the physical hosts, which may negatively impact other latency sensitive VM to VM traffic. As VM downtime and the time to migrate depend on the allocated resources for migration traffic, it is important to manage the network resources for live VM migration traffic. In this work, we improve the performance for both live VM migration traffic and VM to VM communication using three strategies. First, we take advantage out of the path diversity available in modern datacenters and utilize multipath TCP (MPTCP) for live VM traffic. Second, we implement flexible use of queue management strategies such as FQ_CODEL or Hierarchy Token Bucket (HTB). Finally, we orchestrate the process into OpenStack Neutron and connect it together with an SDN control application, which runs on OpenDaylight. An extensive evaluation in our OpenStack testbed using different VM workload patterns and VM sizes shows, that FQ_CODEL can bring down VM to VM latency during ongoing migrations while MPTCP effectively aggregates bandwidth of multiple paths to reduce live VM migration latency and downtime.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2015
National Category
Computer Science
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-38741 (URN)10.1109/CLOUD.2015.24 (DOI)000380473600014 ()978-1-4673-7286-2 (ISBN)
Conference
IEEE Cloud 2015 IEEE 8th International Conference on Cloud Computing, June 27th – July 3rd 2015, New York, USA
Funder
Knowledge Foundation, READY
Available from: 2015-11-27 Created: 2015-11-27 Last updated: 2017-05-04Bibliographically approved
5. Cost-Efficient Resource Scheduling under QoS Constraints for Geo-Distributed Data Centers
Open this publication in new window or tab >>Cost-Efficient Resource Scheduling under QoS Constraints for Geo-Distributed Data Centers
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Geo-distributed Data Centers (DCs) are more and more common in order to provide scalability for the ever increasing compute demands of modern applications. When multiple distributed DCs can serve user requests, it is important to determine, which DC and server to select to fulfil the compute request, given that enough resources are available in terms of CPU and bandwidth. The problem is complicated as every DC has different operational costs associated, such as energy costs, carbon emission cost and bandwidth costs. In this paper, we develop a novel mathematical optimization model that guides the decision maker which DC to select, which server to use to host the compute demands and which DC gateway and network path to use to route the network traffic in order to satisfy the compute, bandwidth and latency demands. Our model includes the queuing delay depending on the traffic load in the model. Our extensive numerical evaluation based on real-world DC locations, demand patterns and resource provision costs shows how operational cost increases with traffic load, and we analyse the impact of different latency bounds on the performance of different virtual networks.

National Category
Communication Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-48481 (URN)
Projects
READY
Funder
Knowledge Foundation
Available from: 2017-05-04 Created: 2017-05-04 Last updated: 2017-05-10
6. Optimizing Virtual Machine Consolidation in Virtualized Datacenters Using Resource Sensitivity
Open this publication in new window or tab >>Optimizing Virtual Machine Consolidation in Virtualized Datacenters Using Resource Sensitivity
2016 (English)In: Cloud Computing Technology and Science (CloudCom), 2016 IEEE International Conference on, IEEE conference proceedings, 2016, 168-175 p.Conference paper, Published paper (Refereed)
Abstract [en]

In virtualized datacenters (vDCs), dynamic consolidation of virtual machines (VMs) is used to achieve both energy-efficiency and load balancing among different physical machines (PMs). Using VM live migrations, we can consolidate VMs on a smaller number of hosts to power down unused PMs and save energy. Most migration schemes are however oblivious to the characteristics of services that run inside VMs, and thus may lead to migrations where VMs competing for the same resource type are packed on the same PM. As a result, VMs may suffer from significant resource contention and noticeable degradation in their performance. Using resource sensitivity values of VMs (ie, quantitative measures to reflect how much a VM is sensitive to its requested resources such as CPU, Mem, and Disk), we have designed a novel VM consolidation approach to optimize placement of VMs on available PMs. We validated our approach using five well-known applications/benchmarks with various resource demand signatures: varying from pure CPU/Mem/Disk-intensive to mixtures of them. Our extensive numerical evaluation illustrates that, for the same power consumption, our approach improve the performance of cloud services by 9 - 12\%, on average, when compared with current sensitivity oblivious approaches.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016
Keyword
Virtualized Datacenters; VM live migration; Optimization; Resource Contention; VM co-location
National Category
Communication Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-45863 (URN)10.1109/CloudCom.2016.36 (DOI)000398536300023 ()978-1-5090-1445-3 (ISBN)
Conference
8th IEEE International Conference on Cloud Computing Technology and Science (cloudCom2016), Luxembourg, 12-15 Dec. 2016
Available from: 2016-09-13 Created: 2016-09-13 Last updated: 2017-08-08Bibliographically approved
7. Robust Optimization for Energy-Efficient Virtual Machine Consolidation in Modern Datacenters
Open this publication in new window or tab >>Robust Optimization for Energy-Efficient Virtual Machine Consolidation in Modern Datacenters
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Energy efficient Virtual Machine (VM) consolidation in modern data centers is typically optimized using methods such as Mixed Integer Programming, which typically require precise input to the model. Unfortunately, many parameters are uncertain or very difficult to predict precisely in the real world. As a consequence, a once calculated solution may be highly infeasible in practice. In this paper, we use methods from robust optimization theory in order to quantify the impact of uncertainty in modern data centers. We study the impact of different parameter uncertainties on the energy efficiency and overbooking ratios such as e.g. VM resource demands, migration related overhead or the power consumption model of the servers used. We also show that setting aside additional resource to cope with uncertainty of workload influences the overbooking ratio of the servers and the energy consumption. We show that, by using our model, Cloud operators can calculate a more robust migration schedule leading to higher total energy consumption. A more risky operator may well choose a more opportunistic schedule leading to lower energy consumption but also higher risk of SLA violation.

National Category
Communication Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-48480 (URN)
Projects
READY
Funder
Knowledge Foundation
Note

The paper is under submission to a journal

Available from: 2017-05-04 Created: 2017-05-04 Last updated: 2017-05-10
8. A Robust Tabu Search Heuristic for VM Consolidation under Demand Uncertainity in Virtualized Datacenters
Open this publication in new window or tab >>A Robust Tabu Search Heuristic for VM Consolidation under Demand Uncertainity in Virtualized Datacenters
2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In virtualized datacenters (vDCs), dynamic consolidation of virtual machines (VMs) is used as one of the most common techniques to achieve both energy- and resource- utilization efficiency. Live migrations of VMs are used for dynamic consolidation but due to dynamic resource demand variation of VMs may lead to frequent and non-optimal migrations. Assuming deterministic workload of the VMs may ensure the most energy/resource efficient VM allocations but eventually may lead to significant resource contention or under-utilization if the workload varies significantly over time. On the other hand, adopting a conservative approach by allocating VMs depending on their peak demand may lead to low utilization, if the peak occurs infrequently or for a short period of time. Therefore, in this work we design a robust VM migration scheme that strikes a balance between protection for resource contention and additional energy costs due to powering on more servers while considering uncertainties on VMs resource demands. We use the theory of Gamma-robustness and derive a robust Mixed Integer Linear programming (MILP) formulation. Due to the complexity, the problem is hard to solve for online optimization and we propose a novel heuristic based on Tabu search. Using several scenarios, we show that that the proposed heuristic can achieve near optimal solution qualities in a short time and scales well with the instance sizes. Moreover, we quantitatively analyze the trade-off between energy cost versus protection level and robustness.

Place, publisher, year, edition, pages
IEEE, 2017
National Category
Communication Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-48479 (URN)10.1109/CCGRID.2017.35 (DOI)978-1-5090-6610-0 (ISBN)
Conference
17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2017)
Projects
READY
Funder
Knowledge Foundation
Note

The paper is publihsed online now: http://dl.acm.org/citation.cfm?id=3101134

Available from: 2017-05-04 Created: 2017-05-04 Last updated: 2017-08-07

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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  • Other style
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  • de-DE
  • en-GB
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