33 research outputs found

    A Tool for Managing the X1.V1 Platform on the Cloud

    Full text link

    Efficiently Conducting Quality-of-Service Analyses by Templating Architectural Knowledge

    Get PDF
    Previously, software architects were unable to effectively and efficiently apply reusable knowledge (e.g., architectural styles and patterns) to architectural analyses. This work tackles this problem with a novel method to create and apply templates for reusable knowledge. These templates capture reusable knowledge formally and can efficiently be integrated in architectural analyses

    A survey on elasticity management in PaaS systems

    Full text link
    [EN] Elasticity is a goal of cloud computing. An elastic system should manage in an autonomic way its resources, being adaptive to dynamic workloads, allocating additional resources when workload is increased and deallocating resources when workload decreases. PaaS providers should manage resources of customer applications with the aim of converting those applications into elastic services. This survey identifies the requirements that such management imposes on a PaaS provider: autonomy, scalability, adaptivity, SLA awareness, composability and upgradeability. This document delves into the variety of mechanisms that have been proposed to deal with all those requirements. Although there are multiple approaches to address those concerns, providers main goal is maximisation of profits. This compels providers to look for balancing two opposed goals: maximising quality of service and minimising costs. Because of this, there are still several aspects that deserve additional research for finding optimal adaptability strategies. Those open issues are also discussed.This work has been partially supported by EU FEDER and Spanish MINECO under research Grant TIN2012-37719-C03-01.Muñoz-Escoí, FD.; Bernabeu Aubán, JM. (2017). A survey on elasticity management in PaaS systems. Computing. 99(7):617-656. https://doi.org/10.1007/s00607-016-0507-8S617656997Ajmani S (2004) Automatic software upgrades for distributed systems. PhD thesis, Department of Electrical and Computer Science, Massachusetts Institute of Technology, USAAjmani S, Liskov B, Shrira L (2006) Modular software upgrades for distributed systems. In: 20th European Conference on Object-Oriented Programming (ECOOP), Nantes, France, pp 452–476Alhamad M, Dillon TS, Chang E (2010) Conceptual SLA framework for cloud computing. In: 4th International Conference on Digital Ecosystems and Technologies (DEST), Dubai, pp 606–610Almeida S, Leitão J, Rodrigues LET (2013) ChainReaction: a causal+ consistent datastore based on chain replication. In: 8th EuroSys Conference, Prague, Czech Republic, pp 85–98Araujo J, Matos R, Maciel PRM, Matias R (2011) Software aging issues on the Eucalyptus cloud computing infrastructure. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), Anchorage, Alaska, USA, pp 1411–1416Arief LB, Speirs NA (2000) A UML tool for an automatic generation of simulation programs. In: Worshop on Software and Performance (WOSP), Ottawa, Canada, pp 71–76Armbrust M, Fox A, Griffith R, Joseph AD, Katz RH, Konwinski A, Lee G, Patterson DA, Rabkin A, Stoica I, Zaharia M (2010) A view of cloud computing. Commun ACM 53(4):50–58Bailis P, Ghodsi A (2013) Eventual consistency today: limitations, extensions, and beyond. Commun ACM 56(5):55–63Bailis P, Ghodsi A, Hellerstein JM, Stoica I (2013) Bolt-on causal consistency. In: Intnl Conf Mgmnt Data (SIGMOD). NY, USA, New York, pp 761–772Balsamo S, Marco AD, Inverardi P, Simeoni M (2004) Model-based performance prediction in software development: a survey. IEEE Trans Softw Eng 30(5):295–310Barham P, Dragovic B, Fraser K, Hand S, Harris TL, Ho A, Neugebauer R, Pratt I, Warfield A (2003) Xen and the art of virtualization. In: 19th ACM Symposium on Operating Systems Principles (SOSP), Bolton Landing, NY, USA, pp 164–177Bennani MN, Menascé DA (2005) Resource allocation for autonomic data centers using analytic performance models. In: 2nd Intnl Conf Auton Comput (ICAC), Seattle, WA, USA, pp 229–240Birman KP (1996) Building Secure and Reliable Network Applications. Manning Publications Co., ISBN 1-884777-29-5Bloom T (1983) Dynamic module replacement in a distributed programming system. PhD thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, USABloom T, Day M (1993) Reconfiguration and module replacement in Argus: theory and practice. Softw Eng J 8(2):102–108Caballer M, Segrelles Quilis JD, Moltó G, Blanquer I (2015) A platform to deploy customized scientific virtual infrastructures on the cloud. Concurr Comput Pract E 27(16):4318–4329Calatrava A, Romero E, Moltó G, Caballer M, Alonso JM (2016) Self-managed cost-efficient virtual elastic clusters on hybrid cloud infrastructures. Future Gener Comp Syst 61:13–25Calcavecchia NM, Caprarescu BA, Nitto ED, Dubois DJ, Petcu D (2012) DEPAS: a decentralized probabilistic algorithm for auto-scaling. Computing 94(8–10):701–730Casalicchio E, Silvestri L (2013) Mechanisms for SLA provisioning in cloud-based service providers. Comput Netw 57(3):795–810Casalicchio E, Menascé DA, Aldhalaan A (2013) Autonomic resource provisioning in cloud systems with availability goals. In: ACM Cloud Autonomic Computing Conference (CAC), FL, USA, Miami, pp 1–10Chang F, Dean J, Ghemawat S, Hsieh WC, Wallach DA, Burrows M, Chandra T, Fikes A, Gruber RE (2008) Bigtable: a distributed storage system for structured data. ACM Trans Comput Syst 26(2):4Copil G, Trihinas D, Truong HL, Moldovan D, Pallis G, Dustdar S, Dikaiakos MD (2014) ADVISE—A framework for evaluating cloud service elasticity behavior. In: 12th International Conference on Service-Oriented Computing (ICSOC), France, Paris, pp 275–290Cotroneo D, Natella R, Pietrantuono R, Russo S (2014) A survey of software aging and rejuvenation studies. ACM J Emerg Technol 10(1):8:1–8:34Coutinho EF, de Carvalho Sousa FR, Rego PAL, Gomes DG, de Souza JN (2015) Elasticity in cloud computing: a survey. Ann Telecommun 70(15):289–309Dawoud W, Takouna I, Meinel C (2011) Elastic VM for cloud resources provisioning optimization. In: 1st International Conference on Advances in Computing and Communications (ACC), Kochi, India, pp 431–445de Juan-Marín R, Decker H, Armendáriz-Íñigo JE, Bernabéu-Aubán JM, Muñoz-EscoíFD (2015) Scalability approaches for causal multicast: a survey. Computing (in press)de Miguel M, Lambolais T, Hannouz M, Betgé-Brezetz S, Piekarec S (2000) UML extensions for the specification and evaluation of latency constraints in architectural models. In: Workshop on Software and Performance (WOSP), Ottawa, Canada, pp 83–88Demers AJ, Greene DH, Hauser C, Irish W, Larson J, Shenker S, Sturgis HE, Swinehart DC, Terry DB (1987) Epidemic algorithms for replicated database maintenance. In: 6th ACM Symposium on Principles of Distributed Computing (PODC), Vancouver, Canada, pp 1–12Dustdar S, Guo Y, Satzger B, Truong HL (2011) Principles of elastic processes. IEEE Internet Comput 15(5):66–71Emeakaroha VC, Brandic I, Maurer M, Dustdar S (2013) Cloud resource provisioning and SLA enforcement via LoM2HiS framework. Concurr Comput Pract E 25(10):1462–1481Felter W, Ferreira A, Rajamony R, Rubio J (2015) An updated performance comparison of virtual machines and Linux containers. In: IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), Philadelphia, PA, USA, pp 171–172Fox A, Brewer EA (1999) Harvest, yield and scalable tolerant systems. In: 7th Workshop on Hot Topics in Operating Systems (HotOS), Rio Rico, Arizona, USA, pp 174–178Galante G, De Bona LCE (2012) A survey on cloud computing elasticity. In: 5th International Conference on Utility and Cloud Computing (UCC), Chicago, IL, USA, pp 263–270Galante G, De Bona LCE, Mury AR, Schulze B, Righi RR (2016) An analysis of public clouds elasticity in the execution of scientific applications: a survey. J Grid Comput 14(2):193–216Gambi A, Hummer W, Truong HL, Dustdar S (2013) Testing elastic computing systems. IEEE Internet Comput 17(6):76–82Garg S, van Moorsel APA, Vaidyanathan K, Trivedi KS (1998) A methodology for detection and estimation of software aging. In: 9th International Symposium on Software Reliability Engineering (ISSRE), Paderborn, Germany, pp 283–292Gey F, Landuyt DV, Joosen W (2015) Middleware for customizable multi-staged dynamic upgrades of multi-tenant SaaS applications. In: 8th IEEE/ACM International Conference on Utility and Cloud Computing (UCC), Limassol, Cyprus, pp 102–111Gilbert S, Lynch NA (2002) Brewer’s conjecture and the feasibility of consistent, available, partition-tolerant web services. SIGACT News 33(2):51–59Gong Z, Gu X, Wilkes J (2010) PRESS: PRedictive Elastic reSource Scaling for cloud systems. In: 6th International Conference on Network and Service Management (CNSM), Niagara Falls, Canada, pp 9–16Grozev N, Buyya R (2014) Inter-cloud architectures and application brokering: taxonomy and survey. Softw Pract Exp 44(3):369–390Hammer M (2009) How to touch a running system. reconfiguration of stateful components. PhD thesis, Facultät für Mathematik, Informatik und Statistik, Ludwig-Maximilians-Universität München, Munich, GermanyHasan MZ, Magana E, Clemm A, Tucker L, Gudreddi SLD (2012) Integrated and autonomic cloud resource scaling. In: IEEE Network Operations and Management Symposium (NOMS), Maui, HI, USA, pp 1327–1334Herbst NR, Kounev S, Reussner R (2013) Elasticity in cloud computing: What it is, and what it is not. In: 10th International Conference on Autonomic Computing (ICAC), San Jose, CA, USA, pp 23–27Hermanns H, Herzog U, Katoen J (2002) Process algebra for performance evaluation. Theor Comput Sci 274(1–2):43–87Horn P (2001) Autonomic computing: IBM’s perspective on the state of information technology. Tech. rep. IBM PressHuebscher MC, McCann JA (2008) A survey of autonomic computing—degrees, models, and applications. ACM Comput Surv 40(3):7Hwang J, Zeng S, Wu F, Wood T (2013) A component-based performance comparison of four hypervisors. In: International Symposium on Integrated Network Management (IM), Ghent, Belgium, pp 269–276IBM (2006) An architectural blueprint for autonomic computing. White paper, 4th edIosup A, Ostermann S, Yigitbasi N, Prodan R, Fahringer T, Epema DHJ (2011) Performance analysis of cloud computing services for many-tasks scientific computing. IEEE Trans Parallel Distrib Syst 22(6):931–945Ivanovic D, Carro M, Hermenegildo MV (2013) A sharing-based approach to supporting adaptation in service compositions. Computing 95(6):453–492Jiang Y, Perng C, Li T, Chang RN (2011) ASAP: A self-adaptive prediction system for instant cloud resource demand provisioning. In: 11th International Conference on Data Mining (ICDM), Vancouver, Canada, pp 1104–1109Johnson PR, Thomas RH (1975) The maintenance of duplicate databases. RFC 677, Network Working Group, Internet Engineering Task ForceKephart JO, Chess DM (2003) The vision of autonomic computing. IEEE Comput 36(1):41–50Kiviti A, Laor D, Costa G, Enberg P, Har’El N, Marti D, Zolotarov V (2014) OSv—Optimizing the operating system for virtual machines. In: USENIX Annual Technical Conference (ATC), Philadelphia, PA, USA, pp 61–72Knauth T, Fetzer C (2011) Scaling non-elastic applications using virtual machines. In: IEEE International Conference on Cloud Computing (CLOUD), Washington, DC, USA, pp 468–475Knauth T, Fetzer C (2014) DreamServer: truly on-demand cloud services. In: International Conference on Systems and Storage (SYSTOR), Haifa, Israel, pp 1–11Kramer J, Magee J (1990) The evolving philosophers problem: dynamic change management. IEEE Trans Softw Eng 16(11):1293–1306Lakshman A, Malik P (2010) Cassandra: a decentralized structured storage system. Oper Syst Rev 44(2):35–40Lang W, Shankar S, Patel JM, Kalhan A (2014) Towards multi-tenant performance SLOs. IEEE Trans Knowl Data Eng 26(6):1447–1463Langner F, Andrzejak A (2013) Detecting software aging in a cloud computing framework by comparing development versions. In: IFIP/IEEE International Symposium on Integrated Network Management (IM), Ghent, Belgium, pp 896–899Lazowska ED, Zahorjan J, Graham GS, Sevcik KC (1984) Quantitative system performance. Computer system analysis using queueing network models. Prentice Hall, Upper Saddle RiverLeitner P, Michlmayr A, Rosenberg F, Dustdar S (2010) Monitoring, prediction and prevention of SLA violations in composite services. In: IEEE International Conference on Web Services (ICWS), Florida, USA, Miami, pp 369–376Li W (2011) Evaluating the impacts of dynamic reconfiguration on the QoS of running systems. J Syst Softw 84(12):2123–2138Lim HC, Babu S, Chase JS, Parekh SS (2009) Automated control in cloud computing: challenges and opportunities. In: 1st ACM Workshop Automated Control Datacenters Clouds (ACDC), Barcelona, Spain, pp 13–18Liu J, Zhou J, Buyya R (2015) Software rejuvenation based fault tolerance scheme for cloud applications. In: 8th IEEE International Conference on Cloud Computing (CLOUD), New York City, NY, USA, pp 1115–1118Lorido-Botran T, Miguel-Alonso J, Lozano JA (2014) A review of auto-scaling techniques for elastic applications in cloud environments. J Grid Comput 12(4):559–592Massie M, Li B, Nicholes B, Vuksan V, Alexander R, Buchbinder J, Costa F, Dean A, Josephsen D, Phaal P, Pocock D (2012) Monitoring with Ganglia. O’Reilly Media, Tracking Dynamic Host and Application Metrics at Scale. ISBN 978-1-4493-2970-9Matias R Jr, Andrzejak A, Machida F, Elias D, Trivedi KS (2014) A systematic differential analysis for fast and robust detection of software aging. In: 33rd IEEE Symposium on Reliable Distributed Systems (SRDS). Nara, Japan, pp 311–320Medina V, García JM (2014) A survey of migration mechanisms of virtual machines. ACM Comput Surv 46(3):30Mell P, Grance T (2011) The NIST definition of cloud computing. Recommendations of the National Institute of Standards and Technology, Special Publication 800-145Menascé DA, Bennani MN (2006) Autonomic virtualized environments. In: International Conference on Autonomic and Autonomous Systems (ICAS), Silicon Valley, California, USA, p 28Menascé DA, Ngo P (2009) Understanding cloud computing: Experimentation and capacity planning. In: 35th International Computer Measurement Group Conference, Dallas, TX, USAMenascé DA, Ruan H, Gomaa H (2007) QoS management in service-oriented architectures. Perform Eval 64(7–8):646–663Miedes E, Muñoz-Escoí FD (2010) Dynamic switching of total-order broadcast protocols. In: International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA), Las Vegas, Nevada, USA, pp 457–463Mohamed M (2014) Generic monitoring and reconfiguration for service-based applications in the cloud. PhD thesis, Université d’Evry-Val d’Essonne, FranceMohamed M, Amziani M, Belaïd D, Tata S, Melliti T (2015) An autonomic approach to manage elasticity of business processes in the cloud. Future Gener Comp Sys 50(C):49–61Mohd Yusoh ZI (2013) Composite SaaS resource management in cloud computing using evolutionary computation. PhD thesis, Sc Eng Faculty, Queensland University of Technology, Brisbane, AustraliaMontero RS, Moreno-Vozmediano R, Llorente IM (2011) An elasticity model for high throughput computing clusters. J Parallel Distrib Comput 71(6):750–757Morabito R, Kjällman J, Komu M (2015) Hypervisors vs. lightweight virtualization: a performance comparison. In: IEEE International Conference on Cloud Engineering (IC2E), Tempe, AZ, USA, pp 386–393Najjar A, Serpaggi X, Gravier C, Boissier O (2014) Survey of elasticity management solutions in cloud computing. In: Mahmood Z (ed) Continued rise of the cloud: advances and trends in cloud computing. Springer, Berlin, pp 235–263Naskos A, Gounaris A, Sioutas S (2015) Cloud elasticity: a survey. In: 1st International Workshop on Algorithmic Aspects of Cloud Computing (ALGOCLOUD), Patras, Greece, pp 151–167Neamtiu I, Dumitras T (2011) Cloud software upgrades: challenges and opportunities. In: IEEE International Workshop on the Maintenance and Evolution of Service-Oriented and Cloud-Based Systems (MESOCA), Williamsburg, VA, USA, pp 1–10Neuman BC (1994) Scale in distributed systems. In: Singhal M, Casavant TL (eds) Readings in Distributed computing systems. IEEE-CS Press, Los Alamitos, pp 463–489Padala P, Shin KG, Zhu X, Uysal M, Wang Z, Singhal S, Merchant A, Salem K (2007) Adaptive control of virtualized resources in utility computing environments. In: EuroSys Conference Lisbon, Portugal, pp 289–302Parnas DL (1994) Software aging. In: 6th International Conference on Software Engineering (ICSE), Sorrento, Italy, pp 279–287Parzen E (1960) A survey on time series analysis. Tech. rep., n. 37, Applied Mathematics and Statistics Laboratory, Stanford University, Stanford, CA, USAPascual-Miret L, González de Mendívil JR, Bernabéu-Aubán JM, Muñoz-Escoí FD (2015) Widening CAP consistency. Tech. rep., IUMTI-SIDI-2015/003, Univ. Politècnica de València, Valencia, SpainPopek GJ, Goldberg RP (1974) Formal requirements for virtualizable third generation architectures. Commun ACM 17(7):412–421Potter S, Nieh J (2005) AutoPod: Unscheduled system updates with zero data loss. In: 2nd International Conference on Autonomic Computing (ICAC), Seattle, WA, USA, pp 367–368Rajagopalan S (2014) System support for elasticity and high availability. PhD thesis, The University of British Columbia, Vancouver, CanadaReinecke P, Wolter K, van Moorsel APA (2010) Evaluating the adaptivity of computing systems. Perform Eval 67(8):676–693Rolia JA, Sevcik KC (1995) The method of layers. IEEE Trans Softw Eng 21(8):689–700Roy N, Dubey A, Gokhale AS (2011) Efficient autoscaling in the cloud using predictive models for workload forecasting. In: 4th IEEE International Conference on Cloud Computing (CLOUD), Washington, DC, USA, pp 500–507Ruiz-Fuertes MI, Muñoz-Escoí FD (2009) Performance evaluation of a metaprotocol for database replication adaptability. In: 28th IEEE Symposium on Reliable Distributed Systems (SRDS), Niagara Falls, New York, USA, pp 32–38Saito Y, Shapiro M (2005) Optimistic replication. ACM Comput Surv 37(1):42–81Seifzadeh H, Abolhassani H, Moshkenani MS (2013) A survey of dynamic software updating. J Softw Evol Process 25(5):535–568Sharma U, Shenoy PJ, Sahu S, Shaikh A (2011) A cost-aware elasticity provisioning system for the cloud. In: International Conference on Distributed Computing Systems (ICDCS), Minneapolis, Minnesota, USA, pp 559–570Shen M, Kshemkalyani AD, Hsu TY (2015) Causal consistency for geo-replicated cloud storage under partial replication. In: International Parallel and Distributed Processing Symposium (IPDPS) Workshop, Hyderabad, India, pp 509–518Shen Z, Subbiah S, Gu X, Wilkes J (2011) CloudScale: elastic resource scaling for multi-tenant cloud systems. In: ACM Symposium on Cloud Computing (SOCC), Cascais, Portugal, p 5Simoes R, Kamienski CA (2014) Elasticity management in private and hybrid clouds. In: 7th IEEE International Conference on Cloud Computing (CLOUD), Anchorage, AK, USA, pp 793–800Singh S, Chana I (2015) QoS-aware autonomic resource management in cloud computing: a systematic review. ACM Comput Surv 48(3):42:1–42:46Smith CU (1980) The prediction and evaluation of the performance of software from extended design specifications. PhD thesis, Department of Computer Science, The University of Texas at Austin, USASmith CU, Williams LG (2003) Software performance engineering. In: Lavagno L, Martin G, Selic B (eds) UML for real. Design of embedded real-time systems, chap 16. Springer, Berlin, pp 343–365Solarski M (2004) Dynamic upgrade of distributed software components. PhD thesis, Fakultät IV Elektronik und Informatik, Technischen Universität Berlin, Berlin, GermanySoltesz S, Pötzl H, Fiuczynski ME, Bavier AC, Peterson LL (2007) Container-based operating system virtualization: a scalable, high-performance alternative to hypervisors. In: European Conference, Lisbon, Portugal, pp 275–287Soules CAN, Appavoo J, Hui K, Wisniewski RW, Silva DD, Ganger GR, Krieger O, Stumm M, Auslander MA, Ostrowski M, Rosenburg BS, Xenidis J (2003) System support for online reconfiguration. In: USENIX Annual Technical Conference. San Antonio, Texas, USA, pp 141–154Sridharan S (2012) A performance comparison of hypervisors for cloud computing. Master Thesis (paper 269), School of Computing, University of North Florida, USAStonebraker M (1986) The case for shared nothing. IEEE Database Eng Bull 9(1):4–9Sun D, Guimarans D, Fekete A, Gramoli V, Zhu L (2015) Multi-objective optimisation of rolling upgrade allowing for failures in clouds. In: 34th IEEE Symposium on Reliable Distributed Systems (SRDS). Montreal, QC, Canada, pp 68–73Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. The MIT Press, CambridgeToosi AN, Calheiros RN, Buyya R (2014) Interconnected cloud computing environments: challenges, taxonomy, and survey. ACM Comput Surv 47(1):7:1–7:47Vaquero González LM, Rodero-Merino L, Cáceres J, Lindner MA (2009) A break in the clouds: towards a cloud definition. Comput Commun Rev 39(1):50–55Varrette S, Guzek M, Plugaru V, Besseron X, Bouvry P (2013) HPC performance and energy-efficiency of Xen, KVM and VMware hypervisors. In: 25th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD). Porto de Galinhas, Pernambuco, Brazil, pp 89–96Vasic N, Novakovic DM, Miucin S, Kostic D, Bianchini R (2012) DejaVu: accelerating resource allocation in virtualized environments. In: 17th nternational Conference on Architectural Support for Programing Languages and Operating Systems (ASPLOS), London, UK, pp 423–436Vaughan-Nichols SJ (2006) New approach to virtualization is a lightweight. IEEE Comput 39(11):12–14Vogels W (2009) Eventually consistent. Commun ACM 52(1):40–44Wada H, Suzuki J, Yamano Y, Oba K (2011) Evolutionary deployment optimization for service-oriented clouds. Softw Pract Exp 41(5):469–493Whitaker A, Cox RS, Shaw M, Gribble SD (2005) Rethinking the design of virtual machine monitors. IEEE Comput 38(5):57–62Wishart DMG (1969) A survey of control theory. J R Stat Soc Ser A-G 132(3):293–319Yataghene L, Amziani M, Ioualalen M, Tata S (2014) A queuing model for business processes elasticity evaluation. In: International Workshop on Advanced Information Systems for Enterprises (IWAISE), Tunis, Tunisia, pp 22–28Zawirski M, Preguiça N, Duarte S, Bieniusa A, Balegas V, Shapiro M (2015) Write fast, read in th

    Session on techniques and resources for storm-scale numerical weather prediction

    Get PDF
    The session on techniques and resources for storm-scale numerical weather prediction are reviewed. The recommendations of this group are broken down into three area: modeling and prediction, data requirements in support of modeling and prediction, and data management. The current status, modeling and technological recommendations, data requirements in support of modeling and prediction, and data management are addressed

    Adaptive CPU Allocation for Resource Isolation and Work Conservation

    Get PDF
    Consolidating multiple workloads on the same physical machine is an effective measure for utilizing resources efficiently and reducing costs. The main objective is to execute multiple demanding workloads using no more than necessary resources while simultaneously maximizing performance. Conventional work-conserving resource managers are designed for this purpose. However, without adequate control, the performance of consolidated workloads may degrade dramatically or become unpredictable because of contention for shared resources. Hence, resource isolation should be enforced according to a sharing policy when there is resource contention among workloads, i.e., each workload should obtain a theoretical share of resources. In reality, it is challenging for state-of-the-art resource managers to achieve both resource isolation and work conservation simultaneously due to complex and dynamic workloads. This thesis proposes adaptive resource allocation to address this sharing problem and studies CPU management as an example. A novel feedback-based resource manager is designed to perform adaptive allocation of CPU resources, taking into account each workload's requirements. First, an application-agnostic metric is proposed as the feedback signal, which can be used to measure the performance change of various applications in a non-invasive and timely way. Second, two alternative feedback-based algorithms are designed to search for the optimal resource allocation for each workload. The adaptive allocation is modelled as a dynamic optimization problem. The algorithms solve this problem by assessing performance changes in response to a change in resource allocation. The algorithms are demonstrated to be capable of handling complex and dynamic workloads. The resource manager proposed in this thesis uses these algorithms to determine the CPU allocation for multiple tenants. A prototype is implemented with four different sharing policies. For three common policies, the experimental evaluation confirms that the resource manager can achieve resource isolation and work conservation simultaneously, while the existing best-practice mechanisms cannot. Moreover, the resource manager can support a novel efficiency policy, which determines CPU sharing based on the overall system efficiency. In addition, a preliminary study shows that the feedback-based methodology for CPU management can be extended to control I/O bandwidth

    Coopetition in an open-source way : lessons from mobile and cloud computing infrastructures

    Get PDF
    An increasing amount of technology is no longer developed in-house. Instead, we are in a new age where technology is developed by a networked community of individuals and organizations, who base their relations to each other on mutual interest. Advances arising from research in platforms, ecosystems, and infrastructures can provide valuable knowledge for better understanding and explaining technology development among a network of firms. More surprisingly, recent research suggests that technology can be jointly developed by rival competing firms in an open-source way. For instance, it is known that the mobile device makers Apple and Samsung continued collaborating in open-source projects while running expensive patent wars in the courts. On top of multidisciplinary theory in open-source software, cooperation among competitors (aka coopetition) and digital infrastructures, I (and my coauthors) explored how rival firms cooperate in the joint development of open-source infrastructures. While assimilating a wide variety of paradigms and analytical approaches, this doctoral research combined the qualitative analysis of naturally occurring data (QA) with the mining of software repositories (MSR) and social network analysis (SNA) within a set of case studies. By turning to the mobile and cloud computing industries in general, and the WebKit and OpenStack opensource infrastructures in particular, we found out that qualitative ethnographic materials, combined with social network visualizations, provide a rich medium that enables a better understanding of competitive and cooperative issues that are simultaneously present and interconnected in open-source infrastructures. Our research contributes back to managerial literature in coopetition strategy, but more importantly to Information Systems by addressing both cooperation and competition within the development of high-networked open-source infrastructures.Yhä suurempaa osaa teknologiasta ei enää kehitetä organisaatioiden omasta toimesta. Sen sijaan, olemme uudella aikakaudella jossa teknologiaa kehitetään verkostoituneessa yksilöiden ja organisaatioiden yhteisössä, missä toimitaan perustuen yhteiseen tavoitteeseen. Alustojen, ekosysteemien ja infrastruktuurien tutkimuksen tulokset voivat tuottaa arvokasta tietämystä teknologian kehittämisestä yritysten verkostossa. Erityisesti tuore tutkimustieto osoittaa että kilpailevat yritykset voivat yhdessä kehittää teknologiaa avoimeen lähdekoodiin perustuvilla käytännöillä. Esimerkiksi tiedetään että mobiililaitteiden valmistajat Apple ja Samsung tekivät yhteistyötä avoimen lähdekoodin projekteissa ja kävivät samaan aikaan kalliita patenttitaistoja eri oikeusfoorumeissa. Perustuen monitieteiseen teoriaan avoimen lähdekoodin ohjelmistoista, yhteistyöstä kilpailijoiden kesken (coopetition) sekä digitaalisista infrastruktuureista, minä (ja kanssakirjoittajani) tutkimme miten kilpailevat yritykset tekevät yhteistyötä avoimen lähdekoodin infrastruktuurien kehityksessä. Sulauttaessaan runsaan joukon paradigmoja ja analyyttisiä lähestymistapoja case-joukon puitteissa, tämä väitöskirjatutkimus yhdisti luonnollisesti esiintyvän datan kvantitatiivisen analyysin ohjelmapakettivarastojen louhintaan ja sosiaalisten verkostojen analyysiin. Tutkiessamme mobiili- ja pilvipalveluiden teollisuudenaloja yleisesti, ja WebKit ja OpenStack avoimen lähdekoodin infrastruktuureja erityisesti, havaitsimme että kvalitatiiviset etnografiset materiaalit yhdistettyinä sosiaalisten verkostojen visualisointiin tuottavat rikkaan aineiston joka mahdollistaa avoimen lähdekoodin infrastruktuuriin samanaikaisesti liittyvien kilpailullisten ja yhteistyökuvioiden hyvän ymmärtämisen. Tutkimuksemme antaa oman panoksensa johdon kirjallisuuteen coopetition strategy -alueella, mutta sitäkin enemmän tietojärjestelmätieteeseen, läpikäymällä sekä yhteistyötä että kilpailua tiiviisti verkostoituneessa avoimen lähdekoodin infrastruktuurien kehitystoiminnassaUma crescente quantidade de tecnologia não é desenvolvida internamente por uma só organização. Em vez disso, estamos em uma nova era em que a tecnologia é desenvolvida por uma comunidade de indivíduos e organizações que baseiam suas relações umas com as outras numa rede de interesse mútuo. Os avanços teórico decorrentes da pesquisa em plataformas computacionais, ecossistemas e infraestruturas digitais fornecem conhecimentos valiosos para uma melhor compreensão e explicação do desenvolvimento tecnológico por uma rede de multiplas empresas. Mais surpreendentemente, pesquisas recentes sugerem que tecnologia pode ser desenvolvida conjuntamente por empresas rivais concorrentes e de uma forma aberta (em código aberto). Por exemplo, sabe-se que os fabricantes de dispositivos móveis Apple e Samsung continuam a colaborar em projetos de código aberto ao mesmo tempo que se confrontam em caras guerras de patentes nos tribunais. Baseados no conhecimento científico de software de código aberto, de cooperação entre concorrentes (também conhecida como coopetição) e de infraestruturas digitais, eu e os meus co-autores exploramos como empresas concorrentes cooperam no desenvolvimento conjunto de infraestruturas de código aberto. Ao utilizar uma variedade de paradigmas e abordagens analíticas, esta pesquisa de doutoramento combinou a análise qualitativa de dados de ocorrência natural (QA) com a análise de repositórios de softwares (MSR) e a análise de redes sociais (SNA) dentro de um conjunto de estudos de casos. Ao investigar as industrias de technologias móveis e de computação em nuvem em geral, e as infraestruturas em código aberto WebKit e OpenStack, em particular, descobrimos que o material etnográfico qualitativo, combinado com visualizações de redes sociais, fornece um meio rico que permite uma melhor compreensão das problemas competitivos e cooperativos que estão simultaneamente presentes e interligados em infraestruturas de código aberto. A nossa pesquisa contribui para a literatura em gestão estratégica e coompetição, mas mais importante para literatura em Sistemas de Informação, abordando a cooperação e concorrência no desenvolvimento de infraestruturas de código aberto por uma rede the indivíduos e organizações em interesse mútuo

    Toward a fully cloudified mobile network infrastructure

    Get PDF
    Cloud computing enables the on-demand delivery of resources for a multitude of services and gives the opportunity for small agile companies to compete with large industries. In the telco world, cloud computing is currently mostly used by mobile network operators (MNO) for hosting non-critical support services and selling cloud services such as applications and data storage. MNOs are investigating the use of cloud computing to deliver key telecommunication services in the access and core networks. Without this, MNOs lose the opportunities of both combining this with over-the-top (OTT) and value-added services to their fundamental service offerings and leveraging cost-effective commodity hardware. Being able to leverage cloud computing technology effectively for the telco world is the focus of mobile cloud networking (MCN). This paper presents the key results of MCN integrated project that includes its architecture advancements, prototype implementation, and evaluation. Results show the efficiency and the simplicity that a MNO can deploy and manage the complete service lifecycle of fully cloudified, composed services that combine OTT/IT- and mobile-network-based services running on commodity hardware. The extensive performance evaluation of MCN using two key proof-of-concept scenarios that compose together many services to deliver novel converged elastic, on-demand mobile-based but innovative OTT services proves the feasibility of such fully virtualized deployments. Results show that it is beneficial to extend cloud computing to telco usage and run fully cloudified mobile-network-based systems with clear advantages and new service opportunities for MNOs and end-users
    corecore