Automated maintenance of service compositions with SLA violation detection and dynamic binding

Web service compositions need to adapt to changes in their constituent web services, in order to maintain functionality and performance. Therefore, service compositions must be able to detect web service failure and performance degradation resulting in the violation of service-level agreements. Automated diagnosis and repair are equally important. However, existing standards and languages for service compositions, such as BPEL, lack constructs for web service monitoring and runtime adaptability, which are pre-requisites for diagnosis and repair. We present a solution for transparent runtime monitoring, as well as automated performance degradation detection, diagnosis, and repair for service compositions expressed as BPEL processes. Our solution uses lightweight monitoring techniques, supports customizable diagnosis and repair strategies, and is compatible with any standards-compliant BPEL engin

Achieving autonomic Web service compositions with models at runtime

[EN] Several exceptional situations may arise in the complex, heterogeneous, and changing contexts where Web service operations run. For instance, a Web service operation may have greatly increased its execution time or may have become unavailable. The contribution of this article is to provide a tool-supported framework to guide autonomic adjustments of context-aware service compositions using models at runtime. During execution, when problematic events arise in the context, models are used by an autonomic architecture to guide changes of the service composition. Under the closed-world assumption, the possible context events are fully known at design time. Nevertheless, it is difficult to foresee all the possible situations arising in uncertain contexts where service compositions run. Therefore, the proposed framework also covers the dynamic evolution of service compositions to deal with unexpected events in the open world. An evaluation demonstrates that our framework is efficient during dynamic adjustments.Alférez-Salinas, GH.; Pelechano Ferragud, V. (2017). Achieving autonomic Web service compositions with models at runtime. Computers & Electrical Engineering. 63:332-352. doi:10.1016/j.compeleceng.2017.08.004S3323526

A survey on elasticity management in PaaS systems

[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

Considering Quality of a Service in an Intentional Approach

International audienceThe success of service-based applications is based on service technologies such as Web services. Nevertheless, the benefits of the Service-Oriented Architecture (SOA) remain mainly at the software level, since business people are often unable to fully exploit its benefits due to their unfamiliarity with such software level technology. The intentional Service-Oriented Architecture (iSOA) suggests a move from the function-driven SOA to intention-driven SOA in order to provide service description understandable by business practitioners. However, such transposition from business to implementation level should also consider Quality of Service (QoS) aspects. In this paper, we propose modeling the Quality of intentional Service (QoiS) by introducing the quality goals and their qualitative and quantitative evaluation. We also propose populating the intentional service registry of the iSOA architecture with the QoiS description

Dynamic adaptation of service compositions with variability models

Web services run in complex contexts where arising events may compromise the quality of the whole system. Thus, it is desirable to count on autonomic mechanisms to guide the self-adaptation of service compositions according to changes in the computing infrastructure. One way to achieve this goal is by implementing variability constructs at the language level. However, this approach may become tedious, difficult to manage, and error-prone. In this paper, we propose a solution based on a semantically rich variability model to support the dynamic adaptation of service compositions. When a problematic event arises in the context, this model is leveraged for decision-making. The activation and deactivation of features in the variability model result in changes in a composition model that abstracts the underlying service composition. These changes are reflected into the service composition by adding or removing fragments of Business Process Execution Language (WS-BPEL) code, which can be deployed at runtime. In order to reach optimum adaptations, the variability model and its possible configurations are verified at design time using Constraint Programming. An evaluation demonstrates several benefits of our approach, both at design time and at runtime.This work has been developed with the support of MICINN under the project everyWare TIN2010-18011 and co-financed with ERDF.Alférez Salinas, GH.; Pelechano Ferragud, V.; Mazo, R.; Salinesi, C.; Díaz, D. (2014). Dynamic adaptation of service compositions with variability models. Journal of Systems and Software. 91:24-47. https://doi.org/10.1016/j.jss.2013.06.034S24479

Application of service composition mechanisms to Future Networks architectures and Smart Grids

Aquesta tesi gira entorn de la hipòtesi de la metodologia i mecanismes de composició de serveis i com es poden aplicar a diferents camps d'aplicació per a orquestrar de manera eficient comunicacions i processos flexibles i sensibles al context. Més concretament, se centra en dos camps d'aplicació: la distribució eficient i sensible al context de contingut multimèdia i els serveis d'una xarxa elèctrica intel·ligent. En aquest últim camp es centra en la gestió de la infraestructura, cap a la definició d'una Software Defined Utility (SDU), que proposa una nova manera de gestionar la Smart Grid amb un enfocament basat en programari, que permeti un funcionament molt més flexible de la infraestructura de xarxa elèctrica. Per tant, revisa el context, els requisits i els reptes, així com els enfocaments de la composició de serveis per a aquests camps. Fa especial èmfasi en la combinació de la composició de serveis amb arquitectures Future Network (FN), presentant una proposta de FN orientada a serveis per crear comunicacions adaptades i sota demanda. També es presenten metodologies i mecanismes de composició de serveis per operar sobre aquesta arquitectura, i posteriorment, es proposa el seu ús (en conjunció o no amb l'arquitectura FN) en els dos camps d'estudi. Finalment, es presenta la investigació i desenvolupament realitzat en l'àmbit de les xarxes intel·ligents, proposant diverses parts de la infraestructura SDU amb exemples d'aplicació de composició de serveis per dissenyar seguretat dinàmica i flexible o l'orquestració i gestió de serveis i recursos dins la infraestructura de l'empresa elèctrica.Esta tesis gira en torno a la hipótesis de la metodología y mecanismos de composición de servicios y cómo se pueden aplicar a diferentes campos de aplicación para orquestar de manera eficiente comunicaciones y procesos flexibles y sensibles al contexto. Más concretamente, se centra en dos campos de aplicación: la distribución eficiente y sensible al contexto de contenido multimedia y los servicios de una red eléctrica inteligente. En este último campo se centra en la gestión de la infraestructura, hacia la definición de una Software Defined Utility (SDU), que propone una nueva forma de gestionar la Smart Grid con un enfoque basado en software, que permita un funcionamiento mucho más flexible de la infraestructura de red eléctrica. Por lo tanto, revisa el contexto, los requisitos y los retos, así como los enfoques de la composición de servicios para estos campos. Hace especial hincapié en la combinación de la composición de servicios con arquitecturas Future Network (FN), presentando una propuesta de FN orientada a servicios para crear comunicaciones adaptadas y bajo demanda. También se presentan metodologías y mecanismos de composición de servicios para operar sobre esta arquitectura, y posteriormente, se propone su uso (en conjunción o no con la arquitectura FN) en los dos campos de estudio. Por último, se presenta la investigación y desarrollo realizado en el ámbito de las redes inteligentes, proponiendo varias partes de la infraestructura SDU con ejemplos de aplicación de composición de servicios para diseñar seguridad dinámica y flexible o la orquestación y gestión de servicios y recursos dentro de la infraestructura de la empresa eléctrica.This thesis revolves around the hypothesis the service composition methodology and mechanisms and how they can be applied to different fields of application in order to efficiently orchestrate flexible and context-aware communications and processes. More concretely, it focuses on two fields of application that are the context-aware media distribution and smart grid services and infrastructure management, towards a definition of a Software-Defined Utility (SDU), which proposes a new way of managing the Smart Grid following a software-based approach that enable a much more flexible operation of the power infrastructure. Hence, it reviews the context, requirements and challenges of these fields, as well as the service composition approaches. It makes special emphasis on the combination of service composition with Future Network (FN) architectures, presenting a service-oriented FN proposal for creating context-aware on-demand communication services. Service composition methodology and mechanisms are also presented in order to operate over this architecture, and afterwards, proposed for their usage (in conjunction or not with the FN architecture) in the deployment of context-aware media distribution and Smart Grids. Finally, the research and development done in the field of Smart Grids is depicted, proposing several parts of the SDU infrastructure, with examples of service composition application for designing dynamic and flexible security for smart metering or the orchestration and management of services and data resources within the utility infrastructure

Microservice Transition and its Granularity Problem: A Systematic Mapping Study

Microservices have gained wide recognition and acceptance in software industries as an emerging architectural style for autonomic, scalable, and more reliable computing. The transition to microservices has been highly motivated by the need for better alignment of technical design decisions with improving value potentials of architectures. Despite microservices' popularity, research still lacks disciplined understanding of transition and consensus on the principles and activities underlying "micro-ing" architectures. In this paper, we report on a systematic mapping study that consolidates various views, approaches and activities that commonly assist in the transition to microservices. The study aims to provide a better understanding of the transition; it also contributes a working definition of the transition and technical activities underlying it. We term the transition and technical activities leading to microservice architectures as microservitization. We then shed light on a fundamental problem of microservitization: microservice granularity and reasoning about its adaptation as first-class entities. This study reviews state-of-the-art and -practice related to reasoning about microservice granularity; it reviews modelling approaches, aspects considered, guidelines and processes used to reason about microservice granularity. This study identifies opportunities for future research and development related to reasoning about microservice granularity.Comment: 36 pages including references, 6 figures, and 3 table

Achieving Autonomic Web Service Compositions with Models at Runtime

Over the last years, Web services have become increasingly popular. It is because they allow businesses to share data and business process (BP) logic through a programmatic interface across networks. In order to reach the full potential of Web services, they can be combined to achieve specifi c functionalities. Web services run in complex contexts where arising events may compromise the quality of the system (e.g. a sudden security attack). As a result, it is desirable to count on mechanisms to adapt Web service compositions (or simply called service compositions) according to problematic events in the context. Since critical systems may require prompt responses, manual adaptations are unfeasible in large and intricate service compositions. Thus, it is suitable to have autonomic mechanisms to guide their self-adaptation. One way to achieve this is by implementing variability constructs at the language level. However, this approach may become tedious, difficult to manage, and error-prone as the number of con figurations for the service composition grows. The goal of this thesis is to provide a model-driven framework to guide autonomic adjustments of context-aware service compositions. This framework spans over design time and runtime to face arising known and unknown context events (i.e., foreseen and unforeseen at design time) in the close and open worlds respectively. At design time, we propose a methodology for creating the models that guide autonomic changes. Since Service-Oriented Architecture (SOA) lacks support for systematic reuse of service operations, we represent service operations as Software Product Line (SPL) features in a variability model. As a result, our approach can support the construction of service composition families in mass production-environments. In order to reach optimum adaptations, the variability model and its possible con figurations are verifi ed at design time using Constraint Programming (CP). At runtime, when problematic events arise in the context, the variability model is leveraged for guiding autonomic changes of the service composition. The activation and deactivation of features in the variability model result in changes in a composition model that abstracts the underlying service composition. Changes in the variability model are refl ected into the service composition by adding or removing fragments of Business Process Execution Language (WS-BPEL) code, which are deployed at runtime. Model-driven strategies guide the safe migration of running service composition instances. Under the closed-world assumption, the possible context events are fully known at design time. These events will eventually trigger the dynamic adaptation of the service composition. Nevertheless, it is diffi cult to foresee all the possible situations arising in uncertain contexts where service compositions run. Therefore, we extend our framework to cover the dynamic evolution of service compositions to deal with unexpected events in the open world. If model adaptations cannot solve uncertainty, the supporting models self-evolve according to abstract tactics that preserve expected requirements.Alférez Salinas, GH. (2013). Achieving Autonomic Web Service Compositions with Models at Runtime [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/34672TESI