An efficient resource sharing technique for multi-tenant databases

Multi-tenancy is one of the key components of cloud computing environment. Multi-tenant database system in SaaS (Software as a Service) has gained a lot of attention in academics, research and business arena. These database systems provide scalability and economic benefits for both cloud service providers and customers(organizations/companies referred as tenants) by sharing same resources and infrastructure in isolation of shared databases, network and computing resources with Service level agreement (SLA) compliances. In a multitenant scenario, active tenants compete for resources in order to access the database. If one tenant blocks up the resources, the performance of all the other tenants may be restricted and a fair sharing of the resources may be compromised. The performance of tenants must not be affected by resource-intensive activities and volatile workloads of other tenants. Moreover, the prime goal of providers is to accomplish low cost of operation, satisfying specific schemas/SLAs of each tenant. Consequently, there is a need to design and develop effective and dynamic resource sharing algorithms which can handle above mentioned issues. This work presents a model embracing a query classification and worker sorting technique to efficiently share I/O, CPU and Memory thus enhancing dynamic resource sharing and improvising the utilization of idle instances proficiently. The model is referred as Multi-Tenant Dynamic Resource Scheduling Model (MTDRSM) .The MTDRSM support workload execution of different benchmark such as TPC-C(Transaction Processing Performance Council), YCSB(The Yahoo! Cloud Serving Benchmark)etc. and on different database such as MySQL, Oracle, H2 database etc. Experiments are conducted for different benchmarks with and without SLA compliances to evaluate the performance of MTDRSM in terms of latency and throughput achieved. The experiments show significant performance improvement over existing Mute Bench model in terms of latency and throughput

Software-Defined Cloud Computing: Architectural Elements and Open Challenges

The variety of existing cloud services creates a challenge for service providers to enforce reasonable Software Level Agreements (SLA) stating the Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid such penalties at the same time that the infrastructure operates with minimum energy and resource wastage, constant monitoring and adaptation of the infrastructure is needed. We refer to Software-Defined Cloud Computing, or simply Software-Defined Clouds (SDC), as an approach for automating the process of optimal cloud configuration by extending virtualization concept to all resources in a data center. An SDC enables easy reconfiguration and adaptation of physical resources in a cloud infrastructure, to better accommodate the demand on QoS through a software that can describe and manage various aspects comprising the cloud environment. In this paper, we present an architecture for SDCs on data centers with emphasis on mobile cloud applications. We present an evaluation, showcasing the potential of SDC in two use cases-QoS-aware bandwidth allocation and bandwidth-aware, energy-efficient VM placement-and discuss the research challenges and opportunities in this emerging area.Comment: Keynote Paper, 3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI 2014), September 24-27, 2014, Delhi, Indi

DBaaS Multitenancy, Auto-tuning and SLA Maintenance in Cloud Environments: a Brief Survey

Cloud computing is a paradigm that presents many advantages to both costumers and service providers, such as low upfront investment, pay-per-use and easiness of use, delivering/enabling scalable services using Internet technologies. Among many types of services we have today, Database as a Service (DBaaS) is the one where a database is provided in the cloud in all its aspects. Examples of aspects related to DBaaS utilization are data storage, resources management and SLA maintenance. In this context, an important feature, related to it, is resource management and performance, which can be done in many different ways for several reasons, such as saving money, time, and meeting the requirements agreed between client and provider, that are defined in the Service Level Agreement (SLA). A SLA usually tries to protect the costumer from not receiving the contracted service and to ensure that the provider reaches the profit intended. In this paper it is presented a classification based on three main parameters that aim to manage resources for enhancing the performance on DBaaS and guarantee that the SLA is respected for both user and provider sides benefit. The proposal is based upon a survey of existing research work efforts

Fog Computing: A Taxonomy, Survey and Future Directions

In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named "Fog computing" has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/ sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features.We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research

A Cost-Aware Mechanism for Optimized Resource Provisioning in Cloud Computing

Due to the recent wide use of computational resources in cloud computing, new resource provisioning challenges have been emerged. Resource provisioning techniques must keep total costs to a minimum while meeting the requirements of the requests. According to widely usage of cloud services, it seems more challenging to develop effective schemes for provisioning services cost-effectively; we have proposed a novel learning based resource provisioning approach that achieves cost-reduction guarantees of demands. The contributions of our optimized resource provisioning (ORP) approach are as follows. Firstly, it is designed to provide a cost-effective method to efficiently handle the provisioning of requested applications; while most of the existing models allow only workflows in general which cares about the dependencies of the tasks, ORP performs based on services of which applications comprised and cares about their efficient provisioning totally. Secondly, it is a learning automata-based approach which selects the most proper resources for hosting each service of the demanded application; our approach considers both cost and service requirements together for deploying applications. Thirdly, a comprehensive evaluation is performed for three typical workloads: data-intensive, process-intensive and normal applications. The experimental results show that our method adapts most of the requirements efficiently, and furthermore the resulting performance meets our design goals

BRAHMA(+): A Framework for Resource Scaling of Streaming and ASAP Time-Varying Workflows

Automatic scaling of complex software-as-a-service application workflows is one of the most important problems concerning resource management in clouds. In this paper, we study the automatic workflow resource scaling problem for streaming and ASAP workflows, and its time-varying variant where the workflow resource requirements change over time. Service components of streaming workflows execute concurrently while those of ASAP workflows execute sequentially. We propose an intelligent framework, BRAHMA(+), which possesses the capability to learn the workflow behavior and construct a knowledge base that serves as its decision making engine. The proposed resource provisioning algorithms leverage this learned information curated in the knowledge base to perform informed and intelligent scaling decisions. Additionally, BRAHMA(+) employs the use of online-learning strategies to keep the knowledge base up-to-date, thereby accommodating the changes in the workflow resource requirements over time. We evaluate the proposed algorithms using CloudSim simulations. Results on streaming and ASAP workflows, with both static and time-varying resource requirements show that the proposed algorithms are effective and produce good cost-quality trade-offs. The proactive and hybrid algorithms meet the service level agreements and restrict deadline violations to a small fraction (3%-5% in the considered scenarios), while only suffering a marginal increase in average cost per component compared to the described baseline algorithms

Cognitive-Aware Network Virtualization Hypervisor for Efficient Resource Provisioning in Software Defined Cloud Networks

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonIntegration of different technologies forms an integral part of modern network engineering and 5G technology deployment. Although Software Defined Networking (SDN) and Network Functions Virtualization (NFV) function well independently, integrating these two technologies present the cooperate advantages to service providers and service users. Operations of cloud computing technologies have been enhanced with the advent of SDN and NFV for efficient solutions deployment and infrastructure management in Software Defined Cloud Datacentre Networks (SDCDCN) where dynamic controllability is indispensable for elastic service provision. The provisioning of joint compute and network resources enabled by SDCN is essential to enforce reasonable Service Level Agreements (SLAs) stating the Quality of Service (QoS) while saving energy consumption and resource wastage. This thesis presents a Cognitive- Aware Network virtualization Hypervisor which was developed from merging the programmable dynamic network control attributes of SDN and the network slicing attributes of NFV to provision joint compute and network resources in SDCDCN for QoS fulfilment and energy efficiency. It focuses on the techniques for allocating Virtual Network Requests on physical hosts and switches considering SLA, QoS, and energy efficiency aspects. The thesis advances the state-of the-art with the following key contributions: A modelling and simulation environment for Software Defined Cloud Datacentre Networks abstracting functionalities and behaviours of virtual and physical network resources. The second is a novel dynamic overbooking algorithm for energy efficiency and SLA enforcement with the migration of virtual machines and network flows. Finally, a performance-aware intelligent overbooking for predicting network resource usage and performance for the next defined time interval considering multiple performance indexes

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