An Algorithm for Network and Data-aware Placement of Multi-Tier Applications in Cloud Data Centers

Today's Cloud applications are dominated by composite applications comprising multiple computing and data components with strong communication correlations among them. Although Cloud providers are deploying large number of computing and storage devices to address the ever increasing demand for computing and storage resources, network resource demands are emerging as one of the key areas of performance bottleneck. This paper addresses network-aware placement of virtual components (computing and data) of multi-tier applications in data centers and formally defines the placement as an optimization problem. The simultaneous placement of Virtual Machines and data blocks aims at reducing the network overhead of the data center network infrastructure. A greedy heuristic is proposed for the on-demand application components placement that localizes network traffic in the data center interconnect. Such optimization helps reducing communication overhead in upper layer network switches that will eventually reduce the overall traffic volume across the data center. This, in turn, will help reducing packet transmission delay, increasing network performance, and minimizing the energy consumption of network components. Experimental results demonstrate performance superiority of the proposed algorithm over other approaches where it outperforms the state-of-the-art network-aware application placement algorithm across all performance metrics by reducing the average network cost up to 67% and network usage at core switches up to 84%, as well as increasing the average number of application deployments up to 18%.Comment: Submitted for publication consideration for the Journal of Network and Computer Applications (JNCA). Total page: 28. Number of figures: 15 figure

Multi-elastic Datacenters: Auto-scaled Virtual Clusters on Energy-Aware Physical Infrastructures

[EN] Computer clusters are widely used platforms to execute different computational workloads. Indeed, the advent of virtualization and Cloud computing has paved the way to deploy virtual elastic clusters on top of Cloud infrastructures, which are typically backed by physical computing clusters. In turn, the advances in Green computing have fostered the ability to dynamically power on the nodes of physical clusters as required. Therefore, this paper introduces an open-source framework to deploy elastic virtual clusters running on elastic physical clusters where the computing capabilities of the virtual clusters are dynamically changed to satisfy both the user application's computing requirements and to minimise the amount of energy consumed by the underlying physical cluster that supports an on-premises Cloud. For that, we integrate: i) an elasticity manager both at the infrastructure level (power management) and at the virtual infrastructure level (horizontal elasticity); ii) an automatic Virtual Machine (VM) consolidation agent that reduces the amount of powered on physical nodes using live migration and iii) a vertical elasticity manager to dynamically and transparently change the memory allocated to VMs, thus fostering enhanced consolidation. A case study based on real datasets executed on a production infrastructure is used to validate the proposed solution. The results show that a multi-elastic virtualized datacenter provides users with the ability to deploy customized scalable computing clusters while reducing its energy footprint.The results of this work have been partially supported by ATMOSPHERE (Adaptive, Trustworthy, Manageable, Orchestrated, Secure, Privacy-assuring Hybrid, Ecosystem for Resilient Cloud Computing), funded by the European Commission under the Cooperation Programme, Horizon 2020 grant agreement No 777154.Alfonso Laguna, CD.; Caballer Fernández, M.; Calatrava Arroyo, A.; Moltó, G.; Blanquer Espert, I. (2018). Multi-elastic Datacenters: Auto-scaled Virtual Clusters on Energy-Aware Physical Infrastructures. Journal of Grid Computing. 17(1):191-204. https://doi.org/10.1007/s10723-018-9449-zS191204171Buyya, R.: High Performance Cluster Computing: Architectures and Systems. Prentice Hall PTR, Upper Saddle River (1999)de Alfonso, C., Caballer, M., Alvarruiz, F., Moltó, G.: An economic and energy-aware analysis of the viability of outsourcing cluster computing to the cloud. Futur. Gener. Comput. Syst. (Int. J. Grid Comput eScience) 29, 704–712 (2013). https://doi.org/10.1016/j.future.2012.08.014Williams, D., Jamjoom, H., Liu, Y.H., Weatherspoon, H.: Overdriver: handling memory overload in an oversubscribed cloud. ACM SIGPLAN Not. 46(7), 205 (2011). https://doi.org/10.1145/2007477.1952709 . http://dl.acm.org/citation.cfm?id=2007477.1952709Valentini, G., Lassonde, W., Khan, S., Min-Allah, N., Madani, S., Li, J., Zhang, L., Wang, L., Ghani, N., Kolodziej, J., Li, H., Zomaya, A., Xu, C.Z., Balaji, P., Vishnu, A., Pinel, F., Pecero, J., Kliazovich, D., Bouvry, P.: An overview of energy efficiency techniques in cluster computing systems. Clust. Comput. 16(1), 3–15 (2013). https://doi.org/10.1007/s10586-011-0171-xDe Alfonso, C., Caballer, M., Hernández, V.: Efficient power management in high performance computer clusters. In: Proceedings of the 1st International Multi-conference on Innovative Developments in ICT, Proceedings of the International Conference on Green Computing 2010 (ICGreen 2010), 39–44 (2010)OpenNebula: OpenNebula Cloud Software https://opennebula.org/ . [Online; accessed 12-June-2017]OpenStack: OpenStack Cloud Software. http://openstack.org . [Online; accessed 12 June 2017]VMWare: VMWare vCenter Server. https://www.vmware.com/products/vcenter-server.html . [Online; accessed 12 June 2017]De Alfonso, C., Blanquer, I.: Automatic consolidation of virtual machines in on-premises cloud platforms. In: IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp 1070–1079 (2017). https://doi.org/10.1109/CCGRID.2017.128Chase, J.S., Irwin, D.E., Grit, L.E., Moore, J.D., Sprenkle, S.E.: Dynamic virtual clusters in a grid site manager. In: Proceedings of the 12th IEEE International Symposium on High Performance Distributed Computing, HPDC ’03, p 90. IEEE Computer Society, Washington, DC (2003). http://dl.acm.org/citation.cfm?id=822087.823392Doelitzscher, F., Held, M., Reich, C., Sulistio, A.: Viteraas: Virtual cluster as a service. In: 2011 IEEE Third International Conference on Cloud Computing Technology and Science (CloudCom), pp 652–657 (2011). https://doi.org/10.1109/CloudCom.2011.101Wei, X., Wang, H., Li, H., Zou, L.: Dynamic deployment and management of elastic virtual clusters. In: 2011 Sixth Annual Chinagrid Conference (ChinaGrid), pp 35–41 (2011). https://doi.org/10.1109/ChinaGrid.2011.31de Assuncao, M.D., di Costanzo, A., Buyya, R.: Evaluating the cost-benefit of using cloud computing to extend the capacity of clusters. In: Proceedings of the 18th ACM International Symposium on High Performance Distributed Computing, HPDC ’09, pp 141–150. ACM, New York (2009). https://doi.org/10.1145/1551609.1551635 . http://doi.acm.org/10.1145/1551609.1551635Marshall, P., Keahey, K., Freeman, T.: Elastic site: Using clouds to elastically extend site resources. In: 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid), pp 43–52 (2010). https://doi.org/10.1109/CCGRID.2010.80Niu, S., Zhai, J., Ma, X., Tang, X., Chen, W.: Cost-effective cloud hpc resource provisioning by building semi-elastic virtual clusters. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, SC ’13, pp 56:1–56:12. ACM, New York (2013). https://doi.org/10.1145/2503210.2503236 . http://doi.acm.org/10.1145/2503210.2503236Bialecki, A., Cafarella, M., Cutting, D., Omalley, O.: Hadoop: a framework for running applications on large clusters built of commodity hardware. Tech. rep. Apache Hadoop. http://hadoop.apache.org (2005)MIT: StarCluster Elastic Load Balancer. http://web.mit.edu/stardev/cluster/docs/0.92rc2/manual/load_balancer.htmlAppliance, C.C.S.: Creating elastic virtual clusters. http://cernvm.cern.ch/portal/elasticclusters (2015)Research project, T.G.: The games research project. http://www.green-datacenters.eu (2013)Cioara, T., Anghel, I., Salomie, I., Copil, G., Moldovan, D., Kipp, A.: Energy aware dynamic resource consolidation algorithm for virtualized service centers based on reinforcement learning. In: 2011 10th International Symposium on Parallel and Distributed Computing (ISPDC), pp 163–169 (2011). https://doi.org/10.1109/ISPDC.2011.32Farahnakian, F., Liljeberg, P., Plosila, J.: Energy-efficient virtual machines consolidation in cloud data centers using reinforcement learning. In: 2014 22nd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp 500–507 (2014). https://doi.org/10.1109/PDP.2014.109Masoumzadeh, S., Hlavacs, H.: Integrating vm selection criteria in distributed dynamic vm consolidation using fuzzy q-learning. In: 2013 9th International Conference on Network and Service Management (CNSM), pp 332–338 (2013). https://doi.org/10.1109/CNSM.2013.6727854Feller, E., Rilling, L., Morin, C.: Energy-aware ant colony based workload placement in clouds. In: 2011 12th IEEE/ACM International Conference on Grid Computing (GRID), pp 26–33 (2011). https://doi.org/10.1109/Grid.2011.13Pop, C.B., Anghel, I., Cioara, T., Salomie, I., Vartic, I.: A swarm-inspired data center consolidation methodology. In: Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics, WIMS ’12, pp 41:1–41:7. ACM, New York (2012). https://doi.org/10.1145/2254129.2254180Marzolla, M., Babaoglu, O., Panzieri, F.: Server consolidation in clouds through gossiping. In: Proceedings of the 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WOWMOM ’11, pp 1–6. IEEE Computer Society, Washington, DC (2011). https://doi.org/10.1109/WoWMoM.2011.5986483Ghafari, S., Fazeli, M., Patooghy, A., Rikhtechi, L.: Bee-mmt: A load balancing method for power consumption management in cloud computing. In: 2013 Sixth International Conference on Contemporary Computing (IC3), pp 76–80 (2013). https://doi.org/10.1109/IC3.2013.6612165Ajiro, Y., Tanaka, A.: Improving packing algorithms for server consolidation. In: International CMG Conference, pp. 399–406. Computer Measurement Group (2007)Verma, A., Ahuja, P., Neogi, A.: pmapper: power and migration cost aware application placement in virtualized systems. In: Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware, Middleware ’08, pp 243–264. Springer, New York (2008)Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28 (5), 755–768 (2012). https://doi.org/10.1016/j.future.2011.04.017Guazzone, M., Anglano, C., Canonico, M.: Exploiting vm migration for the automated power and performance management of green cloud computing systems. In: Proceedings of the First International Conference on Energy Efficient Data Centers, E2DC’12, pp 81–92. Springer, Berlin (2012). https://doi.org/10.1007/978-3-642-33645-4_8Shi, L., Furlong, J., Wang, R.: Empirical evaluation of vector bin packing algorithms for energy efficient data centers. In: 2013 IEEE Symposium on Computers and Communications (ISCC), pp 000,009–000,015 (2013). https://doi.org/10.1109/ISCC.2013.6754915Tomás, L., Tordsson, J.: Improving cloud infrastructure utilization through overbooking. In: Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference on - CAC ’13, p 1. ACM Press, New York (2013). https://doi.org/10.1145/2494621.2494627Dawoud, W., Takouna, I., Meinel, C.: Elastic vm for cloud resources provisioning optimization. In: Abraham, A., Lloret Mauri, J., Buford, J., Suzuki, J., Thampi, S. (eds.) Advances in Computing and Communications, Communications in Computer and Information Science, vol. 190, pp 431–445. Springer, Berlin (2011). https://doi.org/10.1007/978-3-642-22709-7_43Tasoulas, E., Haugerund, H.R., Begnum, K.: Bayllocator: a proactive system to predict server utilization and dynamically allocate memory resources using Bayesian networks and ballooning. In: Proceedings of the 26th International Conference on Large Installation System Administration: Strategies, Tools, and Techniques, pp. 111–122. USENIX Association (2012)Hines, M.R., Gordon, A., Silva, M., Da Silva, D., Ryu, K., Ben-Yehuda, M.: Applications know best: performance-driven memory overcommit with Ginkgo. In: 2011 IEEE Third International Conference on Cloud Computing Technology and Science, pp. 130–137. IEEE. https://doi.org/10.1109/CloudCom.2011.27 (2011)Litke, A.: Manage resources on overcommitted KVM hosts. Tech. rep. IBM. http://www.ibm.com/developerworks/library/l-overcommit-kvm-resources/ (2011)De Alfonso, C., Caballer, M., Alvarruiz, F., Hernández, V.: An energy management system for cluster infrastructures. Comput. Electr. Eng. 39(8), 2579–2590 (2013). https://doi.org/10.1016/j.compeleceng.2013.05.004Moltó, G., Caballer, M, de Alfonso, C.: Automatic memory-based vertical elasticity and oversubscription on cloud platforms. Futur. Gener. Comput. Syst. 56, 1–10 (2016). https://doi.org/10.1016/j.future.2015.10.002Calatrava, A., Romero, E., Moltó, G., Caballer, M., Alonso, J.M.: Self-managed cost-efficient virtual elastic clusters on hybrid Cloud infrastructures. Futur. Gener. Comput. Syst. 61, 13–25 (2016). https://doi.org/10.1016/j.future.2016.01.018 . http://authors.elsevier.com/sd/article/S0167739X16300024 , http://linkinghub.elsevier.com/retrieve/pii/S0167739X16300024Caballer, M., Chatziangelou, M., Calatrava, A., Moltó, G., Pérez, A.: IM integration in the EGI VMOps Dashboard. In: EGI Conference 2017 and INDIGO Summit 2017 (2017)Calatrava, A., Caballer, M., Moltó, G., Pérez, A.: Virtual Elastic Clusters in the EGI LToS with EC3. In: EGI Conference 2017 and INDIGO Summit 2017 (2017)Iosup, A., Li, H., Jan, M., Anoep, S., Dumitrescu, C., Wolters, L., Epema, D.H.: The grid workloads archive. Futur. Gener. Comput. Syst. 24(7), 672–686 (2008). https://doi.org/10.1016/j.future.2008.02.003 . http://www.sciencedirect.com/science/article/pii/S0167739X08000125Nordugrid dataset, the grid workloads archive (Online; accessed 27-March-2017). http://gwa.ewi.tudelft.nl/datasets/gwa-t-3-nordugrid/report/Caballer, M., Blanquer, I., Moltó, G., de Alfonso, C: Dynamic Management of Virtual Infrastructures. J. Grid Comput. 13, 53–70 (2015). https://doi.org/10.1007/s10723-014-9296-5 . http://link.springer.com/article/10.1007/s10723-014-9296-

Transiency-driven Resource Management for Cloud Computing Platforms

Modern distributed server applications are hosted on enterprise or cloud data centers that provide computing, storage, and networking capabilities to these applications. These applications are built using the implicit assumption that the underlying servers will be stable and normally available, barring for occasional faults. In many emerging scenarios, however, data centers and clouds only provide transient, rather than continuous, availability of their servers. Transiency in modern distributed systems arises in many contexts, such as green data centers powered using renewable intermittent sources, and cloud platforms that provide lower-cost transient servers which can be unilaterally revoked by the cloud operator. Transient computing resources are increasingly important, and existing fault-tolerance and resource management techniques are inadequate for transient servers because applications typically assume continuous resource availability. This thesis presents research in distributed systems design that treats transiency as a first-class design principle. I show that combining transiency-specific fault-tolerance mechanisms with resource management policies to suit application characteristics and requirements, can yield significant cost and performance benefits. These mechanisms and policies have been implemented and prototyped as part of software systems, which allow a wide range of applications, such as interactive services and distributed data processing, to be deployed on transient servers, and can reduce cloud computing costs by up to 90\%. This thesis makes contributions to four areas of computer systems research: transiency-specific fault-tolerance, resource allocation, abstractions, and resource reclamation. For reducing the impact of transient server revocations, I develop two fault-tolerance techniques that are tailored to transient server characteristics and application requirements. For interactive applications, I build a derivative cloud platform that masks revocations by transparently moving application-state between servers of different types. Similarly, for distributed data processing applications, I investigate the use of application level periodic checkpointing to reduce the performance impact of server revocations. For managing and reducing the risk of server revocations, I investigate the use of server portfolios that allow transient resource allocation to be tailored to application requirements. Finally, I investigate how resource providers (such as cloud platforms) can provide transient resource availability without revocation, by looking into alternative resource reclamation techniques. I develop resource deflation, wherein a server\u27s resources are fractionally reclaimed, allowing the application to continue execution albeit with fewer resources. Resource deflation generalizes revocation, and the deflation mechanisms and cluster-wide policies can yield both high cluster utilization and low application performance degradation