OStrich: Fair Scheduling for Multiple Submissions

International audienceCampaign Scheduling is characterized by multiple job submissions issued from multiple users over time. This model perfectly suits today's systems since most available parallel environments have multiple users sharing a common infrastructure. When scheduling individually the jobs submitted by various users, one crucial issue is to ensure fairness. This work presents a new fair scheduling algorithm called OStrich whose principle is to maintain a virtual time-sharing schedule in which the same amount of processors is assigned to each user. The completion times in the virtual schedule determine the execution order on the physical processors. Then, the campaigns are interleaved in a fair way by OStrich. For independent sequential jobs, we show that OStrich guarantees the stretch of a campaign to be proportional to campaign's size and the total number of users. The stretch is used for measuring by what factor a workload is slowed down relative to the time it takes on an unloaded system. The theoretical performance of our solution is assessed by simulating OStrich compared to the classical FCFS algorithm, issued from synthetic workload traces generated by two different user profiles. This is done to demonstrate how OStrich benefits both types of users, in contrast to FCFS

A grid broker pricing mechanism for temporal and budget guarantees

We introduce a pricing mechanism for Grid computing, with the aim of showing how a broker can accept the most appropriate jobs to be computed on time and on budget. We analyse the mechanism’s performance via discrete event simulation, and illustrate its viability, the benefits of a new admission policy and to how slack relates to machine heterogeneity

Analyzing the EGEE production grid workload: application to jobs submission optimization

International audienceGrids reliability remains an order of magnitude below clusters on production infrastructures. This work is aims at improving grid application performances by improving the job submission system. A stochastic model, capturing the behavior of a complex grid workload management system is proposed. To instantiate the model, detailed statistics are extracted from dense grid activity traces. The model is exploited in a simple job resubmission strategy. It provides quantitative inputs to improve job submission performance and it enables quantifying the impact of faults and outliers on grid operations

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-

Resource Re-allocation for Data Inter-dependent Continuous Tasks in Grids

Many researchers focus on resource intensive tasks which have to be run continuously over long periods. A Grid may offer resources for these tasks, but they are contested by multiple client agents. Hence, a Grid might be unwilling to allocate its resources for long terms, leading to tasks’ interruptions. This issue becomes more substantial when tasks are data inter-dependent, where one interrupted task may cause an interruption of a bundle of other tasks. In this paper, we discuss a new resource re-allocation strategy for a client, in which resources are re-allocated between the client tasks in order to avoid prolonged interruptions. Those re-allocations are decided by a client agent, but they should be agreed with a Grid and can be performed only by a Grid. Our strategy has been tested within different Grid environments and noticeably improves client utilities in almost all cases

An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them

Non-clairvoyant Scheduling of Multiple Bag-of-Tasks Applications

International audienceThe bag-of-tasks application model, albeit simple, arises in many application domains and has received a lot of attention in the scheduling literature. Previous works propose either theoretically sound solutions that rely on unrealistic assumptions, or ad-hoc heuristics with no guarantees on performance. This work attempts to bridge this gap through the design of non-clairvoyant heuristics based on solid theoretical foundations. The performance achieved by these heuristics is studied via simulations in a view to comparing them both to previously proposed solutions and to theoretical upper bounds on achievable performance. Also, an interesting theoretical result in this work is that a straightforward on-demand heuristic delivers asymptotically optimal performance when the communications or the computations can be neglected

The ISQoS Grid Broker for Temporal and Budget Guarantees

We introduce our Grid broker that uses SLAs in job submission with the aim of ensuring jobs are computed on time and on budget. We demonstrate our broker's ability to perform negotiation and to select preferentially higher priority jobs, in a tender market and discuss the architecture that makes this possible. We additionally show the effects of rescheduling and how careful consideration is required in order to avoid price instability. We therefore make recommendations upon how to maintain this stability, given rescheduling