1 Placement (Scheduling) Optimal mapping of VMs – to physical hosts in a data center (cloud) – across multiple clouds Federation and bursting Multi-cloud service deployment Third-party broker scenarios When? – Admission of new service, upon elasticity, hardware failure, periodically Optimal? – Service Provider perspective Performance (hosts, VMs), cost, guarantees, non- functional criteria (location, isolation, trust, risk, eco- efficiency, etc.) – Infrastructure Provider perspective Provisioning cost, consolidation, isolation, SLA violations, etc.

2 Placement (cont.) Further considerations – Historical performance data – Benchmarking and application profiling – Co-location and (anti)affinity – End-user location – Data constraints (legislations) – Federation (lack of control over remote resources) – Dynamicity - providers, prices, performance, workloads, etc. change over time – (Live) Migration overhead – (end-user) SLAs – perspectives 1.All management actions are SLA-driven 2.Placement = SLA refinement 3.SLAs are just another criteria

Example Approach Combinatorial optimization formulations – Packing formulations for data centers (MMKP) Multi-dimensional (CPU, memory, disk, network), multi-choice (many physical hosts) Knapsack Problems Policies for load balancing, power saving (consolidation), SLA protection Scalability improvements (fractional 2-approximation) – 0-1 integer programming (assignment problems) for multi and federated clouds Optimize service performance and/or cost, with service layout (load balancing), budget, VM configuration, etc. as constraints. Model uncertainty (changing conditions in providers, offers, performance, etc.) and migration overhead – Approximations (greedy heuristics) for scalability Sense PlanAct

4 Placement - Experiences Reservoir (and IBM SUR grant) – Placement optimization within clouds and across federated clouds. SLA protection and/or load balancing, consolidation, revenue maximization OPTIMIS – Bursting and Federated/Multi-cloud deployment based on functional and non-functional criteria (trust, risk, eco-efficiency, cost) Vision Cloud – Placement of compute close to data Various Grid research projects – QoS, SLA management, advance reservations, co-allocation, fair-share scheduling, job management, performance predictions, etc

Outlook and perspectives Placement of services (that use compute, data, and networking) – Compute, data, and/or network intense – Network aware vs. managed networks Holistic view of placement problems for all cloud architectures Interactions with related problems – Time perspective (short - long) Placement and admission control – Abstraction level (low - high) Placement and governance

6 Selected references D. Breitgand, A. Marashini, and J. Tordsson. Policy-Driven Service Placement Optimization in Federated Clouds, IBM Haifa Labs technical report H-0299, 2011 J. Tordsson, R.S. Montero, R.M. Vozmediano, and I.M. Llorente. Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers. Future Generation Computer Systems, 2011, accepted. B. Rochwerger, J. Tordsson, C. Ragusa, D. Breitgand, S. Clayman, A. Epstein, D. Hadas, E. Levy, I. Loy, A. Maraschini, P. Massonet, H. Munoz, K. Nagin, G. Toffetti, and M. Villari. Reservoir - when one cloud is not enough, IEEE Computer 2011, accepted. W. Li, J. Tordsson, and E. Elmroth. Modelling for Dynamic Cloud Scheduling via Migration of Virtual Machines (tentative), in preparation, 2011 P-O Östberg, Virtual Infrastructures for Computational Science, PhD thesis, 2011 J. Tordsson. Portable Tools for Interoperable Grids, PhD thesis, 2009