The increasing popularity of modern virtualizationbased datacenters continues to motivate both industry and academia to provide answers to a large variety of new and challenging questions. In this paper we aim to answer focusing on one such question: how to improve performance and availability of services hosted on IaaS clouds. Our system, structural constraintaware virtual machine placement (SCAVP), supports three types of constraints: demand, communication and availability. We formulate SCAVP as an optimization problem and show its hardness. We design a hierarchical placement approach with four approximation algorithms that efficiently solves the SCAVP problem for large problem sizes. We provide a formal model for the application (to better understand structural constraints) and the datacenter (to effectively capture capabilities), and use the two models as inputs to the placement problem. We evaluate SCAVP in a simulated environment to illustrate the efficiency and importance of the proposed approach.
Abstract. Today's enterprise data centers support thousands of mission-critical business applications composed of multiple distributed heterogeneous components. Application components exhibit complex dependencies on the configuration of multiple data center network, middleware, and related application resources. Applications are also associated with extended life-cycles, migrating from development to testing, staging and production environments, with frequent roll-backs. Maintaining end-to-end data center operational integrity and quality requires careful planning of (1) application deployment design, (2) resource selection, (3) provisioning operation selection, parameterization and ordering, and (4) provisioning operation execution. Current data center management products are focused on workflow-based automation of the deployment processes. Workflows are of limited value because they hard-code many aspects of the process, and are thus sensitive to topology changes. An emerging and promising class of model-based tools is providing new methods for designing detailed deployment topologies based on a set of requirements and constraints. In this paper we describe an approach to bridging the gap between generated "desired state" models and the elemental procedural provisioning operations supported by data center resources. In our approach, we represent the current and desired state of the data center using object models. We use AI planning to automatically generate workflows that bring the data center from its current state to the desired state. We discuss our optimizations to Partial Order Planning algorithms for the provisioning domain. We validated our approach by developing and integrating a prototype with a state of the art provisioning product. We also present initial results of a performance study.
Due to the competitiveness of the computing industry, software developers are pressured to quickly deliver new code releases. At the same time, operators are expected to update and keep production systems stable at all times. To overcome the development-operations barrier, organizations have started to adopt Infrastructure as Code (IaC) tools to efficiently deploy middleware and applications using automation scripts. These automations comprise a series of steps that should be idempotent to guarantee repeatability and convergence. Rigorous testing is required to ensure that the system idempotently converges to a desired state, starting from arbitrary states. We propose and evaluate a model-based testing framework for IaC. An abstracted system model is utilized to derive state transition graphs, based on which we systematically generate test cases for the automation. The test cases are executed in light-weight virtual machine environments. Our prototype targets one popular IaC tool (Chef), but the approach is general. We apply our framework to a large base of public IaC scripts written by operators, showing that it correctly detects non-idempotent automations. (Eds.): Middleware 2013, LNCS 8275, pp. 368-388, 2013. c IFIP International Federation for Information Processing 2013 to testing idempotence and convergence of IaC automations. idemN: This coverage parameter specifies a set of task sequence lengths for which idempotence should be tested. The possible values range from idemN = {1} (idempotence of single tasks) to idemN = {1, . . . , |A|} (maximum sequence length covering all automation tasks). Evidently, higher values produce more test cases, whereas lower values have the risk that problems related to dependencies between "distant" tasks are potentially not detected (see also Section 7.2). repeatN: This parameter controls the number of times each task is (at most) repeated. If the automation is supposed to converge after a single run (most Chef recipes are designed that way, see our evaluation in Section 7), it is usually sufficient to have repeatN = 1, because many idempotence related problems are already detected after executing a task (sequence) twice. However, certain scenarios might require higher values for repeatN , in particular automations that are continuously repeated in order to eventually converge. The tester then has to use domain knowledge to set a reasonable upper bound of repetitions. restart: The boolean parameter restart determines whether tasks are arbitrarily repeated in the middle of the automation (restart = f alse), or the whole automation always gets restarted from scratch (restart = true). Consider our scenario automation with task sequence a 1 , a 2 , a 3 , a 4 . If we require idemN = 3 with restart = true, then the test cases could for instance include the task sequences . If restart = f alse, we have additional test cases, including a 1 , a 2 , a 3 , a 2 , a 3 , ... , a 1 , a 2 , a 3 , a 4 , a 2 , a 3 , ... , etc. f orceP re: This parameter specifies whether different pre-states f...
Abstract-This paper studies the design of low-cost survivable wavelength-division-multiplexing (WDM) networks. To achieve survivability, lightpaths are arranged as a set of rings. Arrangement in rings is also necessary to support SONET/SDH protection schemes such as 4FBLSR above the optical layer. This is expected to be the most common architecture in regional (metro) networks [9]. We assume that we are given a set of lightpaths in an arbitrary network topology and aim at finding a partition of the lightpaths to rings adding a minimum number of lightpaths to the original set. The cost measure that we consider (number of lightpaths) reflects the switching cost of the entire network. In the case of a SONET/SDH higher layer, the number of lightpaths is equal to the number of add-drop multiplexers (ADMs) (since two subsequent lightpaths in a ring can share an ADM at the common node).We prove some negative results on the tractability and approximability of the problem and provide an approximation algorithm with a worst case approximation ratio of 8/5. We study some special cases in which the performance of the algorithm is improved.A similar problem was introduced, motivated, and studied in [9] and recently in [13] (where it was termed minimum ADM problem). However, these two works focused on a ring topology while we generalize the problem to an arbitrary network topology.Index Terms-Optical network design, SONET add/drop multiplexers (ADMs), SONET rings, wavelength-division multiplexing (WDM).
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