Many techniques have been proposed to scale web applications. However, the data interdependencies between the database queries and transactions issued by the applications limit their efficiency. We claim that major scalability improvements can be gained by restructuring the web application data into multiple independent data services with exclusive access to their private data store. While this restructuring does not provide performance gains by itself, the implied simplification of each database workload allows a much more efficient use of classical techniques. We illustrate the data denormalization process on three benchmark applications: TPC-W, RUBiS and RUBBoS. We deploy the resulting service-oriented implementation of TPC-W across an 85-node cluster and show that restructuring its data can provide at least an order of magnitude improvement in the maximum sustainable throughput compared to master-slave database replication, while preserving strong consistency and transactional properties.

Creating good adaptation policies is critical to building complex autonomic systems since it is such policies that define the system configuration used in any given situation. While online approaches based on control theory and rulebased expert systems are possible solutions, each has its disadvantages. Here, a hybrid approach is described that uses modeling and optimization offline to generate suitable configurations, which are then encoded as policies that are used at runtime. The approach is demonstrated on the problem of providing dynamic management in virtualized consolidated server environments that host multiple multi-tier applications. Contributions include layered queuing models for Xen-based virtual machine environments, a novel optimization technique that uses a combination of bin packing and gradient search, and experimental results that show that automatic offline policy generation is viable and can be accurate even with modest computational effort. 1

Abstract—Virtual machine technology enables highly agile system deployments in which components can be cheaply moved, cloned, and allocated controlled hardware resources. In this paper, we examine in the context of multitier Enterprise applications, how these facilities can be used to provide enhanced solutions to the classic problem of ensuring high availability without a loss in performance on a fixed amount of resources. By using virtual machine clones to restore the redundancy of a system whenever component failures occur, we achieve improved availability compared to a system with a fixed redundancy level. By smartly controlling component placement and colocation using information about the multitier system’s flows and predictions made by queuing models, we ensure that the resulting performance degradation is minimized. Simulation results show that our proposed approach provides better availability and significantly lower degradation of system response times compared to traditional approaches. I.

Virtual machine technology enables agile system deployments in which software components can be cheaply moved, replicated, and allocated hardware resources in a controlled fashion. This paper examines how these facilities can be used to provide enhanced solutions to the classic problem of ensuring high availability while maintaining performance. By regenerating software components to restore the redundancy of a system whenever failures occur, we achieve improved availability compared to a system with a fixed redundancy level. Moreover, by smartly controlling component placement and resource allocation using information about application control flow and performance predictions from queuing models, we ensure that the resulting performance degradation is minimized. We consider an environment in which a collection of multitier enterprise applications operates across multiple hosts, racks, clusters, and data centers to maximize failure independence. Simulation results show that our proposed approach provides better availability and significantly lower degradation of system response times compared to traditional approaches. 1.

Abstract—Capacity management of a hosting infrastructure has traditionally focused only on performance goals. However, the quality of service provided to the hosted applications, and ultimately the revenues achieved by the provider, depend also on other aspects, such as security and energy constraints. This paper extends our self-adaptive SLA-driven capacity management solution to capture, in an unified framework, key performance and cost tradeoffs that arise when operating under security attacks and energy constraints. A number of scenarios and strategies based on dynamic SLA contracts are designed to help uncover, via simulation experiments, the main tradeoffs, considering both the provider’s interests (i.e., revenues) and the customer’s interests (i.e., legitimate throughput, response time distribution and costs). Finally, we also assess the cost-effectiveness of our framework under highly variable application service times. I.