1,570 research outputs found
A peer-to-peer infrastructure for resilient web services
This work is funded by GR/M78403 âSupporting Internet Computation in Arbitrary Geographical Locationsâ and GR/R51872 âReflective Application Framework for Distributed Architecturesâ, and by Nuffield Grant URB/01597/G âPeer-to-Peer Infrastructure for Autonomic Storage ArchitecturesâThis paper describes an infrastructure for the deployment and use of Web Services that are resilient to the failure of the nodes that host those services. The infrastructure presents a single interface that provides mechanisms for users to publish services and to find hosted services. The infrastructure supports the autonomic deployment of services and the brokerage of hosts on which services may be deployed. Once deployed, services are autonomically managed in a number of aspects including load balancing, availability, failure detection and recovery, and lifetime management. Services are published and deployed with associated metadata describing the service type. This same metadata may be used subsequently by interested parties to discover services. The infrastructure uses peer-to-peer (P2P) overlay technologies to abstract over the underlying network to deploy and locate instances of those services. It takes advantage of the P2P network to replicate directory services used to locate service instances (for using a service), Service Hosts (for deployment of services) and Autonomic Managers which manage the deployed services. The P2P overlay network is itself constructed using novel Web Services-based middleware and a variation of the Chord P2P protocol, which is self-managing.Postprin
Diagnosis in Policy-Based Autonomic Management
Policy-based Autonomic Management monitors a system and its applications and tweaks performance parameters in real-time based on a set of governing policies. A policy specifies a set of conditions under which one or more of a set of actions are to be performed. It is very common that multiple policiesâ conditions are met simultaneously, each advocating many actions. Deciding which action to perform is a non-trivial task. We propose a method of diagnosing the system to try to determine the best action or actions to perform in a given situation using Abductive Inference. We develop an original method of building a causality graph to facilitate diagnosis directly from a set of policies. Performance of the diagnosis method is measured by implementing diagnosis into an existing autonomic management application and monitoring the performance of a LAMP (Linux, Apache, MySQL, PHP) server being governed by the manager. The results are favourable when compared to previous methods of action selection and to the server running without the autonomic manager
Energy-QoS Tradeoffs in J2EE Hosting Centers
International audienceNowadays, hosting centres are widely used to host various kinds of applications e.g., web servers or scientific applications. Resource management is a major challenge for most organisations that run these infrastructures. Many studies show that clusters are not used at their full capacity which represents a significant source of waste. Autonomic management systems have been introduced in order to dynamically adapt software infrastructures according to runtime conditions. They provide support to deploy, configure, monitor, and repair applications in such environments. In this paper, we report our experiments in using an autonomic management system to provide resource aware management for a clustered application. We consider a standard replicated server infrastructure in which we dynamically adapt the degree of replication in order to ensure a given QoS while minimising energy consumption
Towards Autonomic Service Provisioning Systems
This paper discusses our experience in building SPIRE, an autonomic system
for service provision. The architecture consists of a set of hosted Web
Services subject to QoS constraints, and a certain number of servers used to
run session-based traffic. Customers pay for having their jobs run, but require
in turn certain quality guarantees: there are different SLAs specifying charges
for running jobs and penalties for failing to meet promised performance
metrics. The system is driven by an utility function, aiming at optimizing the
average earned revenue per unit time. Demand and performance statistics are
collected, while traffic parameters are estimated in order to make dynamic
decisions concerning server allocation and admission control. Different utility
functions are introduced and a number of experiments aiming at testing their
performance are discussed. Results show that revenues can be dramatically
improved by imposing suitable conditions for accepting incoming traffic; the
proposed system performs well under different traffic settings, and it
successfully adapts to changes in the operating environment.Comment: 11 pages, 9 Figures,
http://www.wipo.int/pctdb/en/wo.jsp?WO=201002636
Autonomic Management Policy SpeciïŹcation: from UML to DSML
International audienceAutonomic computing is recognized as one of the most promizing solutions to address the increasingly complex task of distributed environments' administration. In this context, many projects relied on software components and architectures to provide autonomic management frameworks. We designed such a component-based autonomic management framework, but observed that the interfaces of a component model are too low-level and difficult to use. Therefore, we introduced UML diagrams for the modeling of deployment and management policies. However, we had to adapt/twist the UML semantics in order to meet our requirements, which led us to define DSMLs. In this paper, we present our experience in designing the Tune system and its support for management policy specification, relying on UML diagrams and on DSMLs. We analyse these two approaches, pinpointing the benefits of DSMLs over UML
Implementing autonomic administration DSLs in TUNe
Software components are recognized as the most adequate approach to support autonomic administration systems. We implemented and experimented with such a system, but observed that the interfaces of a component model are too low-level and difficult to use. Consequently, we designed higher abstraction level languages for modeling administration policies. These languages are specific to our autonomic administration domain. We metamodeled and implemented these DSLs on the Kermeta framework
Collaborative Policy-Based Autonomic Management in IaaS Clouds
With the increasing number of machines (either virtual or physical) in a computing environment, it is becoming harder to monitor and manage these resources. Relying on human administrators, even with tools, is expensive and the growing complexity makes management even harder. The alternative is to look for automated approaches that can monitor and manage computing resources in real time with no human intervention. One of the approaches to this problem is policy-based autonomic management. However, in large systems having one single autonomic manager to manage everything is almost impossible. Therefore, multiple autonomic managers will be needed and these will need to cooperate in the overall management. We propose a management model using multiple autonomic managers organized in a hierarchical fashion to monitor and manage the resources in a computing environment based on provided policies. We develop a communication protocol to facilitate collaboration between different autonomic managers, define the core operations of these managers and introduce algorithms to deal with their deployment and operation. We also introduce an approach for the inference of the communication messages from policies and develop several algorithms for joining and maintaining the management hierarchy. We propose a deployment system that can discover relevant resources in a computing environment automatically to facilitate the deployment of autonomic managers at different levels of a physical system. We then test our approach by implementing it in a small private Infrastructure-as-a-Service (IaaS) cloud and show how this collaboration of autonomic managers in a hierarchical way can help to adopt to high stress situations automatically and reduce the SLA violation rate without adding any new resource to the environment
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A Control Theory Foundation for Self-Managing Computing Systems
The high cost of operating large computing installations has motivated a broad interest in reducing the need for human intervention by making systems self-managing. This paper explores the extent to which control theory can provide an architectural and analytic foundation for building self-managing systems. Control theory provides a rich set of methodologies for building automated self-diagnosis and self-repairing systems with properties such as stability, short settling times, and accurate regulation. However, there are challenges in applying control theory to computing systems, such as developing effective resource models, handling sensor delays, and addressing lead times in effector actions. We propose a deployable testbed for autonomic computing (DTAC) that we believe will reduce the barriers to addressing research problems in applying control theory to computing systems. The initial DTAC architecture is described along with several problems that it can be used to investigate
Towards a generic autonomic architecture for legacy resource management
Half a decade has passed since the objectives and benefits of autonomic computing were stated, yet even the latest system designs and deployments exhibit only limited and isolated elements of autonomic functionality. From an autonomic computing standpoint, all computing systems â old, new or under development â are legacy systems, and will continue to be so for some time to come. In this paper, we propose a generic architecture for developing fully-fledged autonomic systems out of legacy, non-autonomic components, and we investigate how existing technologies can be used to implement this architecture
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