21,949 research outputs found

    Collaborative Policy-Based Autonomic Management in IaaS Clouds

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    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

    Conclusions from the European Roadmap on Control of Computing Systems

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    The use of control-based methods for resource management in real-time computing and communication systems has gained a substantial interest recently. Applications areas include performance control of web-servers, dynamic resource management in embedded systems, traffic control in communication networks, transaction management in database servers, error control in software systems, and autonomic computing. Within the European EU/IST FP6 Network of Exellence ARTIST2 on Embedded System Design a roadmap on Control of Real-Time Computing Systems has recently been completed. The focus of the roadmap is how flexibility, adaptivity, performance and robustness can be achieved in a real-time computing or communication system through the use of control theory. The item that is controlled is in most cases the allocation of computing and communication resources, e.g., the distribution or scheduling of CPU time among different competing tasks, jobs, requests, or transactions, or the communication resources in a network. Due to this, control of computing systems also goes under the name of feedback scheduling. The roadmap is divided into six research areas: control of server systems, control of CPU resources, control of communication networks, error control of software systems, feedback scheduling of control systems, and control middleware. For each area an overview is given and challenges for future research are stated. The aim of this position paper is to summarize the conclusions concerning these research challenges. In this paper, we will only cover the first four of the areas above. A preliminary version of the roadmap can be found on http://www.control.lth.se/user/karlerik/roadmap1.pd

    Real-time transaction processing for autonomic grid application

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    The advances in computing and communication technologies and software have resulted in an explosive growth in computing systems and applications that impact all aspects of our life. Computing systems are expected to be effective and serve useful purpose when they are first introduced and continue to be useful as condition changes. With increase in complexity of systems and applications, their development, configuration, and management challenges are beyond the capabilities of existing tools and methodologies. So the system becomes unmanageable and insecure. So in order to make the systems selfmanageable and secure the concept of Autonomic computing is evolved. Autonomic computing offers a potential solution to these challenging research problems. It is inspired by nature and biological systems (such as the autonomic nervous system) that have evolved to cope with the challenges of scale, complexity, heterogeneity and unpredictability by being decentralized, context aware, adaptive and resilient. This new era of computing is driven by the convergence of biological and digital computing systems and is characterized by being self-defining, self-configuring, self-optimizing, self-protecting, self-healing, context aware and anticipatory. Autonomic computing is a new computing model to self manages computing systems with a minimal human interference. It provides an unprecedented level of self-regulation and hides complexity from Users. The Autonomic computing initiative is inspired by the human body’s autonomic nervous system. The autonomic nervous system monitors the heart- beats, checks blood sugar levels and maintains normal body temperature with out any conscious effort from the human. There is an important distinction between autonomic activity in the human body and autonomic responses in computer systems. Many of the decision made autonomic elements in computer systems make decisions based on tasks, which are chosen to be delegated to the technology. The influences of the autonomic nervous systems may imply that the autonomic computing initiative is concerned only with lowlevel self-managing capability such as reflex reaction. The basic application area of autonomic computing is grid computing. Both autonomic computing and grid computing are proposed as innovations of IT. Autonomic computing aims to present a solution to the rapidly increasing complexity crises in IT industry, as grid computing tries to share and integrate distributed computational resources and data resources. Basic aim is to implement the autonomic computing in grid related study like autonomic task distribution and handling in grids, and autonomic resource allocation. In this thesis paper we presents methods of calculating deadlines of global and local transaction And sub transaction by taking EDF algorithm and measure the performance by taking miss ratio in Different workload. We implement this work in an existing grid. The basic aim is to know autonomic computing better. It is a model to self manage computing Systems with minimal human interference. Self manage has properties like self-configuration, self-optimization, self-healing, self-protection. Autonomic grid computing combines autonomic computing with grid technologies to help companies to reduce the complexity associated with the grid system and hides the complexity from their grid user. Autonomic real-time transaction services incorporate fault tolerance into autonomic grid technology by automatically recovering systems from various failures. Here in this paper Deadlines of global transaction, sub transaction and local transaction are calculated by taking parameters arrival time, execution time, relative deadline, and slack time. We are taking a periodic transaction having λ (transaction arrival rate per second) Tasks are generated at different nodes with Poisson ratio with λ as workload. Miss ratio is the performance metrics. With increase in workload miss ratio first decreased and then rose. The reason was each sub transaction acted as a unit to compete for resources so that more workload the more system resource they consumed. So more transaction missed their deadlines, as they could not get enough resource in time. EDF algorithm has both less global and local miss ratios then other scheduling algorithm. If EDF is compare with FCFS or SJF or HPF it is apparent that both algorithms perform almost identically until no of transaction is low, then EDF misses fewer dead lines than other. Real-time transaction can handled by the grid in autonomic environment and satisfy properties of autonomic computing

    Towards a global autonomic management and integration of heterogeneous networks and multimedia services

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    Abstract. Traditionally, networks and systems are manually managed. It usually takes one or more human operators to manage all aspects of a dynamically evolving computing and communicating system. The operator is tightly integrated in this management process, and his tasks range from defining high-level policies to executing low-level system commands for immediate problem solving. Although this form of human-in-the-loop management was appropriate in the past, it has become increasingly unsuitable for modern networked computing systems and telecommunication. The potential advantage that autonomic computing brings is reducing the cost and complexity of managing Information and Communication Technology Infrastructure(ICT). The objectives of this paper are to underline the characteristics of autonomic architectures and present an outline of our autonomic management architecture based on OGSA (Open Grid Services Architecture) and Peer-to-Peer model. The autonomic management architectures of CISCO and IBM are briefly described and compared with our autonomic management architecture

    Multi-level Autonomic Business Process Management

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    The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-38484-4_14Nowadays, business processes are becoming increasingly complex and heterogeneous. Autonomic Computing principles can reduce this complexity by autonomously managing the software systems and the running processes, their states and evolution. Business Processes that are able to be self-managed are referred to as Autonomic Business Processes (ABP). However, a key challenge is to keep the models of such ABP understandable and expressive in increasingly complex scenarios. This paper discusses the design aspects of an autonomic business process management system able to self-manage processes based on operational adaptation. The goal is to minimize human intervention during the process definition and execution phases. This novel approach, named MABUP, provides four well-defined levels of abstraction to express business and operational knowledge and to guide the management activity; namely, Organizational Level, Technological Level, Operational Level and Service Level. A real example is used to illustrate our proposal.Research supported by CAPES, CNPQ and Spanish Ministry of Science and Innovation.Oliveira, K.; Castro, J.; España Cubillo, S.; Pastor López, O. (2013). Multi-level Autonomic Business Process Management. En Enterprise, Business-Process and Information Systems Modeling. Springer. 184-198. doi:10.1007/978-3-642-38484-4_14S184198España, S., González, A., Pastor, Ó.: Communication Analysis: A Requirements Engineering Method for Information Systems. In: van Eck, P., Gordijn, J., Wieringa, R. (eds.) CAiSE 2009. LNCS, vol. 5565, pp. 530–545. Springer, Heidelberg (2009)Ganek, A.G., Corbi, T.A.: The dawning of the autonomic computing era. IBM Systems Journal 42(1), 5–18 (2003)Gonzalez, A., et al.: Unity criteria for Business Process Modelling. In: Third International Conference on Research Challenges in Information Science, RCIS 2009, pp. 155–164 (2009)Greenwood, D., Rimassa, G.: Autonomic Goal-Oriented Business Process Management. Management, 43 (2007)Haupt, T., et al.: Autonomic execution of computational workflows. In: 2011 Federated Conference on Computer Science and Information Systems, FedCSIS, pp. 965–972 (2011)Kephart, J.O., Chess, D.M.: The vision of autonomic computing. IEEE (2003)Lee, K., et al.: Workflow adaptation as an autonomic computing problem. In: Proceedings of the 2nd Workshop on Workflows in Support of Large-Scale Science, New York, NY, USA, pp. 29–34 (2007)Mosincat, A., Binder, W.: Transparent Runtime Adaptability for BPEL Processes. In: Bouguettaya, A., Krueger, I., Margaria, T. (eds.) ICSOC 2008. LNCS, vol. 5364, pp. 241–255. Springer, Heidelberg (2008)Oliveira, K., et al.: Towards Autonomic Business Process Models. In: International Conference on Software Engineering and Knowledge, SEKE 2012, San Francisco, California, USA (2012)Rahman, M., et al.: A taxonomy and survey on autonomic management of applications in grid computing environments. Concurr. Comput.: Pract. Exper. 23(16), 1990–2019 (2011)Reijers, H.A., Mendling, J.: Modularity in process models: Review and effects. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 20–35. Springer, Heidelberg (2008)Rodrigues Nt., J.A., Monteiro Jr., P.C.L., de O. Sampaio, J., de Souza, J.M., Zimbrão, G.: Autonomic Business Processes Scalable Architecture. In: ter Hofstede, A.H.M., Benatallah, B., Paik, H.-Y. (eds.) BPM Workshops 2007. LNCS, vol. 4928, pp. 78–83. Springer, Heidelberg (2008)Strohmaier, M., Yu, E.: Towards autonomic workflow management systems. ACM Press (2006)Terres, L.D., et al.: Selection of Business Process for Autonomic Automation. In: 2010 14th IEEE International Enterprise Distributed Object Computing Conference, pp. 237–246 (October 2010)Tretola, G., Zimeo, E.: Autonomic internet-scale workflows. In: Proceedings of the 3rd International Workshop on Monitoring, Adaptation and Beyond, New York, NY, USA, pp. 48–56 (2010)Vedam, H., Venkatasubramanian, V.: A wavelet theory-based adaptive trend analysis system for process monitoring and diagnosis. In: Proceedings of the 1997 American Control Conference, vol. 1, pp. 309–313 (June 1997)Wang, Y., Mylopoulos, J.: Self-Repair through Reconfiguration: A Requirements Engineering Approach. In: 2009 IEEE/ACM International Conference on Automated Software Engineering, pp. 257–268 (November 2009)Yu, T., Lin, K.: Adaptive algorithms for finding replacement services in autonomic distributed business processes. In: Proceedings Autonomous Decentralized Systems, ISADS 2005, pp. 427–434 (2005

    An Autonomic Cross-Platform Operating Environment for On-Demand Internet Computing

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    The Internet has evolved into a global and ubiquitous communication medium interconnecting powerful application servers, diverse desktop computers and mobile notebooks. Along with recent developments in computer technology, such as the convergence of computing and communication devices, the way how people use computers and the Internet has changed people´s working habits and has led to new application scenarios. On the one hand, pervasive computing, ubiquitous computing and nomadic computing become more and more important since different computing devices like PDAs and notebooks may be used concurrently and alternately, e.g. while the user is on the move. On the other hand, the ubiquitous availability and pervasive interconnection of computing systems have fostered various trends towards the dynamic utilization and spontaneous collaboration of available remote computing resources, which are addressed by approaches like utility computing, grid computing, cloud computing and public computing. From a general point of view, the common objective of this development is the use of Internet applications on demand, i.e. applications that are not installed in advance by a platform administrator but are dynamically deployed and run as they are requested by the application user. The heterogeneous and unmanaged nature of the Internet represents a major challenge for the on demand use of custom Internet applications across heterogeneous hardware platforms, operating systems and network environments. Promising remedies are autonomic computing systems that are supposed to maintain themselves without particular user or application intervention. In this thesis, an Autonomic Cross-Platform Operating Environment (ACOE) is presented that supports On Demand Internet Computing (ODIC), such as dynamic application composition and ad hoc execution migration. The approach is based on an integration middleware called crossware that does not replace existing middleware but operates as a self-managing mediator between diverse application requirements and heterogeneous platform configurations. A Java implementation of the Crossware Development Kit (XDK) is presented, followed by the description of the On Demand Internet Computing System (ODIX). The feasibility of the approach is shown by the implementation of an Internet Application Workbench, an Internet Application Factory and an Internet Peer Federation. They illustrate the use of ODIX to support local, remote and distributed ODIC, respectively. Finally, the suitability of the approach is discussed with respect to the support of ODIC

    Autonomic Computing

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    Autonomic computing (AC) has as its vision the creation of self-managing systems to address today’s con-cerns of complexity and total cost of ownership while meeting tomorrow’s needs for pervasive and ubiquitous computation and communication. This paper reports on the latest auto-nomic systems research and technologies to influence the industry; it looks behind AC, summarising what it is, the current state-of-the-art research, related work and initiatives, highlights research and technology transfer issues and concludes with further and recommended reading

    Coordinated Autonomic Managers for Energy Efficient Date Centers

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    The complexity of today’s data centers has led researchers to investigate ways in which autonomic methods can be used for data center management. Autonomic managers try to monitor and manage resources to ensure that the components they manage are self-configuring, self-optimizing, self-healing and self-protecting (so called “self-*” properties). In this research, we consider autonomic management systems for data centers with a particular focus on making data centers more energy-aware. In particular, we consider a policy based, multi-manager autonomic management systems for energy aware data centers. Our focus is on defining the foundations – the core concepts, entities, relationships and algorithms - for autonomic management systems capable of supporting a range of management configurations. Central to our approach is the notion of a “topology” of autonomic managers that when instantiated can support a range of different configurations of autonomic managers and communication among them. The notion of “policy” is broadened to enable some autonomic managers to have more direct control over the behavior of other managers through changes in policies. The ultimate goal is to create a management framework that would allow the data center administrator to a) define managed objects, their corresponding managers, management system topology, and policies to meet their operation needs and b) rely on the management system to maintain itself automatically. A data center simulator that computes its energy consumption (computing and cooling) at any given time is implemented to evaluate the impact of different management scenarios. The management system is evaluated with different management scenarios in our simulated data center
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