205,840 research outputs found
Design and Implementation of a Measurement-Based Policy-Driven Resource Management Framework For Converged Networks
This paper presents the design and implementation of a measurement-based QoS
and resource management framework, CNQF (Converged Networks QoS Management
Framework). CNQF is designed to provide unified, scalable QoS control and
resource management through the use of a policy-based network management
paradigm. It achieves this via distributed functional entities that are
deployed to co-ordinate the resources of the transport network through
centralized policy-driven decisions supported by measurement-based control
architecture. We present the CNQF architecture, implementation of the prototype
and validation of various inbuilt QoS control mechanisms using real traffic
flows on a Linux-based experimental test bed.Comment: in Ictact Journal On Communication Technology: Special Issue On Next
Generation Wireless Networks And Applications, June 2011, Volume 2, Issue 2,
Issn: 2229-6948(Online
TCG based approach for secure management of virtualized platforms: state-of-the-art
There is a strong trend shift in the favor of adopting virtualization to get business benefits. The provisioning of virtualized enterprise resources is one kind of many possible scenarios. Where virtualization promises clear advantages it also poses new security challenges which need to be addressed to gain stakeholders confidence in the dynamics of new environment. One important facet of these challenges is establishing 'Trust' which is a basic primitive for any viable business model. The Trusted computing group (TCG) offers technologies and mechanisms required to establish this trust in the target platforms. Moreover, TCG technologies enable protecting of sensitive data in rest and transit. This report explores the applicability of relevant TCG concepts to virtualize enterprise resources securely for provisioning, establish trust in the target platforms and securely manage these virtualized Trusted Platforms
Policy-based techniques for self-managing parallel applications
This paper presents an empirical investigation of policy-based self-management techniques for parallel applications executing in loosely-coupled environments. The dynamic and heterogeneous nature of these environments is discussed and the special considerations for parallel applications are identified. An adaptive strategy for the run-time deployment of tasks of parallel applications is presented. The strategy is based on embedding numerous policies which are informed by contextual and environmental inputs. The policies govern various aspects of behaviour, enhancing flexibility so that the goals of efficiency and performance are achieved despite high levels of environmental variability. A prototype self-managing parallel application is used as a vehicle to explore the feasibility and benefits of the strategy. In particular, several aspects of stability are investigated. The implementation and behaviour of three policies are discussed and sample results examined
Query management in a sensor environment
Traditional sensor network deployments consisted of fixed infrastructures and were relatively small in size. More and more, we see the deployment of ad-hoc sensor networks with heterogeneous devices on a larger scale, posing new challenges for device management and query processing. In this paper, we present our design and prototype implementation of XSense, an architecture supporting metadata and query services for an underlying large scale dynamic P2P sensor network. We cluster sensor devices into manageable groupings to optimise the query process and automatically locate appropriate clusters based on keyword abstraction from queries. We present experimental analysis to show the benefits of our approach and demonstrate improved query performance and scalability
DRS: Dynamic Resource Scheduling for Real-Time Analytics over Fast Streams
In a data stream management system (DSMS), users register continuous queries,
and receive result updates as data arrive and expire. We focus on applications
with real-time constraints, in which the user must receive each result update
within a given period after the update occurs. To handle fast data, the DSMS is
commonly placed on top of a cloud infrastructure. Because stream properties
such as arrival rates can fluctuate unpredictably, cloud resources must be
dynamically provisioned and scheduled accordingly to ensure real-time response.
It is quite essential, for the existing systems or future developments, to
possess the ability of scheduling resources dynamically according to the
current workload, in order to avoid wasting resources, or failing in delivering
correct results on time. Motivated by this, we propose DRS, a novel dynamic
resource scheduler for cloud-based DSMSs. DRS overcomes three fundamental
challenges: (a) how to model the relationship between the provisioned resources
and query response time (b) where to best place resources; and (c) how to
measure system load with minimal overhead. In particular, DRS includes an
accurate performance model based on the theory of \emph{Jackson open queueing
networks} and is capable of handling \emph{arbitrary} operator topologies,
possibly with loops, splits and joins. Extensive experiments with real data
confirm that DRS achieves real-time response with close to optimal resource
consumption.Comment: This is the our latest version with certain modificatio
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