46,270 research outputs found
A Massively Scalable Architecture For Instant Messaging & Presence
This paper analyzes the scalability of Instant Messaging & Presence (IM&P) architectures. We take a queueing-based modelling and analysis approach to ďŹnd the bottlenecks of the current IM&P architecture at the Dutch social network Hyves, as well as of alternative architectures. We use the Hierarchical Evaluation Tool (HIT) to create and analyse models analytically. Based on these results, we recommend a new architecture that provides better scalability than the current one. \u
A methodological approach to BISDN signalling performance
Sophisticated signalling protocols are required to properly handle the complex multimedia, multiparty services supported by the forthcoming BISDN. The implementation feasibility of these protocols should be evaluated during their design phase, so that possible performance bottlenecks are identified and removed. In this paper we present a methodology for evaluating the performance of BISDN signalling systems under design. New performance parameters are introduced and their network-dependent values are extracted through a message flow model which has the capability to describe the impact of call and bearer control separation on the signalling performance. Signalling protocols are modelled through a modular decomposition of the seven OSI layers including the service user to three submodels. The workload model is user descriptive in the sense that it does not approximate the direct input traffic required for evaluating the performance of a layer protocol; instead, through a multi-level approach, it describes the actual implications of user signalling activity for the general signalling traffic. The signalling protocol model is derived from the global functional model of the signalling protocols and information flows using a network of queues incorporating synchronization and dependency functions. The same queueing approach is followed for the signalling transfer network which is used to define processing speed and signalling bandwidth requirements and to identify possible performance bottlenecks stemming from the realization of the related protocols
Towards a user oriented analytical approach to learning design
The London Pedagogy Planner (LPP) is a prototype for a collaborative online planning and design tool that supports lecturers in developing, analysing and sharing learning designs. The tool is based on a developing model of the components involved in learning design and the critical relationships between them. As a decision tool it makes the pedagogical design explicit as an output from the process, capturing it for testing, redesign, reuse and adaptation by the originator, or by others. The aim is to test the extent to which we can engage lecturers in reflecting on learning design, and make them part of the educational community that discovers how best to use technologyâenhanced learning. This paper describes the development of LPP, presents pedagogical benefits of visual representations of learning designs and proposes an analytical approach to learning design based on these visual representations. The analytical approach is illustrated based on an initial evaluation with a small group of lecturers from two partner institutions
Toward a user-oriented analytical approach to learning design
The London Pedagogy Planner (LPP) is a prototype for a collaborative online planning and design tool that supports lecturers in developing, analysing and sharing learning designs. The tool is based on a developing model of the components involved in learning design, and the critical relationships between them. As a decision tool, it makes the pedagogical design explicit as an output from the process, capturing it for testing, redesign, reuse and adaptation by the originator, or by others. The aim is to test the extent to which we can engage lecturers in reflecting on learning design, and make them part of the educational community that discovers how best to use Technology Enhanced Learning (TEL). This paper describes the development of LPP, presents pedagogical benefits of visual representations of learning designs, and proposes an analytical approach to learning design based on these visual representations. The analytical approach is illustrated based on an initial evaluation with the lecturers
Energy-Aware Cloud Management through Progressive SLA Specification
Novel energy-aware cloud management methods dynamically reallocate
computation across geographically distributed data centers to leverage regional
electricity price and temperature differences. As a result, a managed VM may
suffer occasional downtimes. Current cloud providers only offer high
availability VMs, without enough flexibility to apply such energy-aware
management. In this paper we show how to analyse past traces of dynamic cloud
management actions based on electricity prices and temperatures to estimate VM
availability and price values. We propose a novel SLA specification approach
for offering VMs with different availability and price values guaranteed over
multiple SLAs to enable flexible energy-aware cloud management. We determine
the optimal number of such SLAs as well as their availability and price
guaranteed values. We evaluate our approach in a user SLA selection simulation
using Wikipedia and Grid'5000 workloads. The results show higher customer
conversion and 39% average energy savings per VM.Comment: 14 pages, conferenc
Mixed Initiative Systems for Human-Swarm Interaction: Opportunities and Challenges
Human-swarm interaction (HSI) involves a number of human factors impacting
human behaviour throughout the interaction. As the technologies used within HSI
advance, it is more tempting to increase the level of swarm autonomy within the
interaction to reduce the workload on humans. Yet, the prospective negative
effects of high levels of autonomy on human situational awareness can hinder
this process. Flexible autonomy aims at trading-off these effects by changing
the level of autonomy within the interaction when required; with
mixed-initiatives combining human preferences and automation's recommendations
to select an appropriate level of autonomy at a certain point of time. However,
the effective implementation of mixed-initiative systems raises fundamental
questions on how to combine human preferences and automation recommendations,
how to realise the selected level of autonomy, and what the future impacts on
the cognitive states of a human are. We explore open challenges that hamper the
process of developing effective flexible autonomy. We then highlight the
potential benefits of using system modelling techniques in HSI by illustrating
how they provide HSI designers with an opportunity to evaluate different
strategies for assessing the state of the mission and for adapting the level of
autonomy within the interaction to maximise mission success metrics.Comment: Author version, accepted at the 2018 IEEE Annual Systems Modelling
Conference, Canberra, Australi
A Big Data Analyzer for Large Trace Logs
Current generation of Internet-based services are typically hosted on large
data centers that take the form of warehouse-size structures housing tens of
thousands of servers. Continued availability of a modern data center is the
result of a complex orchestration among many internal and external actors
including computing hardware, multiple layers of intricate software, networking
and storage devices, electrical power and cooling plants. During the course of
their operation, many of these components produce large amounts of data in the
form of event and error logs that are essential not only for identifying and
resolving problems but also for improving data center efficiency and
management. Most of these activities would benefit significantly from data
analytics techniques to exploit hidden statistical patterns and correlations
that may be present in the data. The sheer volume of data to be analyzed makes
uncovering these correlations and patterns a challenging task. This paper
presents BiDAl, a prototype Java tool for log-data analysis that incorporates
several Big Data technologies in order to simplify the task of extracting
information from data traces produced by large clusters and server farms. BiDAl
provides the user with several analysis languages (SQL, R and Hadoop MapReduce)
and storage backends (HDFS and SQLite) that can be freely mixed and matched so
that a custom tool for a specific task can be easily constructed. BiDAl has a
modular architecture so that it can be extended with other backends and
analysis languages in the future. In this paper we present the design of BiDAl
and describe our experience using it to analyze publicly-available traces from
Google data clusters, with the goal of building a realistic model of a complex
data center.Comment: 26 pages, 10 figure
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