133 research outputs found
Developing Real-Time Emergency Management Applications: Methodology for a Novel Programming Model Approach
Get PDFThe last years have been characterized by the arising of highly distributed computing
platforms composed of a heterogeneity of computing and communication resources including
centralized high-performance computing architectures (e.g. clusters or large shared-memory
machines), as well as multi-/many-core components also integrated into mobile nodes
and network facilities. The emerging of computational paradigms such as Grid and Cloud
Computing, provides potential solutions to integrate such platforms with data systems, natural
phenomena simulations, knowledge discovery and decision support systems responding to a
dynamic demand of remote computing and communication resources and services.
In this context time-critical applications, notably emergency management systems, are
composed of complex sets of application components specialized for executing specific
computations, which are able to cooperate in such a way as to perform a global goal in a
distributed manner. Since the last years the scientific community has been involved in facing
with the programming issues of distributed systems, aimed at the definition of applications
featuring an increasing complexity in the number of distributed components, in the spatial
distribution and cooperation between interested parties and in their degree of heterogeneity.
Over the last decade the research trend in distributed computing has been focused on
a crucial objective. The wide-ranging composition of distributed platforms in terms of
different classes of computing nodes and network technologies, the strong diffusion of
applications that require real-time elaborations and online compute-intensive processing as
in the case of emergency management systems, lead to a pronounced tendency of systems
towards properties like self-managing, self-organization, self-controlling and strictly speaking
adaptivity.
Adaptivity implies the development, deployment, execution and management of applications
that, in general, are dynamic in nature. Dynamicity concerns the number and the specific
identification of cooperating components, the deployment and composition of the most
suitable versions of software components on processing and networking resources and
services, i.e., both the quantity and the quality of the application components to achieve
the needed Quality of Service (QoS). In time-critical applications the QoS specification
can dynamically vary during the execution, according to the user intentions and the
Developing Real-Time Emergency
Management Applications: Methodology for
a Novel Programming Model Approach
Gabriele Mencagli and Marco Vanneschi
Department of Computer Science, University of Pisa, L. Bruno Pontecorvo, Pisa
Italy
2
2 Will-be-set-by-IN-TECH
information produced by sensors and services, as well as according to the monitored state
and performance of networks and nodes.
The general reference point for this kind of systems is the Grid paradigm which, by
definition, aims to enable the access, selection and aggregation of a variety of distributed and
heterogeneous resources and services. However, though notable advancements have been
achieved in recent years, current Grid technology is not yet able to supply the needed software
tools with the features of high adaptivity, ubiquity, proactivity, self-organization, scalability
and performance, interoperability, as well as fault tolerance and security, of the emerging
applications.
For this reason in this chapter we will study a methodology for designing high-performance
computations able to exploit the heterogeneity and dynamicity of distributed environments
by expressing adaptivity and QoS-awareness directly at the application level. An effective
approach needs to address issues like QoS predictability of different application configurations
as well as the predictability of reconfiguration costs. Moreover adaptation strategies need to
be developed assuring properties like the stability degree of a reconfiguration decision and the
execution optimality (i.e. select reconfigurations accounting proper trade-offs among different
QoS objectives). In this chapter we will present the basic points of a novel approach that lays
the foundations for future programming model environments for time-critical applications
such as emergency management systems.
The organization of this chapter is the following. In Section 2 we will compare the existing
research works for developing adaptive systems in critical environments, highlighting their
drawbacks and inefficiencies. In Section 3, in order to clarify the application scenarios that
we are considering, we will present an emergency management system in which the run-time
selection of proper application configuration parameters is of great importance for meeting the
desired QoS constraints. In Section 4we will describe the basic points of our approach in terms
of how compute-intensive operations can be programmed, how they can be dynamically
modified and how adaptation strategies can be expressed. In Section 5 our approach will
be contextualize to the definition of an adaptive parallel module, which is a building block
for composing complex and distributed adaptive computations. Finally in Section 6 we will
describe a set of experimental results that show the viability of our approach and in Section 7
we will give the concluding remarks of this chapter
Adaptive model predictive control of autonomic distributed parallel computations with variable horizons and switching costs
Get PDFAutonomic computing is a paradigm for building systems capable of adapting their operation when external changes occur, such as workload variations, load surges and changes in the resource availability. The optimal configuration in terms of the number of computing resources assigned to each component must be automatically adjusted to the new environmental conditions. To accomplish the execution goals with the desired Quality of Service, decision-making strategies should be in charge of selecting the best reconfigurations by taking into account metrics like performance, efficiency (avoiding wasting resources), number and frequency of reconfigurations, and their amplitude (performing minimal modifications of the current configuration). This paper presents a decision-making strategy that merges the potential of Model Predictive Control with a cooperative optimization framework. After a description of our approach, we investigate the effect of different switching costs to model the resource allocation problem. We use a control method in which our proactive decision-making strategy (designed to use future prediction horizons) is made adaptive itself by dynamically changing the horizon length on the basis of the prediction errors. Simulations have been used to exemplify our approach and to discuss the effectiveness of the variable-horizon strategy in achieving the best trade-offs between reconfiguration metrics
A Game-Theoretic Approach for Elastic Distributed Data Stream Processing
Get PDFDistributed data stream processing applications are structured as graphs of interconnected modules able to ingest high-speed data and to transform them in order to generate results of interest. Elasticity is one of the most appealing features of stream processing applications. It makes it possible to scale up/down the allocated computing resources on demand in response to fluctuations of the workload. On clouds, this represents a necessary feature to keep the operating cost at affordable levels while accommodating user-defined QoS requirements. In this article, we study this problem from a game-theoretic perspective. The control logic driving elasticity is distributed among local control agents capable of choosing the right amount of resources to use by each module. In a first step, we model the problem as a noncooperative game in which agents pursue their self-interest. We identify the Nash equilibria and we design a distributed procedure to reach the best equilibrium in the Pareto sense. As a second step, we extend the noncooperative formulation with a decentralized incentive-based mechanism in order to promote cooperation by moving the agreement point closer to the system optimum. Simulations confirm the results of our theoretical analysis and the quality of our strategies
QoS-control of Structured Parallel Computations: a Predictive Control Approach
No full textA central issue for parallel applications executed on heterogeneous distributed platforms (e.g. Grids and Clouds) is assuring that performance and cost parameters are optimized throughout the execution. A solution is based on providing application components with adaptation strategies able to select at run-time the best component configuration. In this report we will introduce a preliminary work concerning the exploitation of control-theoretic techniques for controlling the Quality of Service of parallel computations. In particular we will demonstrate how the model-based predictive control strategy can be used based on first-principle performance models of structured parallelism schemes. We will also evaluate the viability of our approach on a first experimental scenario.<br /
Analysis of Control-theoretic Predictive Strategies for the Adaptation of Distributed Parallel Computations
No full textIn adaptive distributed parallel applications the adaptation
process is based on the ability to change some characteristics
of parallel components, such as the parallelism form and
the parallelism degree, in response to unexpected execution
conditions. Although existing research work has studied this
problem, it is of increasing importance to investigate adaptation
strategies able to reach important properties like the
stability of control decisions, i.e. to guarantee that recon-
gurations are eective and durable, and control optimality,
expressed by means of cooperative and non-cooperative
agreements between decisions of dierent controllers. These
properties are crucial in distributed environments like Grids
and Clouds, where recongurations imply a cost both in
terms of a performance degradation as well as a monetary
charge. In this paper we brie
y introduce the basic ideas
of our methodology and we introduce dierent adaptation
strategies based on alternative formulations of the Modelbased
Predictive Control technique. First hints about the
eectiveness of our approach are discussed through experiments
developed in a simulation environment
Run-time mechanisms for fine-grained parallelism on network processors: The TILEPro64 experience
No full textThe efficient parallelization of very ne-grained computations is an old problem still challenging also on modern shared memory architectures. Scalable parallelizations are possi ble if the base mechanisms provided by the run-time support (for inter-thread/inter-process synchronization/communication) are carefully designed and developed on top of parallel architec tures. This requires a deep knowledge of the hardware behavior and the interaction patterns used by the parallelism paradigms. In this paper we present our experience in developing e cient inter-thread interaction mechanisms on the THera TILEPr064 network processor. Although it is a domain-speci c parallel architecture, the TILEPr064 represents a notable example of how advanced architectural structures, such as user-accessible on chip interconnection networks and con gurable cache coherence protocols, are of great importance to design lightweight coop eration mechanisms enabling e cient parallel implementations of ne-grained problems. The paper presents our ideas and an experimental evaluation that compares our proposals with other existing run-time supports
Towards a Systematic Approach to the Dynamic Adaptation of Structured Parallel Computations Using Model Predictive Control
No full textAdaptiveness is an essential feature for distrib- uted parallel applications executed on dynamic environments like Grids and Clouds. Being adaptive means that parallel components can change their configuration at run-time (by modifying their parallelism degree or switching to a differ- ent parallel variant) to face irregular workload or to react to uncontrollable changes of the execution platform. A criti- cal problem consists in the definition of adaptation strategies able to select optimal reconfigurations (minimizing operating costs and reconfiguration overhead) and achieve the stability of control decisions (avoiding unnecessary reconfigurations). This paper presents an approach to apply Model Predictive Control (a form of optimal control studied in Control The- ory) to adaptive parallel computations expressed according to the Structured Parallel Programming methodology. We show that predictive control is amenable to achieve stability and optimality by relying on the predictability of structured parallelism patterns and the possibility to express analyti- cal cost models of their QoS metrics. The approach has been exemplified on two case-studies, providing a first assessment of its potential and feasibility
A cost model for autonomic reconfigurations in high-performance pervasive applications
No full textIn the last years we have seen the diffusion of platforms
including high- performance nodes (e.g. multicores) and
powerful mobile devices (e.g. smartphones) interconnected
by heterogeneous networks. Relevant examples of applications
targeting these kinds of platforms are Emergency Management
and Homeland Protection which provide computing/
communication activities characterized by user-defined
Quality of Service constraints. In this paper we introduce
the ASSISTANT programming model for adaptive parallel
applications. ASSISTANT components are specified in multiple
versions, each one dynamically selected according to an
adaptation strategy aimed to target the required QoS levels.
For these applications a key-issue is a well-defined adaptation
semantics featuring a cost model which describes the
overhead for reconfiguring a component (e.g. when switching
between versions). In this paper we introduce our approach
and we evaluate this cost on a flood management application.
Author Keywords
High-Performance Computing, Adaptivity, Autonomic Computing,
Application Reconfigurations
Proactive elasticity and energy awareness in data stream processing
Get PDFData stream processing applications have a long running nature (24hr/7d) with workload conditions that may exhibit wide variations at run-time. Elasticity is the term coined to describe the capability of applications to change dynamically their resource usage in response to workload fluctuations. This paper focuses on strategies for elastic data stream processing targeting multicore systems. The key idea is to exploit Model Predictive Control, a control-theoretic method that takes into account the system behavior over a future time horizon in order to decide the best reconfiguration to execute. We design a set of energy-aware proactive strategies, optimized for throughput and latency QoS requirements, which regulate the number of used cores and the CPU frequency through the Dynamic Voltage and Frequency Scaling (DVFS) support offered by modern multicore CPUs. We evaluate our strategies in a high-frequency trading application fed by synthetic and real-world workload traces. We introduce specific properties to effectively compare different elastic approaches, and the results show that our strategies are able to achieve the best outcome
- …
