11 research outputs found
Adaptive Knobs for Resource Efficient Computing
Performance demands of emerging domains such as artificial intelligence, machine learning and vision, Internet-of-things etc., continue to grow. Meeting such requirements on modern multi/many core systems with higher power densities, fixed power and energy budgets, and thermal constraints exacerbates the run-time management challenge. This leaves an open problem on extracting the required performance within the power and energy limits, while also ensuring thermal safety. Existing architectural solutions including asymmetric and heterogeneous cores and custom acceleration improve performance-per-watt in specific design time and static scenarios. However, satisfying applicationsâ performance requirements under dynamic and unknown workload scenarios subject to varying system dynamics of power, temperature and energy requires intelligent run-time management.
Adaptive strategies are necessary for maximizing resource efficiency, considering i) diverse requirements and characteristics of concurrent applications, ii) dynamic workload variation, iii) core-level heterogeneity and iv) power, thermal and energy constraints. This dissertation proposes such adaptive techniques for efficient run-time resource management to maximize performance within fixed budgets under unknown and dynamic workload scenarios. Resource management strategies proposed in this dissertation comprehensively consider application and workload characteristics and variable effect of power actuation on performance for pro-active and appropriate allocation decisions. Specific contributions include i) run-time mapping approach to improve power budgets for higher throughput, ii) thermal aware performance boosting for efficient utilization of power budget and higher performance, iii) approximation as a run-time knob exploiting accuracy performance trade-offs for maximizing performance under power caps at minimal loss of accuracy and iv) co-ordinated approximation for heterogeneous systems
through joint actuation of dynamic approximation and power knobs for performance guarantees with minimal power consumption.
The approaches presented in this dissertation focus on adapting existing mapping techniques, performance boosting strategies, software and dynamic approximations to meet the performance requirements, simultaneously considering system constraints. The proposed strategies are compared against relevant state-of-the-art run-time management frameworks to qualitatively evaluate their efficacy
Towards a novel biologically-inspired cloud elasticity framework
With the widespread use of the Internet, the popularity of web applications has
significantly increased. Such applications are subject to unpredictable workload
conditions that vary from time to time. For example, an e-commerce website may
face higher workloads than normal during festivals or promotional schemes. Such
applications are critical and performance related issues, or service disruption can
result in financial losses. Cloud computing with its attractive feature of dynamic
resource provisioning (elasticity) is a perfect match to host such applications.
The rapid growth in the usage of cloud computing model, as well as the rise in
complexity of the web applications poses new challenges regarding the effective
monitoring and management of the underlying cloud computational resources.
This thesis investigates the state-of-the-art elastic methods including the models
and techniques for the dynamic management and provisioning of cloud resources
from a service provider perspective.
An elastic controller is responsible to determine the optimal number of cloud resources,
required at a particular time to achieve the desired performance demands.
Researchers and practitioners have proposed many elastic controllers using versatile
techniques ranging from simple if-then-else based rules to sophisticated
optimisation, control theory and machine learning based methods. However,
despite an extensive range of existing elasticity research, the aim of implementing
an efficient scaling technique that satisfies the actual demands is still a challenge
to achieve. There exist many issues that have not received much attention from
a holistic point of view. Some of these issues include: 1) the lack of adaptability
and static scaling behaviour whilst considering completely fixed approaches; 2)
the burden of additional computational overhead, the inability to cope with the
sudden changes in the workload behaviour and the preference of adaptability
over reliability at runtime whilst considering the fully dynamic approaches; and 3)
the lack of considering uncertainty aspects while designing auto-scaling solutions.
This thesis seeks solutions to address these issues altogether using an integrated
approach. Moreover, this thesis aims at the provision of qualitative elasticity rules.
This thesis proposes a novel biologically-inspired switched feedback control
methodology to address the horizontal elasticity problem. The switched methodology
utilises multiple controllers simultaneously, whereas the selection of a
suitable controller is realised using an intelligent switching mechanism. Each
controller itself depicts a different elasticity policy that can be designed using the
principles of fixed gain feedback controller approach. The switching mechanism
is implemented using a fuzzy system that determines a suitable controller/-
policy at runtime based on the current behaviour of the system. Furthermore,
to improve the possibility of bumpless transitions and to avoid the oscillatory
behaviour, which is a problem commonly associated with switching based control
methodologies, this thesis proposes an alternative soft switching approach. This
soft switching approach incorporates a biologically-inspired Basal Ganglia based
computational model of action selection.
In addition, this thesis formulates the problem of designing the membership functions
of the switching mechanism as a multi-objective optimisation problem. The
key purpose behind this formulation is to obtain the near optimal (or to fine tune)
parameter settings for the membership functions of the fuzzy control system in
the absence of domain expertsâ knowledge. This problem is addressed by using
two different techniques including the commonly used Genetic Algorithm and
an alternative less known economic approach called the Taguchi method. Lastly,
we identify seven different kinds of real workload patterns, each of which reflects
a different set of applications. Six real and one synthetic HTTP traces, one for
each pattern, are further identified and utilised to evaluate the performance of
the proposed methods against the state-of-the-art approaches
Trustworthiness in Mobile Cyber Physical Systems
Computing and communication capabilities are increasingly embedded in diverse objects and structures in the physical environment. They will link the âcyberworldâ of computing and communications with the physical world. These applications are called cyber physical systems (CPS). Obviously, the increased involvement of real-world entities leads to a greater demand for trustworthy systems. Hence, we use "system trustworthiness" here, which can guarantee continuous service in the presence of internal errors or external attacks. Mobile CPS (MCPS) is a prominent subcategory of CPS in which the physical component has no permanent location. Mobile Internet devices already provide ubiquitous platforms for building novel MCPS applications. The objective of this Special Issue is to contribute to research in modern/future trustworthy MCPS, including design, modeling, simulation, dependability, and so on. It is imperative to address the issues which are critical to their mobility, report significant advances in the underlying science, and discuss the challenges of development and implementation in various applications of MCPS
Towards a novel biologically-inspired cloud elasticity framework
With the widespread use of the Internet, the popularity of web applications has
significantly increased. Such applications are subject to unpredictable workload
conditions that vary from time to time. For example, an e-commerce website may
face higher workloads than normal during festivals or promotional schemes. Such
applications are critical and performance related issues, or service disruption can
result in financial losses. Cloud computing with its attractive feature of dynamic
resource provisioning (elasticity) is a perfect match to host such applications.
The rapid growth in the usage of cloud computing model, as well as the rise in
complexity of the web applications poses new challenges regarding the effective
monitoring and management of the underlying cloud computational resources.
This thesis investigates the state-of-the-art elastic methods including the models
and techniques for the dynamic management and provisioning of cloud resources
from a service provider perspective.
An elastic controller is responsible to determine the optimal number of cloud resources,
required at a particular time to achieve the desired performance demands.
Researchers and practitioners have proposed many elastic controllers using versatile
techniques ranging from simple if-then-else based rules to sophisticated
optimisation, control theory and machine learning based methods. However,
despite an extensive range of existing elasticity research, the aim of implementing
an efficient scaling technique that satisfies the actual demands is still a challenge
to achieve. There exist many issues that have not received much attention from
a holistic point of view. Some of these issues include: 1) the lack of adaptability
and static scaling behaviour whilst considering completely fixed approaches; 2)
the burden of additional computational overhead, the inability to cope with the
sudden changes in the workload behaviour and the preference of adaptability
over reliability at runtime whilst considering the fully dynamic approaches; and 3)
the lack of considering uncertainty aspects while designing auto-scaling solutions.
This thesis seeks solutions to address these issues altogether using an integrated
approach. Moreover, this thesis aims at the provision of qualitative elasticity rules.
This thesis proposes a novel biologically-inspired switched feedback control
methodology to address the horizontal elasticity problem. The switched methodology
utilises multiple controllers simultaneously, whereas the selection of a
suitable controller is realised using an intelligent switching mechanism. Each
controller itself depicts a different elasticity policy that can be designed using the
principles of fixed gain feedback controller approach. The switching mechanism
is implemented using a fuzzy system that determines a suitable controller/-
policy at runtime based on the current behaviour of the system. Furthermore,
to improve the possibility of bumpless transitions and to avoid the oscillatory
behaviour, which is a problem commonly associated with switching based control
methodologies, this thesis proposes an alternative soft switching approach. This
soft switching approach incorporates a biologically-inspired Basal Ganglia based
computational model of action selection.
In addition, this thesis formulates the problem of designing the membership functions
of the switching mechanism as a multi-objective optimisation problem. The
key purpose behind this formulation is to obtain the near optimal (or to fine tune)
parameter settings for the membership functions of the fuzzy control system in
the absence of domain expertsâ knowledge. This problem is addressed by using
two different techniques including the commonly used Genetic Algorithm and
an alternative less known economic approach called the Taguchi method. Lastly,
we identify seven different kinds of real workload patterns, each of which reflects
a different set of applications. Six real and one synthetic HTTP traces, one for
each pattern, are further identified and utilised to evaluate the performance of
the proposed methods against the state-of-the-art approaches
Self-aware reliable monitoring
Cyber-Physical Systems (CPSs) can be found in almost all technical areas where they constitute a key enabler for anticipated autonomous machines and devices. They are used in a wide range of applications such as autonomous driving, traffic control, manufacturing plants, telecommunication systems, smart grids, and portable health monitoring systems. CPSs are facing steadily increasing requirements such as autonomy, adaptability, reliability, robustness, efficiency, and performance.
A CPS necessitates comprehensive knowledge about itself and its environment to meet these requirements as well as make rational, well-informed decisions, manage its objectives in a sophisticated way, and adapt to a possibly changing environment. To gain such comprehensive knowledge, a CPS must monitor itself and its environment. However, the data obtained during this process comes from physical properties measured by sensors and may differ from the ground truth. Sensors are neither completely accurate nor precise. Even if they were, they could still be used incorrectly or break while operating. Besides, it is possible that not all characteristics of physical quantities in the environment are entirely known. Furthermore, some input data may be meaningless as long as they are not transferred to a domain understandable to the CPS. Regardless of the reason, whether erroneous data, incomplete knowledge or unintelligibility of data, such circumstances can result in a CPS that has an incomplete or inaccurate picture of itself and its environment, which can lead to wrong decisions with possible negative consequences.
Therefore, a CPS must know the obtained dataâs reliability and may need to abstract information of it to fulfill its tasks. Besides, a CPS should base its decisions on a measure that reflects its confidence about certain circumstances. Computational Self-Awareness (CSA) is a promising solution for providing a CPS with a monitoring ability that is reliable and robust â even in the presence of erroneous data. This dissertation proves that CSA, especially the properties abstraction, data reliability, and confidence, can improve a systemâs monitoring capabilities regarding its robustness and reliability. The extensive experiments conducted are based on two case studies from different fields: the health- and industrial sectors
Building the Future Internet through FIRE
The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate
Building the Future Internet through FIRE
The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate
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CONCEPTUAL DESIGN REPORT
Brookhaven National Laboratory has prepared a conceptual design for a world class user facility for scientific research using synchrotron radiation. This facility, called the ''National Synchrotron Light Source II'' (NSLS-II), will provide ultra high brightness and flux and exceptional beam stability. It will also provide advanced insertion devices, optics, detectors, and robotics, and a suite of scientific instruments designed to maximize the scientific output of the facility. Together these will enable the study of material properties and functions with a spatial resolution of {approx}1 nm, an energy resolution of {approx}0.1 meV, and the ultra high sensitivity required to perform spectroscopy on a single atom. The overall objective of the NSLS-II project is to deliver a research facility to advance fundamental science and have the capability to characterize and understand physical properties at the nanoscale, the processes by which nanomaterials can be manipulated and assembled into more complex hierarchical structures, and the new phenomena resulting from such assemblages. It will also be a user facility made available to researchers engaged in a broad spectrum of disciplines from universities, industries, and other laboratories
Tematski zbornik radova meÄunarodnog znaÄaja. Tom 3 / MeÄunarodni nauÄni skup "Dani ArÄibalda Rajsa", Beograd, 1-2. mart 2013
The Thematic Conference Proceedings contains 138 papers written by eminent scholars in the field of law, security, criminalistics, police studies, forensics, medicine, as well as members of national security system participating in education of the police, army and other security services from Russia, Ukraine, Belarus, China, Poland, Slovakia, Czech Republic, Hungary, Slovenia, Bosnia and Herzegovina, Montenegro, Republic of Srpska and Serbia. Each paper has been reviewed by two competent international reviewers, and the Thematic Conference Proceedings in whole has been reviewed by five international reviewers. The papers published in the Thematic Conference Proceedings contain the overview of con-temporary trends in the development of police educational system, development of the police and contemporary security, criminalistics and forensics, as well as with the analysis of the rule of law activities in crime suppression, situation and trends in the above-mentioned fields, and suggestions on how to systematically deal with these issues. The Thematic Conference Proceedings represents a significant contribution to the existing fund of scientific and expert knowledge in the field of criminalistic, security, penal and legal theory and practice. Publication of this Conference Proceedings contributes to improving of mutual cooperation between educational, scientific and expert institutions at national, regional and international level