5 research outputs found
Adaptive monitoring and control framework in Application Service Management environment
The economics of data centres and cloud computing services have pushed hardware and software requirements to the limits, leaving only very small performance overhead before systems get into saturation. For Application Service Management–ASM, this carries the growing risk of impacting the execution times of various processes. In order to deliver a stable service at times of great demand for computational power, enterprise data centres and cloud providers must implement fast and robust control mechanisms that are capable of adapting to changing operating conditions while satisfying service–level agreements. In ASM practice, there are normally two methods for dealing with increased load, namely increasing computational power or releasing load. The first approach typically involves allocating additional machines, which must be available, waiting idle, to deal with high demand situations. The second approach is implemented by terminating incoming actions that are less important to new activity demand patterns, throttling, or rescheduling jobs. Although most modern cloud platforms, or operating systems, do not allow adaptive/automatic termination of processes, tasks or actions, it is administrators’ common practice to manually end, or stop, tasks or actions at any level of the system, such as at the level of a node, function, or process, or kill a long session that is executing on a database server. In this context, adaptive control of actions termination remains a significantly
underutilised subject of Application Service Management and deserves further consideration. For example, this approach may be eminently suitable for systems with harsh
execution time Service Level Agreements, such as real–time systems, or systems running
under conditions of hard pressure on power supplies, systems running under variable priority, or constraints set up by the green computing paradigm. Along this line of work,
the thesis investigates the potential of dimension relevance and metrics signals decomposition as methods that would enable more efficient action termination. These methods are integrated in adaptive control emulators and actuators powered by neural networks that are used to adjust the operation of the system to better conditions in environments with established goals seen from both system performance and economics perspectives. The behaviour of the proposed control framework is evaluated using complex load and service agreements scenarios of systems compatible with the requirements of on–premises, elastic compute cloud deployments, server–less computing, and micro–services architectures
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Inference Algorithms and Sensorimotor Representations in Brains and Machines
Animals function in a 3D world in which survival depends on robust, well-controlled actions. Historically, researchers in Artificial Intelligence (AI) and neuroscience have explored sensory and motor systems independently. There is a growing body of literature in AI and neuroscience to suggest that they actually work in tandem. While there has been a great deal of work on vision and audition as sensory modalities in these fields, one could argue that a more fundamental modality in biology is haptics, or the sense of touch. In this thesis, we will look at building computational models that integrate tactile sensing with other sensory modalities to perform manipulation-like tasks in robots and discrimination tasks in mice. We will also explore the problem of inference through the lens of Markov Chain Monte Carlo methods (MCMC). We elaborate on the ideas discussed in this thesis in the introduction presented in Chapter 1. A challenging problem one often faces when applying probabilistic mathematical models to the study of sensory-motor systems and other problems involving learning of inference is sampling. Hamiltonian Markov Chain Monte Carlo (HMC) algorithms can efficiently draw representative samples from complex probabilistic models. Most MCMC methods rely on detailed balance to ensure that we can sample from the correct distribution. This constraint can be relaxed in discrete state spaces such as those employed by HMC type methods. In Chapter 2, we study HMC methods without detailed balance to explore faster convergence. Markov jump processes are stochastic processes on discrete state space but continuous in time. In Chapter 3, we use Markov Jump Processes to simulate waiting times along with generalized detailed balance. This waiting time ,we show, helps generate samples faster. Most MCMC methods are plagued by slow simulation times on discrete computing systems. In Chapter 4, we explore HMC in analog circuits where the problem of generating samples from a distribution is mapped to the problem of sampling charge in a capacitor.The second half of this dissertation focuses on the role of haptics in perception and action. Manipulation is a fundamental problem for artificial and biological agents. High dimensional actuators (say, fingers, trunks,etc) are really hard to control. In Chapter 5, we present an approach to learn to actuate dexterous manipulators to grasp objects in simulation. Haptics as a sensory modality is critical to many manipulation tasks. Employing haptics in high dimensional dextrous actuators is challenging. In Chapter 6, we explore how intrinsic curiosity and haptics can be used to learn exploration strategies for discrimination of objects with dextrous hands. A key component to make tactile sensing a possibility is the availability of cheap, efficient, scalable hardware. Chapter 7 presents results for tactile servoing using a physical gelsight sensor. Traditional neuroscience texts delineate sensory and motor systems as two independent systems yet recent results suggest that this may not be entirely complete. That is, there is evidence to suggest that the representations in the cortex is more distributed than is accepted. Finally in Chapter 8, we explore building a computational model of spiking neural data collected from both the barrel and motor cortices during free and active whisking. These works help towards understanding sensorimotor representations in the context of haptics and high dimensional controls. We conclude with a discussion on future directions in Chapter 9