Combating catastrophic forgetting with developmental compression

Generally intelligent agents exhibit successful behavior across problems in several settings. Endemic in approaches to realize such intelligence in machines is catastrophic forgetting: sequential learning corrupts knowledge obtained earlier in the sequence, or tasks antagonistically compete for system resources. Methods for obviating catastrophic forgetting have sought to identify and preserve features of the system necessary to solve one problem when learning to solve another, or to enforce modularity such that minimally overlapping sub-functions contain task specific knowledge. While successful, both approaches scale poorly because they require larger architectures as the number of training instances grows, causing different parts of the system to specialize for separate subsets of the data. Here we present a method for addressing catastrophic forgetting called developmental compression. It exploits the mild impacts of developmental mutations to lessen adverse changes to previously-evolved capabilities and `compresses' specialized neural networks into a generalized one. In the absence of domain knowledge, developmental compression produces systems that avoid overt specialization, alleviating the need to engineer a bespoke system for every task permutation and suggesting better scalability than existing approaches. We validate this method on a robot control problem and hope to extend this approach to other machine learning domains in the future

Computation Offloading in Multi-access Edge Computing using Deep Sequential Model based on Reinforcement Learning

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.Multi-access Edge Computing (MEC) is an emerging paradigm which utilizes computing resources at the network edge to deploy heterogeneous applications and services. In the MEC system, mobile users and enterprises can offload computation-intensive tasks to nearby computing resources to reduce latency and save energy. When users make offloading decisions, the task dependency needs to be considered. Due to the NP-hardness of the offloading problem, the existing solutions are mainly heuristic, and therefore have difficulties in adapting to the increasingly complex and dynamic applications. To address the challenges of task dependency and adapting to dynamic scenarios, we propose a new Deep Reinforcement Learning (DRL) based offloading framework, which can efficiently learn the offloading policy uniquely represented by a specially designed Sequence-to-Sequence (S2S) neural network. The proposed DRL solution can automatically discover the common patterns behind various applications so as to infer an optimal offloading policy in different scenarios. Simulation experiments were conducted to evaluate the performance of the proposed DRL-based method with different data transmission rates and task numbers. The results show that our method outperforms two heuristic baselines and achieves nearly optimal performance.Engineering and Physical Sciences Research Council (EPSRC

Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability

Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive and Scalable Communication Network

Energy and Performance: Management of Virtual Machines: Provisioning, Placement, and Consolidation

Cloud computing is a new computing paradigm that offers scalable storage and compute resources to users on demand through Internet. Public cloud providers operate large-scale data centers around the world to handle a large number of users request. However, data centers consume an immense amount of electrical energy that can lead to high operating costs and carbon emissions. One of the most common and effective method in order to reduce energy consumption is Dynamic Virtual Machines Consolidation (DVMC) enabled by the virtualization technology. DVMC dynamically consolidates Virtual Machines (VMs) into the minimum number of active servers and then switches the idle servers into a power-saving mode to save energy. However, maintaining the desired level of Quality-of-Service (QoS) between data centers and their users is critical for satisfying users’ expectations concerning performance. Therefore, the main challenge is to minimize the data center energy consumption while maintaining the required QoS. This thesis address this challenge by presenting novel DVMC approaches to reduce the energy consumption of data centers and improve resource utilization under workload independent quality of service constraints. These approaches can be divided into three main categories: heuristic, meta-heuristic and machine learning. Our first contribution is a heuristic algorithm for solving the DVMC problem. The algorithm uses a linear regression-based prediction model to detect over-loaded servers based on the historical utilization data. Then it migrates some VMs from the over-loaded servers to avoid further performance degradations. Moreover, our algorithm consolidates VMs on fewer number of server for energy saving. The second and third contributions are two novel DVMC algorithms based on the Reinforcement Learning (RL) approach. RL is interesting for highly adaptive and autonomous management in dynamic environments. For this reason, we use RL to solve two main sub-problems in VM consolidation. The first sub-problem is the server power mode detection (sleep or active). The second sub-problem is to find an effective solution for server status detection (overloaded or non-overloaded). The fourth contribution of this thesis is an online optimization meta-heuristic algorithm called Ant Colony System-based Placement Optimization (ACS-PO). ACS is a suitable approach for VM consolidation due to the ease of parallelization, that it is close to the optimal solution, and its polynomial worst-case time complexity. The simulation results show that ACS-PO provides substantial improvement over other heuristic algorithms in reducing energy consumption, the number of VM migrations, and performance degradations. Our fifth contribution is a Hierarchical VM management (HiVM) architecture based on a three-tier data center topology which is very common use in data centers. HiVM has the ability to scale across many thousands of servers with energy efficiency. Our sixth contribution is a Utilization Prediction-aware Best Fit Decreasing (UP-BFD) algorithm. UP-BFD can avoid SLA violations and needless migrations by taking into consideration the current and predicted future resource requirements for allocation, consolidation, and placement of VMs. Finally, the seventh and the last contribution is a novel Self-Adaptive Resource Management System (SARMS) in data centers. To achieve scalability, SARMS uses a hierarchical architecture that is partially inspired from HiVM. Moreover, SARMS provides self-adaptive ability for resource management by dynamically adjusting the utilization thresholds for each server in data centers.Siirretty Doriast

Edge Computing for Internet of Things

The Internet-of-Things is becoming an established technology, with devices being deployed in homes, workplaces, and public areas at an increasingly rapid rate. IoT devices are the core technology of smart-homes, smart-cities, intelligent transport systems, and promise to optimise travel, reduce energy usage and improve quality of life. With the IoT prevalence, the problem of how to manage the vast volumes of data, wide variety and type of data generated, and erratic generation patterns is becoming increasingly clear and challenging. This Special Issue focuses on solving this problem through the use of edge computing. Edge computing offers a solution to managing IoT data through the processing of IoT data close to the location where the data is being generated. Edge computing allows computation to be performed locally, thus reducing the volume of data that needs to be transmitted to remote data centres and Cloud storage. It also allows decisions to be made locally without having to wait for Cloud servers to respond

Mobile health data: investigating the data used by an mHealth app using different mobile app architectures

Mobile Health (mHealth) has come a long way in the last forty years and is still rapidly evolving and presenting many opportunities. The advancements in mobile technology and wireless mobile communication technology contributed to the rapid evolution and development of mHealth. Consequently, this evolution has led to mHealth solutions that are now capable of generating large amounts of data that is synchronised and stored on remote cloud and central servers, ensuring that the data is distributable to healthcare providers and available for analysis and decision making. However, the amount of data used by mHealth apps can contribute significantly to the overall cost of implementing a new or upscaling an existing mHealth solution. The purpose of this research was to determine if the amount of data used by mHealth apps would differ significantly if they were to be implemented using different mobile app architectures. Three mHealth apps using different mobile app architectures were developed and evaluated. The first app was a native app, the second was a standard mobile Web app and the third was a mobile Web app that used Asynchronous JavaScript and XML (AJAX). Experiments using the same data inputs were conducted on the three mHealth apps. The primary objective of the experiments was to determine if there was a significant difference in the amount of data used by different versions of an mHealth app when implemented using different mobile app architectures. The experiment results demonstrated that native apps that are installed and executed on local mobile devices used the least amount of data and were more data efficient than mobile Web apps that executed on mobile Web browsers. It also demonstrated that mobile apps implemented using different mobile app architectures will demonstrate a significant difference in the amount of data used during normal mobile app usage

A patient agent controlled customized blockchain based framework for internet of things

Although Blockchain implementations have emerged as revolutionary technologies for various industrial applications including cryptocurrencies, they have not been widely deployed to store data streaming from sensors to remote servers in architectures known as Internet of Things. New Blockchain for the Internet of Things models promise secure solutions for eHealth, smart cities, and other applications. These models pave the way for continuous monitoring of patient’s physiological signs with wearable sensors to augment traditional medical practice without recourse to storing data with a trusted authority. However, existing Blockchain algorithms cannot accommodate the huge volumes, security, and privacy requirements of health data. In this thesis, our first contribution is an End-to-End secure eHealth architecture that introduces an intelligent Patient Centric Agent. The Patient Centric Agent executing on dedicated hardware manages the storage and access of streams of sensors generated health data, into a customized Blockchain and other less secure repositories. As IoT devices cannot host Blockchain technology due to their limited memory, power, and computational resources, the Patient Centric Agent coordinates and communicates with a private customized Blockchain on behalf of the wearable devices. While the adoption of a Patient Centric Agent offers solutions for addressing continuous monitoring of patients’ health, dealing with storage, data privacy and network security issues, the architecture is vulnerable to Denial of Services(DoS) and single point of failure attacks. To address this issue, we advance a second contribution; a decentralised eHealth system in which the Patient Centric Agent is replicated at three levels: Sensing Layer, NEAR Processing Layer and FAR Processing Layer. The functionalities of the Patient Centric Agent are customized to manage the tasks of the three levels. Simulations confirm protection of the architecture against DoS attacks. Few patients require all their health data to be stored in Blockchain repositories but instead need to select an appropriate storage medium for each chunk of data by matching their personal needs and preferences with features of candidate storage mediums. Motivated by this context, we advance third contribution; a recommendation model for health data storage that can accommodate patient preferences and make storage decisions rapidly, in real-time, even with streamed data. The mapping between health data features and characteristics of each repository is learned using machine learning. The Blockchain’s capacity to make transactions and store records without central oversight enables its application for IoT networks outside health such as underwater IoT networks where the unattended nature of the nodes threatens their security and privacy. However, underwater IoT differs from ground IoT as acoustics signals are the communication media leading to high propagation delays, high error rates exacerbated by turbulent water currents. Our fourth contribution is a customized Blockchain leveraged framework with the model of Patient-Centric Agent renamed as Smart Agent for securely monitoring underwater IoT. Finally, the smart Agent has been investigated in developing an IoT smart home or cities monitoring framework. The key algorithms underpinning to each contribution have been implemented and analysed using simulators.Doctor of Philosoph

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network

Cross-layer Peer-to-Peer Computing in Mobile Ad Hoc Networks

The future information society is expected to rely heavily on wireless technology. Mobile access to the Internet is steadily gaining ground, and could easily end up exceeding the number of connections from the fixed infrastructure. Picking just one example, ad hoc networking is a new paradigm of wireless communication for mobile devices. Initially, ad hoc networking targeted at military applications as well as stretching the access to the Internet beyond one wireless hop. As a matter of fact, it is now expected to be employed in a variety of civilian applications. For this reason, the issue of how to make these systems working efficiently keeps the ad hoc research community active on topics ranging from wireless technologies to networking and application systems. In contrast to traditional wire-line and wireless networks, ad hoc networks are expected to operate in an environment in which some or all the nodes are mobile, and might suddenly disappear from, or show up in, the network. The lack of any centralized point, leads to the necessity of distributing application services and responsibilities to all available nodes in the network, making the task of developing and deploying application a hard task, and highlighting the necessity of suitable middleware platforms. This thesis studies the properties and performance of peer-to-peer overlay management algorithms, employing them as communication layers in data sharing oriented middleware platforms. The work primarily develops from the observation that efficient overlays have to be aware of the physical network topology, in order to reduce (or avoid) negative impacts of application layer traffic on the network functioning. We argue that cross-layer cooperation between overlay management algorithms and the underlying layer-3 status and protocols, represents a viable alternative to engineer effective decentralized communication layers, or eventually re-engineer existing ones to foster the interconnection of ad hoc networks with Internet infrastructures. The presented approach is twofold. Firstly, we present an innovative network stack component that supports, at an OS level, the realization of cross-layer protocol interactions. Secondly, we exploit cross-layering to optimize overlay management algorithms in unstructured, structured, and publish/subscribe platforms