Adaptive learning-based resource management strategy in fog-to-cloud

Technology in the twenty-first century is rapidly developing and driving us into a new smart computing world, and emerging lots of new computing architectures. Fog-to-Cloud (F2C) is among one of them, which emerges to ensure the commitment for bringing the higher computing facilities near to the edge of the network and also help the large-scale computing system to be more intelligent. As the F2C is in its infantile state, therefore one of the biggest challenges for this computing paradigm is to efficiently manage the computing resources. Mainly, to address this challenge, in this work, we have given our sole interest for designing the initial architectural framework to build a proper, adaptive and efficient resource management mechanism in F2C. F2C has been proposed as a combined, coordinated and hierarchical computing platform, where a vast number of heterogeneous computing devices are participating. Notably, their versatility creates a massive challenge for effectively handling them. Even following any large-scale smart computing system, it can easily recognize that various kind of services is served for different purposes. Significantly, every service corresponds with the various tasks, which have different resource requirements. So, knowing the characteristics of participating devices and system offered services is giving advantages to build effective and resource management mechanism in F2C-enabled system. Considering these facts, initially, we have given our intense focus for identifying and defining the taxonomic model for all the participating devices and system involved services-tasks. In any F2C-enabled system consists of a large number of small Internet-of-Things (IoTs) and generating a continuous and colossal amount of sensing-data by capturing various environmental events. Notably, this sensing-data is one of the key ingredients for various smart services which have been offered by the F2C-enabled system. Besides that, resource statistical information is also playing a crucial role, for efficiently providing the services among the system consumers. Continuous monitoring of participating devices generates a massive amount of resource statistical information in the F2C-enabled system. Notably, having this information, it becomes much easier to know the device's availability and suitability for executing some tasks to offer some services. Therefore, ensuring better service facilities for any latency-sensitive services, it is essential to securely distribute the sensing-data and resource statistical information over the network. Considering these matters, we also proposed and designed a secure and distributed database framework for effectively and securely distribute the data over the network. To build an advanced and smarter system is necessarily required an effective mechanism for the utilization of system resources. Typically, the utilization and resource handling process mainly depend on the resource selection and allocation mechanism. The prediction of resources (e.g., RAM, CPU, Disk, etc.) usage and performance (i.e., in terms of task execution time) helps the selection and allocation process. Thus, adopting the machine learning (ML) techniques is much more useful for designing an advanced and sophisticated resource allocation mechanism in the F2C-enabled system. Adopting and performing the ML techniques in F2C-enabled system is a challenging task. Especially, the overall diversification and many other issues pose a massive challenge for successfully performing the ML techniques in any F2C-enabled system. Therefore, we have proposed and designed two different possible architectural schemas for performing the ML techniques in the F2C-enabled system to achieve an adaptive, advance and sophisticated resource management mechanism in the F2C-enabled system. Our proposals are the initial footmarks for designing the overall architectural framework for resource management mechanism in F2C-enabled system.La tecnologia del segle XXI avança ràpidament i ens condueix cap a un nou món intel·ligent, creant nous models d'arquitectures informàtiques. Fog-to-Cloud (F2C) és un d’ells, i sorgeix per garantir el compromís d’acostar les instal·lacions informàtiques a prop de la xarxa i també ajudar el sistema informàtic a gran escala a ser més intel·ligent. Com que el F2C es troba en un estat preliminar, un dels majors reptes d’aquest paradigma tecnològic és gestionar eficientment els recursos informàtics. Per fer front a aquest repte, en aquest treball hem centrat el nostre interès en dissenyar un marc arquitectònic per construir un mecanisme de gestió de recursos adequat, adaptatiu i eficient a F2C.F2C ha estat concebut com una plataforma informàtica combinada, coordinada i jeràrquica, on participen un gran nombre de dispositius heterogenis. La seva versatilitat planteja un gran repte per gestionar-los de manera eficaç. Els serveis que s'hi executen consten de diverses tasques, que tenen requisits de recursos diferents. Per tant, conèixer les característiques dels dispositius participants i dels serveis que ofereix el sistema és un requisit per dissenyar mecanismes eficaços i de gestió de recursos en un sistema habilitat per F2C. Tenint en compte aquests fets, inicialment ens hem centrat en identificar i definir el model taxonòmic per a tots els dispositius i sistemes implicats en l'execució de tasques de serveis. Qualsevol sistema habilitat per F2C inclou en un gran nombre de dispositius petits i connectats (conegut com a Internet of Things, o IoT) que generen una quantitat contínua i colossal de dades de detecció capturant diversos events ambientals. Aquestes dades són un dels ingredients clau per a diversos serveis intel·ligents que ofereix F2C. A més, el seguiment continu dels dispositius participants genera igualment una gran quantitat d'informació estadística. En particular, en tenir aquesta informació, es fa molt més fàcil conèixer la disponibilitat i la idoneïtat dels dispositius per executar algunes tasques i oferir alguns serveis. Per tant, per garantir millors serveis sensibles a la latència, és essencial distribuir de manera equilibrada i segura la informació estadística per la xarxa. Tenint en compte aquests assumptes, també hem proposat i dissenyat un entorn de base de dades segura i distribuïda per gestionar de manera eficaç i segura les dades a la xarxa. Per construir un sistema avançat i intel·ligent es necessita un mecanisme eficaç per a la gestió de l'ús dels recursos del sistema. Normalment, el procés d’utilització i manipulació de recursos depèn principalment del mecanisme de selecció i assignació de recursos. La predicció de l’ús i el rendiment de recursos (per exemple, RAM, CPU, disc, etc.) en termes de temps d’execució de tasques ajuda al procés de selecció i assignació. Adoptar les tècniques d’aprenentatge automàtic (conegut com a Machine Learning, o ML) és molt útil per dissenyar un mecanisme d’assignació de recursos avançat i sofisticat en el sistema habilitat per F2C. L’adopció i la realització de tècniques de ML en un sistema F2C és una tasca complexa. Especialment, la diversificació general i molts altres problemes plantegen un gran repte per realitzar amb èxit les tècniques de ML. Per tant, en aquesta recerca hem proposat i dissenyat dos possibles esquemes arquitectònics diferents per realitzar tècniques de ML en el sistema habilitat per F2C per aconseguir un mecanisme de gestió de recursos adaptatiu, avançat i sofisticat en un sistema F2C. Les nostres propostes són els primers passos per dissenyar un marc arquitectònic general per al mecanisme de gestió de recursos en un sistema habilitat per F2C.Postprint (published version

Trustee: A Trust Management System for Fog-enabled Cyber Physical Systems

In this paper, we propose a lightweight trust management system (TMS) for fog-enabled cyber physical systems (Fog-CPS). Trust computation is based on multi-factor and multi-dimensional parameters, and formulated as a statistical regression problem which is solved by employing random forest regression model. Additionally, as the Fog-CPS systems could be deployed in open and unprotected environments, the CPS devices and fog nodes are vulnerable to numerous attacks namely, collusion, self-promotion, badmouthing, ballot-stuffing, and opportunistic service. The compromised entities can impact the accuracy of trust computation model by increasing/decreasing the trust of other nodes. These challenges are addressed by designing a generic trust credibility model which can countermeasures the compromise of both CPS devices and fog nodes. The credibility of each newly computed trust value is evaluated and subsequently adjusted by correlating it with a standard deviation threshold. The standard deviation is quantified by computing the trust in two configurations of hostile environments and subsequently comparing it with the trust value in a legitimate/normal environment. Our results demonstrate that credibility model successfully countermeasures the malicious behaviour of all Fog-CPS entities i.e. CPS devices and fog nodes. The multi-factor trust assessment and credibility evaluation enable accurate and precise trust computation and guarantee a dependable Fog-CPS system

Effective and Economical Content Delivery and Storage Strategies for Cloud Systems

Cloud computing has proved to be an effective infrastructure to host various applications and provide reliable and stable services. Content delivery and storage are two main services provided by the cloud. A high-performance cloud can reduce the cost of both cloud providers and customers, while providing high application performance to cloud clients. Thus, the performance of such cloud-based services is closely related to three issues. First, when delivering contents from the cloud to users or transferring contents between cloud datacenters, it is important to reduce the payment costs and transmission time. Second, when transferring contents between cloud datacenters, it is important to reduce the payment costs to the internet service providers (ISPs). Third, when storing contents in the datacenters, it is crucial to reduce the file read latency and power consumption of the datacenters. In this dissertation, we study how to effectively deliver and store contents on the cloud, with a focus on cloud gaming and video streaming services. In particular, we aim to address three problems. i) Cost-efficient cloud computing system to support thin-client Massively Multiplayer Online Game (MMOG): how to achieve high Quality of Service (QoS) in cloud gaming and reduce the cloud bandwidth consumption; ii) Cost-efficient inter-datacenter video scheduling: how to reduce the bandwidth payment cost by fully utilizing link bandwidth when cloud providers transfer videos between datacenters; iii) Energy-efficient adaptive file replication: how to adapt to time-varying file popularities to achieve a good tradeoff between data availability and efficiency, as well as reduce the power consumption of the datacenters. In this dissertation, we propose methods to solve each of aforementioned challenges on the cloud. As a result, we build a cloud system that has a cost-efficient system to support cloud clients, an inter-datacenter video scheduling algorithm for video transmission on the cloud and an adaptive file replication algorithm for cloud storage system. As a result, the cloud system not only benefits the cloud providers in reducing the cloud cost, but also benefits the cloud customers in reducing their payment cost and improving high cloud application performance (i.e., user experience). Finally, we conducted extensive experiments on many testbeds, including PeerSim, PlanetLab, EC2 and a real-world cluster, which demonstrate the efficiency and effectiveness of our proposed methods. In our future work, we will further study how to further improve user experience in receiving contents and reduce the cost due to content transfer

Network-aware video streaming for future media Internet

272 p

Elastic Resource Management in Distributed Clouds

The ubiquitous nature of computing devices and their increasing reliance on remote resources have driven and shaped public cloud platforms into unprecedented large-scale, distributed data centers. Concurrently, a plethora of cloud-based applications are experiencing multi-dimensional workload dynamics---workload volumes that vary along both time and space axes and with higher frequency. The interplay of diverse workload characteristics and distributed clouds raises several key challenges for efficiently and dynamically managing server resources. First, current cloud platforms impose certain restrictions that might hinder some resource management tasks. Second, an application-agnostic approach might not entail appropriate performance goals, therefore, requires numerous specific methods. Third, provisioning resources outside LAN boundary might incur huge delay which would impact the desired agility. In this dissertation, I investigate the above challenges and present the design of automated systems that manage resources for various applications in distributed clouds. The intermediate goal of these automated systems is to fully exploit potential benefits such as reduced network latency offered by increasingly distributed server resources. The ultimate goal is to improve end-to-end user response time with novel resource management approaches, within a certain cost budget. Centered around these two goals, I first investigate how to optimize the location and performance of virtual machines in distributed clouds. I use virtual desktops, mostly serving a single user, as an example use case for developing a black-box approach that ranks virtual machines based on their dynamic latency requirements. Those with high latency sensitivities have a higher priority of being placed or migrated to a cloud location closest to their users. Next, I relax the assumption of well-provisioned virtual machines and look at how to provision enough resources for applications that exhibit both temporal and spatial workload fluctuations. I propose an application-agnostic queueing model that captures the resource utilization and server response time. Building upon this model, I present a geo-elastic provisioning approach---referred as geo-elasticity---for replicable multi-tier applications that can spin up an appropriate amount of server resources in any cloud locations. Last, I explore the benefits of providing geo-elasticity for database clouds, a popular platform for hosting application backends. Performing geo-elastic provisioning for backend database servers entails several challenges that are specific to database workload, and therefore requires tailored solutions. In addition, cloud platforms offer resources at various prices for different locations. Towards this end, I propose a cost-aware geo-elasticity that combines a regression-based workload model and a queueing network capacity model for database clouds. In summary, hosting a diverse set of applications in an increasingly distributed cloud makes it interesting and necessary to develop new, efficient and dynamic resource management approaches