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

Essentiality of managing the resource information in the coordinated fog-to-cloud paradigm

This is the peer reviewed version of the following article: Sengupta, S, Garcia, J, Masip‐Bruin, X. Essentiality of managing the resource information in the coordinated fog‐to‐cloud paradigm. Int J Commun Syst. 2019, which has been published in final form at https://doi.org/10.1002/dac.4286. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Fog-to-cloud (F2C) computing is an emerging computational platform. By combing the cloud, fog, and IoT, it provides an excellent framework for managing and coordinating the resources in any smart computing domain. Efficient management of these kinds of diverse resources is one of the critical tasks in the F2C system. Also, it must be considered that different types of services are offered by any smart system. So, before managing these resources and enabling the various types of services, it is essential to have some comprehensive informational catalogue of resources and services. Hence, after identifying the resource and service-task taxonomy, our main aim in this paper is finding out a solution for properly organizing this information over the F2C system. For that purpose, we are proposing a modified F2C framework where all the information is distributively stored near to the edge of the network. Finally, by presenting some experimental results, we evaluate and validate the performance of our proposing framework.This work has been supported by the Spanish Ministry of Science, Innovation and Universities and by the European Regional Development Fund (FEDER) under contract RTI2018-094532-B-I00 and by the H2020 European Union mF2C project with reference 730929.Peer ReviewedPostprint (published version

SFDDM: a secure distributed database management in combined fog-to-cloud systems

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Technological revolutions have greatly increased the use of IoT devices for our daily life. Driving the fact that everything surrounding us is getting connected what turns into an unstoppable increase in the amount of data produced. This data represents the state of diverse environmental events and helps to control a large set of distinct activities. So, accurate and secure management of this data is essential for any computing platform. Moreover, in order to provide real-time services in a distributed system (i.e., smart city), the data should be properly and securely managed. It is well known that shifting these tasks to the edge (i.e., near to the end users), highly facilitates these two objectives. The recently proposed Fog-to-Cloud (F2C) model is intended to enable data processing near to the edge, which helps to get better latency-sensitive services. However, some challenges remain to accurately and securely manage this data over the system, mainly due to the distributed F2C nature. Thus, considering these facts and challenges, in this paper we propose an architectural solution aimed at building a secure distributed database for F2C systems. Then, considering a real-case scenario, we perform some tests to measure the performance of our proposing schema. Finally, by comparing the performance between traditional cloud, fog/edge based execution model and our proposing SFDDM, we validate the effectiveness of our proposing schema.This work has been supported by the Spanish Ministry of Science, Innovation and Universities and the European Regional Development Fund (FEDER) under contract RTI2018- 094532-B-I00, and by the H2020 European Union mF2C project with reference 730929.Peer ReviewedPostprint (author's final draft

Cryptolepine-Induced Cell Death of Leishmania donovani Promastigotes Is Augmented by Inhibition of Autophagy

Leishmania donovani are the causative agents of visceral leishmaniasis worldwide. Lack of vaccines and emergence of drug resistance warrants the need for improved drug therapy and newer therapeutic intervention strategies against leishmaniasis. In the present study, we have investigated the effect of the natural indoloquinoline alkaloid cryptolepine on L. donovani AG83 promastigotes. Our results show that cryptolepine induces cellular dysfunction in L. donovani promastigotes, which leads to the death of this unicellular parasite. Interestingly, our study suggest that cryptolepine-induced cell death of L. donovani is counteracted by initial autophagic features elicited by the cells. For the first time, we show that autophagy serves as a survival mechanism in response to cryptolepine treatment in L. donovani promastigotes and inhibition of autophagy causes an early increase in the amount of cell death. This study can be exploited for designing better drugs and better therapeutic strategies against leishmaniasis in future

ATP independent type IB topoisomerase of Leishmania donovani is stimulated by ATP: an insight into the functional mechanism

Most type IB topoisomerases do not require ATP and Mg2+ for activity. However, as shown previously for vaccinia topoisomerase I, we demonstrate that ATP stimulates the relaxation activity of the unusual heterodimeric type IB topoisomerase from Leishmania donovani (LdTOP1L/S) in the absence of Mg2+. The stimulation is independent of ATP hydrolysis but requires salt as a co-activator. ATP binds to LdTOP1L/S and increases its rate of strand rotation. Docking studies indicate that the amino acid residues His93, Tyr95, Arg188 and Arg190 of the large subunit may be involved in ATP binding. Site directed mutagenesis of these four residues individually to alanine and subsequent relaxation assays reveal that the R190A mutant topoisomerase is unable to exhibit ATP-mediated stimulation in the absence of Mg2+. However, the ATP-independent relaxation activities of all the four mutant enzymes remain unaffected. Additionally, we provide evidence that ATP binds LdTOP1L/S and modulates the activity of the otherwise ATP-independent enzyme. This study establishes ATP as an activator of LdTOP1L/S in the absence of Mg2