Transportation Ecosystem Framework in Fog to Cloud Environment

Traffic congestion and accidents cause cities to be the principal source of pollutant emissions. The TIMON project initiative aims at providing Real-Time (RT) information and cloud-based services through an open web-based platform and a mobile application to the main actors: drivers, vulnerable road users and businesses. TIMON establishes a cooperative ecosystem to connect people, vehicles, infrastructure and business and contributes to intelligent transport, IoT and Cloud computing. In the first part, this paper provides an overview of TIMON and how it contributes to increasing safety and reducing congestion and emissions. The TIMON ecosystem represents the perfect use case of distributed technologies, as it collects data from IoT sensors, open and closed data sources and user engagement data, processes it and provides useful information not only for road users, but also for scientists and technicians who need real systems to study the data, infrastructure and IT safety management. In the second part, the Cloud deployment of the TIMON system is described in detail and a new, more distributed design is proposed to exploit the potential of current emerging technologies of Fog and Edge computing. Document type: Conference objec

Co-Allocation with Collective Requests in Grid Systems

Abstract: We present a new algorithm for resource allocation in large, heterogeneous grids. Its main advantage over existing co-allocation algorithms is that it supports collective requests with partial resource reservation, where the focus is on better grid utilisation. Alongside the requests that must be fulfilled by each resource, a collective request specifies the total amount of a required resource property without a strict assumption with regard to its distribution. As a consequence, the job becomes much more flexible in terms of its resource assignment and the co-allocation algorithm may therefore start the job earlier. This flexibility increases grid utilisation as it allows an optimisation of job placement that leads to a greater number of accepted jobs. The proposed algorithm is implemented as a module in the XtreemOS grid operating system. Its performance and complexity have been assessed through experiments on the Grid'5000 infrastructure. The results reveal that in most cases the algorithm returns optimal start times for jobs and acceptable, but sometimes suboptimal resource sets

Increasing efficiency of job execution with resource co-allocation in distributed computer systems

Področje porazdeljenih računalniških infrastruktur v računalništvu ni novost, a v industriji in akademskih krogih še vedno žanje veliko zanimanja. Močnejši računalniki, boljša omreženost in hitrejše povezave ter zahtevna porazdeljena opravila pospešujejo razcvet porazdeljenega računalništva. Veliko število računalnikov, povezanih v eno omrežje, ponuja uporabnikom dodatno računsko moč, kadarkoli jo potrebujejo. Toda tak sistem ni poceni in njegovo vzdrževanje ni preprosto, zato so lahko stroški z njim neupravičeno visoki, če infrastruktura ni učinkovito izkoriščena. Trenutno najzanimivejši obliki porazdeljenih sistemov sta omrežno računalništvo in računalništvo v oblaku. V tej doktorski disertaciji pod drobnogled vzamemo pogoste načine upravljanja z viri v obeh omenjenih oblikah. Proučimo pristope razvrščanja na omrežjih s porazdeljenim in centralnim upravljanjem infrastrukture ter izpostavimo ključne lastnosti opravil, ki se pogosto izvajajo na porazdeljeni infrastrukturi. V doktorskem delu predstavimo prednosti razvrščanja opravil, ki lahko prilagodijo svoje delovanje količini dodeljenih virov, in predlagamo dva pristopa razvrščanja. Prvi omogoča sočasno dodeljevanje virov opravilom v porazdeljeni infrastrukturi s porazdeljenim upravljanjem z viri, kar pomeni, da hkrati teče več avtonomnih razvrščevalnikov, ki nimajo globalnega pogleda na stanje vseh virov na vozliščih. Pri tem pristopu se omejimo na razvrščevalnike, ki v danem trenutku v sistem umeščajo zadnje prispelo opravilo. Predlagani pristop podpira kolektivne zahteve, to so zahteve po množici vozlišč, ki morajo kot celota ugoditi tem zahtevam. Pristop smo implementirali za sistem XtreemOS ter preskusili njegovo delovanje v realnem in umetnem okolju. Rezultati potrjujejo, da s pristopom računsko infrastrukturo obremenimo varčneje, hkrati pa se opravila začnejo izvajati ob zgodnejšem času. Cena izboljšav je nekoliko daljše trajanje iskanja primernih vozlišč. Drugi pristop predvideva odloženo dodeljevanje virov opravilom v porazdeljeni infrastrukturi s centralnim upravljanjem z viri, kar pomeni, da imamo le en razvrščevalnik, ki v danem trenutku umešča več opravil sočasno. Predlagali smo preiskovanje opravil v paketih in hkratno prilagajanje opravil po virih tako, da izboljšamo njihovo sobivanje in posledično učinkovitost izrabe vozlišč. Predlagani pristop smo implementirali tako, da deluje skupaj z razvrščevalnikom Haizea, in preskusili delovanje. Rezultati potrjujejo, da lahko s prilagajanjem manjše množice opravil izboljšamo učinkovitost izrabe celotne računske infrastrukture, saj prihranki pri tem več kot odtehtajo dodatno delo prilagajanja opravil. Predlagana pristopa razvrščevalnikom omogočata boljši izkoristek infrastrukture in večjo verjetnost, da bo razvrščevalnik našel primerne vire za opravilo. Z opisi pristopov in poskusi, predstavljenimi v tem delu, prispevamo k oblikovanju novih rešitev za razvrščevalnike na področju omrežnega in oblačnega računalništva, v zaključkih pa navajamo tudi nekatere mogoče razširitve.The field of distributed computer systems, while not new in computer science, is still the subject of a lot of interest in both industry and academia. More powerful computers, faster and more ubiquitous networks, and complex distributed applications are accelerating the growth of distributed computing. Large numbers of computers interconnected in a single network provide additional computing power to users whenever required. Such systems are, however, expensive and complex to manage, which can lead to unduly high expenses unless the infrastructure is efficiently utilised. Currently the most attractive forms of distributed systems are grid and cloud computing. In this dissertation we review some of the resource management approaches commonly used in grid and cloud computing. We examine scheduling approaches in systems with distributed and centralised infrastructure management and highlight the key properties of the applications for which distributed infrastructures are typically used. We present the advantages of scheduling flexible jobs which can scale themselves to the amount of allocated resources, and propose two scheduling approaches. The first approach supports co-allocation of computer resources to jobs on distributed infrastructures with distributed resource management. The latter implies that the system can use multiple autonomous schedulers, which do not have global control over the state of the resources on the nodes. We focus on schedulers that only map a single job to the infrastructure at a time. We propose an approach that supports collective demands, i.e. requests for a set of nodes that must collectively meet the specified demands for resources. We implemented this approach in the XtreemOS operating system and evaluated it in real and simulated environments. The results show that the use of collective demands extends search times, but this is compensated by the fact that the scheduled jobs load the infrastructure more sparingly and allow the jobs to start earlier. The second approach is applicable to offline resource scheduling in distributed infrastructures with global control over the resources. In other words, there is a single central scheduler that can schedule a whole set of jobs simultaneously. For such a set-up we propose analysing the jobs in a batch in order to pair and scale them into co-located subsets and thus improve utilisation. We implemented the proposed approach to run with the Haizea scheduler and evaluated its activity. The results show that the adjusting of a small job subset improves the utilisation of the infrastructure and the savings obtained more than outweigh the extra work needed for the adjusting. The proposed approaches allow the schedulers to better utilise the infrastructure and increase the likelihood of finding the appropriate resources for the job. Through the approaches described and experiments presented, we contribute to the formulation of new solutions for schedulers in the fields of grid and cloud computing. Some possible extensions are given in the conclusions

Increasing efficiency of job execution with resource co-allocation in distributed computer systems

The field of distributed computer systems, while not new in computer science, is still the subject of a lot of interest in both industry and academia. More powerful computers, faster and more ubiquitous networks, and complex distributed applications are accelerating the growth of distributed computing. Large numbers of computers interconnected in a single network provide additional computing power to users whenever required. Such systems are, however, expensive and complex to manage, which can lead to unduly high expenses unless the infrastructure is efficiently utilised. Currently the most attractive forms of distributed systems are grid and cloud computing. In this dissertation we review some of the resource management approaches commonly used in grid and cloud computing. We examine scheduling approaches in systems with distributed and centralised infrastructure management and highlight the key properties of the applications for which distributed infrastructures are typically used. We present the advantages of scheduling flexible jobs which can scale themselves to the amount of allocated resources, and propose two scheduling approaches. The first approach supports co-allocation of computer resources to jobs on distributed infrastructures with distributed resource management. The latter implies that the system can use multiple autonomous schedulers, which do not have global control over the state of the resources on the nodes. We focus on schedulers that only map a single job to the infrastructure at a time. We propose an approach that supports collective demands, i.e. requests for a set of nodes that must collectively meet the specified demands for resources. We implemented this approach in the XtreemOS operating system and evaluated it in real and simulated environments. The results show that the use of collective demands extends search times, but this is compensated by the fact that the scheduled jobs load the infrastructure more sparingly and allow the jobs to start earlier. The second approach is applicable to offline resource scheduling in distributed infrastructures with global control over the resources. In other words, there is a single central scheduler that can schedule a whole set of jobs simultaneously. For such a set-up we propose analysing the jobs in a batch in order to pair and scale them into co-located subsets and thus improve utilisation. We implemented the proposed approach to run with the Haizea scheduler and evaluated its activity. The results show that the adjusting of a small job subset improves the utilisation of the infrastructure and the savings obtained more than outweigh the extra work needed for the adjusting. The proposed approaches allow the schedulers to better utilise the infrastructure and increase the likelihood of finding the appropriate resources for the job. Through the approaches described and experiments presented, we contribute to the formulation of new solutions for schedulers in the fields of grid and cloud computing. Some possible extensions are given in the conclusions

Angleško-slovenski glosar virtualizacijske terminologije: usklajevanje tehničnega, terminološkega in organizacijskega dela

With the growing adoption of using virtual machines over physical hosts as a form of resource consolidation, The English-Slovene Glossary of Virtualization-related Terms encompassing management of virtual machines, cloud orchestration and data storage seemed like the next logical step.The Glossary of Virtualization-related Terms has been translated into Slovene and reviewed by experts in the fields of cloud computing, virtualization technologies and linguists. Close to 6000 terms had been localized for the Slovene market, using the advanced version of Poedit application – the editor for translating apps and websites. PoEdit automatically displays translation equivalents either from its own base (built-in translation memory) or from the base of previously translated words and phrases, which had been created and offered as opensource by other users. Based on these, it makes suggestions and, over time, learns enough to fill in frequently used strings. The translated text was then imported into its original page location – the Graphic User Interface (visible on buttons on the dashboard) of the customized ManageIQ Enterprise Virtualization Manager (EVM) software used by administrators of public and private clouds. Hence the main criterion was brevity and precision in transfering meaning across languages. This is where we encountered most problems – neologisms and existing words that acquire new meaning as a result of rapid development of virtualization technology. To avoid merely adding a suffix while the core of the word remains the same in Slovene (e.g. tenant, tenant-ov) and also to encourage further additions, comments or suggested changes the glossary has been made available on Wikipedia, the online encyclopedia

Application of Unsupervised Anomaly Detection techniques to Moisture Content data from wood constructions

Wood is considered one of the most important construction materials, as well as a natural material prone to degradation, with fungi being the main reason for wood failure in a temperate climate. Visual inspection of wood or other approaches for monitoring are time-consuming, and the incipient stages of decay are not always visible. Thus, visual decay detection and such manual monitoring could be replaced by automated real-time monitoring systems. The capabilities of such systems can range from simple monitoring, periodically reporting data, to the automatic detection of anomalous measurements that may happen due to various environmental or technical reasons. In this paper, we explore the application of Unsupervised Anomaly Detection (UAD) techniques to wood Moisture Content (MC) data. Specifically, data were obtained from a wood construction that was monitored for four years using sensors at different positions. Our experimental results prove the validity of these techniques to detect both artificial and real anomalies in MC signals, encouraging further research to enable their deployment in real use cases

Managing the cloud continuum: lessons learnt from a real fog-to-cloud deployment

The wide adoption of the recently coined fog and edge computing paradigms alongsideconventional cloud computing creates a novel scenario, known as the cloud continuum, whereservices may benefit from the overall set of resources to optimize their execution. To operatesuccessfully, such a cloud continuum scenario demands for novel management strategies, enablinga coordinated and efficient management of the entire set of resources, from the edge up to thecloud, designed in particular to address key edge characteristics, such as mobility, heterogeneity andvolatility. The design of such a management framework poses many research challenges and hasalready promoted many initiatives worldwide at different levels. In this paper we present the resultsof one of these experiences driven by an EU H2020 project, focusing on the lessons learnt from a realdeployment of the proposed management solution in three different industrial scenarios. We thinkthat such a description may help understand the benefits brought in by a holistic cloud continuummanagement and also may help other initiatives in their design and development processes.This research was funded by H2020 mF2C Project, grant number 730929, and for UPCauthors by the Spanish Ministry of Science, Innovation and Universities and FEDER, grant numberRTI2018-094532-B-I00.Peer ReviewedPostprint (published version