LOGOS: Enabling Local Resource Managers for the Efficient Support of Data-Intensive Workflows within Grid Sites

In this study we discuss how to enable grid sites for the support of data-intensive workflows. Usually, within grid sites, tasks and resources are administrated by local resource managers (LRMs). Many of LRMs have been designed for managing compute-intensive applications. Therefore, data-intensive workflow applications might not perform well on such environments due to the number and size of data transfers between tasks. To improve the performance of such kind of applications it is necessary to redefine the scheduling policies integrated on LRMs. This paper proposes a novel scheme for efficiently supporting data-intensive workflows in LRMs within grid sites. Such scheme is partially implemented in our grid middleware LOGOS and used to improve the performance of a well known LRM: HTCondor. The core of LOGOS is a novel communication-aware scheduling algorithm (PPSA) capable of finding near-optimal solutions. Experiments conducted in this study showed that our approach leads to performance improvements up to 52 % in the management of data-intensive workflow applications

Towards Distributed Mobile Computing

In the latest years, we observed an exponential growth of the market of the mobile devices. In this scenario, it assumes a particular relevance the rate at which mobile devices are replaced. According to the International Telecommunicaton Union in fact, smart-phone owners replace their device every 20 months, on average. The side effect of this trend is to deal with the disposal of an increasing amount of electronic devices which, in many cases, arestill working. We believe that it is feasible to recover such an unexploited computational power. Through a change of paradigm in fact, it is possible to achieve a two-fold objective: 1) extend the mobile devices lifetime, 2) enable a new opportunity to speed up mobile applications. In this paper we aim at providing a survey of state-of-art solutions aim at going in the direction of a Distributed Mobile Computing paradigm. We put in evidence the challenges to be addressed in order to implement this paradigm and we propose some possible future improvements

Distributed computing practice for large-scale science and engineering applications

It is generally accepted that the ability to develop large-scale distributed applications has lagged seriously behind other developments in cyberinfrastructure. In this paper, we provide insight into how such applications have been developed and an understanding of why developing applications for distributed infrastructure is hard. Our approach is unique in the sense that it is centered around half a dozen existing scientific applications; we posit that these scientific applications are representative of the characteristics, requirements, as well as the challenges of the bulk of current distributed applications on production cyberinfrastructure (such as the US TeraGrid). We provide a novel and comprehensive analysis of such distributed scientific applications. Specifically, we survey existing models and methods for large-scale distributed applications and identify commonalities, recurring structures, patterns and abstractions. We find that there are many ad hoc solutions employed to develop and execute distributed applications, which result in a lack of generality and the inability of distributed applications to be extensible and independent of infrastructure details. In our analysis, we introduce the notion of application vectors: a novel way of understanding the structure of distributed applications. Important contributions of this paper include identifying patterns that are derived from a wide range of real distributed applications, as well as an integrated approach to analyzing applications, programming systems and patterns, resulting in the ability to provide a critical assessment of the current practice of developing, deploying and executing distributed applications. Gaps and omissions in the state of the art are identified, and directions for future research are outlined

High Performance CDR Processing with MapReduce

A call detail record (CDR) is a data record produced by telecommunication equipment consisting of call detail transaction logs. It contains valuable information for many purposes in several domains, such as billing, fraud detection and analytical purposes. However, in the real world these needs face a big data challenge. Billions of CDRs are generated every day and the processing systems are expected to deliver results in a timely manner. The capacity of our current production system is not enough to meet these needs. Therefore a better performing system based on MapReduce and running on Hadoop cluster was designed and implemented. This paper presents an analysis of the previous system and the design and implementation of the new system, called MS2. In this paper also empirical evidence is provided to demonstrate the efficiency and linearity of MS2. Tests have shown that MS2 reduces overhead by 44% and speeds up performance nearly twice compared to the previous system. From benchmarking with several related technologies in large-scale data processing, MS2 was also shown to perform better in the case of CDR batch processing.  When it runs on a cluster consisting of eight CPU cores and two conventional disks, MS2 is able to process 67,000 CDRs/second

Toward High-Performance Computing and Big Data Analytics Convergence: The Case of Spark-DIY

Convergence between high-performance computing (HPC) and big data analytics (BDA) is currently an established research area that has spawned new opportunities for unifying the platform layer and data abstractions in these ecosystems. This work presents an architectural model that enables the interoperability of established BDA and HPC execution models, reflecting the key design features that interest both the HPC and BDA communities, and including an abstract data collection and operational model that generates a unified interface for hybrid applications. This architecture can be implemented in different ways depending on the process- and data-centric platforms of choice and the mechanisms put in place to effectively meet the requirements of the architecture. The Spark-DIY platform is introduced in the paper as a prototype implementation of the architecture proposed. It preserves the interfaces and execution environment of the popular BDA platform Apache Spark, making it compatible with any Spark-based application and tool, while providing efficient communication and kernel execution via DIY, a powerful communication pattern library built on top of MPI. Later, Spark-DIY is analyzed in terms of performance by building a representative use case from the hydrogeology domain, EnKF-HGS. This application is a clear example of how current HPC simulations are evolving toward hybrid HPC-BDA applications, integrating HPC simulations within a BDA environment.This work was supported in part by the Spanish Ministry of Economy, Industry and Competitiveness under Grant TIN2016-79637-P(toward Unification of HPC and Big Data Paradigms), in part by the Spanish Ministry of Education under Grant FPU15/00422 TrainingProgram for Academic and Teaching Staff Grant, in part by the Advanced Scientific Computing Research, Office of Science, U.S.Department of Energy, under Contract DE-AC02-06CH11357, and in part by the DOE with under Agreement DE-DC000122495,Program Manager Laura Biven

Programming Languages for Data-Intensive HPC Applications: a Systematic Mapping Study

A major challenge in modelling and simulation is the need to combine expertise in both software technologies and a given scientific domain. When High-Performance Computing (HPC) is required to solve a scientific problem, software development becomes a problematic issue. Considering the complexity of the software for HPC, it is useful to identify programming languages that can be used to alleviate this issue. Because the existing literature on the topic of HPC is very dispersed, we performed a Systematic Mapping Study (SMS) in the context of the European COST Action cHiPSet. This literature study maps characteristics of various programming languages for data-intensive HPC applications, including category, typical user profiles, effectiveness, and type of articles. We organised the SMS in two phases. In the first phase, relevant articles are identified employing an automated keyword-based search in eight digital libraries. This lead to an initial sample of 420 papers, which was then narrowed down in a second phase by human inspection of article abstracts, titles and keywords to 152 relevant articles published in the period 2006–2018. The analysis of these articles enabled us to identify 26 programming languages referred to in 33 of relevant articles. We compared the outcome of the mapping study with results of our questionnaire-based survey that involved 57 HPC experts. The mapping study and the survey revealed that the desired features of programming languages for data-intensive HPC applications are portability, performance and usability. Furthermore, we observed that the majority of the programming languages used in the context of data-intensive HPC applications are text-based general-purpose programming languages. Typically these have a steep learning curve, which makes them difficult to adopt. We believe that the outcome of this study will inspire future research and development in programming languages for data-intensive HPC applications.Additional co-authors: Sabri Pllana, Ana Respício, José Simão, Luís Veiga, Ari Vis

Teenustele orienteeritud ja tõendite-teadlik mobiilne pilvearvutus

Arvutiteaduses on kaks kõige suuremat jõudu: mobiili- ja pilvearvutus. Kui pilvetehnoloogia pakub kasutajale keerukate ülesannete lahendamiseks salvestus- ning arvutusplatvormi, siis nutitelefon võimaldab lihtsamate ülesannete lahendamist mistahes asukohas ja mistahes ajal. Täpsemalt on mobiilseadmetel võimalik pilve võimalusi ära kasutades energiat säästa ning jagu saada kasvavast jõudluse ja ruumi vajadusest. Sellest tulenevalt on käesoleva töö peamiseks küsimuseks kuidas tuua pilveinfrastruktuur mobiilikasutajale lähemale? Antud töös uurisime kuidas mobiiltelefoni pilveteenust saab mobiilirakendustesse integreerida. Saime teada, et töö delegeerimine pilve eeldab mitmete pilve aspektide kaalumist ja integreerimist, nagu näiteks ressursimahukas töötlemine, asünkroonne suhtlus kliendiga, programmaatiline ressursside varustamine (Web APIs) ja pilvedevaheline kommunikatsioon. Nende puuduste ületamiseks lõime Mobiilse pilve vahevara Mobile Cloud Middleware (Mobile Cloud Middleware - MCM) raamistiku, mis kasutab deklaratiivset teenuste komponeerimist, et delegeerida töid mobiililt mitmetele pilvedele kasutades minimaalset andmeedastust. Teisest küljest on näidatud, et koodi teisaldamine on peamisi strateegiaid seadme energiatarbimise vähendamiseks ning jõudluse suurendamiseks. Sellegipoolest on koodi teisaldamisel miinuseid, mis takistavad selle laialdast kasutuselevõttu. Selles töös uurime lisaks, mis takistab koodi mahalaadimise kasutuselevõttu ja pakume lahendusena välja raamistiku EMCO, mis kogub seadmetelt infot koodi jooksutamise kohta erinevates kontekstides. Neid andmeid analüüsides teeb EMCO kindlaks, mis on sobivad tingimused koodi maha laadimiseks. Võrreldes kogutud andmeid, suudab EMCO järeldada, millal tuleks mahalaadimine teostada. EMCO modelleerib kogutud andmeid jaotuse määra järgi lokaalsete- ning pilvejuhtude korral. Neid jaotusi võrreldes tuletab EMCO täpsed atribuudid, mille korral mobiilirakendus peaks koodi maha laadima. Võrreldes EMCO-t teiste nüüdisaegsete mahalaadimisraamistikega, tõuseb EMCO efektiivsuse poolest esile. Lõpuks uurisime kuidas arvutuste maha laadimist ära kasutada, et täiustada kasutaja kogemust pideval mobiilirakenduse kasutamisel. Meie peamiseks motivatsiooniks, et sellist adaptiivset tööde täitmise kiirendamist pakkuda, on tagada kasutuskvaliteet (QoE), mis muutub vastavalt kasutajale, aidates seeläbi suurendada mobiilirakenduse eluiga.Mobile and cloud computing are two of the biggest forces in computer science. While the cloud provides to the user the ubiquitous computational and storage platform to process any complex tasks, the smartphone grants to the user the mobility features to process simple tasks, anytime and anywhere. Smartphones, driven by their need for processing power, storage space and energy saving are looking towards remote cloud infrastructure in order to solve these problems. As a result, the main research question of this work is how to bring the cloud infrastructure closer to the mobile user? In this thesis, we investigated how mobile cloud services can be integrated within the mobile apps. We found out that outsourcing a task to cloud requires to integrate and consider multiple aspects of the clouds, such as resource-intensive processing, asynchronous communication with the client, programmatically provisioning of resources (Web APIs) and cloud intercommunication. Hence, we proposed a Mobile Cloud Middleware (MCM) framework that uses declarative service composition to outsource tasks from the mobile to multiple clouds with minimal data transfer. On the other hand, it has been demonstrated that computational offloading is a key strategy to extend the battery life of the device and improves the performance of the mobile apps. We also investigated the issues that prevent the adoption of computational offloading, and proposed a framework, namely Evidence-aware Mobile Computational Offloading (EMCO), which uses a community of devices to capture all the possible context of code execution as evidence. By analyzing the evidence, EMCO aims to determine the suitable conditions to offload. EMCO models the evidence in terms of distributions rates for both local and remote cases. By comparing those distributions, EMCO infers the right properties to offload. EMCO shows to be more effective in comparison with other computational offloading frameworks explored in the state of the art. Finally, we investigated how computational offloading can be utilized to enhance the perception that the user has towards an app. Our main motivation behind accelerating the perception at multiple response time levels is to provide adaptive quality-of-experience (QoE), which can be used as mean of engagement strategy that increases the lifetime of a mobile app