A component framework for communication in distributed applications

PurposeThe purpose of this paper is threefold. Firstly, a new concept, teacher buoyancy, is introduced. Based on the significance to study how teachers bounce back from minor and frequent setbacks (vs. major adversities emphasized in resilience) in their daily work and the research on buoyancy by Martin and Marsh, a dual-component framework to conceptualize this new concept is introduced. Secondly, the development of a new instrument, the Teacher Buoyancy Scale (TBS), to measure it is presented. Thirdly, results of a study using the TBS are reported, which provide insights into how teacher buoyancy can be fostered.Design/methodology/approachThe study employed a quantitative design. A total of 258 teachers taking a part-time initial teacher education (ITE) program completed the TBS. Their responses were analyzed by exploratory factor analysis (EFA). In addition to descriptive statistics and reliability coefficients, Pearson correlation coefficients were calculated to examine the relationship among the factors.FindingsThe data analysis indicated five factors, namely, Coping with difficulties, Bouncing back cognitively and emotionally, Working hard and appraising difficulties positively, Caring for one's well-being and Striving for professional growth. These factors can be readily interpreted by the dual-component framework. Correlations among the factors further revealed that enabling factors can be subdivided into more proximal personal strengths relating to direct coping, and more distal personal assets pertaining to personal well-being. It is the latter that correlates most highly with perceived teacher buoyancy.Originality/valueThe most original contribution of this paper is the proposal of the new concept of teacher buoyancy which is teachers' capacity to deal with the everyday challenges that most teachers face in their teaching. The delineation between buoyancy and resilience sharpens the focus of the problem domain that is most relevant to teachers. The development of the TBS provides a useful and reliable instrument to examine teacher buoyancy in future studies.

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Swarm-Like Distributed Algorithm for Scheduling a Microservice-Based Application to the Cloud Servers

Electronics ◽

10.3390/electronics10131553 ◽

2021 ◽

Vol 10 (13) ◽

pp. 1553

Author(s):

Marian Rusek ◽

Grzegorz Dwornicki

Keyword(s):

Distributed Applications ◽

Practical Implementation ◽

Live Migration ◽

Cloud Infrastructure ◽

Application Development ◽

Cloud Management ◽

Cloud Application ◽

Cloud Servers ◽

Distributed Cloud ◽

Better Than

Introduction of virtualization containers and container orchestrators fundamentally changed the landscape of cloud application development. Containers provide an ideal way for practical implementation of microservice-based architecture, which allows for repeatable, generic patterns that make the development of reliable, distributed applications more approachable and efficient. Orchestrators allow for shifting the accidental complexity from inside of an application into the automated cloud infrastructure. Existing container orchestrators are centralized systems that schedule containers to the cloud servers only at their startup. In this paper, we propose a swarm-like distributed cloud management system that uses live migration of containers to dynamically reassign application components to the different servers. It is based on the idea of “pheromone” robots. An additional mobile agent process is placed inside each application container to control the migration process. The number of parallel container migrations needed to reach an optimal state of the cloud is obtained using models, experiments, and simulations. We show that in the most common scenarios the proposed swarm-like algorithm performs better than existing systems, and due to its architecture it is also more scalable and resilient to container death. It also adapts to the influx of containers and addition of new servers to the cloud automatically.

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Detection of stable properties in distributed applications

Proceedings of the sixth annual ACM Symposium on Principles of distributed computing - PODC '87 ◽

10.1145/41840.41851 ◽

1987 ◽

Cited By ~ 17

Author(s):

Jean-Michael Helary ◽

Claude Jard ◽

Noël Plouzeau ◽

Michel Raynal

Keyword(s):

Distributed Applications

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Computational storage: an efficient and scalable platform for big data and HPC applications

Journal Of Big Data ◽

10.1186/s40537-019-0265-5 ◽

2019 ◽

Vol 6 (1) ◽

Cited By ~ 2

Author(s):

Mahdi Torabzadehkashi ◽

Siavash Rezaei ◽

Ali HeydariGorji ◽

Hosein Bobarshad ◽

Vladimir Alves ◽

...

Keyword(s):

Big Data ◽

High Performance ◽

Distributed Processing ◽

Data Access ◽

Distributed Applications ◽

Process Data ◽

Storage Devices ◽

Hadoop Mapreduce ◽

Big Data Applications ◽

Application Processor

AbstractIn the era of big data applications, the demand for more sophisticated data centers and high-performance data processing mechanisms is increasing drastically. Data are originally stored in storage systems. To process data, application servers need to fetch them from storage devices, which imposes the cost of moving data to the system. This cost has a direct relation with the distance of processing engines from the data. This is the key motivation for the emergence of distributed processing platforms such as Hadoop, which move process closer to data. Computational storage devices (CSDs) push the “move process to data” paradigm to its ultimate boundaries by deploying embedded processing engines inside storage devices to process data. In this paper, we introduce Catalina, an efficient and flexible computational storage platform, that provides a seamless environment to process data in-place. Catalina is the first CSD equipped with a dedicated application processor running a full-fledged operating system that provides filesystem-level data access for the applications. Thus, a vast spectrum of applications can be ported for running on Catalina CSDs. Due to these unique features, to the best of our knowledge, Catalina CSD is the only in-storage processing platform that can be seamlessly deployed in clusters to run distributed applications such as Hadoop MapReduce and HPC applications in-place without any modifications on the underlying distributed processing framework. For the proof of concept, we build a fully functional Catalina prototype and a CSD-equipped platform using 16 Catalina CSDs to run Intel HiBench Hadoop and HPC benchmarks to investigate the benefits of deploying Catalina CSDs in the distributed processing environments. The experimental results show up to 2.2× improvement in performance and 4.3× reduction in energy consumption, respectively, for running Hadoop MapReduce benchmarks. Additionally, thanks to the Neon SIMD engines, the performance and energy efficiency of DFT algorithms are improved up to 5.4× and 8.9×, respectively.

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