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With the advancement of IoT, the number of smart and connected devices is increasing. These geographically distributed devices produce and consume a huge amount of heterogeneous and dynamic data known as ‘Big Data’ at the network edge that is close to the end users. Therefore, a new requirement of data management and computing capacity at the network edge has been evolved with respect to user mobility and diverse requirements of applications. Since the traditional cloud data-centers are not capable of handling such extensive data as well as user mobility, it has become indispensable to rethink about the resource allocation and management in the cloud infrastructure. In this case, distributed computing models such as fog computing, mobile clouds and vehicular networks come into play.

 

The article, ‘Mobility-aware application scheduling in fog computing’ by Luiz F. Bittencourt et al., discusses the advantageous aspects of fog computing in the context of faster data processing and computing at the edges of the network for the applications dependent on users’ geographical location. It gives an overview of the hierarchical fog computing infrastructure and illustrates the possible development of user access point called ‘cloudlets’ with the utilization of computation and storage facility. Applications can be classified into different categories based on the user mobility and Quality of Service (QoS) requirements of the applications. These classes can influence the design of scheduling strategies for fog computing infrastructure.

 

The article depicts that by putting application classes and fog computing scheduling policies together while considering user mobility can reduce network delay which makes the applications perform better. To find out more detailed information, please follow the link,

https://www.computer.org/cms/Computer.org/magazines/whats-new/2017/07/mcd2017020026.pdf.

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Orchestrating Scientific Workflows on the Cloud
Junaid Arshad
FEB 19, 2015 15:33 PM
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By Junaid Arshad

Cloud Computing

One of the most compelling benefits of Cloud computing has been its ability for on-demand provision of extraordinarily scalable resources. This has led to a widespread adoption of cloud computing for diverse application domains including both industry and academia. By virtue of its commercial viability, it has become an extremely attractive prospect especially for Small and Medium Enterprises (SMEs). An excellent example of cloud adoption within this industry segment is the EU funded CloudSME project which is aimed at this specific industry segment and has successfully demonstrated the feasibility of cloud computing technology for SMEs in general and for SMEs in simulation and manufacturing in particular. For academia, the ability to call upon gigantic compute and storage resources at tiny or no cost at all is a godsend and has transformed research across diverse scientific disciplines. The EU funded ER-Flow project is a very good example and is aimed at facilitating scientists from disciplines such as Astrophysics, Heliophysics and Computational Chemistry to be able to run their computations on the community supported clouds at no cost.

Cloud Orchestration

Cloud orchestration services have recently emerged to improve the overall usability of the cloud infrastructures i.e. to simplify user interaction, deployment of instances and so on. These services allow configuring, managing, maintaining, deploying and scaling on the cloud. They provide flexibility in instance, service and infrastructure deployments with a few clicks/commands. A cloud orchestration service will facilitate deploying an entire infrastructure in accordance with the requirements of the workflow. Having said that, the automation of processes underpins success of cloud computing which becomes a major challenge when managing heterogeneous environments with disparate systems as emphasized here. Cloud orchestration attempts to address the issue of messy automation by:

  1. Integrating cloud resources across heterogeneous platforms to simplify service construction, deployment and management
  2. Dynamic creation and reconfiguration of cloud services
  3. Provision of applications as ready-to-deploy services agnostic of underlying infrastructure  
  4. Dynamic management of relationships among cloud services
  5. Real-time monitoring and management of underlying infrastructure

Evolution of Scientific Workflows

Grid technologies (Globus, gLite and UNICORE) have been being used extensively for scientific workflow execution. However, a number of outstanding issues have been identified with grid technologies spanning across functionality provided, deployment and maintenance which hamper usage and advancements within the scientific workflows. Cloud computing has been identified as the next generation technology to address the challenges posed by contemporary grid technologies by providing flexibility in deployment, maintenance and use of the infrastructure required for workflow execution. Within the ER-Flow project, we have chosen cloud orchestration technologies to support cloud based workflow execution mainly due to the flexibility and on the fly deployment benefits

 

 

Cloud Orchestration Services

Recently, there has been growing number of available solutions for orchestrating services on the Cloud. These include Juju, Heat, Puppet, Chef, BOSH and OCCO. However, each of these solutions has different focus and therefore address different problems and can be adopted as complimentary solutions. The figure below presents one such comparison of these solutions.

http://www.zerobanana.com/images/orchestration-config-management

Source: http://www.zerobanana.com/

Due to their focus on hassle-free service orchestration, we shortlisted JuJu, Heat and OCCO for our analysis which is presented in the figure below.

Comparison of Cloud Orchestration Solutions

Based on our analysis, we have selected JuJu as the orchestration solution to facilitate cloud based workflow execution. Our selection of JuJu is based on the following factors which we believe will prove detrimental in near future.

·         Multi-cloud support - supports Azure, Openstack, EC2 and HP clouds compared to just Openstack by Heat

·         TOSCA compliance - JuJu is progressing rapidly with TOSCA in collaboration with IBM

·         Greater organizational support - Canonical  has been recently joined by IBM to support JuJu

·         Focus on the service layer - JuJu facilitates provision of dedicated deployment environments without the hassle of managing underlying infrastructure

·         Programmatic interface to managing service deployment lifecycle - API for creation, deletion, maintenance of services

We are thrilled by the flexibility and ease of use offered by JuJu Charms and look to continue our work with it to support workflows from diverse scientific disciplines to be executed on the cloud.

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