Final TANGO Toolbox is released as Open Source

Here below the set of tools available for the final version of Tango toolbox. Final version is here! Each tool is accompanied by a Readme file with the description of the component, installation guidelines for developers and users and the relationship with other TANGO components, license and link to the downloadable software are provided. This time, also a video tutorial for each tool is available. Final version integrates TANGO components to make it possible to explore trade-off on additional non-functional behavior such as security, robustness and maintainability. This version is focused on enhancing programmer productivity in order to offer a compact solution having a feedback on tool training in last two versions of the project (Alpha and Beta) and having also the objective in productivity issues that programmers could face in daily tasks. In order to provide some examples of use of our tools several scenarios and the related workflows are described. All components can be used independently or in integration with other components to approach different situations, as are shown in these examples.
Date: 
Monday, October 15, 2018

Energy-aware Cost Prediction and Pricing of Virtual Machines in Cloud Computing Environments

With the increasing cost of electricity, Cloud providers consider energy consumption as one of the major cost factors to be maintained within their infrastructure. Consequently, various proactive and reactive management mechanisms are used to efficiently manage the cloud resources and reduce the energy consumption and cost. These mechanisms support energy-awareness at the level of Physical Machines (PM) as well as Virtual Machines (VM) to make corrective decisions.

Energy-aware Self-Adaptation for Application Execution on Heterogeneous Parallel Architectures

Hardware in HPC environments in recent years has become ever more heterogeneous in order to improve computational performance and as an aspect of managing power and energy constraints. This increase in heterogeneity requires middleware abstractions to eliminate additional complexities that it brings. In this paper we present a self-adaptation framework which includes aspects such as automated configuration, deployment and redeployment of applications to different heterogeneous infrastructure.

Towards Virtual Machine Energy-Aware Cost Prediction in Clouds

Pricing mechanisms employed by different service providers significantly influence the role of cloud computing within the IT industry. With the increasing cost of electricity, Cloud providers consider power consumption as one of the major cost factors to be maintained within their infrastructures. Consequently, modelling a new pricing mechanism that allow Cloud providers to determine the potential cost of resource usage and power consumption has attracted the attention of many researchers.

A methodology for efficient code optimizations and memory management

Virtual Machines (VMs) live migration is one of the important approaches to improve resource utilisation and support energy efficiency in Clouds. However, VMs live migration leads to performance loss and additional costs due to increased migration time and energy overhead. This paper introduces a Performance and Energybased Cost Prediction Framework to estimate the total cost of VMs live migration by considering the resource usage and power consumption, while maintaining the expected level of performance.

Workflow Simulation Aware and Multi-Threading Effective Task Scheduling for Heterogeneous Computing

Efficient application scheduling is critical for achieving high performance in heterogeneous computing systems. This problem has proved to be NP-complete, heading research efforts in obtaining low complexity heuristics that produce good quality schedules.

Containers vs. Virtual Machines: Performance Evaluation of HPC Mini-Apps on the Cloud

This paper was presented at 33rd UK Performance Engineering Workshop,

For further details we will upload the content very soon!

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Towards Energy Aware Cloud Computing Application Construction

The energy consumption of cloud computing continues to be an area of significant concern as data center growth continues to increase. This paper reports on an energy efficient interoperable cloud architecture realised as a cloud toolbox that focuses on reducing the energy consumption of cloud applications holistically across all deployment models. The architecture supports energy efficiency at service construction, deployment and operation.

Rapid and accurate energy models through calibration with IPMI and RAPL

Energy consumption in Cloud and High Performance Computing platforms is a significant issue and affects aspects such as the cost of energy and the cooling of the data center. Host level monitoring and prediction provides the groundwork for improving energy efficiency through the placement of workloads. Monitoring must be fast and efficient without unnecessary overhead, to enable scalability. This precludes the use of Watt meters attached per host, requiring alternative approaches such as integrated measurements and models.

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