Final TANGO Toolbox is released as Open Source
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.
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.
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.
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.
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.
This paper was presented at 33rd UK Performance Engineering Workshop,
For further details we will upload the content very soon!
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.
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.