GAME-SCORE: Game-based energy-aware cloud scheduler and simulator for computational clouds

Energy-awareness remains one of the main concerns for today's cloud computing (CC) operators. The optimisation of energy consumption in both cloud computational clusters and computing servers is usually related to scheduling problems. The definition of an optimal scheduling policy which does not negatively impact to system performance and task completion time is still challenging. In this work, we present a new simulation tool for cloud computing, GAME-SCORE, which implements a scheduling model based on the Stackelberg game. This game presents two main players: a) the scheduler and b) the energy-efficiency agent. We used the GAME-SCORE simulator to analyse the efficiency of the proposed game-based scheduling model. The obtained results show that the Stackelberg cloud scheduler performs better than static energy-optimisation strategies and can achieve a fair balance between low energy consumption and short makespan in a very short tim

Cloud Management Optimization – Issues and Developments

Cloud computing is the current technology paradigm that portends the greatest potentials to revolutionize the way IT activities are conducted. Cloud computing influences most known areas of activities in human endeavour. The cloud provides easy to use applications that can be accessed online at any place and time. Cloud computing also allows organisations and enterprises to create and deploy own applications. In addition, the cloud offers extendable storage facilities, inclusive of processing capabilities. The cloud utilizes various data centres with physical machines or servers for storage and computing purposes. These data centres consume a high amount of energy. The electricity utilization is high and it continues to increase. The servers and cooling machines consume a lot of energy, hence the need to manage this in an optimal way. The study was executed by means of review of some literature available on cloud management optimisation. This study examines issues and developments of cloud management optimisation and also present a recommendation for future research. The result only 25% of the core papers examined discussed the issue of cloud virtualization as it relates to cloud management optimisation. This outcome will offer insight for further work in cloud management optimisation

Optimization Techniques For Low Energy Consumption In Green Cloud Computing

Computing in the cloud can assist businesses in shifting their focus to the development of solid business applications that will bring about genuine value to the businesses. Green computing, often known as environmentally sustainable computing, is the definition of green computing. It is a reference to the efforts that are made to maximise the usage of power consumption & energy efficiency while simultaneously minimising the cost & the amount of CO2 emission. To conduct a study on optimisation techniques & procedures that assist us in optimising low energy consumption & evaluating multiple parameters in order to obtain the desired output is the primary purpose of this research. Energy-Conscious Multisite Computation Offloading Techniques (EMOGC) for Green Cloud Computing is the methodology that was utilised throughout this project. Simulation & analysis are presented in The Energy-Conscious Multisite Computation Offloading Techniques for Green Cloud Computing in order to investigate time-efficient scheduling on multisite, which is responsible for optimising energy, time, & cost at the optimum time. This strategy seeks to finish the application within the allotted amount of time while also consuming as little power as feasible from the connected devices. According to the findings of this research, it is clear that the explored technique is effective in obtaining high throughput (HT) while simultaneously minimising the execution time, which in turn enhances the data rate in Green Cloud Computing (GCC)

Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing

The availability of many-core computing platforms enables a wide variety of technical solutions for systems across the embedded, high-performance and cloud computing domains. However, large scale manycore systems are notoriously hard to optimise. Choices regarding resource allocation alone can account for wide variability in timeliness and energy dissipation (up to several orders of magnitude). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing covers dynamic resource allocation heuristics for manycore systems, aiming to provide appropriate guarantees on performance and energy efficiency. It addresses different types of systems, aiming to harmonise the approaches to dynamic allocation across the complete spectrum between systems with little flexibility and strict real-time guarantees all the way to highly dynamic systems with soft performance requirements. Technical topics presented in the book include: • Load and Resource Models• Admission Control• Feedback-based Allocation and Optimisation• Search-based Allocation Heuristics• Distributed Allocation based on Swarm Intelligence• Value-Based AllocationEach of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments

Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing

The availability of many-core computing platforms enables a wide variety of technical solutions for systems across the embedded, high-performance and cloud computing domains. However, large scale manycore systems are notoriously hard to optimise. Choices regarding resource allocation alone can account for wide variability in timeliness and energy dissipation (up to several orders of magnitude). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing covers dynamic resource allocation heuristics for manycore systems, aiming to provide appropriate guarantees on performance and energy efficiency. It addresses different types of systems, aiming to harmonise the approaches to dynamic allocation across the complete spectrum between systems with little flexibility and strict real-time guarantees all the way to highly dynamic systems with soft performance requirements. Technical topics presented in the book include: Load and Resource Models Admission Control Feedback-based Allocation and Optimisation Search-based Allocation Heuristics Distributed Allocation based on Swarm Intelligence Value-Based Allocation Each of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments.Note.-- EUR 6,000 BPC fee funded by the EC FP7 Post-Grant Open Access Pilo

Cloud engineering is search based software engineering too

Many of the problems posed by the migration of computation to cloud platforms can be formulated and solved using techniques associated with Search Based Software Engineering (SBSE). Much of cloud software engineering involves problems of optimisation: performance, allocation, assignment and the dynamic balancing of resources to achieve pragmatic trade-offs between many competing technical and business objectives. SBSE is concerned with the application of computational search and optimisation to solve precisely these kinds of software engineering challenges. Interest in both cloud computing and SBSE has grown rapidly in the past five years, yet there has been little work on SBSE as a means of addressing cloud computing challenges. Like many computationally demanding activities, SBSE has the potential to benefit from the cloud; ‘SBSE in the cloud’. However, this paper focuses, instead, of the ways in which SBSE can benefit cloud computing. It thus develops the theme of ‘SBSE for the cloud’, formulating cloud computing challenges in ways that can be addressed using SBSE

Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing

The availability of many-core computing platforms enables a wide variety of technical solutions for systems across the embedded, high-performance and cloud computing domains. However, large scale manycore systems are notoriously hard to optimise. Choices regarding resource allocation alone can account for wide variability in timeliness and energy dissipation (up to several orders of magnitude). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing covers dynamic resource allocation heuristics for manycore systems, aiming to provide appropriate guarantees on performance and energy efficiency. It addresses different types of systems, aiming to harmonise the approaches to dynamic allocation across the complete spectrum between systems with little flexibility and strict real-time guarantees all the way to highly dynamic systems with soft performance requirements. Technical topics presented in the book include: • Load and Resource Models• Admission Control• Feedback-based Allocation and Optimisation• Search-based Allocation Heuristics• Distributed Allocation based on Swarm Intelligence• Value-Based AllocationEach of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments