• Energy Research

The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high-performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to address issues such as scalability, elasticity, reliability, security, sustainability, and application models. The proposed manifesto addresses them by identifying the major open challenges in Cloud computing, emerging trends, and impact areas. It then offers research directions for the next decade, thus helping in the realisation of Future Generation Cloud Computing.

In many production clouds, with the notable exception of Google, aggregation-based VM placement policies are used to provision datacenter resources energy and performance efficiently. However, if VMs with similar workloads are placed onto the same machines, they might suffer from contention, particularly, if they are competing for similar resources. High levels of resource contention may degrade VMs performance, and, therefore, could potentially increase users' costs and infrastructure's energy consumption. Furthermore, segregation-based methods result in stranded resources and, therefore, less economics. The recent industrial interest in segregating workloads opens new directions for research. In this paper, we demonstrate how aggregation and segregation-based VM placement policies lead to variabilities in energy efficiency, workload performance, and users' costs. We, then, propose various approaches to aggregation-based placement and migration. We investigate through a number of experiments, using Microsoft Azure and Google's workload traces for more than twelve thousand hosts and a million VMs, the impact of placement decisions on energy, performance, and costs. Our extensive simulations and empirical evaluation demonstrate that, for certain workloads, aggregation-based allocation and consolidation is ~9.61% more energy and ~20.0% more performance efficient than segregation-based policies. Moreover, various aggregation metrics, such as runtimes and workload types, offer variations in energy consumption and performance, therefore, users' costs.<br>

One of the key problems facing cloud applications is to reduce their energy consumption, which can increase the working lifetime of a machine, decrease the operation costs of cloud providers, and the environmental impact caused by power consumption. It is very important to design and evaluate an energy-efficient cloud. Recently, two open problems are raised in the literature: 1) what is the optimal solution (the lower bound) for the total energy consumption? and 2) what is the energy-efficiency for a scheduling algorithm? In this paper, we consider two major scheduling policies: 1) always power-on physical machines (PMs) once turning-on and 2) turning-off (hibernating) idle PMs, both with possible virtual machine migrations during evaluation. Focusing on compute-intensive applications on cloud, we propose analytical methods to settle down the two open problems. Our theoretical results are validated by experimental results in different scheduling scenarios and can be applied in cloud computing environments to help energy-efficient design.