Internet of Things (IoT) coupled with big data analytics is emerging as the core of smart and sustainable systems which bolsters economic, environmental and social sustainability. Cloud-based data centers provide high performance computing power to analyze voluminous IoT data to provide invaluable insights to support decision making. However, multifarious servers in data centers appear to be the black hole of superfluous energy consumption that contributes to 23% of the global carbon dioxide (CO₂) emissions in ICT (Information and Communication Technology) industry. IoT-related energy research focuses on low-power sensors and enhanced machine-to-machine communication performance. To date, cloud-based data centers still face energy–related challenges which are detrimental to the environment. Virtual machine (VM) consolidation is a well-known approach to affect energy-efficient cloud infrastructures. Although several research works demonstrate positive results for VM consolidation in simulated environments, there is a gap for investigations on real, physical cloud infrastructure for big data workloads. This research work addresses the gap of conducting real physical cloud infrastructure-based experiments. The primary goal of setting up a real physical cloud infrastructure is for the evaluation of dynamic VM consolidation approaches which include integrated algorithms from existing relevant research. An open source VM consolidation framework, Openstack NEAT is adopted and experiments are conducted on a Multi-node Openstack Cloud with Apache Spark as the big data platform. Open sourced Openstack has been deployed because it enables rapid innovation, and boosts scalability as well as resource utilization. Additionally, this research work investigates the performance based on service level agreement (SLA) metrics and energy usage of compute hosts. Relevant results concerning the best performing combination of algorithms are presented and discussed.

Virtualization, either at OS- or hardware level, plays an important role in cloud computing. It enables easier automation and faster deployment in distributed environments. While virtualized infrastructures provide a level of management flexibility, they lack practical abstraction of the distributed resources. A developer in such an environment still needs to deal with all the complications of building a distributed software system. Different orchestration systems are built to provide that abstraction; however, they do not solve the inherent challenges of distributed systems, such as synchronization issues or resilience to failures. This paper introduces Nefele, a decentralized process orchestration system that automatically deploys and manages individual processes, rather than containers/VMs, within a cluster. Nefele is inspired by the Single System Image (SSI) vision of mitigating the intricacies of remote execution, yet it maintains the flexibility and performance of virtualized infrastructures. Nefele offers a set of APIs for building cloud-native applications that lets the developer easily build, deploy, and scale applications in a cloud environment. We have implemented and deployed Nefele on a cluster in our datacenter and evaluated its performance. Our evaluations show that Nefele can effectively deploy, scale, and monitor processes across a distributed environment, while it incorporates essential primitives to build a distributed software system.