Yahav Biran, Sudeep Pasricha, George Collins, Joel Dubow
Adv. Sci. Technol. Eng. Syst. J. 2(6), 1-12 (2017);
Cloud providers seek to maximize their market share. Traditionally, they deploy datacenters with sufficient capacity to accommodate their entire computing demand while maintaining geographical affinity to its customers. Achieving these goals by a single cloud provider is increasingly unrealistic from a cost of ownership perspective. Moreover, the carbon emissions from underutilized datacenters place an increasing demand on electricity and is a growing factor in the cost of cloud provider datacenters. Cloud-based systems may be classified into two categories: serving systems and analytical systems. We studied two primary workload types, on-demand video streaming as a serving system and MapReduce jobs as an analytical systems and suggested two unique energy mix usage for processing that workloads. The recognition that on-demand video streaming now constitutes the bulk portion of traffic to Internet consumers provides a path to mitigate rising energy demand. On-demand video is usually served through Content Delivery Networks (CDN), often scheduled in backend and edge datacenters. This publication describes a CDN deployment solution that utilizes green energy to supply on-demand streaming workload. A cross-cloud provider collaboration will allow cloud providers to both operate near their customers and reduce operational costs, primarily by lowering the datacenter deployments per provider ratio. Our approach optimizes cross-datacenters deployment. Specifically, we model an optimized CDN-edge instance allocation system that maximizes, under a set of realistic constraints, green energy utilization. The architecture of this cross-cloud coordinator service is based on Ubernetes, an open source container cluster manager that is a federation of Kubernetes clusters. It is shown how, under reasonable constraints, it can reduce the projected datacenter’s carbon emissions growth by 22% from the currently reported consumption. We also suggest operating datacenters using energy mix sources as a VoltDB-based fast data system to process offline workloads such as MapReduce jobs. We show how cross-cloud coordinator service can reduce the projected data- centers carbon emissions growth by 21% from the currently expected trajectory when processing offline MapReduce jobs.
Anwar Mohamed Fanan, Nick Riley, Meftah Mehdawi
Adv. Sci. Technol. Eng. Syst. J. 2(6), 13-19 (2017);
Cognitive Radio (CR) encompasses a number of technologies which enable adaptive self-programing of systems at different levels to provide more effective use of the increasingly congested radio spectrum. CRs have potential to use spectrum allocated to TV services, which is not used by the primary user (TV), without causing disruptive interference to licensed users by using appropriate propagation modelling in TV White Spaces (TVWS). In this paper we address two related aspects of channel occupancy prediction for cognitive radio. Firstly, we continue to investigate the best propagation model among three propagation models (Extended-Hata, Davidson-Hata and Egli) for use in the TV band, whilst also finding the optimum terrain data resolution to use (1000, 100 or 30 m). We compare modelled results with measurements taken in randomly-selected locations around Hull UK, using the two comparison criteria of implementation time and accuracy, when used for predicting TVWS system performance. Secondly, we describe how such models can be integrated into a database-driven tool for CR channel selection within the TVWS environment by creating a flexible simulation system for creating a TVWS database.