A Space Efficient Data Management Scheme on Content Delivery Networks for Online Video Provisioning

In this paper CDNs (Content Delivery Networks) have been widely implemented to provide scalable cloud services. Such networks support resource pooling by permitting virtual machines or physical servers to be dynamically activated and deactivated consistent with current user demand. This paper examines on-line video replication and placement problems in Content delivery networks an efficient video provisioning scheme should simultaneously utilize system resources to reduce total energy consumption and limit replication overhead. We inclined to propose a scheme known as adaptive information placement that may dynamically place and reorganize video replicas among cache servers on subscribers’ arrival and departure. Both the analyses and simulation results show that adaptive information placement will reduce the number of activated cache servers with restricted replication overhead. Additionally, adaptive information placements performance is approximate to the optimal solution

Power Consumption and Carbon Emission Equivalent for Virtualized Resources – An Analysis: Virtual Machine and Container Analysis for Greener Data Center

The International Energy Agency (IEA) revealed that the worldwide energy-related carbon dioxide (CO2) situation has hit a historic high of 33.1 Giga tonnes (Gt) of CO2. 85% of the rise in emissions was due to China, India, and the United States. The increase in emissions in India was 4.8%, or 105 Mega tonnes (Mt) of CO2, with the increase in emissions being evenly distributed across the transportation and industrial sectors, according to Beloglazov et al (2011). Environmental contamination brought on by carbon emissions is harmful to the environment. As a result, there is an urgent need for the IT sectors to develop effective and efficient technology to eliminate such carbon emissions. The primary focus is on lowering carbon emissions due to widespread awareness of the issue

Towards Carbon-Aware Spatial Computing: Challenges and Opportunities

Carbon-aware spatial computing (CASC) is focused on reducing the carbon footprint of spatial computing itself and leveraging spatial computing techniques to minimize carbon emissions in other domains. The significance of CASC lies in its potential to mitigate anthropogenic climate change by offering numerous societal applications, such as carbon-aware supply chain development and carbon-aware site selection. CASC is challenging because of the spatiotemporal variability and the high dimensionality of carbon emissions data, involving spatial coordinates and timestamps. Related work, known as carbon-aware computing, mostly focuses on job scheduling of cloud computing, and there is a lack of surveys and review papers detailing the potential of CASC on variant domains and applications. In this paper, we provide the vision of CASC by proposing a taxonomy of sub-domains within CASC and introducing ideas beyond job scheduling, such as carbon-smart site selection. We also briefly review the literature in selected sub-domains and highlight research challenges and opportunities. Given the societal importance of the topic, we encourage the scientific community to use this brief survey to expand the field of study into other related sub-domains and advance CASC more broadly

FedZero: Leveraging Renewable Excess Energy in Federated Learning

Federated Learning (FL) is an emerging machine learning technique that enables distributed model training across data silos or edge devices without data sharing. Yet, FL inevitably introduces inefficiencies compared to centralized model training, which will further increase the already high energy usage and associated carbon emissions of machine learning in the future. Although the scheduling of workloads based on the availability of low-carbon energy has received considerable attention in recent years, it has not yet been investigated in the context of FL. However, FL is a highly promising use case for carbon-aware computing, as training jobs constitute of energy-intensive batch processes scheduled in geo-distributed environments. We propose FedZero, a FL system that operates exclusively on renewable excess energy and spare capacity of compute infrastructure to effectively reduce the training's operational carbon emissions to zero. Based on energy and load forecasts, FedZero leverages the spatio-temporal availability of excess energy by cherry-picking clients for fast convergence and fair participation. Our evaluation, based on real solar and load traces, shows that FedZero converges considerably faster under the mentioned constraints than state-of-the-art approaches, is highly scalable, and is robust against forecasting errors

Optimized Contract-based Model for Resource Allocation in Federated Geo-distributed Clouds

In the era of Big Data, with data growing massively in scale and velocity, cloud computing and its pay-as-you-go modelcontinues to provide significant cost benefits and a seamless service delivery model for cloud consumers. The evolution of small-scaleand large-scale geo-distributed datacenters operated and managed by individual Cloud Service Providers (CSPs) raises newchallenges in terms of effective global resource sharing and management of autonomously-controlled individual datacenter resourcestowards a globally efficient resource allocation model. Earlier solutions for geo-distributed clouds have focused primarily on achievingglobal efficiency in resource sharing, that although tries to maximize the global resource allocation, results in significant inefficiencies inlocal resource allocation for individual datacenters and individual cloud provi ders leading to unfairness in their revenue and profitearned. In this paper, we propose a new contracts-based resource sharing model for federated geo-distributed clouds that allows CSPsto establish resource sharing contracts with individual datacentersapriorifor defined time intervals during a 24 hour time period. Based on the established contracts, individual CSPs employ a contracts cost and duration aware job scheduling and provisioning algorithm that enables jobs to complete and meet their response time requirements while achieving both global resource allocation efficiency and local fairness in the profit earned. The proposed techniques are evaluated through extensive experiments using realistic workloads generated using the SHARCNET cluster trace. The experiments demonstrate the effectiveness, scalability and resource sharing fairness of the proposed model

Eco-friendly Powering and Delay-aware Task Scheduling in Geo-distributed Edge-cloud System: A Two-timescale Framework

n/

Carbon-aware Load Balancing for Geo-distributed Cloud Services

Abstract—Recently, datacenter carbon emission has become an emerging concern for the cloud service providers. Previous works are limited on cutting down the power consumption of the datacenters to defuse such a concern. In this paper, we show how the spatial and temporal variabilities of the electricity carbon footprint can be fully exploited to further green the cloud running on top of geographically distributed datacenters. We jointly consider the electricity cost, service level agreement (SLA) requirement, and emission reduction budget. To navigate such a three-way tradeoff, we take advantage of Lyapunov optimization techniques to design and analyze a carbon-aware control framework, which makes online decisions on geographical load balancing, capacity right-sizing, and server speed scaling. Results from rigorous mathematical analyses and real-world trace-driven empirical evaluation demonstrate its effectiveness in both minimizing electricity cost and reducing carbon emission. I