Application live-upgrading and error-recovery using code-data decoupling

When applications have critical bugs that present security vulnerabilities or may result in serious failures with potential massive business level impact, these applications have to be updated as fast as possible to minimize the harm of the bug. However, mission-critical or other user-facing applications may maintain critical internal state that has to be serialized and restored during the update process introducing signi1cant cost and delay. Instead of serializing the internal state we propose to implement applications in such a way that the application state is fully decoupled (e.g. in a different address space or shared memory segment) from the application logic. Such a decoupling allows for example that upgrades can happen without serialization of the data, even allowing side-by-side execution of the updated and the failing version of the application and thereby reducing application downtime during the update process. Furthermore, this decoupling also allows applications to recover easily from failures by recovering the previous data of the crashed application instance

SHIELD: Sustainable Hybrid Evolutionary Learning Framework for Carbon, Wastewater, and Energy-Aware Data Center Management

Today's cloud data centers are often distributed geographically to provide robust data services. But these geo-distributed data centers (GDDCs) have a significant associated environmental impact due to their increasing carbon emissions and water usage, which needs to be curtailed. Moreover, the energy costs of operating these data centers continue to rise. This paper proposes a novel framework to co-optimize carbon emissions, water footprint, and energy costs of GDDCs, using a hybrid workload management framework called SHIELD that integrates machine learning guided local search with a decomposition-based evolutionary algorithm. Our framework considers geographical factors and time-based differences in power generation/use, costs, and environmental impacts to intelligently manage workload distribution across GDDCs and data center operation. Experimental results show that SHIELD can realize 34.4x speedup and 2.1x improvement in Pareto Hypervolume while reducing the carbon footprint by up to 3.7x, water footprint by up to 1.8x, energy costs by up to 1.3x, and a cumulative improvement across all objectives (carbon, water, cost) of up to 4.8x compared to the state-of-the-art

MOSAIC: A Multi-Objective Optimization Framework for Sustainable Datacenter Management

In recent years, cloud service providers have been building and hosting datacenters across multiple geographical locations to provide robust services. However, the geographical distribution of datacenters introduces growing pressure to both local and global environments, particularly when it comes to water usage and carbon emissions. Unfortunately, efforts to reduce the environmental impact of such datacenters often lead to an increase in the cost of datacenter operations. To co-optimize the energy cost, carbon emissions, and water footprint of datacenter operation from a global perspective, we propose a novel framework for multi-objective sustainable datacenter management (MOSAIC) that integrates adaptive local search with a collaborative decomposition-based evolutionary algorithm to intelligently manage geographical workload distribution and datacenter operations. Our framework sustainably allocates workloads to datacenters while taking into account multiple geography- and time-based factors including renewable energy sources, variable energy costs, power usage efficiency, carbon factors, and water intensity in energy. Our experimental results show that, compared to the best-known prior work frameworks, MOSAIC can achieve 27.45x speedup and 1.53x improvement in Pareto Hypervolume while reducing the carbon footprint by up to 1.33x, water footprint by up to 3.09x, and energy costs by up to 1.40x. In the simultaneous three-objective co-optimization scenario, MOSAIC achieves a cumulative improvement across all objectives (carbon, water, cost) of up to 4.61x compared to the state-of-the-arts

Innovation Mashups: Academic Rigor Meets Social Networking Buzz

Exploring new options for publishing and content delivery offers an enormous opportunity to improve the state of the art and further modernize academic and professional publications. Traditional organizations such as the IEEE Computer Society, ACM, and Usenix have been encountering increasing competition from new ways of rapid publishing and dissemination, including social networking sites (Facebook, Twitter, LinkedIn, Google+), blogs with enabled commenting, video posting (YouTube), Slashdot, and many other types of media. Liking is replacing traditional impact factors, comments left on authors\u27 webpages or blogs are replacing formal reviews, and site visits have more relevance than the number of article citations