Our society is increasingly digital, and its processes are increasingly digitalized. As an emerging technology for the digital society, graphs provide a universal abstraction to represent concepts and objects, and the relationships between them. However, processing graphs at a massive scale raises numerous sustainability challenges; becoming energy-aware could help graph-processing infrastructure alleviate its climate impact. Graph Greenifier aims to address this challenge in the conceptual framework offered by the Graph Massivizer architecture. We present an early vision of how Graph Greenifier could provide sustainability analysis and decision-making capabilities for extreme graph-processing workloads. Graph Greenifier leverages an advanced digital twin for data center operations, based on the OpenDC open-source simulator, a novel toolchain for workload-driven simulation of graph processing at scale, and a sustainability predictor. The input to the digital twin combines monitoring of the information and communication technology infrastructure used for graph processing with data collected from the power grid. Graph Greenifier thus informs providers and consumers on operational sustainability aspects, requiring mutual information sharing, reducing energy consumption for graph analytics, and increasing the use of electricity from renewable sources.

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Cloud datacenters provide a backbone to our digital society. Inaccurate capacity procurement for cloud datacenters can lead to significant performance degradation, denser targets for failure, and unsustainable energy consumption. Although this activity is core to improving cloud infrastructure, relatively few comprehensive approaches and support tools exist for mid-tier operators, leaving many planners with merely rule-of-thumb judgement. We derive requirements from a unique survey of experts in charge of diverse datacenters in several countries. We propose Capelin, a data-driven, scenario-based capacity planning system for mid-tier cloud datacenters. Capelin introduces the notion of portfolios of scenarios, which it leverages in its probing for alternative capacity-plans. At the core of the system, a trace-based, discrete-event simulator enables the exploration of different possible topologies, with support for scaling the volume, variety, and velocity of resources, and for horizontal (scale-out) and vertical (scale-up) scaling. Capelin compares alternative topologies and for each gives detailed quantitative operational information, which could facilitate human decisions of capacity planning. We implement and open-source Capelin, and show through comprehensive trace-based experiments it can aid practitioners. The results give evidence that reasonable choices can be worse by a factor of 1.5-2.0 than the best, in terms of performance degradation or energy consumption.

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Resource contention is one of the major problems in cloud datacenters. Many types of resource contention occur, with important impact on the performance and sometimes even the reliability of applications running in cloud datacenters. Cloud applications run together on the same physical machines with different workloads resulting in non-synchronized accesses to the shared resources. This leads to cases where co-hosted applications are contending for the common resources and not receiving the demanded resource amounts. In this work, we investigate the contention in CPU resources, as CPU is allowed to be over-committed by typical SLAs. We propose a CPU-contention predictor for the demanding business-critical workloads, which require low resource contention to deliver the required performance to customers. Our predictor is based on a set of regression models and metrics which we evaluate extensively. We tune the predictor with data collected from a real-world cloud operation spanning multiple datacenters and servicing business-critical workloads.

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Our society is digital: industry, science, governance, and individuals depend, often transparently, on the inter-operation of large numbers of distributed computer systems. Although the society takes them almost for granted, these computer ecosystems are not available for all, may not be affordable for long, and raise numerous other research challenges. Inspired by these challenges and by our experience with distributed computer systems, we envision Massivizing Computer Systems, a domain of computer science focusing on understanding, controlling, and evolving successfully such ecosystems. Beyond establishing and growing a body of knowledge about computer ecosystems and their constituent systems, the community in this domain should also aim to educate many about design and engineering for this domain, and all people about its principles. This is a call to the entire community: there is much to discover and achieve.

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In the new Digital Economy, massive computer systems, often grouped in datacenters, serve as factories "producing" cloud services with massive consumption. However, to afford cloud services globally, we must address new research challenges in designing, operating, and using modern datacenters. We must also address challenges in educating and training the next generation of datacenter engineers. Addressing such challenges, in this work we present our vision on OpenDC: we envision the exploration of various datacenter concepts and technologies, using existing and new scientific methods, enabling new education practices and topics, and leading to the creation of new software and data artifacts. We present the datacenter concepts and technologies we are currently planning to explore using OpenDC. We identify the scientific methods we want to use, and explain our vision of education practices. We present the architecture and open-source program underlying the OpenDC software, and the format and open-access data we use for datacenter experiments. We conclude with an open invitation for the community to join our effort.@en

The Mnemos resource management and scheduling architecture uses portfolio scheduling, topology-aware virtual-resource management, and state information to self-adapt to significant workload changes and to analyze risks. Simulations with real-world workload traces reveal the potential for significant cost savings.@en