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.

Graphs are ubiquitous abstractions enabling reusable computing tools for graph processing with applications in every domain. Diverse workloads, standard models and languages, algebraic frameworks, and suitable and reproducible performance metrics will be at the core of graph processing ecosystems in the future. Academics, start-ups, and big tech companies such as Google, Face book, and Microsoft have introduced various systems for managing and processing the growing presence of big graphs. An increasing number of use cases revealed RDBMS performance problems in managing highly connected data, motivating various startups and innovative products, such as Neo4j, Sparksee, and the current Amazon Neptune. Microsoft Trinity along with Azure SQL DB have provided an early distributed database-oriented approach to big graph management.

High-quality designs of distributed systems and services are essential for our digital economy and society. Threatening to slow down the stream of working designs, we identify the mounting pressure of scale and complexity of (eco-)systems, of ill-defined and wicked problems, and of unclear processes, methods, and tools. We envision design itself as a core research topic in distributed systems, to understand and improve the science and practice of distributed (eco-)system design. Toward this vision, we propose the AtLarge design framework, accompanied by a set of 8 core design principles. We also propose 10 key challenges, which we hope the community can address in the following 5 years. In our experience so far, the proposed framework and principles are practical, and lead to pragmatic and innovative designs for large-scale distributed systems.

Big data processing systems are becoming increasingly more present in cloud workloads. Consequently, they are starting to incorporate more sophisticated mechanisms from traditional database and distributed systems. We focus in this work on the use of caching policies, which for big data raise important new challenges. Not only they must respond to new variants of the trade-off between hit rate, response time, and the space consumed by the cache, but they must do so at possibly higher volume and velocity than web and database workloads. Previous caching policies have not been tested experimentally with big data workloads. We address these challenges in this work. We propose the Read Density family of policies, which is a principled approach to quantify the utility of cached objects through a family of utility functions that depend on the frequency of reads of an object. We further design the Approximate Histogram, which is a policy-based technique based on an array of counters. This technique promises to achieve runtime-space efficient computation of the metric required by the cache policy. We evaluate through trace-based simulation the caching policies from the Read Density family, and compare them with over ten state-of-the-art alternatives. We use two workload traces representative for big data processing, collected from commercial Spark and MapReduce deployments. While we achieve comparable performance to the state-of-art with less parameters, meaningful performance improvement for big data workloads remain elusive.

Datacenters act as cloud-infrastructure to stakeholders across industry, government, and academia. To meet growing demand yet operate efficiently, datacenter operators employ increasingly more sophisticated scheduling systems, mechanisms, and policies. Although many scheduling techniques already exist, relatively little research has gone into the abstraction of the scheduling process itself, hampering design, tuning, and comparison of existing techniques. In this work, we propose a reference architecture for datacenter schedulers. The architecture follows five design principles: components with clearly distinct responsibilities, grouping of related components where possible, separation of mechanism from policy, scheduling as complex workflow, and hierarchical multi-scheduler structure. To demonstrate the validity of the reference architecture, we map to it state-of-the-art datacenter schedulers. We find scheduler-stages are commonly underspecified in peer-reviewed publications. Through trace-based simulation and real-world experiments, we show underspecification of scheduler-stages can lead to significant variations in performance.

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.

Online gaming applications entertain hundreds of millions of daily active players and often feature vastly complex architecture. Among online games, Minecraft-like games simulate unique (e.g., modifiable) environments, are virally popular, and are increasingly provided as a service. However, the performance of Minecraft-like services, and in particular their scalability, is not well understood. Moreover, currently no benchmark exists for Minecraft-like games. Addressing this knowledge gap, in this work we design and use the Yardstick benchmark to analyze the performance of Minecraft-like services. Yardstick is based on an operational model that captures salient characteristics of Minecraft-like services. As input workload, Yardstick captures important features, such as the most-popular maps used within the Minecraft community. Yardstick captures system- and application-level metrics, and derives from them service-level metrics such as frequency of game-updates under scalable workload. We implement Yardstick, and, through real-world experiments in our clusters, we explore the performance and scalability of popular Minecraft-like servers, including the official vanilla server, and the community-developed servers Spigot and Glowstone. Our findings indicate the scalability limits of these servers, that Minecraft-like services are poorly parallelized, and that Glowstone is the least viable option among those tested.

In the late-1950s, leasing time on an IBM 704 cost hundreds of dollars per minute. Today, cloud computing, that is, using IT as a service, on-demand and pay-per-use, is a widely used computing paradigm that offers large economies of scale. Born from a need to make platform as a service (PaaS) more accessible, fine-grained, and affordable, serverless computing has garnered interest from both industry and academia. This article aims to give an understanding of these early days of serverless computing: what it is, where it comes from, what is the current status of serverless technology, and what are its main obstacles and opportunities.

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.

The question 'Can big data and HPC infrastructure converge?' has important implications for many operators and clients of modern computing. However, answering it is challenging. The hardware is currently different, and fast evolving: big data uses machines with modest numbers of fat cores per socket, large caches, and much memory, whereas HPC uses machines with larger numbers of (thinner) cores, non-trivial NUMA architectures, and fast interconnects. In this work, we investigate the convergence of big data and HPC infrastructure for one of the most challenging application domains, the highly irregular graph processing. We contrast through a systematic, experimental study of over 300,000 core-hours the performance of a modern multicore, Intel Knights Landing (KNL) and of traditional big data hardware, in processing representative graph workloads using state-of-the-art graph analytics platforms. The experimental results indicate KNL is convergence-ready, performance-wise, but only after extensive and expert-level tuning of software and hardware parameters.