Alexandru Uta

• PhD
• Professor (Assistant) at Leiden University

In Function-as-a-Service (FaaS) serverless, large applications are split into short-lived stateless functions. Deploying functions is mutually profitable: users need not be concerned with resource management, while providers can keep their servers at high utilization rates running thousands of functions concurrently on a single machine. It is exact...

Due to the complexity and size of modern software systems, the amount of logs generated is tremendous. Hence, it is infeasible to manually investigate these data in a reasonable time, thereby requiring automating log analysis to derive insights about the functioning of the systems. Motivated by an industry use-case, we zoom-in on one integral part...

Traditional datacenter analysis is based on high-level, coarse-grained metrics. This obscures our vision of datacenter behavior, as we do not observe the full picture nor subtleties that might make up these high-level, coarse metrics. There is room for operational improvement based on fine-grained temporal and spatial, low-level metric data. We lev...

Our modern society and competitive economy depend on a strong digital foundation and, in turn, on sustained research and innovation in computer systems and networks (CompSys). With this manifesto, we draw attention to CompSys as a vital part of ICT. Among ICT technologies, CompSys covers all the hardware and all the operational software layers that...

With the ever-increasing dataset sizes, several file formats such as Parquet, ORC, and Avro have been developed to store data efficiently, save the network, and interconnect bandwidth at the price of additional CPU utilization. However, with the advent of networks supporting 25-100 Gb/s and storage devices delivering 1, 000, 000 reqs/sec, the CPU h...

In serverless computing, applications are executed under lightweight virtualization and isolation environments, such as containers or micro virtual machines. Typically, their memory allocation is set by the user before deployment. All other resources, such as CPU, are allocated by the provider statically and proportionally to memory allocations. Th...

ions such as dataframes are only as efficient as their underlying runtime system. The de-facto distributed data processing framework, Apache Spark, is poorly suited for the modern cloud-based data-science workloads due to its outdated assumptions: static datasets analyzed using coarse-grained transformations. In this paper, we introduce the Indexed...

Ensuring the success of big graph processing for the next decade and beyond.

Improving datacenter operations is vital for the digital society. We posit that doing so requires our community to shift, from operational aspects taken in isolation to holistic analysis of datacenter resources, energy, and workloads. In turn, this shift will require new analysis methods, and open-access, FAIR datasets with fine temporal and spatia...

Distributed data processing ecosystems are widespread and their components are highly specialized, such that efficient interoperability is urgent. Recently, Apache Arrow was chosen by the community to serve as a format mediator, providing efficient in-memory data representation. Arrow enables efficient data movement between data processing and stor...

With the ever-increasing dataset sizes, several file formats like Parquet, ORC, and Avro have been developed to store data efficiently and to save network and interconnect bandwidth at the price of additional CPU utilization. However, with the advent of networks supporting 25-100 Gb/s and storage devices delivering 1, 000, 000 reqs/sec the CPU has...

Graphs are by nature unifying abstractions that can leverage interconnectedness to represent, explore, predict, and explain real- and digital-world phenomena. Although real users and consumers of graph instances and graph workloads understand these abstractions, future problems will require new abstractions and systems. What needs to happen in the...

In this document, we describe LDBC Graphalytics, an industrial-grade benchmark for graph analysis platforms. The main goal of Graphalytics is to enable the fair and objective comparison of graph analysis platforms. Due to the diversity of bottlenecks and performance issues such platforms need to address, Graphalytics consists of a set of selected d...

Although graph processing has become a topic of interest in many domains, we still observe a lack of representative datasets for in-depth performance and scalability analysis. Neither data collections, nor graph generators provide enough diversity and control for thorough analysis. To address this problem, we propose a heuristic method for scaling...

All computing infrastructure suffers from performance variability, be it bare-metal or virtualized. This phenomenon originates from many sources: some transient, such as noisy neighbors, and others more permanent but sudden, such as changes or wear in hardware, changes in the underlying hypervisor stack, or even undocumented interactions between th...

Performance variability has been acknowledged as a problem for over a decade by cloud practitioners and performance engineers. Yet, our survey of top systems conferences reveals that the research community regularly disregards variability when running experiments in the cloud. Focusing on networks, we assess the impact of variability on cloud-based...

In a dynamic world of software development, the architectural styles are continuously evolving, adapting to new technologies and trends. Microservice architecture (MSA) is gaining adoption among industry practitioners due to its advantages compared to the monolithic architecture. Although MSA builds on the core concepts of Service Oriented Architec...

Today's low-power devices, such as smartphones and wearables, form a very heterogeneous ecosystem. Applications in such a system typically follow a reactive pattern based on stream analytics, i.e., sensing, processing, and actuating. Despite the simplicity of this pattern, deciding where to place the processing tasks of an application to achieve en...

As data science gets deployed more and more into operational applications, it becomes important for data science frameworks to be able to perform computations in interactive, sub-second time. Indexing and caching are two key techniques that can make interactive query processing on large datasets possible. In this demo, we show the design, implement...

MapReduce ecosystems are (still) widely popular for big data processing in data centers. To address the diverse non-functional requirements arising from many and increasingly more sophisticated users, the community has developed many scheduling policies for MapReduce workloads. Although some individual policies can dynamically optimize for single a...

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, serverles...

Graphs are a natural fit for modeling concepts used in solving diverse problems in science, commerce, engineering, and governance. Responding to the diversity of graph data and algorithms, many parallel and distributed graph-processing systems exist. However, until now these platforms use a static model of deployment: they only run on a pre-defined...

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...

Public cloud computing platforms are a cost-effective solution for individuals and organizations to deploy various types of workloads, ranging from scientific applications, business-critical workloads, e-governance to big data applications. Co-locating all such different types of workloads in a single datacenter leads not only to performance degrad...

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 challe...

Scientific domains such as astronomy or bioinformatics produce increasingly large amounts of data that need to be analyzed. Such analyses are modeled as scientific workflows - applications composed of many individual tasks that exhibit data dependencies. Typically, these applications suffer from significant variability in the interplay between achi...

Data-intensive scientific workflows exhibit inter-task dependencies that generate file-based communication schemes. In such scenarios, traditional disk-based storage systems often limit overall application performance and scalability. To overcome the storage bottleneck, in-memory runtime distributed file systems speed up application I/O. Such syste...

Data-intensive scientific workflows are composed of many tasks that exhibit data precedence constraints leading to communication schemes expressed by means of intermediate files. In such scenarios, the storage layer is often a bottleneck, limiting overall application scalability, due to large volumes of data being generated during runtime at high I...

In many-task computing (MTC), applications such as scientific workflows or parameter sweeps communicate via intermediate files; application performance strongly depends on the file system in use. The state of the art uses runtime systems providing in-memory file storage that is designed for data locality: files are placed on those nodes that write...

MemFS is a fully-symmetrical, in-memory distributed runtime file system. Its design is based on uniformly distributing file stripes across the storage nodes belonging to an application by means of a distributed hash function, purposefully sacrificing data locality for balancing both network traffic and memory consumption. This way, reading and writ...

Many scientific computations can be expressed as Many-Task Computing (MTC) applications. In such scenarios, application processes communicate by means of intermediate files, in particular input, temporary data generated during job execution (stored in a runtime file system), and output.