Beyond Microbenchmarks: The SPEC-RG Vision for a Comprehensive Serverless Benchmark

Serverless computing services, such as Function-as-a-Service (FaaS), hold the attractive promise of a high level of abstraction and high performance, combined with the minimization of operational logic. Several large ecosystems of serverless platforms, both open- and closed-source, aim to realize this promise. Consequently, a lucrative market has emerged. However, the performance trade-offs of these systems are not well-understood. Moreover, it is exactly the high level of abstraction and the opaqueness of the operational-side that make performance evaluation studies of serverless platforms challenging. Learning from the history of IT platforms, we argue that a benchmark for serverless platforms could help address this challenge. We envision a comprehensive serverless benchmark, which we contrast to the narrow focus of prior work in this area. We argue that a comprehensive benchmark will need to take into account more than just runtime overhead, and include notions of cost, realistic workloads, more (open-source) platforms, and cloud integrations. Finally, we show through preliminary real-world experiments how such a benchmark can help compare the performance overhead when running a serverless workload on state-of-the-art platforms

The Workflow Trace Archive: Open-Access Data from Public and Private Computing Infrastructures -- Technical Report

Realistic, relevant, and reproducible experiments often need input traces collected from real-world environments. We focus in this work on traces of workflows---common in datacenters, clouds, and HPC infrastructures. We show that the state-of-the-art in using workflow-traces raises important issues: (1) the use of realistic traces is infrequent, and (2) the use of realistic, {\it open-access} traces even more so. Alleviating these issues, we introduce the Workflow Trace Archive (WTA), an open-access archive of workflow traces from diverse computing infrastructures and tooling to parse, validate, and analyze traces. The WTA includes ${>}48$ million workflows captured from ${>}10$ computing infrastructures, representing a broad diversity of trace domains and characteristics. To emphasize the importance of trace diversity, we characterize the WTA contents and analyze in simulation the impact of trace diversity on experiment results. Our results indicate significant differences in characteristics, properties, and workflow structures between workload sources, domains, and fields.Comment: Technical repor

The workflow trace archive:Open-access data from public and private computing infrastructures

Realistic, relevant, and reproducible experiments often need input traces collected from real-world environments. In this work, we focus on traces of workflows - common in datacenters, clouds, and HPC infrastructures. We show that the state-of-the-art in using workflow-traces raises important issues: (1) the use of realistic traces is infrequent and (2) the use of realistic, open-access traces even more so. Alleviating these issues, we introduce the Workflow Trace Archive (WTA), an open-access archive of workflow traces from diverse computing infrastructures and tooling to parse, validate, and analyze traces. The WTA includes {>}48>48 million workflows captured from {>}10>10 computing infrastructures, representing a broad diversity of trace domains and characteristics. To emphasize the importance of trace diversity, we characterize the WTA contents and analyze in simulation the impact of trace diversity on experiment results. Our results indicate significant differences in characteristics, properties, and workflow structures between workload sources, domains, and fields

The Future is Big Graphs! A Community View on Graph Processing Systems

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 next decade for big graph processing to continue to succeed?Comment: 12 pages, 3 figures, collaboration between the large-scale systems and data management communities, work started at the Dagstuhl Seminar 19491 on Big Graph Processing Systems, to be published in the Communications of the AC

Characterization of a big data storage workload in the cloud

The proliferation of big data processing platforms has led to radically different system designs, such as MapReduce and the newer Spark. Understanding the workloads of such systems facilitates tuning and could foster new designs. However, whereas MapReduce workloads have been characterized extensively, relatively little public knowledge exists about the characteristics of Spark workloads in representative environments. To address this problem, in this work we collect and analyze a 6-month Spark workload from a major provider of big data processing services, Databricks. Our analysis focuses on a number of key features, such as the long-term trends of reads and modifications, the statistical properties of reads, and the popularity of clusters and of file formats. Overall, we present numerous findings that could form the basis of new systems studies and designs. Our quantitative evidence and its analysis suggest the existence of daily and weekly load imbalances, of heavy-tailed and bursty behaviour, of the relative rarity of modifications, and of proliferation of big data specific formats

An innovation intermediary for Nairobi, Kenya: Designing student-centric services for university- industry collaboration

Engaging students as partners in university-industry collaboration (UIC) through challenge-based and real-life projects creates significant value for all participants through novel educational approaches, talent recruitment, user-driven innovation, new resources, and research-related opportunities. However, as these practices have developed iteratively over time in industrialized countries and are highly context dependent, it is unclear how they can be best transferred to emerging economy contexts. In this paper, we present a research and design process of creating an innovation intermediary to foster student-centric UIC in Nairobi, Kenya. Seen as a set of services that reside on a multilevel platform, the intermediary aims to add value to the existing ecosystem through open access knowledge sharing, promoting partnerships, and mentoring for impact in an integrative, complementary way. Through a four-step qualitative research process involving interviews and co-creation workshops with local stakeholders, we examine the ecosystem, define value creation, design the services of the intermediary, and propose a step-wise model for further diffusion. We note the importance of establishing a solid rationale for collaboration, understanding the expected value to be created, creating a neutral space for the collaboration, and planning the implementation in detail. We contribute to transferring student-centric UIC practices into emerging economy contexts.ITESO, A.C