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. The 1950s saw the emergence of two technologies that are currently shaping the world: containerization in shipping and timesharing in computing. By allowing shipping to become standardized and automated, the former gave rise to manufacturing and retail ecosystems, and ultimately to the economic phenomenon of globalization 1. By enabling multiple clients to share the same physical infrastructure, time-sharing gave rise to cloud computing and the modern digital ecosystems, which are key drivers for growth in knowledge-based societies 2. Whereas few could afford the costs of time-sharing services and paid dearly for simple computer simulations in the late-1950s, today over 80% of companies use the hundreds of services accessible as cloud computing (source: Studies by European Commission 3 and Cloudability (2018)), along with many private individuals. Following with remarkable regularity the evolution observed in the history of containerization, cloud services have adapted to offer better fitting containers that require less time to load (boot) and provide higher automation in handling (orchestrating) containers on behalf of the client. Serverless computing promises more: to achieve full-automation in managing fine-grained containers. Already,
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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-)system, 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. arXiv:1902.05416v1 [cs.DC] 14 Feb 2019 2. Why Focus on MCS Design?We argue in this section for the timely and important need to focus on MCS design. Not only is (good) design needed (Section 2.1), but we identify an increasing need for good design (Section 2.2) and designers (Section 2.3).We also analyze what good design needs to address, that is, complex challenges from system design (Section 2.4) and from MCS design (Section 2.5).3. We anonymize the venue, but consider it relevant because its held year is after 2014, the venue is a conference, and its ranking is A in CORE18 and green in MSAR14. For comparison, ICDCS has these rankings too.4. We anonymize the university, but consider the course relevant because it is large, it took place after 2014, and the university is ranked in the top-150 (in computer science) in both the THE and the QS 2018 World University Rankings (out of nearly 1,000 universities), and in Webometrics of July 18 (out of over 28,000).
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Our society is digital: industry, science, governance, and individuals depend, often transparently, on the interoperation 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.
Big data analytics is the often complex process of examining large andvaried data sets to uncover information. The aim of this paper is to describe how Real TimeOperation Center structuring drilling data in an informative and systematic manner throughdigital solution that can help organizations make informed business decisions and leverage business value to deliver wells efficiently and effectively. Real Time Operation Center process of collecting largechunks of structured/unstructured data, segregating and analyzing it and discovering thepatterns and other useful business insights from it. The methods were based on structuringa detailed workflow, RACI, quality check list for every single process of the provision of real-timedrilling data and digitally transform into valuable information through robust auditableprocess, quality standards and sophisticated software. The paper will explain RTOC DataManagement System and how it helped the organization determining which data is relevantand can be analyzed to drive better business decisions in the future. The big data platform, in-house built-in software, andautomated dashboards have helped the company build the links between different assets,analyzing technical gaps, creating opportunities and moving away from manual data entry(e.g. Excel) which was causing data errors, disconnection between information and wastedworker hours due to inefficiency. These solutions leverage analytics and unlock the valuefrom data to enhance operational efficiency, drive performance and maximize profitability. As a result, the company has successfully delivered 160 wells in 2019 (6% higher than 2019 Business Plan and 10% higher than number of delivered wellsin 2018) more efficiently with 28.2 days per 10kft fornew wells (10% better than 2018), without compromising the well objectives and quality of the wells. Moreover, despite increasing complexity, the highest level ofconfidence on data analytics has permitted the company to go beyond their normaloperating envelop and set a major record for drilling the world's fifth longest well as amilestone in 2019.
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