Related papers: How to Evaluate Distributed Coordination Systems? …
Distributed stream processing engines are designed with a focus on scalability to process big data volumes in a continuous manner. We present the Theodolite method for benchmarking the scalability of distributed stream processing engines.…
The paper introduces PDSP-Bench, a novel benchmarking system designed for a systematic understanding of performance of parallel stream processing in a distributed environment. Such an understanding is essential for determining how Stream…
With rapid growth in the amount of unstructured data produced by memory-intensive applications, large scale data analytics has recently attracted increasing interest. Processing, managing and analyzing this huge amount of data poses several…
Now we live in an era of big data, and big data applications are becoming more and more pervasive. How to benchmark data center computer systems running big data applications (in short big data systems) is a hot topic. In this paper, we…
Large-scale distributed computing systems often contain thousands of distributed nodes (machines). Monitoring the conditions of these nodes is important for system management purposes, which, however, can be extremely resource demanding as…
In recent IoT (Internet of Things) and Web 2.0 technologies, a critical problem arises with respect to storing and processing the large amount of collected data. In this paper we develop and evaluate distributed infrastructures for storing…
Blockchain and Cloud Computing are two of the main topics related to the distributed computing paradigm, and in the last decade, they have seen exponential growth in their adoption. Cloud computing has long been established as the main…
Software design patterns present general code solutions to common software design problems. Modern software systems rely heavily on containers for running their constituent service components. Yet, despite the prevalence of ready-to-use…
Distributed systems store data objects redundantly to balance the data access load over multiple nodes. Load balancing performance depends mainly on 1) the level of storage redundancy and 2) the assignment of data objects to storage nodes.…
We argue that recent developments in proof-of-work consensus mechanisms can be used in accordance with advancements in formal verification techniques to build a distributed payment protocol that addresses important economic drawbacks from…
We consider the estimation of Dirichlet Process Mixture Models (DPMMs) in distributed environments, where data are distributed across multiple computing nodes. A key advantage of Bayesian nonparametric models such as DPMMs is that they…
Complex engineered systems require coordinated design choices across heterogeneous components under multiple conflicting objectives and uncertain specifications. Monotone co-design provides a compositional framework for such problems by…
This report presents the design of the Scope infrastructure for extensible and portable benchmarking. Improvements in high- performance computing systems rely on coordination across different levels of system abstraction. Developing and…
Map construction methods automatically produce and/or update road network datasets using vehicle tracking data. Enabled by the ubiquitous generation of georeferenced tracking data, there has been a recent surge in map construction…
Comparative evaluation of several systems is a recurrent task in researching. It is a key step before deciding which system to use for our work, or, once our research has been conducted, to demonstrate the potential of the resulting model.…
Conventional latency metrics are formulated based on a broad definition of traditional monolithic services, and hence lack the capacity to address the complexities inherent in modern services and distributed computing paradigms.…
We consider a parallel system of $m$ identical machines prone to unpredictable crashes and restarts, trying to cope with the continuous arrival of tasks to be executed. Tasks have different computational requirements (i.e., processing time…
Distributed Software Systems are used these days by many people in the real time operations and modern enterprise applications. One of the most important and essential attributes of measurements for the quality of service of distributed…
Machine Learning (ML) and Deep Learning (DL) innovations are being introduced at such a rapid pace that researchers are hard-pressed to analyze and study them. The complicated procedures for evaluating innovations, along with the lack of…
Machine Learning systems rely on data for training, input and ongoing feedback and validation. Data in the field can come from varied sources, often anonymous or unknown to the ultimate users of the data. Whenever data is sourced and used,…