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Graph and hardware-specific optimisations lead to orders of magnitude improvements in performance, energy, and cost over conventional graph processing methods. Typical big data platforms, such as Apache MapReduce and Apache Spark, rely on generic ...

Graph processing is increasingly popular given the wide range of phenomena represented as graphs (e.g., social media networks, pharmaceutical drug compounds, or fraud networks, among others). The increasing amount of data available requires new ...

Graphs can represent various phenomena and are increasingly used to tackle complex problems. Among the challenges associated with graph processing is the ability to analyze and mine massive-scale graphs. While the massive scale is usually associated ...

Serverless computing offers an affordable and easy way to code lightweight functions that can be invoked based on some events to perform simple tasks. For more complicated processing, multiple serverless functions can be orchestrated as a directed ...

Our society is increasingly digital, and its processes are increasingly digitalized. As an emerging technology for the digital society, graphs provide a universal abstraction to represent concepts and objects, and the relationships between them. However,...

It is our great pleasure to welcome you to the 2023 ACM/SPEC Workshop on Serverless, Extreme-Scale, and Sustainable Graph Processing Systems. This is the first such workshop, aiming to facilitate the exchange of ideas and expertise in the broad field of ...

Datacenters are the backbone of our digital society, used by the industry, academic researchers, public institutions, etc. To manage resources, data centers make use of sophisticated schedulers. Each scheduler offers a different set of capabilities and ...

Serverless platforms have exploded in popularity in recent years, but, today, these platforms are still unsuitable for large classes of applications. They perform well for batch-oriented workloads that perform coarse transformations over data ...

Cloud-native systems are dynamic in nature as they always have to react to changes in the environment, e.g., how users utilize the system. Self-adaptive cloud-native systems manage those changes by predicting how future environmental changes will impact ...

In this paper we report our experiences from the migration of an AI model inference process, used in the context of an E-health platform to the Function as a Service model. To that direction, a performance analysis is applied, across three available ...

As the next generation of diverse workloads like autonomous driving and augmented/virtual reality evolves, computation is shifting from cloud-based services to the edge, leading to the emergence of a cloud-edge compute continuum. This continuum promises ...

The ability to split applications across different locations in the continuum (edge/cloud) creates needs for application break down into smaller and more distributed chunks. In this realm the Function as a Service approach appears as a significant ...

While systemic failure/overload in recoverable networks with load redistribution is a common phenomenon, current ability to evaluate and moreover mitigate the corresponding systemic risk is vastly insufficient due to complexity of the problem and ...

The ambition of this talk is to seed discussions around how cloud native technologies can help research on performance engineering, but also what are the interesting performance engineering challenges to solve with cloud native technologies.

Cloud ...

Market analysts are agreed that serverless computing has strong market potential, with projected compound annual growth rates varying between 21% and 28% through 2028 and a projected market value of 36.8 billion by that time. Although serverless ...

Fluid approximations are useful for representing transient behaviour of queueing systems. For layered queues a fluid model has previously been derived indirectly via transformation first to a PEPA model, or via recursive neural networks. This paper ...

The continuous adoption of embedded systems in the most diverse application domains contributes to the increasing complexity of their development. Hardware/Software Co-Design methodologies are usually employed to tackle the challenges deriving from even ...

It is our great pleasure to welcome you to the 8th Workshop on Challenges in Performance Methods for Software Development - WOSP-C 2023. This year's workshop continues its tradition of being the forum for the discussion of emerging or unaddressed ...

Cloud computing has become the major computational paradigm for the deployment of all kind of applications, ranging from mobile apps to complex AI algorithms. On the other side, the rapid growth of IoT market has led to the need of processing the data ...

Reliable job execution is important in High Performance Computing clusters. Understanding the failure distribution and failure pattern of jobs helps HPC cluster managers design better systems, and users design fault tolerant systems. Machine learning is ...