An Architecture to Stimulate Behavioral Development of Academic Cloud Users

Academic cloud infrastructures are constructed and maintained so they minimally constrain their users. Since they are free and do not limit usage patterns, academics developed such behavior that jeopardizes fair and flexible resource provisioning. For efficiency, related work either explicitly limits user access to resources, or introduce automatic rationing techniques. Surprisingly, the root cause (i.e., the user behavior) is disregarded by these approaches. This article compares academic cloud user behavior to its commercial equivalent. We deduce, that academics should behave like commercial cloud users to relieve resource provisioning. To encourage commercial like behavior, we propose an architectural extension to existing academic infrastructure clouds. First, every user's energy consumption and efficiency is monitored. Then, energy efficiency based leader boards are used to ignite competition between academics and reveal their worst practices. Leader boards are not sufficient to completely change user behavior. Thus, we introduce engaging options that encourage academics to delay resource requests and prefer resources more suitable for the infrastructure's internal provisioning. Finally, we evaluate our extensions via a simulation using real life academic resource request traces. We show a potential resource utilization reduction (by the factor of at most 2.6) while maintaining the unlimited nature of academic clouds

Performance Benchmarking of Infrastructure-as-a-Service (IaaS) Clouds with CloudWorkBench

The continuing growth of the cloud computing market has led to an unprecedented diversity of cloud services with different performance characteristics. To support service selection, researchers and practitioners conduct cloud performance benchmarking by measuring and objectively comparing the performance of different providers and configurations (e.g., instance types in different data center regions). In this tutorial, we demonstrate how to write performance tests for IaaS clouds using the Web-based benchmarking tool Cloud WorkBench (CWB). We will motivate and introduce benchmarking of IaaS cloud in general, demonstrate the execution of a simple benchmark in a public cloud environment, summarize the CWB tool architecture, and interactively develop and deploy a more advanced benchmark together with the participants

VM Image Repository and Distribution Models for Federated Clouds: State of the Art, Possible Directions and Open Issues

The emerging trend of Federated Cloud models enlist virtualization as a significant concept to offer a large scale distributed Infrastructure as a Service collaborative paradigm to end users. Virtualization leverage Virtual Machines (VM) instantiated from user specific templates labelled as VM Images (VMI). To this extent, the rapid provisioning of VMs with varying user requests ensuring Quality of Service (QoS) across multiple cloud providers largely depends upon the image repository architecture and distribution policies. We discuss the possible state-of-art in VMI storage repository and distribution mechanisms for efficient VM provisioning in federated clouds. In addition, we present and compare various representative systems in this realm. Furthermore, we define a design space, identify current limitations, challenges and open trends for VMI repositories and distribution techniques within federated infrastructure

Investigating the Encoding of Words in BERT's Neurons using Feature Textualization

Pretrained language models (PLMs) form the basis of most state-of-the-art NLP technologies. Nevertheless, they are essentially black boxes: Humans do not have a clear understanding of what knowledge is encoded in different parts of the models, especially in individual neurons. The situation is different in computer vision, where feature visualization provides a decompositional interpretability technique for neurons of vision models. Activation maximization is used to synthesize inherently interpretable visual representations of the information encoded in individual neurons. Our work is inspired by this but presents a cautionary tale on the interpretability of single neurons, based on the first large-scale attempt to adapt activation maximization to NLP, and, more specifically, large PLMs. We propose feature textualization, a technique to produce dense representations of neurons in the PLM word embedding space. We apply feature textualization to the BERT model (Devlin et al., 2019) to investigate whether the knowledge encoded in individual neurons can be interpreted and symbolized. We find that the produced representations can provide insights about the knowledge encoded in individual neurons, but that individual neurons do not represent clearcut symbolic units of language such as words. Additionally, we use feature textualization to investigate how many neurons are needed to encode words in BERT.Comment: To be published in 'BlackboxNLP 2023: The 6th Workshop on Analysing and Interpreting Neural Networks for NLP'. Camera-ready versio

Bringing scientific workflows to Amazon SWF

In response to the ever-increasing needs of scientific applications for resources, Cloud computing emerged as an alternative on-demand and cost-effective resource provisioning approach. In this context, Cloud providers have recognised the importance of workflow applications to science and provide their own native solutions, such as the Amazon Simple Workflow Service (SWF). Nevertheless, an important downside of SWF is its incompatibility with existing workflow systems, and lack of means for reusing scientific legacy code. Similarly, existing workflow middlewares and applications require non-trivial extensions to take advantage of Cloud resources. We present in this paper a software engineering solution that allows the scientific workflow community access the Amazon Cloud through one single front-end converter, and propose a legacy wrapper service for executing legacy code using SWF. Empirical results using a real-world scientific workflow demonstrate that our automatically generated SWF application performs almost as fast as a native manually-optimised version, and outperforms other workflow middleware systems using the Amazon Cloud.(VLID)2199150Accepted versio

Mitochondrial molecular chaperones

After synthesis in the cytosol, most mitochondrial proteins must traverse mitochondrial membranes to reach their functional location. During this process, proteins become unfolded and then refold to attain their native conformation after crossing the lipid bilayers. Mitochondrial molecular chaperones play an essential mechanistic role at various steps of this process. They facilitate presequence translocation, unfolding of the cytosol-localized domains of precursor proteins, movement across the mitochondrial membranes and, finally, folding of newly imported proteins within the matrix