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The recent past has seen the adoption of multi-cloud deployments by enterprises due to availability, features, and regulatory requirements. A typical deployment involves parts of an application/workloads running inside a private cloud with…
Crowd trajectory prediction plays a crucial role in public safety and management, where it can help prevent disasters such as stampedes. Recent works address the problem by predicting individual trajectories and considering surrounding…
This paper proposes a conceptual model for a secure and performance-efficient workload management model in cloud environments. In this model, a resource management unit is employed for energy and performance proficient allocation of virtual…
Graph clustering is an unsupervised machine learning method that partitions the nodes in a graph into different groups. Despite achieving significant progress in exploiting both attributed and structured data information, graph clustering…
Real-time computation of data streams over affordable virtualized infrastructure resources is an important form of data in motion processing architecture. However, processing such data streams while ensuring strict guarantees on quality of…
Decentralized applications rely on non-centralized technical infrastructures and coordination principles. Without trusted third parties, their execution is not controlled by entities exercising centralized coordination but is instead…
Control planes of cloud frameworks trade off between scheduling granularity and performance. Centralized systems schedule at task granularity, but only schedule a few thousand tasks per second. Distributed systems schedule hundreds of…
Distributed dataflow systems enable the use of clusters for scalable data analytics. However, selecting appropriate cluster resources for a processing job is often not straightforward. Performance models trained on historical executions of…
Big data processing applications are becoming more and more complex. They are no more monolithic in nature but instead they are composed of decoupled analytical processes in the form of a workflow. One type of such workflow applications is…
Shared training approaches, such as multi-task learning (MTL) and gradient-based meta-learning, are widely used in various machine learning applications, but they often suffer from negative transfer, leading to performance degradation in…
Scientific workflows are widely used to automate scientific data analysis and often involve processing large quantities of data on compute clusters. As such, their execution tends to be long-running and resource intensive, leading to…
Crowdwork often entails tackling cognitively-demanding and time-consuming tasks. Crowdsourcing can be used for complex annotation tasks, from medical imaging to geospatial data, and such data powers sensitive applications, such as health…
Cloud Computing is an emerging area for accessing computing resources. In general, Cloud service providers offer services that can be clustered into three categories: SaaS, PaaS and IaaS. This paper discusses the Cloud workload analysis.…
Workflow is a common term used to describe a systematic breakdown of tasks that need to be performed to solve a problem. This concept has found best use in scientific and business applications for streamlining and improving the performance…
The data stream model has been defined for new classes of applications involving massive data being generated at a fast pace. Web click stream analysis and detection of network intrusions are two examples. Cluster analysis on data streams…
Over the last years, scientific workflows have become mature enough to be used in a production style. However, despite the increasing maturity, there is still a shortage of tools for searching, adapting, and reusing workflows that hinders a…
A vast and growing number of IoT applications connect physical devices, such as scientific instruments, technical equipment, machines, and cameras, across heterogenous infrastructure from the edge to the cloud to provide responsive,…
The dynamic nature of resource allocation and runtime conditions on Cloud can result in high variability in a job's runtime across multiple iterations, leading to a poor experience. Identifying the sources of such variation and being able…
Resource selection and task placement for distributed execution poses conceptual and implementation difficulties. Although resource selection and task placement are at the core of many tools and workflow systems, the methods are ad hoc…
In this work, we focus on the efficiency and scalability of pairwise constraint-based active clustering, crucial for processing large-scale data in applications such as data mining, knowledge annotation, and AI model pre-training. Our goals…