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Modern production data processing and machine learning pipelines on the cloud are critical components for many cloud-based companies. These pipelines are typically composed of complex workflows represented by directed acyclic graphs (DAGs).…
Recent breakthroughs in generative artificial intelligence have triggered a surge in demand for machine learning training, which poses significant cost burdens and environmental challenges due to its substantial energy consumption.…
Cloud computing is a powerful new technology that is widely used in the business world. Recently, we have been investigating the benefits it offers to scientific computing. We have used three workflow applications to compare the performance…
Finding the right cloud configuration for workloads is an essential step to ensure good performance and contain running costs. A poor choice of cloud configuration decreases application performance and increases running cost significantly.…
This paper presents how an existing framework for offline performance optimization can be applied to microservice applications during the Release phase of the DevOps life cycle. Optimization of resource allocation configuration parameters…
The localization speed and accuracy in the indoor scenario can greatly impact the Quality of Experience of the user. While many individual machine learning models can achieve comparable positioning performance, their prediction mechanisms…
Analyzing large datasets with distributed dataflow systems requires the use of clusters. Public cloud providers offer a large variety and quantity of resources that can be used for such clusters. However, picking the appropriate resources…
We propose throughput and cost optimal job scheduling algorithms in cloud computing platforms offering Infrastructure as a Service. We first consider online migration and propose job scheduling algorithms to minimize job migration and…
This paper presents a theoretical discussion for environmentally-conscious job deployment and migration in cloud environments, aiming to minimize the environmental impact of resource provisioning while incorporating sustainability…
The emerging edge-hub-cloud paradigm has enabled the development of innovative latency-critical cyber-physical applications in the edge-cloud continuum. However, this paradigm poses multiple challenges due to the heterogeneity of the…
A robotic network is a system with multiple robots connected by a communication network. Certain tasks that cannot be accomplished with available robotic resources are candidates for the use of cloud robotics, which overcomes the…
As more IoT applications gradually move towards the cloud-edge collaborative mode, the containerized scheduling of workflows extends from the cloud to the edge. However, given the high delay of the communication network, loose coupling of…
In the swiftly evolving domain of cloud computing, the advent of serverless systems underscores the crucial need for predictive auto-scaling systems. This necessity arises to ensure optimal resource allocation and maintain operational…
Cloud computing enables the dynamic provisioning of server resources. To exploit this opportunity, a policy is needed for dynamically allocating (and deallocating) servers in response to the current load conditions. In this paper we…
A current trend in networking and cloud computing is to provide compute resources over widely dispersed places exemplified by initiatives like Network Function Virtualisation. This paves the way for a widespread service deployment and can…
Accurate prediction of application performance is critical for enabling effective scheduling and resource management in resource-constrained dynamic edge environments. However, achieving predictable performance in such environments remains…
Cloud data centers face increasing pressure to reduce operational energy consumption as big data workloads continue to grow in scale and complexity. This paper presents a workload aware and energy efficient scheduling framework that…
Modern day continued demand for resource hungry services and applications in IT sector has led to development of Cloud computing. Cloud computing environment involves high cost infrastructure on one hand and need high scale computational…
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…
The demand for stringent interactive quality-of-service has intensified in both mobile edge computing (MEC) and cloud systems, driven by the imperative to improve user experiences. As a result, the processing of computation-intensive tasks…