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As cloud computing services rapidly expand their customer base, it has become important to share cloud resources, so as to provide them economically. In cloud computing services, multiple types of resources, such as processing ability,…
Cloud-based computing infrastructure provides an efficient means to support real-time processing workloads, e.g., virtualized base station processing, and collaborative video conferencing. This paper addresses resource allocation for a…
Cloud computing has recently emerged as a major trend in distributed computing. We proposed a platform for selecting and configuring automatically an appropriate cloud environment that meets a set of consumer and provider requirements. It…
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…
Security-constrained unit commitment (SCUC) model is used for power system day-ahead scheduling. However, current SCUC model uses a static network to deliver power and meet demand optimally. A dynamic network can provide a lower optimal…
As cloud computing usage grows, cloud data centers play an increasingly important role. To maximize resource utilization, ensure service quality, and enhance system performance, it is crucial to allocate tasks and manage performance…
Assigning tasks efficiently in cloud computing is a challenging problem and is considered an NP-hard problem. Many researchers have used metaheuristic algorithms to solve it, but these often struggle to handle dynamic workloads and explore…
This paper presents a method called sampling-computation-optimization (SCO) to design batch Bayesian optimization. SCO does not construct new high-dimensional acquisition functions but samples from the existing one-site acquisition function…
Many robotic applications that are critical for robot performance require immediate feedback, hence execution time is a critical concern. Furthermore, it is common that robots come with a fixed quantity of hardware resources; if an…
Recent literature including our past work provide analysis and solutions for using (i) erasure coding, (ii) parallelism, or (iii) variable slicing/chunking (i.e., dividing an object of a specific size into a variable number of smaller…
Security-constrained unit commitment (SCUC) is a computationally complex process utilized in power system day-ahead scheduling and market clearing. SCUC is run daily and requires state-of-the-art algorithms to speed up the process. The…
Cloud computing has been gaining popularity in the recent years. Several studies are being proceeded to build cloud applications with exquisite quality based on users demands. In achieving the same, one of the applied criteria is checkpoint…
Cloud performance diagnosis and prediction is a challenging problem due to the stochastic nature of the cloud systems. Cloud performance is affected by a large set of factors including (but not limited to) virtual machine types, regions,…
Configuration space complexity makes the big-data software systems hard to configure well. Consider Hadoop, with over nine hundred parameters, developers often just use the default configurations provided with Hadoop distributions. The…
Distributed analytics engines such as Spark are a common choice for processing extremely large datasets. However, finding good configurations for these systems remains challenging, with each workload potentially requiring a different setup…
The last years have seen a steep rise in data generation worldwide, with the development and widespread adoption of several software projects targeting the Big Data paradigm. Many companies currently engage in Big Data analytics as part of…
Choosing the right resource can speed up job completion, better utilize the available hardware, and visibly reduce costs, especially when renting computers in the cloud. This was demonstrated in earlier studies on HEPCloud. However, the…
Maintaining web-services is a mission-critical task where any down-time means loss of revenue and reputation (of being a reliable service provider). In the current competitive web services market, such a loss of reputation causes extensive…
Distributed dataflow systems like Spark and Flink enable data-parallel processing of large datasets on clusters of cloud resources. Yet, selecting appropriate computational resources for dataflow jobs is often challenging. For efficient…
Bag of Distributed Tasks (BoDT) can benefit from decentralised execution on the Cloud. However, there is a trade-off between the performance that can be achieved by employing a large number of Cloud VMs for the tasks and the monetary…