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The increasing demand for diverse, mobile applications with various degrees of Quality of Service requirements meets the increasing elasticity of on-demand resource provisioning in virtualized cloud computing infrastructures. This paper…
Cloud computing systems promise to offer subscription-oriented, enterprise-quality computing services to users worldwide. With the increased demand for delivering services to a large number of users, they need to offer differentiated…
In cloud computing, users scale their resources (computational) based on their need. There is massive literature dealing with such resource scaling algorithms. These works ignore a fundamental constrain imposed by all Cloud Service…
Cloud robotics enables robots to offload high-dimensional motion planning and reasoning to remote servers. However, for continuous manipulation tasks requiring high-frequency control, network latency and jitter can severely destabilize the…
In business process landscapes, a common challenge is to provide the necessary computational resources to enact the single process steps. One well-known approach to solve this issue in a cost-efficient way is to use the notion of…
The rapid expansion of cloud services and their unpredictable workload demands present significant challenges in resource management. Traditional resource management approaches, primarily based on static rules and thresholds, often fail to…
The next generation of High Energy Physics experiments are expected to generate exabytes of data---two orders of magnitude greater than the current generation. In order to reliably meet peak demands, facilities must either plan to provision…
Autoscaling is a critical component for efficient resource utilization with satisfactory quality of service (QoS) in cloud computing. This paper investigates proactive autoscaling for widely-used scaling-per-query applications where scaling…
Cloud-related parameterizations remain a leading source of uncertainty in climate projections. Although machine learning holds promise for Earth system models (ESMs), many data-driven parameterizations lack interpretability, physical…
Forecast combination is widely recognized as a preferred strategy over forecast selection due to its ability to mitigate the uncertainty associated with identifying a single "best" forecast. Nonetheless, sophisticated combinations are often…
Cloud computing is becoming an essential component of modern computer and communication systems. The available resources at the cloud such as computing nodes, storage, databases, etc. are often packaged in the form of virtual machines (VMs)…
Optimizing resource allocation for analytical workloads is vital for reducing costs of cloud-data services. At the same time, it is incredibly hard for users to allocate resources per query in serverless processing systems, and they…
Cloud Computing is an emerging area. The main aim of the initial search-and-rescue period after strong earthquakes is to reduce the whole number of mortalities. One main trouble rising in this period is to and the greatest assignment of…
Cloud providers have introduced pricing models to incentivize long-term commitments of compute capacity. These long-term commitments allow the cloud providers to get guaranteed revenue for their investments in data centers and computing…
Production planning must account for uncertainty in a production system, arising from fluctuating demand forecasts. Therefore, this article focuses on the integration of updated customer demand into the rolling horizon planning cycle. We…
We consider a hierarchical edge-cloud architecture in which services are provided to mobile users as chains of virtual network functions. Each service has specific computation requirements and target delay performance, which require placing…
The use of cloud technology can offer significant savings for the deployment of services, provided that the service is able to make efficient use of the available virtual resources to meet service-level requirements. To avoid software…
Simple exponential smoothing is widely used in forecasting economic time series. This is because it is quick to compute and it generally delivers accurate forecasts. On the other hand, its multivariate version has received little attention…
Cloud computing is emerging as an important platform for business, personal and mobile computing applications. In this paper, we study a stochastic model of cloud computing, where jobs arrive according to a stochastic process and request…
Short-term load forecasting (STLF) is challenging due to complex time series (TS) which express three seasonal patterns and a nonlinear trend. This paper proposes a novel hybrid hierarchical deep learning model that deals with multiple…