Related papers: Accelerating R-based Analytics on the Cloud
Efficient load balancing is crucial in cloud computing environments to ensure optimal resource utilization, minimize response times, and prevent server overload. Traditional load balancing algorithms, such as round-robin or least…
MONC is a highly scalable modelling tool for the investigation of atmospheric flows, turbulence and cloud microphysics. Typical simulations produce very large amounts of raw data which must then be analysed for scientific investigation. For…
Substantial difficulties in adopting cloud services are often encountered during upgrades of existing software systems. A reliable early stage analysis can facilitate an informed decision process of moving systems to cloud platforms. It can…
Cloud Computing researches involve a tremendous amount of entities such as users, applications, and virtual machines. Due to the limited access and often variable availability of such resources, researchers have their prototypes tested…
Over the last several years, the computation landscape for conducting data analytics has completely changed. While in the past, a lot of the activities have been undertaken in isolation by companies, and research institutions, today's…
Cloud services must typically be distributed across a large number of machines in order to make use of multiple compute and storage resources. This opens the programmer to several sources of complexity such as concurrency, order of message…
In this paper we describe our work on designing a web based, distributed data analysis system based on the popular MapReduce framework deployed on a small cloud; developed specifically for analyzing web server logs. The log analysis system…
We investigate the feasibility of high performance scientific computation using cloud computers as an alternative to traditional computational tools. The availability of these large, virtualized pools of compute resources raises the…
With the growing amount of data, data processing workloads and the management of their resource usage becomes increasingly important. Since managing a dedicated infrastructure is in many situations infeasible or uneconomical, users…
Genetic Algorithms (GAs) are a powerful technique to address hard optimisation problems. However, scalability issues might prevent them from being applied to real-world problems. Exploiting parallel GAs in the cloud might be an affordable…
Cloud computing is gradually gaining popularity among businesses due to its distinct advantages over self-hosted IT infrastructures. Business Intelligence (BI) is a highly resource intensive system requiring large-scale parallel processing…
Serverless computing offers elasticity unmatched by conventional server-based cloud infrastructure. Although modern data processing systems embrace serverless storage, such as Amazon S3, they continue to manage their compute resources as…
Seismology has entered the petabyte era, driven by decades of continuous recordings of broadband networks, the increase in nodal seismic experiments, and the recent emergence of Distributed Acoustic Sensing (DAS). This review explains how…
The pay-as-you-go model supported by existing cloud infrastructure providers is appealing to most application service providers to deliver their applications in the cloud. Within this context, elasticity of applications has become one of…
Elasticity is a form of self-adaptivity in cloud-based software systems that is typically restricted to the infrastructure layer and realized through auto-scaling. However, both reactive and proactive forms of infrastructure auto-scaling…
Cloud computing has become increasingly popular. Many options of cloud deployments are available. Testing cloud performance would enable us to choose a cloud deployment based on the requirements. In this paper, we present an innovative…
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.…
In this paper, we draw the specifications of a novel benchmark for comparing parallel processing frameworks in the context of big data applications hosted in the cloud. We aim at filling several gaps in already existing cloud data…
High intensive computation applications can usually take days to months to finish an execution. During this time, it is common to have variations of the available resources when considering that such hardware is usually shared among a…
This paper proposes an architectural framework for the efficient orchestration of containers in cloud environments. It centres around resource scheduling and rescheduling policies as well as autoscaling algorithms that enable the creation…