Building Cloud Applications for Challenged Networks

NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium

Rowe

et al. 2016

Self Cite

Decision making in cloud environments is quite challenging due to the diversity in service offerings and pricing models, especially considering that the cloud market is an incredibly fast moving one. In addition, there are no hard and fast rules; each customer has a specific set of constraints (e.g. budget) and application requirements (e.g. minimum computational resources). Machine learning can help address some of the complicated decisions by carrying out customer-specific analytics to determine the most suitable instance type(s) and the most opportune time for starting or migrating instances. We employ machine learning techniques to develop an adaptive deployment policy, providing an optimal match between the customer demands and the available cloud service offerings. We provide an experimental study based on extensive set of job executions over a major public cloud infrastructure.

Section: Resultsmentioning

confidence: 99%

Daleel: Simplifying cloud instance selection using machine learning

Samreen

NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium

Rowe

et al. 2016

Self Cite

“…Moreover, multi-tenancy is a very uncharted land (see Table 2). As edge devices access the network via different ISPs and potentially operate across various administrative domains [51], the ability to deal with multi-organisation/multi-tenant environments becomes a must for any orchestration technology.…”

Section: Edge and Fog Computingmentioning

confidence: 99%

Research challenges in nextgen service orchestration

Vaquero

Cuadrado

Future Generation Computer Systems

et al. 2019

Self Cite

Fog/edge computing, function as a service, and programmable infrastructures, like software-defined networking or network function virtualisation, are becoming ubiquitously used in modern Information Technology infrastructures. These technologies change the characteristics and capabilities of the underlying computational substrate where services run (e.g. higher volatility, scarcer computational power, or programmability). As a consequence, the nature of the services that can be run on them changes too (smaller codebases, more fragmented state, etc.). These changes bring new requirements for service orchestrators, which need to evolve so as to support new scenarios where a close interaction between service and infrastructure becomes essential to deliver a seamless user experience. Here, we present the challenges brought forward by this new breed of technologies and where current orchestration techniques stand with regards to the new challenges. We also present a set of promising technologies that can help tame this brave new world.

“…We assume that the relative additional disk write latency in rPi systems is caused by the relatively slow write speeds on flash memory used in SD cards [12]. Software deployments that predominantly use disk reads and memory access, avoiding disk writes, may therefore be even more valuable for efficiency on these devices than on cloud-based servers -otherwise the network latency gains reported 7 For this we needed to switch to the Barbossa release, version 0.8.0. in §I may be eclipsed by slower disk access. Of the applications tested in §II, the httpd, Ruby and ZooKeeper types are therefore most likely to be suited to the rPi environment.…”

Section: I/o Overheadmentioning

confidence: 99%

On Using Micro-Clouds to Deliver the Fog

Porter

Ribeiro

et al. 2017

IEEE Internet Comput.

Self Cite

Cloud computing has demonstrated itself to be a scalable and cost-efficient solution for many real-world applications. However, its modus operandi is not ideally suited to resource-constrained environments that are characterized by limited network bandwidth and high latencies. With the increasing proliferation and sophistication of edge devices, the idea of fog computing proposes to offload some of the computation to the edge. To this end, micro-clouds-which are modular and portable assemblies of small single-board computers-have started to gain attention as infrastructures to support fog computing by offering isolated resource provisioning at the edge in a cost-effective way. We investigate the feasibility and readiness of micro-clouds for delivering the vision of fog computing. Through a number of experiments, we showcase the potential of micro-clouds formed by collections of Raspberry Pi computers to host a range of fog-related applications, particularly for locations where there is limited network bandwidths and long latencies.