Titulo Estágio
Evaluating Big-Data Analytics in the Cloud
Áreas de especialidade
Engenharia de Software
Local do Estágio
CISUC
Enquadramento
In the past decade, the computer and information industry has experienced rapid changes in both platform scale and scope of applications. Computers, smart phones, clouds and social networks demand not only high performance but also a high degree of machine intelligence. In fact, we are entering an era of big data analysis and cognitive computing. This trendy movement is observed by the pervasive use of mobile phones, storage and computing clouds, revival of artificial intelligence in practice, extended supercomputer applications, and widespread deployment of Internet of Things (IoT) platforms. To face these new computing and communication paradigm, we must upgrade the Cloud and IoT ecosystems with new capabilities such as machine learning, IoT sensing, data analytics, and cognitive power that can mimic or augment human intelligence.
In this internship, we propose to assess some of the critical challenges to realize the complete vision of a Data PaaS in the Cloud: Elasticity, Data replication, System administration and Tuning.
Objetivo
In practice, the expected outcomes of this internship are contributions in the critical challenges:
- Elasticity: An open question is whether the same cloud storage service can support both transactions and analytics; how caching best fits into the overall picture is also unclear. To provide elasticity, database engines and analysis platforms in a Data PaaS will need to operate well on top of resources that can be allocated quickly during workload peaks but possibly preempted for users paying for premium service.
- Data replication. Latency across geographically distributed datacenters makes it difficult to keep replicas consistent yet offer good throughput and response time to updates. Multi-master replication is a good alternative, when conflicting updates on different replicas can be automatically synchronized. But the resulting programming model is not intuitive to mainstream programmers. Thus, the challenge is how best to trade-off availability, consistency performance, programmability, and cost.
- System administration and tuning. In the world of Data PaaS, database and system administrators simply do not exist. Therefore, all administrative tasks must be automated, such as capacity planning, resource provisioning, and physical data management. Resource control parameters must also be set automatically and be highly responsive to changes in load, such as buffer pool size and admission control limits.
- A research paper, to be submitted and presented at a top international conference, describing the approach and main results obtained from the experiments.
Plano de Trabalhos - Semestre 1
[Some tasks might overlap; M=Month]
T1 (M1 – M3): Knowledge transfer and state of the art literature review on Data PaaS in the Cloud.
T2 (M3) Design critical mechanisms for Data PaaS in the Cloud, using the information gathered in task T1 as basis.
T3 (M3) Identification of target systems to be used in the experiments.
T4 (M3 – M4) Implementation of a proof of concept prototype.
T5 (M5): Writing the Intermediate report.
Plano de Trabalhos - Semestre 2
[Some tasks might overlap; M=Month]
T6 (M6): Integration of the intermediate defense comments and completion of the Big Data Cloud Services.
T7 (M6 – M7): Implementation of the architecture and critical mechanisms for Data PaaS in the Cloud, and execution of tests (functional).
T8 (M8): Execution of experiments and analysis of results.
T9 (M9): Write a research paper and submission to a top international conference on Big Data and Cloud areas (IEEE Big Data Congress, IEEE Services Computing Conference, IEEE International Conference on Data Engineering – ICDE, etc.).
T10 (M10): Writing the thesis.
Condições
The work will be carried out in the facilities of the Department of Informatics Engineering at the University of Coimbra (CISUC - Software and Systems Engineering Group), where a work place and necessary computer resources will be provided.
Orientador
Jorge Bernardino
jorge@isec.pt 📩