Congresso Sociedade Brasileira de Computação CSBC2016 Porto Alegre (Brazil) Workshop on Cloud Networks & Cloudscape Brazil Wagner Meira - Federal University of Minas Gerais, Brazil Interdisciplinary Research for Cloud Computing: Future and challenges

1. WCN Panel
Interdisciplinary Research for Cloud
Computing: Future and challenges
Data Science, Knowledge Discovery,
Mining and Learning
Wagner Meira Jr.
Universidade Federal de Minas Gerais, Brazil

2. Panel Questions
1) How are the basic premises and challenges of a
given research area being affected by the rise of
cloud computing?
2) What are the changes we already see in those
areas?
3) Where are we heading? Are there new holy grails?

3. Context: Data Science, Knowledge
Discovery, Mining and Learning
● Data science (and related areas) aims to extract
actionable knowledge from large volumes of data (semi-)
automatically.
● Enablers:
– Data storage reached unprecedent scale and is still increasing
(e.g. IoT)
– Computational power is cheaper than ever.
– Techniques are mature and have broad applicability.
– Increasing and broad interest for using computational analytics
and intelligence in daily activities. (That's the difference!)

5. Question 1: How does cloud
computing change premises?
● Latency:
– Interactive tasks (e.g., visualization) become more challenging
● Bandwidth:
– Transferring large amounts of data is not trivial, affecting application setup and computation.
● Computational power:
– Elasticity brings an additional variable for analytics, since we suddenly may vary the
resource usage.
● Storage:
– Larger storage availability allows trading computation and communication.
● Streaming vs. Batch:
– Better connectivity enables better integration of streaming and batch tasks.
● Computing workload:
– How to exploit reference locality and other strategies for multi-user scenarios.

6. Question 2: What are the
changes we already see?
● Issues:
– QoS: virtualization enables computing management, but several SLA
dimensions are still open.
– Scalability: parallel programming environments are in place, not
necessarily being accessible and capable of efficiently solving all
algorithmical demands
– Privacy and security: Very few proposals that are clear, effective and
covering a broad spectrum. Legal issues are a challenge.
●
Commercially, Hadoop enabled several services, and it is being
extended to Spark.
●
There is still a huge market of data-related applications and
services to be integrated into the clouds, and much more to come.

9. Question 3: Where are we heading?
●
Data will keep increasing in terms of volume, diversity and complexity, demanding novel
models and algorithms, and making QoS, scalability, privacy and security even more
challenging:
– Multimedia data
– Small data
– IoT
●
Data scientists' role is to design data flows, not necessarily to code them.
●
Data-driven decision making will become commonplace
– Agents
– Analytics applications
– Smart cities
●
Cloud-aware algorithms: strategy for making algorithms cost-effective considering their
intensity both in terms of computation and communication:
– Hybrid Memory Cube
– GPU

10. Thank you!
Interdisciplinary Research for Cloud
Computing: Future and challenges
Data Science, Knowledge Discovery,
Mining and Learning
Wagner Meira Jr.
Universidade Federal de Minas Gerais, Brazil