GENI-related research activities of CSE, Aalto

Zhonghong Ou (zhonghong.ou@aalto.fi)

Post-doc researcherDepartment of Computer Science and Engineering (CSE)Aalto University, Finland

Internet of Things

Mario Di Francescomario.francesco@aalto.fi

GENI Meeting at KTHMario Di Francesco (http://www.uta.edu/faculty/mariodf) – September 15, 2014

Heterogeneous and multimedia datain the Internet of Things How to store data of

different types oreven multimedia?

What is the impacton performance?

GENI Meeting at KTHMario Di Francesco (http://www.uta.edu/faculty/mariodf) – September 15, 2014

A storage infrastructure for heterogeneous and multimedia data

General data model Document-oriented

database infrastructure– support for replication– live updates– web-friendly querying

system– support for binary data– metadata

Mario Di Francesco, Mayank Raj, Na Li, and Sajal K. Das, "A Storage Infrastructure for Heterogeneous and Multimedia Data in the Internet of Things", The 2012 IEEE International Conference on Internet of Things (iThings 2012), Pages 26-33, November 2012

{ "ts": "2009-07-30T13:31:37.459Z", "tags": ["POSTPROCESS"], "device_id": "OO14.4FO1.OOOO.O1AB", "sensor_id": [1,2], "data": [700,15.3], "_attachments": { "hello.txt”: { "content_type": "text/plain", "data": "SGVsbG8gd29ybGQh" } }}

GENI Meeting at KTHMario Di Francesco (http://www.uta.edu/faculty/mariodf) – September 15, 2014

Database performance for IoT data

What is the best solution to store IoT data in the cloud?– performance evaluation of different classes of databases

Thi Anh MaiPhan, Jukka K. Nurminen, and Mario Di Francesco, “Cloud Databases for Internet-of-Things Data”, The 2014 IEEE International Conference on Internet of Things (iThings 2014), September 2014

Bulk insert latency Multimedia insert and query latency

CIVIS

Jukka K. Nurminenjukka.nurminen@aalto.fi

Delay-sensitive mobile cloud

Dedicated radioDSRC

Cellular technology LTE

Key idea:• No new radios• Processing in the cloud• Short delay vs. resource use

Topics:• Optimal way to update cloud

data• Distributed cloud

Business:• Nokia/HERE cloud• SMEs for new apps

CIVIS Social network and big data analysis for sustainable energy use

• Transaction to a distributed energy paradigm.• Empowerment of local communities.• ICT as enablers of sustainable social dynamics.• Social dimension relevant to obtain CO2 emissions

reduction, energy efficiency and to achieve social goals.

Information NetworkEnergy

System

Social Network

Mobile Cloud Gaming

Matti Siekkinenmatti.siekkinen@aalto.fi

(Mobile) Cloud Gaming

• Game rendered in the cloud and streamed to an end-user device through a thin client

• Latency is a key challenge: even 100 ms can be too much for the most demanding games

• Extremely distributed cloud infrastructure proven to be beneficial using a prototype in test scenarios- Eg. Cloudlets over Wi-Fi, or LTE with server in operator premises

• TODO: scalability and overall plausibility tests would require access to a real-world test network such as the GENI- How sparse/dense would the cloud network need to be to support even

the most demanding games?

SIGMONA (SDN Concept in Generalized Mobile Network Architectures)

Sakari Luukkainensakari.luukkainen@aalto.fi

SIGMONA

• Cloud computing has been emerging as a promising approach to reduce cost for mobile operators

• Cloudification of the mobile network has momentums• One significant source of expense is the use of dedicated

network hardware to provide the required services• Solution: Network Function Virtulisation (NFV)

• Focus- Distribution of cloud elements in the architecture of a mobile network- VM migration and its requirements and performance between cells or

regions

Performance evaluation of public clouds

Zhonghong Ouzhonghong.ou@aalto.fi

Performance valuation of public clouds• Amazon EC2 & Rackspace Cloud

• Introduced in 2006• Provisioning various categories of instances, diversified types of

instances within the same category

• Hardware heterogeneity likely from• Hardware upgrade and replacement

• Research problems• Homogeneous vs. heterogeneous?• Performance variation?

• Did experiments in Amazon EC2 and Rackspace for two periods• 2011 & 2012

Findings

• Amazon EC2 uses diversified hardware to host the same type of instances. Hardware diversity is the primary culprit for performance variation in the cloud.

• Different VM scheduling mechanisms are used in EC2, which exacerbates performance variations, especially for networking related operations.

• In general, the variation between the fast instances and slow instances can reach 40%. In some applications, the variation can even approach up to 60%.

• By selecting fast instances within the same instance type, Amazon EC2 users can acquire up to 30% of cost saving, if the fast instances have a relatively low probability.

Related publications[1] Z. Ou, H. Zhuang, J. K. Nurminen, A. Ylä-Jääski, and P. Hui. Exploiting

hardware heterogeneity within the same instance type of Amazon EC2. 4th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud), 2012. (covered by BBC News, The Register, ACM TechNews etc)

[2] Z. Ou, H. Zhuang, A. Lukyanenko, J.K. Nurminen, P. Hui, V. Mazalov, A. Yla-Jaaski. Is the Same Instance Type Created Equal? Exploiting Heterogeneity of Public Clouds," IEEE Transactions on Cloud Computing, vol.1, no.2, pp.201 - 214, July-December 2013.

[3] H. Zhuang, X. Liu, Z. Ou, and K. Aberer. Impact of instance seeking strategies on resource allocation in cloud data centers. 2013 IEEE Sixth International Conference on Cloud Computing (CLOUD ’13), 27 – 34, June 2013.

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