Satisfying strong application requirements in data-intensive cloud computing environments

In today's data-intensive cloud systems, there is a tension between resource limitations and strict requirements. In an effort to scale up in the cloud, many systems today have unfortunately forced users to relax their requirements. However, users still have to deal with constraints, such as strict time deadlines or limited dollar budgets. Several applications critically rely on strongly consistent access to data hosted in clouds. Jobs that are time-critical must receive priority when they are submitted to shared cloud computing resources. This thesis presents systems that satisfy strong application requirements, such as consistency, dollar budgets, and real-time deadlines, for data-intensive cloud computing environments, in spite of resource limitations, such as bandwidth, congestion, and resource costs, while optimizing system metrics, such as throughput and latency. Our systems cover a wide range of environments, each with their own strict requirements. Pandora gives cloud users with deadline or budget constraints the optimal solution for transferring bulk data within these constraints. Vivace provides applications with a strongly consistent storage service that performs well when replicated across geo-distributed data centers. Natjam ensures that time-critical Hadoop jobs immediately receive cluster resources even when less important jobs are already running. For each of these systems, we designed new algorithms and techniques aimed at making the most of the limited resources available. We implemented the systems and evaluated their performance under deployment using real-world data and execution traces.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2013

Author dc:creator

• Cho, Brian

Contributors dc:contributor

• Gupta, Indranil
• Abdelzaher, Tarek F.
• Godfrey, Philip B.
• Aguilera, Marcos K.

• Cloud Computing
• strong requirements
• bulk data transfer
• data consistency
• priority
• Hadoop

Statement dc:rights

• Copyright 2012 Brian Cho

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/42371

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/42371