About me: I am a Computer Science Ph.D. student at UC Santa Barbara's Distributed Systems Lab (DSL). I work in the intersection of data management and distributed systems to advance the area of Global-Scale Data Management. Also, I have worked in collaboration with HP Labs and Microsoft Research to build data management designs on emerging memory hardware technology.
E-mail: nawab@cs.ucsb.edu
My C.V. can be found here

Research Statement: Data is generated and consumed throughout the world. Processing this data is essential to drive todays Internet Services and Big Data Analytics. In my work, I study and build data management systems for global-scale environments with the goal of understanding the fundamental challenges and design practices to support such an environment.
Looking forward, the demand for global-scale data management will surpass those of current human-driven Internet Services and Big Data Analytics with the emergence of autonomous applications such as Internet of Things (IoT), mobile agents (self-driving cars and robotics), and data-driven sciences ranging from the social sciences to medicine. My ongoing work explores the opportunities and challenges in augmenting edge computing technology and application-level research with data management capabilities that support the next 10x and 100x explosion in globally connected devices and data.
Click here for more about my work on global-scale data management

Projects and publications
Processing Geo-Replicated Data: This line of work studies and design transaction processing protocols for geo-replicated data. The large latency between datacenters is a challenge to performance. This invites designing new protocols that target geo-replication.

[20] COP: Planning Conflicts for Faster Parallel Transactional Machine Learning
(EDBT 2017)

[19] The Challenges of Global-scale Data Management
(SIGMOD 2016 Tutorial) [pptx]

[18] DB-Risk: The Game of Global Database Placement
(SIGMOD 2016 Demo) [demo]

[17] Chariots : A Scalable Shared Log for Data Management in Multi-Datacenter Cloud Environments
(EDBT 2015)

[16] Mind your Ps and Vs: A perspective on the challenges of big data management and privacy concerns
(BigComp 2015)

[15] Low-Latency Multi-Datacenter Databases using Replicated Commits.
(VLDB 2013)

[14] Managing Geo-replicated Data in Multi-datacenters.
(Springer Databases in Networked Information Systems 2013)

[13] Serializability, not Serial: Concurrency Control and Availability in Multi-Datacenter Datastores.
(VLDB 2012)


Speed-of-Light Limit on Coordination Latency: Transaction latency on geo-replicated data is limited by the speed-of-light. A theoretical lower-bound of transaction latency is developed to understand the limits imposed by large communication latency. Using insights from the lower-bound, a protocol called Helios is developed that theoretically achieves the lower-bound and approach it in real experiments

[12] Minimizing Commit Latency of Transactions in Geo-Replicated Data Stores
(SIGMOD 2015)

[11] Message Futures: Fast Commitment of Transactions in Multi-datacenter Environments.
(CIDR 2013)


Data processing on emerging memory technology: In collaboration with HP Labs, I worked on designing data stores for non-volatile memory architectures. I studied the implications of emerging flush-on-fail CPU technology on the durability cost of transactions. Also, as an intern in MSR Redmond I worked on the Time-Split Bw-tree (TSBw-tree) that integrates the algorithms of the Time-split B-tree within the lock-free implementation of the Bwtree.

[10] High Performance Temporal Indexing on Modern Hardware
(ICDE 2015)

[9] Procrastination Beats Prevention: Timely Sufficient Persistence for Efficient Crash Resilience
(EDBT 2015)

[8] Zero-Overhead NVM Crash Resilience.
(FAST 2015 WiP Session + Poster session)

[7] Zero-Overhead NVM Crash Resilience
(NVMW 2015)


Fair resource allocation for Wireless Mesh Networks: This project tackles the problem of unfairness in Wireless Mesh Networks, where TCP flows experience different performance characteristics depending on their location in the network. A MAC-layer solution is developed to transparently improve TCP fairness. The proposed MAC layer, called TMAC, uses a timestamp-ordering technique to achieve fairness.

[6] Fair Packet Scheduling in Wireless Mesh Networks.
(Elsevier Journal of Ad Hoc Networks 2014)

[5] MAC-Layer Protocol for TCP Fairness in Wireless Mesh Networks.
(ICCC 2012)

[4] TMAC: Timestamp-ordered MAC for CSMA/CA Wireless Mesh Networks.
(ICCCN 2011)

[3] TMAC: Timestamp-Ordered MAC Protocol for Wireless Mesh Networks.
(MS Thesis 2011)


Other work on large-scale data processing

[2] Graph Summarization for Geo-correlated Trends Detection in Social Networks
(SIGMOD 2016 Undergraduate Research Poster Competition)

[1] MaaT: Effective and scalable coordination of distributed transactions in the cloud.
(VLDB 2014)