I am a computer systems researcher. My research interests are in the areas of big data, data streams and systems software, focusing on designing efficient computer systems for big data and stream processing.
My research improves the performance, cost, and energy efficiency of database systems. I have designed and implemented practical and scalable systems that improve performance and energy-efficiency of modern database systems.
Systems for Big Data
System Support for Stream Processing
Computer Systems, Operating Systems, and Clouds
Peafowl: In-application CPU Scheduling to Reduce Power Consumption of In-memory Key-Value Stores
E Asyabi, A Bestavros, E Sharafzadeh,T Zhu-
ACM Symposium on Cloud Computing 2020 (SoCC '20)
CTS: An operating system cpu scheduler to mitigate tail latency for latency-sensitive multi-threaded applications
E Asyabi, E Sharafzadeh, SA SanaeeKohroudi, M Sharifi-
Journal of Parallel and Distributed Computing (2019)
Yawn: A CPU Idle-state Governor for Datacenter Applications
E Sharafzadeh, SAS Kohroudi, E Asyabi, M Sharifi -
Proceedings of the 10th ACM SIGOPS Asia-Pacific Workshop on Systems (2019)
TerrierTail: mitigating tail latency of cloud virtual machines
E Asyabi, SA SanaeeKohroudi, M Sharifi, A Bestavros -
IEEE Transactions on Parallel and Distributed Systems (2018)
ppXen: A hypervisor CPU scheduler for mitigating performance variability in virtualized clouds
E Asyabi, M Sharifi, A Bestavros-
Future Generation Computer Systems (2018)
Kani: a QoS-aware hypervisor-level scheduler for cloud computing environments
E Asyabi, A Azhdari, M Dehsangi, MG Khan, M Sharifi, SV Azhari -
Cluster Computing (2016)
cCluster: a core clustering mechanism for workload-aware virtual machine scheduling
M Dehsangi, E Asyabi, M Sharifi, SV Azhari -
2015 3rd International Conference on Future Internet of Things and Cloud (2015)
Einblick is an MIT-based startup founded by Tim Kraska . Einblick is powered by an in-memory analytics engine developed out of MIT and Brown University academic research. Einblick allows data scientists to significantly more quickly build accurate high performance models, and make them available to decision makers.
In Einblick, I was researching an OLAP architecture that tries to process data mostly in the cache (i.e., in-cache execution). In other words, this architecture should have avoided expensive memory accesses as much as possible when processing in-memory data. Over my time in Einblick, I designed a new architecture based on the Arrow framework for in-cache execution of queries like hash aggregation and build it over the summer. Our experiments showed the new architecture was up four times faster. The main reason for this speed-up was significant lower cache misses in our new architecture.
Teaching Fellow for Fundamentals of Computing Systems (CS350)
Boston University: Spring 2019, Fall 2019, Spring 2020, and Fall 2020
Teaching Fellow for Advanced Software Systems (CS410)
Boston University: Fall 2017