PDL People
					    
					  
                    
                        
                        George Amvrosiadis
                    
                     
                    
                      
                        | Contact: 
 | www | | 
                      
                        | Office: 
 | RMCIC 2311 | 
                      
                        | Mailing Address: | Electrical & Computer Engineering Carnegie Mellon University
 5000 Forbes Avenue
 Pittsburgh, PA 15213-3891
 | 
                      
                        | Position: 
 | Associate Research Professor 
 | 
                      
                        | Projects: | Delta FS, Mimir, Zoned Storage | 
                    
                     
                    Research Interests: 
                    
My research interests lie in the areas of:
  -  Scalable storage performance and reliability;
  
-  Operating and distributed systems;
  
-  Data analysis and modeling
  
My current primary research objectives are:
  -  Reimagine file systems as transient, light-weight services
    that applications can instantiate, scale, and optimize on-demand. The
    goal is to propose a (timely!) alternative to parallel file
    systems with fully synchronous namespaces, which will remain performant
    as we transition to exascale systems and beyond. 
 
 
-  Develop efficient mechanisms that turn the OS into a gray
    box. A step in that direction is Duet [4], a framework that notifies
    processes when data of interest becomes cache-resident. This allows
    groups of processes with overlapping data accesses to adjust their
    workflows in order to improve their collective efficiency. Duet has been
    used to improve the efficiency of maintenance tasks [4], job schedulers
    [5], and file system monitoring tools [1]. 
 
 
-  Discover and fix pain points in systems through data analysis
    and modeling of field data. Some examples: improving the reliability
    of enterprise backup systems [2, 5], characterizing the effect of
    temperature on the reliability of datacenter hardware [6, 8], and
    mitigating the impact of maintenance work by predicting when a storage
    device is expected to be idle [7].