Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recent work has made the case for geo-distributed analytics, where data collected and stored at multiple datacenters and edge sites world-wide is analyzed in situ to drive operational and management decisions. A key issue in such systems is ensuring low response times for analytics queries issued against geo-distributed data. A central determinant of response time is the query execution plan (QEP). Current query optimizers do not consider the network when deriving QEPs, which is a key drawback as the geo-distributed sites are connected via WAN links with heterogeneous and modest bandwidths, unlike intra-datacenter networks.  propose CLARINET, a novel WAN-aware query optimizer. Deriving a WAN-aware QEP requires working jointly with the execution layer of analytics frameworks that places tasks to sites and performs scheduling.  design efficient heuristic solutions in CLARINET to make such a joint decision on the QEP. Our experiments with a real prototype deployed across EC2 datacenters, and large-scale simulations using production workloads show that CLARINET improves query response times by ≥ 50% compared to state-of-the-art WAN-aware task placement and scheduling.
['Raajay Viswanathan', 'Ganesh Ananthanarayanan', 'Aditya Akella']
I am writing a research paper on a idea CLARINET: WAN-aware optimization for analytics queries
[{"from": "human", "value": "I am writing a research paper on a idea CLARINET: WAN-aware optimization for analytics queries"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recent work has made the case for geo-distributed analytics, where data collected and stored at multiple datacenters and edge sites world-wide is analyzed in situ to drive operational and management decisions. A key issue in such systems is ensuring low response times for analytics queries issued against geo-distributed data. A central determinant of response time is the query execution plan (QEP). Current query optimizers do not consider the network when deriving QEPs, which is a key drawback as the geo-distributed sites are connected via WAN links with heterogeneous and modest bandwidths, unlike intra-datacenter networks.  propose CLARINET, a novel WAN-aware query optimizer. Deriving a WAN-aware QEP requires working jointly with the execution layer of analytics frameworks that places tasks to sites and performs scheduling.  design efficient heuristic solutions in CLARINET to make such a joint decision on the QEP. Our experiments with a real prototype deployed across EC2 datacenters, and large-scale simulations using production workloads show that CLARINET improves query response times by \u2265 50% compared to state-of-the-art WAN-aware task placement and scheduling."}]