Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Elasticity is very important to the scale-out distributed systems running on today's large-scale multi-tenant clouds, regardless public or private. An elastic distributed data processing system must have the capability of: 1 dynamically balancing the computing load among workers due to their performance heterogeneity and dynamicity; 2 fast recovering the lost memory state of failure workers with acceptable overheads during the regular execution.#R##N##R##N#Unfortunately,  found that the design of the state-of-the-art distributed graph computing system only works ll in small sized dedicated clusters.  implement a distributed graph computing prototype, X-Graph, and demonstrate the capabilities of being elastic in three ways. First,  present menger, a novel two-level graph partition framework, which further splits one worker-level partition into several sub-partitions as the basic migration units, and each has the "migration affinity" with one of the other workers. Second,  implement a dynamical load balancer based on menger, which prefers the worker that has the affinity of the sub-partition to be migrated as the destination, and completely avoids the costly sophistical graph re-partitioning algorithms. Third,  implement a differentiated replication frame-work, which supports parallel recovery for lost partitions just like general-purpose dataflow systems.
['Lu Lu', 'Xuanhua Shi', 'Hai Jin']
I am writing a research paper on a idea Towards Truly Elastic Distributed Graph Computing in the Cloud
[{"from": "human", "value": "I am writing a research paper on a idea Towards Truly Elastic Distributed Graph Computing in the Cloud"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Elasticity is very important to the scale-out distributed systems running on today's large-scale multi-tenant clouds, regardless public or private. An elastic distributed data processing system must have the capability of: 1 dynamically balancing the computing load among workers due to their performance heterogeneity and dynamicity; 2 fast recovering the lost memory state of failure workers with acceptable overheads during the regular execution.#R##N##R##N#Unfortunately,  found that the design of the state-of-the-art distributed graph computing system only works ll in small sized dedicated clusters.  implement a distributed graph computing prototype, X-Graph, and demonstrate the capabilities of being elastic in three ways. First,  present menger, a novel two-level graph partition framework, which further splits one worker-level partition into several sub-partitions as the basic migration units, and each has the \"migration affinity\" with one of the other workers. Second,  implement a dynamical load balancer based on menger, which prefers the worker that has the affinity of the sub-partition to be migrated as the destination, and completely avoids the costly sophistical graph re-partitioning algorithms. Third,  implement a differentiated replication frame-work, which supports parallel recovery for lost partitions just like general-purpose dataflow systems."}]