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Importance of Load Balancing
For clouding computing, load balancing enables cloud manage systems to efficiently allocate the resources of the cloud at the time of virtual machine requests.

scheduling algorithms
Opportunistic Load Balancing (OLB) is the algorithm that assigns workload to nodes in free order. It is simple but does not consider the expected execution time of each node. load balance Min-Min (LBMM) assigns sub-tasks to nodes which requires minimum execution time.

Load Balancing policies
Workload and Client Aware Policy (WCAP) is "implemented in a dis-centralized manner with low overhaed." It specifies the unique and special property (USP) of the requests and computing nodes. With the information of USP, the schedule can decide the most suitable node to complete a request. WCPA makes the most of computing nodes by reducing their idle time. Also, it reduce performance time through searches bases on content information.

a comparative study of algorithms
Honeyhive algorithm is inspired by the "behavior of a colony of honeybees foraging and harvest food." Forager bees searched for food, returned to the hive and describes the food they found through "waggle dance." "Waggle dance" can show the quantity, quality and distance of the food. For the honeyhive algorithm, every server first plays a forger bee role and satisfy requests from virtual servers. With the service done, each server evaluate the profitability of just-serviced virtual server. Then a server will adjust the advert board, which serves as "waggle dance" and records the profitability of virtual servers. If the calculated profitability is high, a server will continue to service current virtual server. Otherwise, it will keep waiting. Biased Random Sampling bases its job allocation on the network represented by a directed graph. For each execution node in this graph, in-degree means available resources and out-degree means allocated jobs. In-degree will decrease during job execution while out-degree will increase after job allocation.

Active Clustering is self-aggregation algorithm to rewire the network.

The result of experiment is that"Active Clustering and Random Sampling Walk predictably perform better as the number of processing nodes is increased" while Honeyhive algorithm does not show the increasing pattern.