Amazon Technologies, Inc.

Pranav Rao Perampalli Nekkar

Aravind Srinivasan

Mihir Sathe

Systems and methods are described for allocating requests to implement new workloads within a set of servers. Each server can have a given proportion of various resources, based on a hardware configuration of the server. Placing multiple workloads that do not match that proportion can result in stranded resources, which are unused but cannot be used due to a lack of other required resources. Embodiments of the present disclosure include load balancer that routes workload requests based on the proportion of resources expected to be used by the workload, by selecting a target server based on whether the target server hosts other workloads that are correlated or anti-correlated with the requested workload. To reduce maintained state information, the load balancer can characterize workloads in terms of clusters, rather than maintaining information as to individual workloads.

Efficient allocation of workloads based on correlated workload clusters

Systems and methods are described for allocating requests to implement new workloads within a dynamic set of servers. Existing load balancing techniques can result in 'focus firing' on new servers added to the set, since a load balancer may view a new server as underloaded. With sufficient intensity, focus firing can result in overshooting target load for the new server, and the new server in fact becoming overloaded. The present disclosure modifies selection of servers as potential targets for a workload by at least partly biasing against selection of young servers. The bias imposed can be scaled to avoid overloading new servers.

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Seattle, WA, United States of America

Pranav Rao Perampalli Nekkar