Impact of Client Choice on Distributed Resource Allocation in Edge Computing

Through using edge computing services, mobile devices can run complex tasks with the help of network-based computing resources. However, servers in the edge cloud are not only constrained to limited resources, but also must make allocation decisions with only limited information available. The clients requesting computing resources may also have limited information about the servers available to them. We focus on a distributed resource allocation method in which servers operate independently and do not communicate with each other, but interact with clients to make allocation decisions for those clients' tasks. We follow a two-round bidding approach to assign tasks to edge cloud servers. Servers may choose to preempt previous tasks to allocate more useful ones, and clients may choose to track the outcomes of their tasks to inform their future decisions. Results show that user learning improves system performance by 50-80% when servers are heterogeneous in pricing aggressiveness.