As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
We propose a dynamic resource provisioning with hotspot anticipation scheme, called NN-Player+DRP-HA that employs a finite state machine model to monitor the movement of avatars in a virtual world. Furthermore, we use a finite state machine to represent possible avatar states and state transitions. By combining the state of each avatar in a game zone with a neural network (NN) predictor, we may figure out potential workload produced by hotspots, and then allocate appropriate computing resources to support the game zone. Experimental results support that the proposed NN-Player+DRP-HA scheme can avoid most of under-allocation events with an acceptable over-allocation rate. Compared with a representative dynamic resource provisioning method, called NN-Player+DRP, the proposed NN-Player+DRP-HA reduces the probability of under-allocation events from 2.16% to 0.42% (80% improvement) in terms of CPU capacity of a VM, under the premise of controlling the CPU over-allocation rate within the CPU capacity of one VM.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.