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This paper proposes and evaluates a scalable dynamic load distribution scheme for multi-server distributed virtual environment systems, where users are highly skewed rather than uniformly distributed over a virtual environment. In the proposed scheme, an overloaded server initiating load distribution selects a set of servers to be involved in load distribution by dynamically adapting to the workload status of other servers, unlike the existing approaches. Upon completion of server selection, the intiating server repartitions the regions dedicated to the involved servers using a graph partitioning algorithm so that all the involved servers have the roughly equal workload. The involved servers then migrate their workloads with each other in a peer-to-peer manner according to the result of repartitioning. We present and analyze the simulation results that compare the performance of the proposed scheme with that of the existing schemes.

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A distributed virtual environment (DVE) is a software system that allows users in a network to interact with each other by sharing a common view of their states. As users are geographically distributed over large networks like the internet and the number of users increases, scalability is a key aspect to consider for real-time interaction. Various solutions have been proposed to improve the scalability in DVE systems but they are either focused on only specific aspects or customized to a target application. In this paper, we classify the approaches for improving scalability of DVE into five categories: communication architecture, interest management, concurrency control, data replication, and load distribution. We then propose a scalable network framework for DVEs, ATLAS. Incorporated with our various scalable schemes, ATLAS meets the scalability of a system as a whole. The integration experiences of ATLAS with several virtual reality systems ensure the versatility of the proposed solution.

The advancement of personal devices such as PDAs and mobile phones become ubiquitous and their increasing computing capabilities allow users to perform tasks that used to be performed on workstations. However, we cannot expect to run all tasks on top of personal devices because of the limited resources. Application Offloading allow us to overcome this issue by porting part of an application to a nearby server or workstation with more capabilities. In this paper we present a new application offloading mechanism to perform offloading based on the execution history of applications, and adaptable to the current conditions of the environment and device. We record the consumed resources and the state of the device and surrogates, and we use that information to perform an adaptable offloading. We show that our scheme can prevent the overhead of profiling and also reduce the execution time of an application against pure runtime offloading.