Efficient Client-to-Server Assignments for Distributed Virtual Environments

Distributed Virtual Environments (DVEs) are distributed systems that allow multiple geographically distributed clients (users) to interact simultaneously in a computer-generated, shared virtual world. Applications of DVEs can be seen in many areas nowadays, such as online games, military simulations, collaborative designs, etc. To support large-scale DVEs with real-time interactions among thousands or more distributed clients, a geographically distributed server architecture (GDSA) is generally needed, and the virtual world can be partitioned into many distinct zones to distribute the load among the servers. Due to the geographic distributions of clients and servers in such architectures, it is essential to efficiently assign the participating clients to servers to enhance users ’ experience in interacting within the DVE. This problem is termed the client assignment problem. In this paper, we propose a two-phase approach, consisting of an initial assignment phase and a refined assignment phase to address this problem. Both phases are shown to be NP-hard, and several heuristic assignment algorithms are then devised based on this two-phase approach. Via extensive simulation studies with realistic settings, we evaluate these algorithms in terms of their performances in enhancing interactivity of the DVE. 1

Server assignment in mirrored server environments

Massively multiplayer online games (MMOGs) may use peer-to-peer, client-server, or mirrored-server environments. Since there exist multiple server options in mirrored server environments, a problem arises when deciding to which server each player should connect. We propose three distinct algorithms that assign players within Quality of Service (QoS) as each player joins and leaves, taking into consideration whether a player already in QoS can be moved to place a newly joining player in QoS without sacrificing QoS for the moved player. Our results show that for certain numbers of servers and values of QoS, our algorithms increase the total number of players in QoS over a static player to server assignment, and bears adapting to a wider variety of environments

Interactivity-Constrained Server Provisioning in Large-Scale Distributed Virtual Environments

Maintaining interactivity is one of the key challenges in distributed virtual environments (DVEs). In this paper, we consider a new problem, termed the interactivity-constrained server provisioning problem, whose goal is to minimize the number of distributed servers needed to achieve a prespecified level of interactivity. We identify and formulate two variants of this new problem and show that they are both NP-hard via reductions to the set covering problem. We then propose several computationally efficient approximation algorithms for solving the problem. The main algorithms exploit dependencies among distributed servers to make provisioning decisions. We conduct extensive experiments to evaluate the performance of the proposed algorithms. Specifically, we use both static Internet latency data available from prior measurements and topology generators, as well as the most recent, dynamic latency data collected via our own large-scale deployment of a DVE performance monitoring system over PlanetLab. The results show that the newly proposed algorithms that take into account interserver dependencies significantly outperform the well-established set covering algorithm for both problem variants

Adaptive Client to Mirrored-Server Assignment for Massively Multiplayer Online Games

The Mirrored Server (MS) architecture for network games uses multiple mirrored servers across multiple locations to alleviate the bandwidth bottleneck and to reduce the client-to-server delay time. Response time in MS can be reduced by optimally assigning clients to their mirrors. The goal of optimal client-to-mirror-assignment (CMA) is to achieve the minimum average client-to-mirror delay considering player joins (CMA-J) and leaves (CMA-L), and mirrors with limited capacity. The existing heuristic solution considers only CMA-J, and thus the average delay of the remaining players may increase when one or more players leave. Furthermore, the solution ignores mirror capacity, which may overload mirrors. In this paper we present a resource usage model for the MS architecture, and formally state the CMA problem. For both CMA-J and CMA-L we propose a polynomial time optimal solution and a faster heuristic algorithm that obtains near optimal CMA. Our simulations on randomly generated MS topologies show that our algorithms significantly reduce the average delay of the existing solution. We also compare the merits of the solutions in terms of their optimality and running time efficiency

Heuristics for Client Assignment and Load Balancing Problems in Online Games

Massively multiplayer online games (MMOGs) have been very popular over the past decade. The infrastructure necessary to support a large number of players simultaneously playing these games raises interesting problems to solve. Since the computations involved in solving those problems need to be done while the game is being played, they should not be so expensive that they cause any noticeable slowdown, as this would lead to a poor player perception of the game. Many of the problems in MMOGs are NP-Hard or NP-Complete, therefore we must develop heuristics for those problems without negatively affecting the player experience as a result of excessive computation. In this dissertation, we focus on a few of the problems encountered in MMOGs – the Client Assignment Problem (CAP) and both centralized and distributed load balancing – and develop heuristics for each. For the CAP we investigate how best to assign players to servers while meeting several conditions for satisfactory play, while in load balancing we investigate how best to distribute load among game servers subject to several criteria. In particular, we develop three heuristics - a heuristic for a variant of the CAP called Offline CAP-Z, a heuristic for centralized load balancing called BreakpointLB, and a heuristic for distributed load balancing called PLGR. We develop a simulator to simulate the operations of an MMOG and implement our heuristics to measure performance against adapted heuristics from the literature. We find that in many cases we are able to produce better results than those adapted heuristics, showing promise for implementation into production environments. Further, we believe that these ideas could also be easily adapted to the numerous other problems to solve in MMOGs, and they merit further consideration and augmentation for future research

QuLa: service selection and forwarding table population in service-centric networking using real-life topologies

The amount of services located in the network has drastically increased over the last decade which is why more and more datacenters are located at the network edge, closer to the users. In the current Internet it is up to the client to select a destination using a resolution service (Domain Name System, Content Delivery Networks ...). In the last few years, research on Information-Centric Networking (ICN) suggests to put this selection responsibility at the network components; routers find the closest copy of a content object using the content name as input. We extend the principle of ICN to services; service routers forward requests to service instances located in datacenters spread across the network edge. To solve this problem, we first present a service selection algorithm based on both server and network metrics. Next, we describe a method to reduce the state required in service routers while minimizing the performance loss caused by this data reduction. Simulation results based on real-life networks show that we are able to find a near-optimal load distribution with only minimal state required in the service routers