Dynamic Load Balancing for Massively Multiplayer Online Games Using OPNET

In recent years, there has been an important growth of online gaming. Today’s Massively Multiplayer Online Games (MMOGs) can contain millions of synchronous players scattered across the world and participating with each other within a single shared game. Traditional Client/Server architectures of MMOGs exhibit different problems in scalability, reliability, and latency, as well as the cost of adding new servers when demand is too high. P2P architecture provides considerable support for scalability of MMOGs. It also achieves good response times by supporting direct connections between players. In this paper, we have proposed a novel dynamic load balancing for massively multiplayer online games (MMOGs) based this hybrid Peer-to-Peer architecture. We have divided the game world space into several regions. Each region in the game world space is controlled and managed by using both a super-peer and a clone-super-peer. The region's super-peer is responsible for distributing the game update among the players inside the region, as well as managing the game communications between the players. However, the clone-super-peer is responsible for controlling the players' migration from one region to another, in addition to be the super-peer of the region when the super-peer leaves the game. We have designed and evaluated the dynamic load balancing for MMOGs based on hybrid P2P architecture. We have used OPNET Modeler 18.0 to simulate and evaluate the proposed system. Our dynamic load balancer is responsible for distributing the load among the regions in the game world space. The position of the load balancer is located between the game server and the regions. The results, following extensive experiments, show that low delay and higher traffic communication can be achieved using dynamic load balancing for MMOGs based on hybrid P2P system

Towards Earlier Fault Detection by Value-Driven Prioritization of Test Cases Using Fuzzy TOPSIS

In industrial software testing, development projects typically set up and maintain test suites containing large numbers of test cases. Executing a large number of test cases can be expensive in terms of effort and wall-clock time. Moreover, indiscriminate execution of all available test cases typically lead to sub-optimal use of testing resources. On the other hand, selecting too few test cases for execution might leave a large number of faults undiscovered. Limiting factors such as allocated budget and time constraints for testing further emphasizes the importance of test case prioritization in order to identify test cases that enable earlier detection of faults while respecting such constraints. In this paper, we propose a multi-criteria decision making approach for prioritizing test cases in order to detect faults earlier. This is achieved by applying the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) decision making technique combined with fuzzy principles. Our solution is based on important criteria such as fault detection probability, execution time, complexity, and other test case properties. By applying the approach on a train control management subsystem from Bombardier Transportation in Sweden, we demonstrate how it helps, in a systematic way, to identify test cases that can lead to early detection of faults while respecting various criteria.IMPRIN