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Dynamic Resource Provisioning in Massively Multiplayer Online Games

Todays Massively Multiplayer Online Games (MMOGs) can include millions of concurrent players spread across the world and interacting with each other within a single session. Faced with high resource demand variability and with misfit resource renting policies, the current industry practice is to overprovision for each game tens of self-owned data centers, making the market entry affordable only for big companies. Focusing on the reduction of entry and operational costs, we investigate a new dynamic resource provisioning method for MMOG operation using external data centers as low-cost resource providers. First, we identify in the various types of player interaction a source of short-term load variability, which complements the long-term load variability due to the size of the player population. Then, we introduce a combined MMOG processor, network, and memory load model that takes into account both the player interaction type and the population size. Our model is best used for estimating the MMOG resource demand dynamically, and thus, for dynamic resource provisioning based on the game world entity distribution. We evaluate several classes of online predictors for MMOG entity distribution and propose and tune a neural network-based predictor to deliver good accuracy consistently under real-time performance constraints. We assess using trace-based simulation the impact of the data center policies on the quality of resource provisioning. We find that the dynamic resource provisioning can be much more efficient than its static alternative even when the external data centers are busy, and that data centers with policies unsuitable for MMOGs are penalized by our dynamic resource provisioning method. Finally, we present experimental results showing the real-time parallelization and load balancing of a real game prototype using data center resources provisioned using our method and show its advantage against a rudimentary client threshold approach.

Efficient management of data center resources for massively multiplayer online games

Todays massively multiplayer online games (MMOGs) can include millions of concurrent players spread across the world. To keep these highly-interactive virtual environments online, a MMOG operator may need to provision tens of thousands of computing resources from various data centers. Faced with large resource demand variability, and with misfit resource renting policies, the current industry practice is to maintain for each game tens of self-owned data centers. In this work we investigate the dynamic resource provisioning from external data centers for MMOG operation. We introduce a novel MMOG workload model that represents the dynamics of both the player population and the player interactions. We evaluate several algorithms, including a novel neural network predictor, for predicting the resource demand. Using trace-based simulation, we evaluate the impact of the data center policies on the resource provisioning efficiency; we show that dynamic provisioning can be much more efficient than its static alternative.

Prediction-based real-time resource provisioning for massively multiplayer online games

Massively Multiplayer Online Games (MMOGs) are a class of computationally intensive client-server applications with severe real-time Quality of Service (QoS) requirements, such as the number of updates per second each client needs to receive from the servers for a fluent and realistic experience. To guarantee the QoS requirements, game providers currently over-provision a large amount of their resources, which makes the overall efficiency of provisioning and utilization of resources rather low and prohibits any but the largest providers from joining the market. To address this deficiency, we propose a new prediction-based method for dynamic resource provisioning and scaling of MMOGs in distributed Grid environments. Firstly, a load prediction service anticipates the future game world entity distribution from historical trace data using a fast and flexible neural network-based method. On top of it, we developed generic analytical game load models used to foresee future hot-spots that congest the game servers and make the overall environment fragmented and unplayable. Finally, a resource allocation service performs dynamic load distribution, balancing, and migration of entities that keep the game servers reasonably loaded such that the real-time QoS requirements are maintained. Experimental results based on a realistic simulation environment demonstrate the advantages of our prediction service compared to other conventional methods, especially due to its ability to adapt to different user load patterns, and a reduction of the average over-allocation from 250% (in the case of static over-provisioning) to around 25% using our dynamic provisioning method.

Cost-efficient hosting and load balancing of Massively Multiplayer Online Games

Massively Multiplayer Online Games (MMOG) are a class of computationally-intensive client-server applications with severe real-time Quality of Service (QoS) requirements, such as the number of updates per second each client needs to receive from the servers for a fluent and realistic experience. To guarantee the QoS requirements, game providers over-provision to game sessions a large amount of their resources, which is very inefficient and prohibits any but the largest providers from joining the market. In this paper, we present a new approach for cost-efficient hosting of MMOG sessions on Cloud resources, provisioned on-demand in the correct amount based on the current number of connected players. Simulation results on real MMOG traces demonstrate that compute Clouds can reduce the hosting costs by a factor between two and five. The resource allocation is driven by a load balancing algorithm that appropriately distributes the load such that the QoS requirements are fulfilled at all times. Experimental results on a fast-paced game demonstrator executed on resources owned by a specialised hosting company demonstrate that our algorithm is able to adjust the number of game servers and load distribution to the highly dynamic client load, while maintaining the QoS in 99.34% of the monitored events.

Multi-objective energy-efficient workflow scheduling using list-based heuristics

Abstract Workflow applications are a popular paradigm used by scientists for modelling applications to be run on heterogeneous high-performance parallel and distributed computing systems. Today, the increase in the number and heterogeneity of multi-core parallel systems facilitates the access to high-performance computing to almost every scientist, yet entailing additional challenges to be addressed. One of the critical problems today is the power required for operating these systems for both environmental and financial reasons. To decrease the energy consumption in heterogeneous systems, different methods such as energy-efficient scheduling are receiving increasing attention. Current schedulers are, however, based on simplistic energy models not matching the reality, use techniques like DVFS not available on all types of systems, or do not approach the problem as a multi-objective optimisation considering both performance and energy as simultaneous objectives. In this paper, we present a new Pareto-based multi-objective workflow scheduling algorithm as an extension to an existing state-of-the-art heuristic capable of computing a set of tradeoff optimal solutions in terms of makespan and energy efficiency. Our approach is based on empirical models which capture the real behaviour of energy consumption in heterogeneous parallel systems. We compare our new approach with a classical mono-objective scheduling heuristic and state-of-the-art multi-objective optimisation algorithm and demonstrate that it computes better or similar results in different scenarios. We analyse the different tradeoff solutions computed by our algorithm under different experimental configurations and we observe that in some cases it finds solutions which reduce the energy consumption by up to 34.5% with a slight increase of 2% in the makespan.

A new business model for massively multiplayer online games

Today, highly successful Massively Multiplayer Online Games (MMOGs) have millions of registered users and hundreds of thousands of active concurrent players. To sustain their highly variable load, game operators over-provision a large static infrastructure capable of sustaining the game peak load, even though a large portion of the resources is unused most of the time. This inefficient resource utilisation has negative economic impacts by preventing any but the largest hosting centres from joining the market and dramatically increases prices. In this paper, we propose a new business model of hosting and operating MMOGs based on Cloud computing principles involving four actors: resource provider, game operator, game provider, and client. Our model efficiently provisions on-demand virtualised resources to game sessions based on their dynamic client load, which dramatically decreases prices and gives small and medium enterprises the opportunity of joining the market through zero initial investment. We validate our new model and its underlying business relationships through trace-based simulations utilising six months worth of monitoring data from a real-life MMOG using emulated resources from 16 of the largest Cloud resource providers currently on the market. We demonstrate that our model can operate state-of-the-art MMOGs with an average monthly gross profit of nearly

Neural Network-Based Load Prediction for Highly Dynamic Distributed Online Games

6 million excluding game purchase prices, overheads and taxation, while being able to maintain and control the QoS offered to all clients. Finally, we show how our approach is capable of operating next generation very highly interactive MMOGs with a small increase of 5.8% in the subscription price.

DIPAS: A distributed performance analysis service for grid service-based workflows

We propose a neural network-based prediction method for the future entity layout in massively multiplayer online games. Our service has the potential to timely foresee critical hot-spots in fast-paced First Person Shooter action games that saturate the game servers which no longer respond to user actions at the required rate. Using our service, proactive load balancing (and redistribution) actions can be triggered. We show results based on a realistic simulation environment that demonstrate the advantages of our method compared to other conventional ones, especially due to its ability to adapt to different load patterns.

The impact of virtualisation on the performance and operational costs of Massively Multiplayer Online Games

Grid workflows are executed on diverse resources whose interactions are highly complex and hardly predicted. Often the user and the workflow middleware services want to be informed about the performance behavior of workflows, as early as possible, so that they can steer the execution of workflows to compensate for the performance loss or execution failures. This paper describes a distributed performance analysis service that supports tracing execution, analyzing performance overheads, and searching for performance problems of Web services-based workflows in the Grid. We present how the user and the Grid workflow middleware can utilize the distributed performance analysis service in order to optimize the execution of workflows.

Enhancing Grids for Massively Multiplayer Online Computer Games

To serve millions of players, Massively Multiplayer Online Game (MMOG) operators pre-provision and then maintain thousands of computer resources. We investigate a hybrid resource provisioning model that uses smaller and cheaper data centers, complemented during peak hours by virtualised cloud computing resources. Through trace-based simulation and empirical experimentation, we assess the impact of provisioning virtualised cloud resources, analyse the virtualisation overhead, and compare provisioning of virtualised resources with resource ownership. Using a simple cost model, we also investigate the costs of hosting MMOGs on the resources leased independently from three commercial cloud providers, including Amazon.