Cognitive Resource Optimization for the Decomposed Cloud Gaming Platform

Massively Multiplayer Online Cloud Game (MMOCG) is a combination of cloud gaming and Massively Multiplayer Online Game (MMOG). Running MMOGs on the cloud can reduce the cost for the players, attract potential players, and meet the nowadays preference of mobile devices. However, the MMOCGs introduce new challenges that need to be addressed. MMOGs usually consume more computational resources because of their sophisticated graphics. Hosting MMOCGs can be more expensive for game operators than hosting other genres of games on the cloud. In order to earn a decent profit, the cost of hosting MMOCGs must be reduced to an acceptable cost. Moreover, MMOCG introduce extra cloud servers to MMOG, which increased the burden of network transmission. So, the MMOCGs must have a good quality of service (QoS) and quality of experience (QoE) to retain the players. In this thesis, we study the server allocation problem to improve MMOCGs in many aspects, including money cost, QoS, and QoE. We proposed three different server allocation strategies to address the challenges of MMOCGs in different situations. We first consider the MMOCG system with several cloud gaming platforms that lie in different locations in the world, each can provide a type of server that can host MMOG on them. The cloud gaming system only obtains the information necessary for gaming. With only the log-in information, a server allocation problem is defined to reduce the money cost of hosting MMOCGs and provide an acceptable network latency for the players. The problem is formulated into an optimization problem that aims to minimize the weighted combination of server rental cost, data transfer cost, and network latency. A genetic algorithm is developed to solve the minimization problem. Extensive experiments are conducted to evaluate the proposed method and compare it with the state-of-the-art as well. The experimental results show that the method gives a lower money cost and a shorter network latency most of the time. We then consider the MMOCG system that can collect the past logging in and out data of the players. We proposed a server allocation problem that aims to reduce the cost of cloud server rental and improve the QoE of the system. A genetic algorithm with gaming time prediction is proposed to solve the problem. The gaming time of the players logging into the system is predicted by the model trained by a neural network. We conduct experiments to evaluate our method and the proposed gaming time prediction model. The results show that our prediction model can achieve almost highly accurate prediction and the server allocation strategy we proposed outperforms the prior art in both server rental and QoE. At last, we consider the influence of QoE on the movement of players during gaming time and propose a server allocation problem aiming to improve the QoE of players during gaming time. We propose a genetic algorithm with trace prediction to address this problem. A trace prediction that predicts the future positions of players for a few time steps when they log into the MMOCG system. Experiments are conducted to evaluate our method by measuring the network latency, mean opinion score, and the time consuming computation. The results show that our method outperforms the genetic algorithm without trace prediction in over 50% of the cases.

Cloud gaming has been very popular in recent years, but issues relating to maintaining low interaction delay to guarantee satisfactory user experience are still prevalent. We observe that the server-side processing delay in cloud gaming system could be heavily influenced by how the resources are partitioned among processes. However, finding the optimal partitioning policy that minimizes the response delay faces several critical challenges. First, fine-grained resource partitioning is non-trivial due to the limitations of hardwre-based resource isolation techniques. Second, game wokload is highly dynamic and unpredictable, making the design of efficient resource partitioning policy more challenging. In this article, we propose an online resource partitioning framework for reducing response delay in cloud gaming, which has several promising properties. First, we divide the processes into disjoint groups and partition resources among process groups, which greatly simplifies the resource partitioning problem while ensuring high partitioning effectiveness. Second, to tackle dynamic workload changes, we classify game workloads into several clusters and maintain separate process grouping plan for each cluster. Third, we leverage reinforcement learning to adaptively choose the best actions for minimizing response delay in real time. We evaluate the proposed framework in a real cloud gaming environment using several real games. The experimental results show that our approach can reduce the response delay by 22 to 41 percent compared to a system without resource partitioning, and outperforms other resource partitioning policies significantly.

Cloud gaming has been a prospective paradigm through which users can realistically play high-end games on its low-end devices by outsourcing both computation and rendering jobs to remote cloud computers. The paper contains a thorough survey of cloud gaming systems, including its architecture, technology that allows the system, and performance issues. The paper describes the general architecture of cloud games, such as non-transparent and transparent ones, and outlines the major Quality of Service (QoS) and Quality of Experience (QoE) metrics, such as latency, bitrate, energy usage, and delay sensitivity of the users. Moreover, different optimization schemes embraced in the cloud gaming systems are discussed, such as distributed architecture, resources distribution schemes, sharing of GPUs, compression data schemes, adaptive transmission and edge computing contribution to latency minimization. The paper also evaluates available commercial cloud gaming services and business models to identify current trends in the market and challenges in deploying the services. According to the literature review, the paper highlights that efficient control of latency, efficient management of resources, and edge-assisted architectures are essential in enhancing scalability and user experience. Lastly, the paper provides the possible direction and technological solutions required to make cloud gaming a mass adoption technology.

In this paper, we study the problem of how to dispatch the play requests to the cloud servers in a cloud gaming system. We show that the dispatching strategy of play requests may heavily affect the total service cost of the cloud gaming system. The play request dispatching problem can be considered as a variant of the dynamic bin packing problem. However, we show that the classical bin packing algorithms such as First Fit and Best Fit are not efficient in dispatching the play requests in cloud gaming due to the diurnal workload pattern of online games. To address this issue, we propose an efficient request dispatching algorithm that assigns the play requests according to the predicted ending times of the game sessions. Through extensive evaluations using real online gaming traces, we show that the proposed dispatching algorithm can significantly reduce the resource waste of the cloud servers and thus decrease the total service cost compared to the First Fit and Best Fit algorithms.

The emerging Cloud Gaming Service provides a highly accessible video gaming experience. With Cloud Gaming, potential players without enough local resource can access high-quality gaming using low-spec devices. With advancing technology, we consider that if the processing power at low-spec devices can be well harvested, the quality delivered on Cloud Gaming can be further improved. Therefore, we propose a Hybrid-Streaming System that aimed at improving the graphic quality delivered by Cloud Gaming. By utilizing the available rendering power from both the Cloud Server and client PC, the system distributes rendering operations to both sides to achieve the desired improvement. Quantitative results show the proposed method improves graphics quality, as well as reducing the server’s workload while attaining acceptable network bandwidth consumption levels.

Cloud gaming enables people playing graphically intensive games from their less powerful, or even outdated computing devices. It is challenging to realize cloud gaming as it requires minimal latency in server-side processing, rendering and streaming, which are expensive in terms of resource requirements, e.g., powerful GPU servers. Commercial gaming providers, e.g., Google Stadia, Amazon Luna, etc., hardly disclose any information on how they optimize gaming performance and cloud cost. In this work, we aim to investigate resource cost optimization for such cloud gaming systems. In contrast to previous work which have been focusing more on theoretical approaches, we deliver a fully functional, cost-optimized cloud-native gaming system, called CloudNPlay, implemented entirely on AWS Lambda, EC2 and other AWS services. We have conducted extensive evaluations on the gaming performance including latency and frame rates; as well as cloud resource cost reduction. The results indicate that games hosted on CloudNPlay are highly playable. More importantly, CloudNPlay reduces the resource cost by around 24% compared to a non-optimized deployment. We plan to open-source CloudNPlay to facilitate further research in cloud gaming.

Background: Patients’ loyalty to visit and use the services provided by the primary health centers (PHCs) is an important requirement of a patient referral system in many countries. The aim of this study was to examine the influence of internal service factors (service provider, service process, and service environment) on service quality and behavioural loyalty of patients in Indonesian PHCs.Methods: A cross-sectional study was conducted in 14 districts in Aceh Province, Indonesia between September and December 2020. Data were collected in 102 PHCs that were selected randomly from 137 PHCs that have an Inpatient Unit in the province. A proportional number of patients were recruited from each PHC and 389 patients were included. The demographic data, three components of internal service factors (service provider, service process, and service environment), the service quality and behavioural loyalty were assessed using a validated questionnaire. Hypothesis testing was conducted by using the structural equation model (SEM).Results: Our data suggested that two elements of internal service factors (service provider and service environment) had a positive and significant influence on service quality of the PHCs with p<0.001 and p=0.021, respectively. Service quality had a positive and significant influence of behavioural loyalty of patients to the PHCs (p=0.003). Service quality however did not serve as an intervening variable between internal service factors (service provider, service process, and service environment) and behavioural loyalty of patients, with p=0.091, p=0.230 and p=0.260, respectively. Conclusions: Service provider and service environment are two main factors that influence the service quality and the service quality directly influence the behavioural loyalty on PHC users. Therefore, to increase the patients’ loyalty to use the PHC services, the quality of the services should be improved by levelling up the quality of providers and both physical and social environments in the PHCs.

Purpose – The purpose of this study is to identify an appropriate factor structure that may be utilized to effectively measure a hotel’s performance relative to service quality in a mid-scale setting. Customer perceptions of service quality in mid-scale hotels have largely been ignored; the focus of researchers has been the upscale (4-star) and luxury (5-star) segments. Design/methodology/approach – A 27-item questionnaire is utilized to measure service quality with an initial sample size of over 2,500 respondents. Principle component analysis is utilized to determine the factor structure and regression analysis to determine which factors may serve as predictors of a hotel’s ability to meet customers’ expectations and to provide value. Findings – A three-dimensional model emerged from the data, consistent with the theorizing of Rust and Oliver (1994), which includes the service product, service delivery and service environment. The service environment is the strongest predictor of a hotel’s ability to meet guests’ expectations and to provide guests with value within this context, which is inconsistent with findings in upscale and luxury hotels. Research limitations/implications – The generalizability of this research may be challenged, as the study was conducted within the context of an oceanfront resort destination dominated by leisure travelers; however, the study may be replicated in additional settings to determine if a similar bundling of service quality attributes occurs in other mid-scale settings including business hotels, as well as economy hotels. Practical implications – A three-factor model may be more appropriate for assessing service quality in a mid-scale (3-star) environment. In this setting, the service environment and service product may be more important measures of service quality than service delivery. This is an important finding, as many mid-scale and select-service, as well as new mid-scale, lifestyle hotel concepts, attempt to drive profitability by deemphasizing service delivery or by utilizing technology to facilitate service delivery. These findings may also assist operators of mid-scale hotels in improving guests’ perceptions of quality, which has been found to increase perceived value and may positively influence purchase or revisit intentions (Kashyap and Bojanic, 2000). Originality/value – Service quality research has been conducted, almost exclusively, in first-class (4-star) and luxury (5-star) hotels, while the majority of hotels do not fall into these categories. Although guest expectations relative to service quality may be lower in more moderately priced, mid-scale hotels, service quality remains a critical variable that influences a guest’s decision to return or recommend a hotel to others. Many travelers now utilize online reviews to minimize purchase risk by seeking information relative to service quality when selecting a hotel. Consequently, it is more important than ever that service quality is understood in hotels at all service levels. The present research contributes to filling this gap in the literature.

Virtual reality (VR) based games are a type of game that provides immersive gaming experience, allowing players to dive into the virtual world of the game being played. VR-based games require a high minimum computer specification, so thin clients cannot play VR-based games properly. This research aims to see how to enable thin computers to play VR-based games by utilizing cloud gaming technology. Using a high specification computer as a server, an android device as a VR headset, this Final Project implements a VR headset device so that it can be used in conjunction with cloud gaming services to be able to play VR-based games on thin computers and see how well the implementation by seeing the result from computer resources used and the Quality of Services. With Parsec cloud gaming services, the application carried out in this Final Project can run well on computers with low specifications. CPU usage on the client computer when the service is running is high at 91% usage, with 2818 MB RAM usage. Quality of Service is obtained when setting the highest quality preset, with a throughput of 16MB with a delay of about 2 ms. VR games that are played can run well with a minimum bandwidth of 15 Mbps selected from the Frame per Second (FPS) results obtained to reach 56 FPS with medium quality settings.

Cloud computing is the transformation of information and communication technology from client or server-based computers. Cloud computing technology benefits users no longer need to invest heavily in software or maintenance software applications. So by developing the Cloud, there is a Cloud gaming which is a process to install a remote interactive game in the cloud and issued in the form of video to the user's device via the internet. In this journal, we need a cloud gaming application called Skyegrid. This study aims to implement cloud-based mobile gaming cloud implementation using an open-source server (Skyegrid) to find out Resources, Service Quality and Experience Quality in this study. In this study Skyegrid is used to discuss some popular games, namely games called Rainbow Six Siege and Monster Hunter World. The result of this study is that Skyegrid's cloud gaming mobile system is able to implement PC games that can be run on Android smartphones and reduce the use of CPU and RAM resources, and use low cost using skyegrid cloud gaming using personal computers that require greater costs.

A service level agreement (SLA) is part of a contract between a cloud service provider (CSP) and a cloud service user (CSU) which defines and describes different agreed properties of the service. The negotiation of the SLA between a CSU and a CSP is a pivotal process in the SLA life cycle. An SLA may include many negotiable quality of service (QoS) parameters with each QoS parameter having multiple acceptable values. Different combinations of acceptable values for all QoS parameters grow exponentially as number of QoS parameters and their selectable values increase. Manually negotiating for all of the QoS parameters considering their various options can be an erroneous and diligent task with lower utility. In this paper, a time efficient flip-flop negotiation strategy for the SLAs in cloud services is presented. The flip-flop negotiation strategy is based on a time dependent 3D utility function and on a dynamic concession strategy using the polynomial extrapolation of opponent's concession. This negotiation strategy is useful for time-critical cloud services as it operates with a motive of reaching an agreement at earlier time without compromising over the utility level of the agreement. Moreover, a multi-provider concurrent negotiation method is presented in this work using the flip-flop negotiation strategy. Multiple experiments are performed (using the implementation of flip-flop negotiation strategy) to test the presented work. The results of the experiments enforce the validity of the flip-flop negotiation strategy for such cloud services where time is an important factor during the SLA negotiation process.

Cloud computing is being adopted by commercial and governmental organizations driven by the need to reduce the operational cost of their information technology resources and search for a scalable and flexible way to provide and release their software services. In this computing model, the Quality of Services (QoS) is agreed between service providers and their customers through Service Level Agreements (SLA). There is thus a need for systematic approaches with which to assess the quality of cloud services and their compliance with the SLA. In previous work, we introduced a generic method for Monitoring cloud Services using models at RunTime (MoS@RT), which allows the monitoring requirements or the metric operationalizations of these requirements to be changed at runtime without the modification of the underlying infrastructure. In this paper, we present the design of a monitoring infrastructure that supports the proposed method with its instantiation to a specific platform and reports the results of an experiment carried out to evaluate the perceived efficacy of 58 undergraduate students when using the infrastructure to configure the monitoring of cloud services deployed on the Microsoft Azure platform. The results show that the participants perceived MoS@RT to be easy to use, useful, and they also expressed their intention to use the method in the future. Although further experiments must be carried out to strengthen these results, MoS@RT has proved to be a promising monitoring method for cloud services.

Cloud gaming has been very popular recently, but providing satisfactory gaming experiences to players at a modest cost is still challenging. Colocating several games onto one server could improve server utilization. To enable efficient colocations while providing Quality of Service (QoS) guarantees, a precise quantification of performance interference among colocated games is required. However, achieving such precise interference prediction is very challenging for games due to the complexity introduced by the contention on many shared resources across CPU and GPU. Moreover, the distinctive properties of cloud gaming require that the prediction model should be constructed beforehand and the prediction should be made instantaneously at request arrivals, which further increases the difficulty. The existing solutions are either not applicable or not effective due to many limitations. In this paper, we present GAugur, a novel methodology that enables highly accurate prediction of the performance interference among games arbitrarily colocated. By leveraging machine learning technologies, GAugur is able to capture the complex relationship between the interference and the contention features of colocated games. We evaluate GAugur through extensive experiments using a large number of real popular games. The results show that GAugur is able to identify whether a colocated game satisfies QoS requirement within an average error of 5%, and is able to quantify the performance degradation of a colocated game within an average error of 7.9%, which significantly outperforms the alternatives. Moreover, GAugur incurs an offline profiling cost linear to the number of games, and negligible overhead for online prediction. We apply GAugur to guiding efficient game colocations for cloud gaming. Experimental results show that GAugur is able to increase the resource utilization by 20% to 60%, and improve the overall performance by up to 15%, compared to the state-of-the-art solutions.

Cloud gaming (CG) has gradually gained popularity. By leveling shared computing resources on the cloud, CG technology allows those without expensive hardware to enjoy AAA games using a low-end device. However, the bandwidth requirement for streaming game video is high, which can cause backbone network congestion for large-scale deployment and expensive bandwidth bills. To address this challenge, the authors proposed an innovative edge-assisted computing architecture that collaboratively uses AI-powered foveated rendering (FR) and super-resolution (SR). Using FR, the cloud server can stream gaming video in lower resolution, significantly reducing the transmitted data volume. The edge server will then upscale the video using a game-specific SR model, recovering the quality of the video, especially for the areas players pay the most attention. The authors built a prototype system called FRSR and did thorough, objective comparative experiments to demonstrate that this architecture can reduce bandwidth usage by 39.47% compared with classic CG implementation for similar perceived quality.

In order to select and rank the best services in a cloud computing environment, the end-to-end quality of service (QoS) values of cloud services have to be computed. For a new SaaS provider, the deployment of its software application in the cloud is a challenging job. It has to find a hosting service (IaaS) that hosts its service. The primary goal of the SaaS provider is to make its service at the top of the ranked list of cloud services returned to end users through satisfying their QoS requirements. In this paper, we propose a mechanism to map the users' QoS requirements of cloud services to the right QoS specifications of SaaS then map them to best IaaS service that offers the optimal QoS guarantees. Then together SaaS and IaaS services can provide the best service offer to end users. As a result of the mapping, the end-to-end QoS values can be calculated. We propose a set of rules to perform the mapping process. We hierarchically model the QoS specifications of cloud services using the Analytic Hierarchy Process (AHP) method. The AHP based model helps to facilitate the mapping process across the cloud layers, and to rank the candidate cloud services for end users. We use a case study to illustrate and validate our solution approach.