Dissecting Cloud Gaming Performance with DECAF

Cloud gaming platforms have witnessed tremendous growth over the past two years with a number of large Internet companies including Amazon, Facebook, Google, Microsoft, and Nvidia publicly launching their own platforms. While cloud gaming platforms continue to grow, the visibility in their performance and relative comparison is lacking. This is largely due to absence of systematic measurement methodologies which can generally be applied. As such, in this paper, we implement DECAF, a methodology to systematically analyze and dissect the performance of cloud gaming platforms across different game genres and game platforms. DECAF is highly automated and requires minimum manual intervention. By applying DECAF, we measure the performance of three commercial cloud gaming platforms including Google Stadia, Amazon Luna, and Nvidia GeForceNow, and uncover a number of important findings. First, we find that processing delays in the cloud comprise majority of the total round trip delay experienced by users, accounting for as much as 73.54% of total user-perceived delay. Second, we find that video streams delivered by cloud gaming platforms are characterized by high variability of bitrate, frame rate, and resolution. Platforms struggle to consistently serve 1080p/60 frames per second streams across different game genres even when the available bandwidth is 8-20× that of platform's recommended settings. Finally, we show that game platforms exhibit performance cliffs by reacting poorly to packet losses, in some cases dramatically reducing the delivered bitrate by up to 6.6× when loss rates increase from 0.1% to 1%. Our work has important implications for cloud gaming platforms and opens the door for further research on comprehensive measurement methodologies for cloud gaming.

The effect of queue size on the throughput, in group failure mode, for the loaded transport channel

The external data flow decreases the throughput of the transport connection. The indicator of this external load is the queue size in front of the protocol data. In this article, using a mathematical model in analytical and numerical forms, the relation between the throughput of the channel and the protocol parameters are presented including the queue size parameter. In this work the effect of the queue size on time-out duration has been shown, which is one of the important parameters and it’s studied weakly in researches. Also, the relation between round-trip delay, the reliability of the transmission of the information segments with queue size are also shown.

Hierarchical Fault Detection and Recovery Framework For Self-Healing WSN

Wireless Sensor Network (WSN) contains several sensor nodules that are linked to each other wirelessly. Errors in WSN may perhaps be because of several causes which bring about hardware damage, power thwarts, incorrect sensor impression, faulty communication, sensor deficiencies, etc. This damages the network process. In this paper, we propose to develop a Hierarchical Fault Detection and Recovery Framework (HDFR) for Self-Healing WSN. This framework consists of three modules: Fault detection, fault confirmation and fault recovery. In fault detection module, Particle Swarm Optimization (PSO) algorithm is applied for estimating the discrete Round Trip Paths (RTPs). Along the established RTPs, round trip delay (RTD) time values are estimated. Then based on the RTD, the suspected nodes are identified. In fault confirmation module, the nodes are confirmed to be either in FAULTY or ACTIVE state. In fault recovery module, the primary controller (PC) will establish an alternate route via the secondary controllers (SC) by excluding the faulty nodes. Then, it will resend the stored packets to the sink via the newly established route. By experimental results, it is shown that the HDFR framework achieves better detection accuracy and packet delivery ratio.

Multi-Layer Latency Aware Workload Assignment of E-Transport IoT Applications in Mobile Sensors Cloudlet Cloud Networks

These days, with the emerging developments in wireless communication technologies, such as 6G and 5G and the Internet of Things (IoT) sensors, the usage of E-Transport applications has been increasing progressively. These applications are E-Bus, E-Taxi, self-autonomous car, E-Train and E-Ambulance, and latency-sensitive workloads executed in the distributed cloud network. Nonetheless, many delays present in cloudlet-based cloud networks, such as communication delay, round-trip delay and migration during the workload in the cloudlet-based cloud network. However, the distributed execution of workloads at different computing nodes during the assignment is a challenging task. This paper proposes a novel Multi-layer Latency (e.g., communication delay, round-trip delay and migration delay) Aware Workload Assignment Strategy (MLAWAS) to allocate the workload of E-Transport applications into optimal computing nodes. MLAWAS consists of different components, such as the Q-Learning aware assignment and the Iterative method, which distribute workload in a dynamic environment where runtime changes of overloading and overheating remain controlled. The migration of workload and VM migration are also part of MLAWAS. The goal is to minimize the average response time of applications. Simulation results demonstrate that MLAWAS earns the minimum average response time as compared with the two other existing strategies.

Quantitative Study on the Impact of Energy Consumption Based Dynamic Selfishness in MANETs

Cooperative communication and resource limitation are two main characteristics of mobile ad hoc networks (MANETs). On one hand, communication among the nodes in MANETs highly depends on the cooperation among nodes because of the limited transmission range of the nodes, and multi-hop communications are needed in most cases. On the other hand, every node in MANETs has stringent resource constraints on computations, communications, memory, and energy. These two characteristics lead to the existence of selfish nodes in MANETs, which affects the network performance in various aspects. In this paper, we quantitatively investigate the impacts of node selfishness caused by energy depletion in MANETs in terms of packet loss rate, round-trip delay, and throughput. We conducted extensive measurements on a proper simulation platform incorporating an OMNeT++ and INET Framework. Our experimental results quantitatively indicate the impact of node selfishness on the network performance in MANETs. The results also imply that it is important to evaluate the impact of node selfishness by jointly considering selfish nodes’ mobility models, densities, proportions, and combinations.

Evaluation of the efficiency of forward error correction of transport protocol data blocks

A model of a transport connection controlled by a transport protocol with the technology of forward error correction in the selective failure mode in the form of a discrete-time Markov chain is proposed. The model takes into account the influence of the protocol parameters, the level of errors in the communication channels, the round-trip delay and the technological parameters of forward error correction on the throughput of the transport connection. The analysis of the dependence of the advantages of the transport protocol with forward error correction over the classical transport protocol is carried out.

A Reliable Transport Scheme for Human Opportunistic Networks

The human opportunistic networks (ONs) formed by hand-held smart devices can facilitate peer-to-peer communication when humans are on the move, despite contemporaneous end-to-end paths rarely existing. In some scenarios, where network resources, especially power, are scarce and the traffic is large, the data delivery is prone to poor user experience and unbounded delay, although the best effort mechanism “store-carry-forward” is used. To cope with that, most transport/routing schemes obtain an acceptable latency at the cost of energy resources. In real-life human ONs, however, excessive energy consumption will trigger passive participation of the relays in message forwarding, so as to save their limited energy resource. Thus, the reliability of these schemes may get worse in real-life human ONs. In this paper, a reliable transport scheme is developed by making an optimal trade-off between the file round-trip delay and the energy consumption of relays. We make use of acknowledgements and coding at the source to enable successful file delivery. When setting up the network model, the cache management rule referred as “full-duplex” strategy is formulated, and then a mathematical model is established to analyze the proposed scheme. This model describes the evolution of packet dissemination and allows both the mean file round-trip delay and the energy consumption up to the reception of the last acknowledgement by the source to be expressed. Subsequently, a new function based on these two metrics is proposed to reflect the number of files that can be delivered under time and energy constraints. Through optimization procedure, the configurations that can maximize the function are obtained; thus, the optimization of these two metrics is achieved. Numerous simulations and comparisons are conducted and the results verify the accuracy of the analytical model. Comparison results show that with limited energy and passive relays, the proposed transport scheme can significantly reduce the energy consumption of file delivery, which obviously alleviates the selfish behavior of nodes. Therefore, the reliability and stability of the communication service in human ONs are enhanced.