Peningkatan quality of experience pada permainan online multiplayer berbasis Arduino dengan menggunakan MQTT server

Online multiplayer games require internet networks to play with opposing players more exciting because multiple players can fight each other. The game experiences lag, which is expressed as the quality of experience (QoE), is one of the most common problems for online multiplayer games, causing the games less exciting to play. This study examined the implementation of Message Queue Telemetry Transport (MQTT) as a communication protocol in multiplayer online games using Arduino and compared its performance against HTTP. QoE used data collected using the mean opinion score (MOS) method. The MQTT resulted in an average QoE score of 3.9 (Pingpong) and 4 (TicTacToe) MOS units, while on HTTP 3.8 (PingPong and TicTacToe). The use of the MQTT communication protocol can improve the QoE of multiplayer online game players compared to HTTP.Permainan online multiplayer memerlukan jaringan internet agar dapat bermain lebih menarik dengan pemain lawan karena beberapa pemain bisa saling melawan satu sama lain. Salah satu kondisi permainan ini yang paling umum adalah permainan mengalami lag, yang dinyatakan sebagai quality of experience (QoE), sehingga permainan kurang menarik untuk dimainkan. Penelitian ini melakukan kajian implementasi Message Queue Telemetry Transport (MQTT) sebagai protokol komunikasi penghubung pada permainan online multiplayer di papan berbasis Arduino dan membandingkan kinerja QoE-nya terhadap HTTP. Metode mean opinion score (MOS) digunakan untuk merekam data yang diperlukan untuk menganalisis QoE. MQTT memperoleh rata-rata skor QoE sebesar 3,9 (Pingpong) dan 4 (TicTacToe) satuan MOS, sedangkan HTTP memperoleh rata-rata skor sebesar 3,8 (PingPong dan TicTacToe). Penggunaan protokol komunikasi MQTT dapat meningkatkan QoE pemain dalam permainan online karena skor rata-rata QoE-nya lebih tinggi dibandingkan dengan HTTP

The effect of 48V mild hybrid technology on fuel consumption of a passenger car by using simulation cycle

The ASEAN's legislation has become more regulatory towards electric vehicles for automotive manufacturers to ensure the environment is preserved better for future generations. The ASEAN roadmap 2025 requirement in optimizing a conventional vehicle's fuel consumption is implemented with hybrid technology in targeting the automotive industry worldwide to achieve energy-efficient vehicles. This research aims to develop a vehicle model via 1D simulation cycle and implement the 48V mild hybrid to lower vehicle fuel consumption considering perspective in drive cycles data. The vehicle model used in this research is a D-segment vehicle powered by a 1.8L TGDI engine. The base model will be created using a GT Suite software where data is compared and analyzed with actual vehicle measurement. There will be two models produced; with and without Belt-Alternator-Starter (BAS) system. They will be further investigated for their functions based on NEDC and RDC drive cycles for fuel consumption. However, implementing the add-on technology from this simulation improved overall vehicle fuel consumption by 7.7% in NEDC and 1.7% in RDC. The results obtained for the optimization of the vehicle have shown difference by the results of each engine characteristics such as engine fuel flow rate, speed, torque, the BAS functions, and state of charge. The research proposes its findings to understand the practical usage of 48V mild hybrid system in fuel reduction and provide reliable proof to use as a reference for initiative studies

ESTUDO DE MAPEAMENTO SISTEMÁTICO SOBRE AS TENDÊNCIAS E DESAFIOS DO CLOUD GAMING

Os jogos digitais constituem hoje um dos principais mercados na área do entretenimento. Como fatores para o contínuo crescimento da indústria têm-se as diversas transformações apoiadas pelas inovações tecnológicas. Entre essas inovações está a computação em nuvem, que trouxe uma extensa gama de possibilidades e permitiu a concepção de uma nova forma de jogar: o Cloud Gaming. A fim de apresentar um panorama sobre as dificuldades e possíveis caminhos em direção ao aumento da adoção do Cloud Gaming, este trabalho analisou, através de um mapeamento sistemático, as tendências e desafios na utilização da computação em nuvem para jogos digitais. Após a definição e execução do protocolo de mapeamento, diversos critérios de seleção e exclusão foram aplicados aos estudos encontrados. Em seguida, uma análise geral e, posteriormente, das respostas das questões de pesquisa foram realizadas e tiveram seus dados apresentados e interpretados através de gráficos, tabelas, além de descrição textual. Foram identificados como problemas e desafios a limitação de banda e compressão dos vídeos, alocação de recursos de rede para servidores com máquinas virtuais, entre outros. Como possíveis tendências, os estudos evidenciaram o foco no streaming baseado em gráficos, ao invés do streaming baseado em vídeo e virtualização de GPU

Opportunistic Deployment of Distributed Edge Clouds for Latency-critical Applications

The growing number of latency-critical applications are posing novel challenges for network operators, cloud/hosting companies, and application providers. Edge Computing is the strongest candidate for providing low-latency responses, but it is not yet clear what edge infrastructures will be like. This paper introduces a new platform for enabling an edge infrastructure according to a disaggregated distributed cloud architecture and an opportunistic model based on bare-metal providers. Results from a multi-server online gaming application deployed in a real geo-distributed edge infrastructure show the feasibility, performance and cost efficiency of the solution

TAME: an Efficient Task Allocation Algorithm for Integrated Mobile Gaming

We consider an integrated mobile gaming platform, in which the mobile device (e.g., smartphone) of a player can offload some game tasks toward a server as well as some neighboring mobile devices. The advantages of such a platform are manyfold: it can lead to an improved game experience, to a better use of energy resources, and, while offloading tasks to other mobile users, to the exploitation of the unused computing and storage resources of the mobile equipments, thus reducing the bandwidth and computing costs of the overall system. In this context, we formulate the problem of offloading the game computational tasks as an optimization problem that minimizes the maximum energy consumption across a set of mobile devices, under the constraints of a maximum response time and a limited availability of computation, communication and storage resources. In light of the problem complexity, we then propose a heuristic, called TAME, which is shown to closely approximate the optimal solution in all scenarios we considered. TAME also outperforms state-of-the-art algorithms under both synthetic and real scenarios, which have been devised based on a realistic and detailed energy consumption model for computation and communication resources. Our results, although tailored to mobile gaming, could be extended to other applications where it may be beneficial to offload computational and storage tasks through device-to-device communications, as enabled by Wi-Fi, Bluetooth, or the upcoming 5G technology

Self-Adaptive Decentralized Monitoring in Software-Defined Networks

The Software-Defined Networking (SDN) paradigm can allow network management solutions to automatically and frequently reconfigure network resources. When developing SDNbased management architectures, it is of paramount importance to design a monitoring system that can provide timely and consistent updates to heterogeneous management applications. To support such applications operating with low latency requirements, the monitoring system should scale with increasing network size and provide precise network views with minimum overhead on the available resources. In this paper we present a novel, self-adaptive, decentralized framework for resource monitoring in SDN. Our framework enables accurate statistics to be collected with limited burden on the network resources. This is realized through a self-tuning, adaptive monitoring mechanism that automatically adjusts its settings based on the traffic dynamics. We evaluate our proposal based on a realistic use case scenario, where a content distribution service and an on-demand gaming platform are deployed within an ISP network. The results show that reduced monitoring latencies are obtained with the proposed framework, thus enabling shorter reconfiguration control loops. In addition, the proposed adaptive monitoring method achieves significant gain in terms of monitoring overhead, while preserving the performance of the services considered

Optimizing Information Leakage in Multicloud Storage Services

Many schemes have been recently advanced for storing data on multiple clouds. Distributing data over multiple cloud storage providers automatically provides users with a certain degree of information leakage control, for no single point of attack can leak all the information. However, unplanned distribution of data chunks can lead to high information disclosure even while using multiple clouds. In this paper, we study an important information leakage problem caused by unplanned data distribution in multicloud storage services. Then, we present StoreSim, an information leakage aware storage system in multicloud. StoreSim aims to store syntactically similar data on the same cloud, thus minimizing the user's information leakage across multiple clouds. We design an approximate algorithm to efficiently generate similarity-preserving signatures for data chunks based on MinHash and Bloom filter, and also design a function to compute the information leakage based on these signatures. Next, we present an effective storage plan generation algorithm based on clustering for distributing data chunks with minimal information leakage across multiple clouds. Finally, we evaluate our scheme using two real datasets from Wikipedia and GitHub. We show that our scheme can reduce the information leakage by up to 60% compared to unplanned placement. Furthermore, our analysis on system attackability demonstrates that our scheme makes attacks on information more complex

A Hybrid Edge-Cloud Architecture for Reducing On-Demand Gaming Latency

International audienceThe cloud was originally designed to provide general-purpose computing using commodity hardware and its focus was on increasing resource consolidation as a means to lower cost. Hence, it was not particularly adapted to the requirements of multimedia applications that are highly latency sensitive and require specialized hardware, such as graphical processing units. Existing cloud infrastructure is dimensioned to serve general-purpose workloads and to meet end-user requirements by providing high throughput. In this paper, we investigate the effectiveness of using this general-purpose infrastructure for serving latency-sensitive multimedia applications. In particular, we examine on-demand gaming, also known as cloud gaming, which has the potential to change the video game industry. We demonstrate through a large-scale measurement study that the existing cloud infrastructure is unable to meet the strict latency requirements necessary for acceptable on-demand game play. Furthermore, we investigate the effectiveness of incorporating edge servers, which are servers located near end-users (e.g., CDN servers), to improve end-user coverage. Specifically, we examine an edge-server-only infrastructure and a hybrid infrastructure that consists of using edge servers in addition to the cloud. We find that a hybrid infrastructure significantly improves the number of end-users served. However, the number of satisfied end-users in a hybrid deployment largely depends on the various deployment parameters. Therefore, we evaluate various strategies that determine two such parameters, namely, the location of on-demand gaming servers and the games that are placed on these servers. We find that, through both a careful selection of on-demand gaming servers and the games to place on these servers, we significantly increase the number of end-users served over the basic random selection and placement strategies

Control plane optimization in Software Defined Networking and task allocation for Fog Computing

As the next generation of mobile wireless standard, the fifth generation (5G) of cellular/wireless network has drawn worldwide attention during the past few years. Due to its promise of higher performance over the legacy 4G network, an increasing number of IT companies and institutes have started to form partnerships and create 5G products. Emerging techniques such as Software Defined Networking and Mobile Edge Computing are also envisioned as key enabling technologies to augment 5G competence. However, as popular and promising as it is, 5G technology still faces several intrinsic challenges such as (i) the strict requirements in terms of end-to-end delays, (ii) the required reliability in the control plane and (iii) the minimization of the energy consumption. To cope with these daunting issues, we provide the following main contributions. As first contribution, we address the problem of the optimal placement of SDN controllers. Specifically, we give a detailed analysis of the impact that controller placement imposes on the reactivity of SDN control plane, due to the consistency protocols adopted to manage the data structures that are shared across different controllers. We compute the Pareto frontier, showing all the possible tradeoffs achievable between the inter-controller delays and the switch-to-controller latencies. We define two data-ownership models and formulate the controller placement problem with the goal of minimizing the reaction time of control plane, as perceived by a switch. We propose two evolutionary algorithms, namely Evo-Place and Best-Reactivity, to compute the Pareto frontier and the controller placement minimizing the reaction time, respectively. Experimental results show that Evo-Place outperforms its random counterpart, and Best-Reactivity can achieve a relative error of <= 30% with respect to the optimal algorithm by only sampling less than 10% of the whole solution space. As second contribution, we propose a stateful SDN approach to improve the scalability of traffic classification in SDN networks. In particular, we leverage the OpenState extension to OpenFlow to deploy state machines inside the switch and minimize the number of packets redirected to the traffic classifier. We experimentally compare two approaches, namely Simple Count-Down (SCD) and Compact Count-Down (CCD), to scale the traffic classifier and minimize the flow table occupancy. As third contribution, we propose an approach to improve the reliability of SDN controllers. We implement BeCheck, which is a software framework to detect ``misbehaving'' controllers. BeCheck resides transparently between the control plane and data plane, and monitors the exchanged OpenFlow traffic messages. We implement three policies to detect misbehaving controllers and forward the intercepted messages. BeCheck along with the different policies are validated in a real test-bed. As fourth contribution, we investigate a mobile gaming scenario in the context of fog computing, denoted as Integrated Mobile Gaming (IMG) scenario. We partition mobile games into individual tasks and cognitively offload them either to the cloud or the neighbor mobile devices, so as to achieve minimal energy consumption. We formulate the IMG model as an ILP problem and propose a heuristic named Task Allocation with Minimal Energy cost (TAME). Experimental results show that TAME approaches the optimal solutions while outperforming two other state-of-the-art task offloading algorithms