2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), 2019
The advancement of technology has pushed the cloud computing capabilities to the edge networks, p... more The advancement of technology has pushed the cloud computing capabilities to the edge networks, paving the way for network operators and multimedia service providers to leverage video caching and transcoding to the Mobile Edge computing (MEC) servers. However, because of high load demands in peak hours and the limited bandwidth, congestions may occur, leading to lower network performance. This problem is worsened by the redundancies of the same content requests and the expectation for the highest video quality, which exhausts the cellular spectrum and the backhaul links. Collaboration between edge servers, to cache and transcode videos, is proposed and proved its efficiency to store highly requested contents and relieve the load on the backhaul links. Meanwhile, Device-to-device (D2D) offloading is considered to alleviate cellular spectrum utilization. In this paper, we will extend the Collaborative Edge (CE) network to include the mobile users (D2D) caching and offloading and create a CE-D2D dual framework. Still, this framework does not present the perfect solution for exhaustive bandwidth utilization. In fact, users’ mobiles have small storage capacities and very scarce bandwidth availability, which limits the number of cached videos and constraints sharing large sized contents. Additionally, existing D2D approaches are considering video requests as simple contents similar to any HTML page request. However, in realistic cases, a lower bitrate version of the content can be offered to the viewers, if the bandwidth (cellular or mobile) is unavailable. Hence, to maximize the caching efficiency, we formulate the CE-D2D framework as a linear program, where MEC servers and users’ mobiles collaborate to cache and offload different chunks of the requested content constrained by cache and bandwidth availability. In this way, instead of caching and serving full popular videos, as done in previous works, we will only cache popular chunks within different helpers, which maximizes the efficiency of caching in small storage devices.
2019 IEEE Global Communications Conference (GLOBECOM), 2019
During the last decade, empowered by the technological advances of mobile devices and the revolut... more During the last decade, empowered by the technological advances of mobile devices and the revolution of wireless mobile network access, crowdsourced live streaming has become more popular. Ensuring a stable high-quality playback experience is necessary to maximize the number of viewers and profits for content providers. Additionally, because of the instability of network conditions and the heterogeneity of the end-users capabilities, transcoding the original video into multiple bitrates is required. Video transcoding is a computationally exhaustive process, where generally a single cloud instance needs to be reserved to produce one single video bitrate representation. On-demand renting of resources or inadequate resources pre-renting may cause delay of the video playback or serving the viewers with a lower quality. On the other hand, if resources provisioning is much higher than required, the extra resources will be wasted. In this paper, we introduce our resources reservation framework for geo-distributed cloud sites, to maximize the Quality of Experience (QoE) of viewers and minimize the cost to the content providers. First, we formulate an offline optimization problem to allocate transcoding resources at the viewers' proximity, while creating a trade off between the network cost and viewers QoE. Second, based on the optimizer resource allocation decisions on historical live videos, we create our time series datasets containing historical records of the optimal resources needed at each geo-distributed cloud site. Finally, we adopt machine learning to build our distributed time series forecasting models to proactively forecast the exact needed transcoding resources ahead of time at each geo-distributed cloud site.
2021 IEEE Global Communications Conference (GLOBECOM), 2021
LoRa supports the exponential growth of connected devices. In this paper, we investigate green Lo... more LoRa supports the exponential growth of connected devices. In this paper, we investigate green LoRa wireless networks powered by both the grid power and a renewable energy source. The grid power compensates for the randomness and intermittency of the harvested energy. We propose an efficient and smart resource management scheme of the limited number of channels and spreading factors (SFs) with the objective of improving the LoRa gateway (LG) energy efficiency. We formulate the problem of grid power consumption minimization while satisfying the quality of service demands. The optimal resource management problem is solved by decoupling the formulated problem into two sub-problems: channel and SF assignment problem and energy management problem. Next, we develop an adaptable resource management schemes based on Reinforcement Learning (RL) taking into account the channel and energy correlation. Simulations results show that the proposed resource management schemes offer efficient use of renewable energy in LoRa wireless networks.
2020 International Wireless Communications and Mobile Computing (IWCMC), 2020
Leveraging video caching to collaborative Mobile Edge Computing (MEC) servers is an emerging para... more Leveraging video caching to collaborative Mobile Edge Computing (MEC) servers is an emerging paradigm, where cloud computing services are extended to edge networks to allocate multimedia contents close to end-users. However, despite minimizing the traffic over the content delivery networks (CDN), congestions may occur in peak hours characterized by high load demands. Involving users' devices in data offloading through Device-to-Device (D2D) connections has proved its efficiency in relieving the cellular spectrum utilization. In this paper, the Collaborative Edge network (CE) and the devices (D2D) cluster are combined to form a CE-D2D framework aiming at maximizing video caching and efficiently using cellular and backhaul bandwidths. However, since we are dealing with large sized contents, the small storage and bandwidth capacities offered by users limit the number of cached videos and restrict offloading large volume data. This makes the CE-D2D framework, so far, an incomplete solution for multimedia contents. Therefore, we propose a caching strategy to cache only the chunks of videos to be watched and instead of caching or offloading each video content by one edge node (as performed in literature), helpers (MEC and mobiles) will collaborate to store and share different chunks to optimize the storage/transmission resources usage. In this work, we model both CE and D2D frameworks as linear programs and schedule the collaboration between them constrained by resource availability. Due to the NP-hardness of the problem, we introduce an online heuristic that presents a proactive chunks caching (HLPC) and a near-optimal data offloading with polynomial complexity.
The increasing trend to migrate applications, computation and storage into more robust systems le... more The increasing trend to migrate applications, computation and storage into more robust systems leads to the emergence of mega data centers hosting tens of thousands of servers. As a result, designing a data center network that interconnects this massive number of servers, and providing efficient and fault-tolerant routing service are becoming an urgent need and a challenge that will be addressed in this thesis. Since this is a hot research topic, many solutions are proposed like adapting new interconnection technologies and new algorithms for data centers. However, many of these solutions generally suffer from performance problems, or can be quite costly. In addition, devoted efforts have not focused on quality of service and power efficiency on data center networks. So, in order to provide a novel solution that challenges the drawbacks of other researches and involves their advantages, we propose to develop new data center interconnection networks that aim to build a scalable, cost...
Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of... more Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. Designing accurate models using such data streams, to predict future insights and revolutionize the decision-taking process, inaugurates pervasive systems as a worthy paradigm for a better quality-of-life. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges. In this context, a wise cooperation and resource scheduling should be envisaged among IoT devices (e.g., smartphones, smart vehic...
2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), 2019
Data Center Networks (DCNs) are attracting immense interest from the industry, research and acade... more Data Center Networks (DCNs) are attracting immense interest from the industry, research and academia to keep pace with the increase of Internet services demands. One of the major concerns that draws the attention of researchers is the exponential growth of the energy consumption and carbon emission of the DCNs. Studies conducted to identify the causes of the increasing energy consumption have proved that the growing size of computing demand, the over-provisioning of the networking resources, the under-utilization of the infrastructure, the fault-tolerance, the high bandwidth exigence and the inefficient hardware and cooling structure are leading to considerable energy waste. Therefore, in recent years, new data center (DC) architectures are proposed where new hardware types and new technologies are implemented for the sake of energy efficiency. Other efforts are focusing on designing algorithms and strategies to enhance the utilization of the network resources. Replacing brown power by renewable energy was also one of the attractive ideas to minimize the energy costs. In this survey paper, we will present energy-related problems in data centers and review the state of the art of the research literature on energy efficient architectures, techniques, technologies, resource management, and thermal control and monitoring. Additionally, we present the challenges facing each approach and the strategies to build a green DC. This paper serves as a specification document that shows step by step how to minimize the energy consumption of different components of the system.
2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), 2019
The advancement of technology has pushed the cloud computing capabilities to the edge networks, p... more The advancement of technology has pushed the cloud computing capabilities to the edge networks, paving the way for network operators and multimedia service providers to leverage video caching and transcoding to the Mobile Edge computing (MEC) servers. However, because of high load demands in peak hours and the limited bandwidth, congestions may occur, leading to lower network performance. This problem is worsened by the redundancies of the same content requests and the expectation for the highest video quality, which exhausts the cellular spectrum and the backhaul links. Collaboration between edge servers, to cache and transcode videos, is proposed and proved its efficiency to store highly requested contents and relieve the load on the backhaul links. Meanwhile, Device-to-device (D2D) offloading is considered to alleviate cellular spectrum utilization. In this paper, we will extend the Collaborative Edge (CE) network to include the mobile users (D2D) caching and offloading and create a CE-D2D dual framework. Still, this framework does not present the perfect solution for exhaustive bandwidth utilization. In fact, users’ mobiles have small storage capacities and very scarce bandwidth availability, which limits the number of cached videos and constraints sharing large sized contents. Additionally, existing D2D approaches are considering video requests as simple contents similar to any HTML page request. However, in realistic cases, a lower bitrate version of the content can be offered to the viewers, if the bandwidth (cellular or mobile) is unavailable. Hence, to maximize the caching efficiency, we formulate the CE-D2D framework as a linear program, where MEC servers and users’ mobiles collaborate to cache and offload different chunks of the requested content constrained by cache and bandwidth availability. In this way, instead of caching and serving full popular videos, as done in previous works, we will only cache popular chunks within different helpers, which maximizes the efficiency of caching in small storage devices.
2019 IEEE Global Communications Conference (GLOBECOM), 2019
During the last decade, empowered by the technological advances of mobile devices and the revolut... more During the last decade, empowered by the technological advances of mobile devices and the revolution of wireless mobile network access, crowdsourced live streaming has become more popular. Ensuring a stable high-quality playback experience is necessary to maximize the number of viewers and profits for content providers. Additionally, because of the instability of network conditions and the heterogeneity of the end-users capabilities, transcoding the original video into multiple bitrates is required. Video transcoding is a computationally exhaustive process, where generally a single cloud instance needs to be reserved to produce one single video bitrate representation. On-demand renting of resources or inadequate resources pre-renting may cause delay of the video playback or serving the viewers with a lower quality. On the other hand, if resources provisioning is much higher than required, the extra resources will be wasted. In this paper, we introduce our resources reservation framework for geo-distributed cloud sites, to maximize the Quality of Experience (QoE) of viewers and minimize the cost to the content providers. First, we formulate an offline optimization problem to allocate transcoding resources at the viewers' proximity, while creating a trade off between the network cost and viewers QoE. Second, based on the optimizer resource allocation decisions on historical live videos, we create our time series datasets containing historical records of the optimal resources needed at each geo-distributed cloud site. Finally, we adopt machine learning to build our distributed time series forecasting models to proactively forecast the exact needed transcoding resources ahead of time at each geo-distributed cloud site.
2021 IEEE Global Communications Conference (GLOBECOM), 2021
LoRa supports the exponential growth of connected devices. In this paper, we investigate green Lo... more LoRa supports the exponential growth of connected devices. In this paper, we investigate green LoRa wireless networks powered by both the grid power and a renewable energy source. The grid power compensates for the randomness and intermittency of the harvested energy. We propose an efficient and smart resource management scheme of the limited number of channels and spreading factors (SFs) with the objective of improving the LoRa gateway (LG) energy efficiency. We formulate the problem of grid power consumption minimization while satisfying the quality of service demands. The optimal resource management problem is solved by decoupling the formulated problem into two sub-problems: channel and SF assignment problem and energy management problem. Next, we develop an adaptable resource management schemes based on Reinforcement Learning (RL) taking into account the channel and energy correlation. Simulations results show that the proposed resource management schemes offer efficient use of renewable energy in LoRa wireless networks.
2020 International Wireless Communications and Mobile Computing (IWCMC), 2020
Leveraging video caching to collaborative Mobile Edge Computing (MEC) servers is an emerging para... more Leveraging video caching to collaborative Mobile Edge Computing (MEC) servers is an emerging paradigm, where cloud computing services are extended to edge networks to allocate multimedia contents close to end-users. However, despite minimizing the traffic over the content delivery networks (CDN), congestions may occur in peak hours characterized by high load demands. Involving users' devices in data offloading through Device-to-Device (D2D) connections has proved its efficiency in relieving the cellular spectrum utilization. In this paper, the Collaborative Edge network (CE) and the devices (D2D) cluster are combined to form a CE-D2D framework aiming at maximizing video caching and efficiently using cellular and backhaul bandwidths. However, since we are dealing with large sized contents, the small storage and bandwidth capacities offered by users limit the number of cached videos and restrict offloading large volume data. This makes the CE-D2D framework, so far, an incomplete solution for multimedia contents. Therefore, we propose a caching strategy to cache only the chunks of videos to be watched and instead of caching or offloading each video content by one edge node (as performed in literature), helpers (MEC and mobiles) will collaborate to store and share different chunks to optimize the storage/transmission resources usage. In this work, we model both CE and D2D frameworks as linear programs and schedule the collaboration between them constrained by resource availability. Due to the NP-hardness of the problem, we introduce an online heuristic that presents a proactive chunks caching (HLPC) and a near-optimal data offloading with polynomial complexity.
The increasing trend to migrate applications, computation and storage into more robust systems le... more The increasing trend to migrate applications, computation and storage into more robust systems leads to the emergence of mega data centers hosting tens of thousands of servers. As a result, designing a data center network that interconnects this massive number of servers, and providing efficient and fault-tolerant routing service are becoming an urgent need and a challenge that will be addressed in this thesis. Since this is a hot research topic, many solutions are proposed like adapting new interconnection technologies and new algorithms for data centers. However, many of these solutions generally suffer from performance problems, or can be quite costly. In addition, devoted efforts have not focused on quality of service and power efficiency on data center networks. So, in order to provide a novel solution that challenges the drawbacks of other researches and involves their advantages, we propose to develop new data center interconnection networks that aim to build a scalable, cost...
Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of... more Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. Designing accurate models using such data streams, to predict future insights and revolutionize the decision-taking process, inaugurates pervasive systems as a worthy paradigm for a better quality-of-life. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges. In this context, a wise cooperation and resource scheduling should be envisaged among IoT devices (e.g., smartphones, smart vehic...
2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), 2019
Data Center Networks (DCNs) are attracting immense interest from the industry, research and acade... more Data Center Networks (DCNs) are attracting immense interest from the industry, research and academia to keep pace with the increase of Internet services demands. One of the major concerns that draws the attention of researchers is the exponential growth of the energy consumption and carbon emission of the DCNs. Studies conducted to identify the causes of the increasing energy consumption have proved that the growing size of computing demand, the over-provisioning of the networking resources, the under-utilization of the infrastructure, the fault-tolerance, the high bandwidth exigence and the inefficient hardware and cooling structure are leading to considerable energy waste. Therefore, in recent years, new data center (DC) architectures are proposed where new hardware types and new technologies are implemented for the sake of energy efficiency. Other efforts are focusing on designing algorithms and strategies to enhance the utilization of the network resources. Replacing brown power by renewable energy was also one of the attractive ideas to minimize the energy costs. In this survey paper, we will present energy-related problems in data centers and review the state of the art of the research literature on energy efficient architectures, techniques, technologies, resource management, and thermal control and monitoring. Additionally, we present the challenges facing each approach and the strategies to build a green DC. This paper serves as a specification document that shows step by step how to minimize the energy consumption of different components of the system.
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