Gagangeet Singh Aujla

Data Offloading in 5G-Enabled Software-Defined Vehicular Networks: A Stackelberg-Game-Based Approach

Data offloading using vehicles is one of the most challenging tasks to perform due to the high mobility of vehicles. There are many solutions available for this purpose, but due to the inefficient management of data along with the control decisions, these solutions are not adequate to provide data offloading by making use of the available networks. Moreover, with the advent of 5G and related technologies, there is a need to cope with high speed and traffic congestion in the existing infrastructure used for data offloading. Hence, to make intelligent decisions for data offloading, an SDN-based scheme is presented in this article. In the proposed scheme, an SDNbased controller is designed that makes decisions for data offloading by using the priority manager and load balancer. Using these two managers in SDN-based controllers, traffic routing is managed efficiently even with an increase in the size of the network. Moreover, a single-leader multi-follower Stackelberg game for network selection is also used for data offloading. The proposed scheme is evaluated with respect to several parameters where its performance was found to be superior in comparison to the existing schemes.

Stackelberg Game for Energy-aware Resource Allocation to Sustain Data Centers Using RES

Smart Grid (SG) has emerged as one of the most powerful technologies of the modern era for an efficient energy management by integrating information and communication technologies (ICT) in the existing infrastructure. Among various ICT, cloud computing (CC) has emerged as one of the leading service providers which uses geo-distributed data centers (DCs) to serve the requests of users in SG. In recent times, with an increase in service requests by end users for various resources, there has been an exponential increase in the number of servers deployed at various DCs. With an increase in the size, the energy consumption of DCs has increased many folds which leads to an increase in overall operational cost of DCs. However, efficient resource allocation among these geo-distributed DCs may play a vital role in reducing the energy consumption of DCs. Moreover, with an increase in harmful emissions, the use of renewable energy sources (RES) can benefit DCs, SG, and society at large. Keeping focus on these points, in this paper, an energy-aware resource allocation scheme is proposed using a Stackelberg game for energy management in cloud-based DCs. For this purpose, a cloud controller is used to receive the requests of users which then distributes these requests among geo-distributed DCs in such a way that the energy consumption of DCs is sustained by RES. However, if energy consumption of DCs is not sustained by RES then the energy is drawn from the grid. The requests of users are routed to the DC which is offered lowest energy tariff from the grid. For this purpose, a Stackelberg game for energy trading is also proposed to select the grid offering lowest energy tariff to DCs. The proposed scheme is evaluated using various performance metrics using Google workload traces. The results obtained show the effectiveness of the proposed scheme.

SDN-based energy management scheme for sustainability of data centers: An analysis on renewable energy sources and electric vehicles participation

Abstract Energy management is becoming one of the major issues from last many years due to an exponential increase in the smart device users for accessing various services from the geo-distributed cloud data centers (DCs) using Internet. During this time, there is an emergence of new technology called as cloud computing which provides on-demand pay-per-use services such as storage, computation, and network to the end users. These services are provided to the end users by various geographically located DCs which are hosted by different service providers such as—Microsoft, Amazon, IBM etc. With an increase in dependence of end users, DCs have expanded both in size and number. With such an expansion, these DCs consume huge amount of energy to accomplish their routine tasks. Such an increase in energy consumption generates a lot of load on the power grid. Moreover, the carbon emissions from these DCs has a global impact on the environment. To mitigate these issues, the integration of DCs with renewable energy sources (RES) can be helpful to reduce the carbon emissions and to ease the load on the power grid. For this purpose, a SDN-based energy management scheme for sustainability of DCs using RES is proposed in this paper. 1 To achieve the aforementioned objectives, an energy-efficient flow scheduling algorithm is proposed using SDN. Moreover, a charging-discharging scheme for penetration of electric vehicles (EVs) is also presented to manage the intermittency of renewable energy. An energy trading and reward point scheme is designed to attract the EVs to participate in the proposed energy management scheme. The efficacy of the proposed scheme is proved using realistic weather traces. The results obtained clearly show the effectiveness of the proposed scheme for sustainability of DCs.

MEnSuS: An efficient scheme for energy management with sustainability of cloud data centers in edge–cloud environment

Abstract Cloud computing (CC) is one of the most popular technologies which provides on-demand ubiquitous services to the geo-located end users. Such services are hosted by physical infrastructure deployed at massive data centers (DCs) at various geographic locations. For handling millions of service requests, DCs consume a large amount of energy which increases the overall operational expenditure, grid load, and carbon footprints. So, to handle these issues, the integration of DCs with renewable energy sources (RES) is one of the solutions used by the research community from past many years. However, with an increase in smart communities such as — smart cities, smart healthcare, and Internet of things (IoT), the dependence of users on CC has increased many folds. Hence, to deal with the service requests and the data generated from smart devices (vehicles, IoT sensors, and actuators) locally, a recent paradigm popularly known as Edge computing has emerged. But, to execute various applications smoothly, there is a movement of high volume of data across different geographically separated nodes which create a huge burden on the network infrastructure. Moreover, the interoperability of various mobile devices is one of the major concerns for effective network infrastructure usage. Therefore, to deal with above challenges, an emerging paradigm known as software defined networks (SDN) can be a suitable choice. Hence, in this paper, an efficient scheme for energy management with sustainability (MEnSuS) of Cloud Data Centers in Edge–Cloud Environment using SDN is presented. In the proposed scheme, a support vector machine-based workload classification approach is presented. Moreover, a two-stage game for workload scheduling for sustainability of DCs is designed. In order to achieve energy efficiency and optimal utilization of network and computing resources, different consolidation schemes are also presented. The proposed scheme is evaluated using Google workload traces and the results obtained prove the effectiveness.

Optimal Decision Making for Big Data Processing at Edge-Cloud Environment: An SDN Perspective

With the evolution of Internet and extensive usage of smart devices for computing and storage, cloud computing has become popular. It provides seamless services such as e-commerce, e-health, e-banking, etc., to the end users. These services are hosted on massive geodistributed data centers (DCs), which may be managed by different service providers. For faster response time, such a data explosion creates the need to expand DCs. So, to ease the load on DCs, some of the applications may be executed on the edge devices near to the proximity of the end users. However, such a multiedge-cloud environment involves huge data migrations across the underlying network infrastructure, which may generate long migration delay and cost. Hence, in this paper, an efficient workload slicing scheme is proposed for handling data-intensive applications in multiedge-cloud environment using software-defined networks (SDN). To handle the inter-DC migrations efficiently, an SDN-based control scheme is presented, which provides energy-aware network traffic flow scheduling. Finally, a multileader multifollower Stackelberg game is proposed to provide cost-effective inter-DC migrations. The efficacy of the proposed scheme is evaluated on Google workload traces using various parameters. The results obtained show the effectiveness of the proposed scheme.

SDN-Based Data Center Energy Management System Using RES and Electric Vehicles

Cloud Computing (CC) has emerged as a leading technology for providing on-demand services such as, network access, data storage, computation to end users for smooth execution of various applications. Such services are provided over physical servers hosted by large data centers (DCs) which may be geographically located. In recent times, with an increase in service requests for various resources, DCs have expanded drastically in terms of number of servers. With such an increase in high end servers, the energy consumption of DCs has escalated many folds which may lead to additional burden on the grid. Moreover, the escalation in energy consumption of DCs has an impact on carbon footprints in the environment. Hence, the integration of renewable energy sources (RES) with DCs may ease the load of grid to a great extent. However, due to intermittent nature of RES, it is a difficult task to sustain DCs using RES. Hence, to sustain the energy consumption of DCs using RES, the penetration of electric vehicles (EVs) can be a major leap. To resolve these issues, a software defined network (SDN)-based DC energy management system using RES and EVs is designed in this paper. In the proposed scheme, a charging- discharging mechanism for penetration of EVs is formulated to cope with the intermittent nature of RES. The results obtained clearly depict that the penetration of EVs played a major role to manage the energy consumption of DC using RES.

EnergyChain: Enabling Energy Trading for Smart Homes using Blockchains in Smart Grid Ecosystem

The amalgamation of information and communication technologies in power industry has led to a revolution known as smart grid (SG). The energy consumers interact with the power utility using a bidirectional communication channel for energy trading in SG ecosystem. However, the traditional energy trading mechanisms strongly rely on trusted third parties which act as a single point of failure. Therefore, it is important to equip SG with a decentralized and secure energy trading system which can execute contracts and handle negotiations among various trading parties. Hence, in this paper, EnergyChain, a blockchain model for storing and accessing the data generated by smart homes in a secure manner is proposed. EnergyChain works in following phases: 1) a miner node is selected on the basis of power capacity of various smart homes, 2) a block creation and validation scheme is presented, and 3) a transaction handling mechanism is designed for secure energy trading. After evaluation, the superiority of EnergyChain is validated. The results obtained show that EnergyChain outperforms the traditional scheme in terms of communication costs and computation time.

Renewable Energy-based Multi-Indexed Job Classification and Container Management Scheme for Sustainability of Cloud Data Centers

Cloud computing has emerged as one of the most popular technologies of the modern era for providing on-demand services to the end users. Most of the computing tasks in cloud data centers are performed by geodistributed data centers which may consume a hefty amount of energy for their operations. However, the usage of renewable energy resources with appropriate server selection and consolidation can mitigate the energy related issues in cloud environment. Hence, in this paper, we propose a renewable energy-aware multi-indexed job classification and scheduling scheme using container as-a-service for data centers sustainability. In the proposed scheme, incoming workloads from different devices are transferred to the data center which has sufficient amount of renewable energy available with it. For this purpose, a renewable energy-based host selection and container consolidation scheme is also designed. The proposed scheme has been evaluated using Google workload traces. The results obtained prove 15%, 28%, and 10.55% higher energy savings in comparison to the existing schemes of its category.

DROpS: A demand response optimization scheme in SDN-enabled smart energy ecosystem

Abstract With an exponential increase in the utilization of intellective appliances, meeting the energy demand of consumers by traditional power grids is a significant challenge. In integration, the amalgamation of electric vehicles, industrial Internet-of-Things (IoT), and smart communities with power grids has escalated the global energy demand. Consequently, the desideratum for a reliable energy supply and resilient energy ecosystem has incentivized the evolution of Smart Grids (SG). Such intelligent grids are equipped with autonomous controllers and advanced technologies like advanced metering infrastructure, smart sensors, and accounting management software. However, the existing demand response management and energy supply techniques are lagging behind in meeting the desired objectives in the SG ecosystem. In order to handle these challenges, a Demand Replication Optimization Scheme (DROpS) for the smart energy ecosystem is designed in this paper. In addition, a Multi-Leader Multi-Follower Stackelberg game is formulated in this paper to operate with the proposed scheme. However, the success of DROpS depends heavily on real-time communication between the consumers and the suppliers. Hence, dynamic and scalable network architecture is required to handle the seamless data generated by a sizably voluminous number of connected sensors, devices, and smart appliances deployed in the SG. For the successful operation of the architecture, a Software-Defined Networking (SDN)-enabled control scheme for flow management is additionally developed. In the proposed scheme, the SG ecosystem is divided into multiple zones such that the dedicated virtual SDN controllers are deployed for network resource utilization in an optimized manner. The proposed scheme is evaluated using a real smart home test bed and data traces from the Haryana power grid. The effectiveness of the proposed scheme is demonstrated in terms of significant gains observed for load variation and latency.

EVaaS: Electric vehicle-as-a-service for energy trading in SDN-enabled smart transportation system

Abstract The increased adoption of electric vehicles (EVs) in the daily life of consumers have led towards the emergence of greener smart cities. However, the problem of energy stability, i.e., balancing the demand and supply, remains persistent in the context of charging stations (CSs). To solve this problem, a unique conceptual solution using EVs has been presented in this paper. The proposed solution deals with the problem of managing the miscellaneous power or power deficit at the CSs by utilizing EVs-as-a-service (EVaaS). On one hand, EVaaS provides opportunities to the owner of EVs to earn profit and on the other hand, it helps to balance the demand and supply at the CSs. This concept works in two steps; (1) EV-as-a-buyer: EVs act as energy buyers and CSs act as energy sellers, and (2) EV-as-a-seller: EVs act as energy sellers and CSs act as energy buyers. In EVaaS paradigm, the CSs are placed in residential, commercial, and industrial areas which broadcast their price for buying (or selling) the deficit (or excess) power from (or to) the EVs. The EVs would then decide whether to charge (or discharge) their battery power from (or at) which CSs based on the factors such as–price and distance. If both the parties come to an agreement, then the EVs would travel to the specified location and exchange the energy with CSs. For the smooth movement of EVs in the smart city, a mobility model is also designed. In addition, this approach also utilizes the software defined networking (SDN) paradigm for enabling faster communication between the entities involved. For this purpose, a flow management scheme is designed for efficient data transfer between EVs and CSs. Through this study, it has been shown that such a strategy for energy trading would help the CSs to balance their load requirements as well as provide profit to the EV owners. The results prove that SDN improves the communication in terms of delay, throughput and network utilization over the conventional networks; while EVs can be successfully utilized to manage the load requirements of various CSs to gain a significant amount of profit.