In recent years, social media has emerged as a powerful tool for sustainability marketing. It leverages its extensive reach and interactive characteristics to raise awareness of environmental issues, promote community engagement, and influence policy development. This paper comprehensively analyzes different strategies used on social media platforms to support sustainability goals. We have classified these strategies into six broad categories: campaigns and education to increase awareness; activities that engage and build communities; efforts to advocate and influence policies; initiatives related to corporate social responsibility and branding; projects that use crowdsourcing and collaboration; and behavior change campaigns. We assess the effectiveness of these strategies based on case studies and key performance metrics for sustainable practices, public behaviors, and corporate marketing. Additionally, the presented survey highlights issues such as misinformation, engagement fatigue, and authenticity, necessitating solutions to address these concerns. This study highlights the vital role of social media in achieving sustainability objectives and outlines future research directions to improve its effectiveness in supporting these goals. Therefore, this research offers valuable insights for practitioners, policymakers, and scholars aiming to leverage social media platforms to advance sustainability goals.

The rapid advancement of mobile edge computing (MEC) has revolutionized the distributed computing landscape. With the help of MEC, the traditional centralized cloud computing architecture can be extended to the edge of networks, enabling real-time processing of resources and time-sensitive applications. Nevertheless, the problem of efficiently assigning the services to the computing resources is a challenging and prevalent issue due to the dynamic and distributed nature of the edge network's architecture. Thus, we require intelligent real-time decision-making and effective optimization algorithms to allocate resources, such as network bandwidth, memory, and CPU. This paper proposes an MEC architecture to allocate the resources in the network to optimize the quality of services (QoS). In this regard, the resource allocation problem is formulated as a bi-objective optimization problem, including minimizing cost and energy with quality and deadline constraints. A hybrid cascading-based meta-heuristic called GA-PSO is embedded with the proposed MEC architecture to achieve these objectives. Finally, it is compared with three existing approaches to establish its efficacy. The experimental results report statistically better cost and energy in all the considered instances, making it practical and validating its effectiveness.

The Internet of Things (IoT) has revolutionized the industrial field with numerous facilities and advancements. The industrial IoT system demands delay-aware workload execution with the aid of a fog computing platform, and precise resource allocation is required in fog nodes (FNs) to execute the fluctuating industrial IoT workloads with minimal cost and delay. In view of the issue mentioned above, we introduce an autonomic workload prediction and resource allocation framework that efficiently allocates resources among FNs. In the proposed framework, the workloads are predicted in the analysis phase with the guidance of the deep autoencoder (DAE) model, and the FNs are scaled based on the demand of Industrial IoT workloads. The crow search algorithm (CSA) is integrated with the framework for optimal FN selection to improve cost and delay objectives. The proposed scheme is evaluated and compared with the existing optimization models in terms of execution cost, request rejection ratio, throughput, and response time. The simulation results establish that the proposed scheme outperformed other optimization models. The method provided a suitable solution for the optimal FN placement problems in efficiently executing dynamic industrial IoT workloads.

With the rapid surge in cloud services, cloud load balancing has become a paramount research issue. The major part of a cloud computing system's operational costs is attributed to energy consumption. Therefore, to provide better QoS, considering the energy minimization factor in load balancing is essential. This paper addresses the latency and energy-aware load balancing problem in a cloud computing system. Specifically, two fundamental performance criteria–response time and energy–for the load balancing problem are considered. To solve this problem, first, the load balancing problem is formulated as an optimization problem. Then it is modeled as a cooperative game so that the solution of the game, called the Nash bargaining solution (NBS), can simultaneously optimize both criteria. The existence and computation of NBS are analyzed theoretically, and an efficient algorithm, called${{\sf L}}$atency and${{\sf E}}$nergy a${{\sf W}}$are load balancIng${{\sf S}}$cheme (${{\sf LEWIS}}$), is proposed to compute the NBS. Further, to assess the efficacy of${{\sf LEWIS}}$, it is compared with three other approaches, i.e.,${\mathsf {Coop\_{RT}}}$,${\mathsf {Coop\_{EN}}}$, and${\mathsf {NCG}}$, on problem instances of various settings. The experimental results show that${{\sf LEWIS}}$not only provides less response time while consuming less energy but also gauntness fairness to the end-users.

When a set of self-interested users shares multiple resources in a distributed system, we face the problem of allocating resources, called the load balancing problem. In particular, load balancing is defined as allocating the load to the servers of the distributed system such that jobs’ response time is minimized, and the utilization of servers is improved. In this article, the load balancing problem in a distributed system consists of a finite set of servers, and a finite set of users is studied. The load balancing problem considered here is a bi-objective problem with two highly probable conflicting objectives: (i) minimizing jobs’ response time (ii) providing the fair utilization of servers. In order to satisfy these two objectives simultaneously, both the objectives are considered in an integrated manner. Next, the load balancing problem is formulated as a noncooperative game; and to solve the game (i.e., to find the Nash equilibrium), a distributed load balancing algorithm (DLBA) is proposed. An experimental study is carried out to ascertain the efficacy of the proposed DLBA. Further, we compare DBLA with three existing load balancing approaches to evaluate its comparative effectiveness. The experimental results validate the effectiveness of the DLBA over the existing approaches.

With Cloud computing becoming mainstream for the execution of various applications, the multi-objective scheduling algorithms for providing the most suitable services to users have gained much attention. As provisioning Cloud services that satisfy end-users quality of service (QoS) requirements is complex and challenging, scheduling algorithms for cloud computing tend to focus on optimizing the execution cost or the execution time within user-defined deadline constraints. This paper addresses the problem of efficiently allocating Cloud services among competing jobs to achieve multiple end-users QoS. We design and develop a framework called Autonomic Resource Provisioning and Scheduling (ARPS) framework. ARPS framework has the decision-making capability to schedule the jobs at the best resources within the deadline and optimizes both the execution time and the cost simultaneously. The ARPS framework is also integrated with the spider monkey optimization (SMO) algorithm based scheduling mechanism. Our proposed mechanism is intended to solve a multi-objective optimization problem, including minimizing processing time, cost, and energy consumption. We study the effectiveness of the proposed scheduling mechanism through extensive simulation analysis using Cloudsim To assess the relative performance of our method, we compare it against four existing mechanisms. Experimental results show that the proposed mechanism outperforms its counterparts.