In recent years, cloud computing’s popularity has skyrocketed due to its capacity to supply customers with versatile and inexpensive computing resources. Multi-cloud computing, in which many cloud providers are employed to host applications, increases the potential benefits of cloud computing. The benefits include increased scalability, dependability, and cost-effectiveness. However, there are also substantial obstacles to overcome when it comes to optimizing resource allocation in multi-cloud computing. There is a need for more advanced methodologies that can optimize resource allocation across several clouds in a way that is more energy-efficient, cost-effective, and environmentally friendly than traditional approaches to multi-cloud computing resource allocation.

While several researchers have developed models for allocating resources in multi-cloud environments, these approaches frequently have their own set of problems.

Some models, for instance, rely on a single person making all the decisions, which might introduce instabilities and prevent the system from expanding. Some models may not account for the heterogeneity of cloud resources, which can lead to inefficient use of those assets. The majority of the currently available models also fail to account for fluctuations in the job, which can lead to subpar performance and unnecessary resource consumption. The contributions of several researchers in this field are as follows.

Hu et al. (2018) proposed a multi-objective scheduling algorithm for multi-cloud environments to reduce the workflow makespan and scheduling cost of data centers. Their approach utilized Particle Swarm Optimization to customize job scheduling based on location and order of data transmission. Although their simulation study showed promising results, their algorithm did not consider the impact of renewable energy sources or the carbon footprint of data centers [1]. Xu and Buyya (2020) proposed a workload shift approach that addressed the CO2 emissions issue by shifting jobs among multi-clouds located in different time zones. They also utilized renewable energy sources, such as solar energy, to reduce the usage of non-renewable energy. Their approach was effective in reducing CO2 emissions by 40% while maintaining a near-average response time for user requests. However, their approach did not consider load balancing or resource utilization, which are important factors in multi-cloud environments [2]. Cai et al. (2021) proposed a distributed job scheduling approach for multi-cloud environments that considered multiple objectives, including cost, energy consumption, time consumed, throughput, load balancing, and resource utilization. Their approach utilized intelligent algorithms based on the aforementioned objectives, as well as a sine function for model implementation. Although their simulation analysis demonstrated high scheduling efficiency with enhanced security, their approach did not consider the impact of renewable energy sources on energy consumption or CO2 emissions [3]. Renugadevi and Geetha (2021) developed a model for a geographically distributed multi-cloud environment that utilized solar energy as the main source of energy. Their model considered electricity prices, favorable location, CO2 emissions, and carbon taxes in energy management and resource allocation. The type of task was customized in response to the deadline of the task, which resulted in adaptive management of the multi-cloud model and workload algorithm. However, their approach did not consider load balancing or resource utilization, which are critical factors for optimal performance in multi-cloud environments [4]. Gaurang Patel et al. (2015) present study of task scheduling algorithms and modification of load balanced min-min algorithm. The proposed algorithm is based on survey of load balancing algorithm for static meta-task scheduling in grid computing. It select task based on maximum completion time and improve resources utilization and makespan [5]. Zhang et al. (2021) proposed a distributed deployment approach for tasks in multi-cloud environments, based on reinforcement learning. Their approach performed two steps: job offloading and task scheduling based on cloud center decisions. Their simulation analysis showed that their approach had the least latency in a heterogeneous environment, compared to existing approaches. However, their approach did not consider the impact of energy sources on energy consumption or CO2 emissions [6]. Sangeetha et al. (2022) utilized deep learning Neural Networks to control routing capabilities in multi-cloud environments to manage space and task allocation. Their approach minimized the delay associated with data processing and storage across clouds. Their simulation analysis demonstrated improved performance in terms of delay and costs associated with resource allocation in multi-cloud environments. However, their approach did not consider the impact of renewable/non-renewable energy sources on energy consumption or CO2 emissions [7]. Cao et al. (2022) presented the carbon footprint of data centers as a major source of intensive CO2 emissions and suggested that data centers increasingly switch to renewable energy to minimize the negative effects of CO2 emissions and boost energy circulation. They further suggested the integration of Artificial Intelligence to address the challenges and put the framework in place in real-world scenarios. However, they did not propose a specific approach to integrate AI or address the challenge of load balancing or resource utilization in multi-cloud environments [8]. Jun-Qing Li et al. (2021) proposed a hybrid technique of greedy and simulated annealing for scheduling crane transportation process. The objective was to reduce the completion time and energy consumption. It works in two steps: the first step is the scheduling of jobs and the second step is the assignment of machines [9]. Yu Du et al. (2022) develop a multi-objective optimization algorithm for distribution algorithm estimation and deep q-network to solve the problem of scheduling job shops. The problem was the processing speed of the machine, idle time, setup time, and transportation between machines. The proposed model was validated on CPLEX. The results showed that the proposed method performed effectively for job shop scheduling [10]. Ju Du (2022) presented a deep Q-network (DQN) model for multi-objective flexible job shop scheduling for crane transportation and setup times. It included 12 states and 7 actions for the scheduling process. The result showed that the proposed method produced an effective and efficient output. DQN also has the option to use dispatching rules according to situations [11]. Haider Ali et al. (2021) explored IOT and its application in the area of smart cities and also discussed wireless sensors. The authors also discussed the multiprocessor chip on-chip (MPSoC) used in daily IOT-based gadgets. At the end of this survey, the author also mentioned the future directions [12]. Umair Ullah Tariq et al. (2021) proposed a novel energy-aware scheduler with constraints on Voltage Frequency Island (VFI)-based heterogeneous NoC-MPSoCs deploying re-timing integrated with DVFS for real-time streaming applications. The authors proposed R-CTG which is a task-level timing approach integrated with non-linear programming and a voltage-level approach. The comparison results showed that the proposed method performed better in terms of latency and energy consumption [13].

More recent efforts have aimed to optimize multi-cloud resource allocation using machine learning and deep reinforcement learning (DRL) approaches. However, there is still a large knowledge gap in the application of DRL to multi-cloud resource allocation, especially in the areas of energy efficiency, CO2 emissions, and cost optimization.