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Cloud-service agreements entail the provisioning of virtual infrastructures at specified availability levels. The agreements specify the infrastructure prices and penalties borne by providers when assured availabilities are not met. Hence, backup resources should be optimally provisioned by balancing the provisioning costs with penalties. We develop stochastic dynamic optimization models of the backup provisioning problem by integrating methods from Operations Management, Statistics and Machine Learning, and Computer Science. We present two sets of dynamic provisioning strategies: periodic policies where resources are adjusted at regular intervals and aperiodic policies that allow flexible timing of such interventions. A closed-loop optimization model under conservative resource control and a certainty-equivalent optimization model under aggressive resource control are developed for periodic management. Similarly, we propose two strategies for aperiodic management: single intervention with single look-ahead and multiple interventions with single look-ahead. We develop computationally efficient online algorithms for both periodic and aperiodic models. We evaluate the models through computational experiments and validate with Amazon EC2 use cases. We provide cloud service providers practical guidelines for efficiently formulating flexible service agreements and cost-effectively managing the virtual infrastructure resources. The proposed resource optimization framework is most suitable to IaaS and can easily be extended to PaaS environments. A cloud service agreement entails the provisioning of a required set of virtual infrastructure resources at a specified level of availability to a client. The agreement also lays out the price charged to the client and a penalty to the provider when the assured availability is not met. The availability assurance involves backup resource provisioning, and the provider needs to allocate backups cost-effectively by balancing the resource-provisioning costs with the potential penalty costs. We develop stochastic dynamic optimization models of the backup resource-provisioning problem, leading to cost-effective resource-management policies in different practical settings. We present two sets of dynamic provisioning strategies: periodic policies, where resources are adjusted at regular intervals, and aperiodic policies that allow flexible timing of such interventions. A closed-loop (CL) optimization model under conservative resource control and a certainty-equivalent (CE) optimization model under aggressive resource control are developed for periodic resource management. Similarly, aperiodic resource management is modeled by using two different strategies: single intervention with single look-ahead (SISL) and multiple interventions with single look-ahead (MISL). Online optimization algorithms for both the periodic and aperiodic models are developed. The worst-case behavior of the algorithms is studied by using competitive ratio analysis and the expected behavior by using computational investigations. By using these studies, managerial guidelines for choosing the best resource-management strategy under different client-specific, service-specific, and system-specific resource-optimization conditions are presented. We validate our models based on use cases constructed from Amazon Elastic Compute Cloud (EC2) with their actual pricing and service-credit data. The practical guidelines from this study will aid contract administrators in cloud data centers to both efficiently formulate service-level agreements and cost-effectively manage the virtual infrastructure resources committed in such agreements.

The search algorithm based on symbiotic organisms’ interactions is a relatively recent bio-inspired algorithm of the swarm intelligence field for solving numerical optimization problems. It is meant to optimize applications based on the simulation of the symbiotic relationship among the distinct species in the ecosystem. The task scheduling problem is NP complete, which makes it hard to obtain a correct solution, especially for large-scale tasks. This paper proposes a modified symbiotic organisms search-based scheduling algorithm for the efficient mapping of heterogeneous tasks to access cloud resources of different capacities. The significant contribution of this technique is the simplified representation of the algorithm’s mutualism process, which uses equity as a measure of relationship characteristics or efficiency of species in the current ecosystem to move to the next generation. These relational characteristics are achieved by replacing the original mutual vector, which uses an arithmetic mean to measure the mutual characteristics with a geometric mean that enhances the survival advantage of two distinct species. The modified symbiotic organisms search algorithm (G_SOS) aims to minimize the task execution time (makespan), cost, response time, and degree of imbalance, and improve the convergence speed for an optimal solution in an IaaS cloud. The performance of the proposed technique was evaluated using a CloudSim toolkit simulator, and the percentage of improvement of the proposed G_SOS over classical SOS and PSO-SA in terms of makespan minimization ranges between 0.61–20.08% and 1.92–25.68% over a large-scale task that spans between 100 to 1000 Million Instructions (MI). The solutions are found to be better than the existing standard (SOS) technique and PSO.

One of the significant challenges for cloud providers is how to manage resources wisely and how to form a viable service level agreement (SLA) with consumers to avoid any violation or penalties. Some consumers make an agreement for a fixed amount of resources, these being the required resources that are needed to execute its business. Consumers may need additional resources on top of these fixed resources, known as– marginal resources that are only consumed and paid for in case of an increase in business demand. In such contracts, both parties agree on a pricing model in which a consumer pays upfront only for the fixed resources and pays for the marginal resources when they are used. A marginal resource allocation is a challenge for service provider particularly small- to medium-sized service providers as it can affect the usage of their resources and consequently their profits. This paper proposes a novel marginal resource allocation decision support model to assist cloud providers to manage the cloud SLAs before its execution, covering all possible scenarios, including whether a consumer is new or not, and whether the consumer requests the same or different marginal resources. The model relies on the capabilities of the user-based collaborative filtering method with an enhanced top-k nearest neighbor algorithm and a fuzzy logic system to make a decision. The proposed framework assists cloud providers manage their resources in an optimal way and avoid violations or penalties. Finally, the performance of the proposed model is shown through a cloud scenario which demonstrates that our proposed approach can assists cloud providers to manage their resources wisely to avoid violations.

The lucrative features of cloud computing such as pay-as-you-go pricing model and dynamic resource provisioning (elasticity) attract clients to host their applications over the cloud to save up-front capital expenditure and to reduce the operational cost of the system. However, the efficient management of hired computational resources is a challenging task. Over the last decade, researchers and practitioners made use of various techniques to propose new methods to address cloud elasticity. Amongst many such techniques, control theory emerges as one of the popular methods to implement elasticity. A plethora of research has been undertaken on cloud elasticity including several review papers that summarise various aspects of elasticity. However, the scope of the existing review articles is broad and focused mostly on the high-level view of the overall research works rather than on the specific details of a particular implementation technique. While considering the importance, suitability and abundance of control theoretical approaches, this paper is a step forward towards a stand-alone review of control theoretic aspects of cloud elasticity. This paper provides a detailed taxonomy comprising of relevant attributes defining the following two perspectives, i.e., control-theory as an implementation technique as well as cloud elasticity as a target application domain. We carry out an exhaustive review of the literature by classifying the existing elasticity solutions using the attributes of control theoretic perspective. The summarized results are further presented by clustering them with respect to the type of control solutions, thus helping in comparison of the related control solutions. In last, a discussion summarizing the pros and cons of each type of control solutions are presented. This discussion is followed by the detail description of various open research challenges in the field.

Cloud users have recently expanded dramatically. The cloud service providers (CSPs) have also increased and have therefore made their infrastructure more complex. The complex infrastructure needs to be distributed appropriately to various users. Also, the advances in cloud computing have led to the development of interconnected cloud computing environments (ICCEs). For instance, ICCEs include the cloud hybrid, intercloud, multi-cloud, and federated clouds. However, the sharing of resources is not facilitated by specific proprietary technologies and access interfaces used by CSPs. Several CSPs provide similar services but have different access patterns. Data from various CSPs must be obtained and processed by cloud users. To ensure that all ICCE tenants (users and CSPs) benefit from the best CSPs, efficient resource management was suggested. Besides, it is pertinent that cloud resources be monitored regularly. Cloud monitoring is a service that works as a third-party entity between customers and CSPs. This paper discusses a complete cloud monitoring survey in ICCE, focusing on cloud monitoring and its significance. Several current open-source monitoring solutions are discussed. A taxonomy is presented and analyzed for cloud resource management. This taxonomy includes resource pricing, assignment of resources, exploration of resources, collection of resources, and disaster management.

Service composition in multi-Cloud environments must coordinate self-interested participants, automate service selection, (re)configure distributed services, and deal with incomplete information about Cloud providers and their services. This work proposes an agent-based approach to compose services in multi-Cloud environments for different types of Cloud services: one-time virtualized services, e.g., processing a rendering job, persistent virtualized services, e.g., infrastructure-as-a-service scenarios, vertical services, e.g., integrating homogenous services, and horizontal services, e.g., integrating heterogeneous services. Agents are endowed with a semi-recursive contract net protocol and service capability tables (information catalogs about Cloud participants) to compose services based on consumer requirements. Empirical results obtained from an agent-based testbed show that agents in this work can: successfully compose services to satisfy service requirements, autonomously select services based on dynamic fees, effectively cope with constantly changing consumers’ service needs that trigger updates, and compose services in multiple Clouds even with incomplete information about Cloud participants.

With the rise of Financial Operations (FinOps), cloud resource management requires the enforcement of strict budgetary guardrails rather than soft cost objectives. However, discrete Virtual Machine (VM) types often cause structural infeasibility, which existing methods fail to address. We formulate the Budget-Constrained VM Resizing problem under temporal hard constraints and establish the NP-hardness of the scalarized problem as a completeness result. To solve this, we propose the Budget-aware Dual (BD) solver, which utilizes a dual variable as a shadow price to dynamically steer candidate decisions toward budget feasibility without opaque penalty tuning. Extensive experiments demonstrate that BD significantly improves budget feasibility and operational stability compared to the baselines. In the run-rate setting, BD reduces candidate budget violations to zero once the budget enters feasible regimes at α≥0.6 and substantially reduces operational churn, decreasing the change rate from 53.95% to 7.80% in an oscillatory workload scenario. BD also exhibits near-linear scalability and remains more than 100× faster than NSGA-II at large fleet sizes. This framework provides a theoretically grounded and scalable approach for balancing economic efficiency, operational stability, and strict budget compliance.

Cloud computing has become essential for modern digital services, yet efficiently allocating compute, storage, and network resources in large-scale and highly dynamic environments remains a significant challenge. Traditional rule-based approaches often struggle to cope with workload variability, multi-tenancy, and the need for real-time multi-objective optimization. In response, recent research has increasingly explored artificial intelligence techniques to improve prediction, scheduling, and automated resource control in cloud infrastructures. This study presents a comprehensive survey of AI-based methods for cloud resource allocation, including machine learning, deep learning, reinforcement learning, and hybrid approaches. It systematically analyzes selected studies published between 2020 and 2026, examining their learning paradigms, optimization objectives (e.g., performance, cost, energy efficiency), experimental validation strategies, and reported limitations. While classical optimization techniques are briefly discussed to contextualize the evolution of the field, the core analysis is strictly centered on AI-driven approaches. The study concludes by identifying the key challenges that persist in intelligent cloud resource management and outlines promising directions for future research toward more adaptive, reliable, and scalable optimization frameworks.

Cloud computing's exponential expansion requires better resource management methods to solve the existing struggle between system performance and energy efficiency and functional scalability. Traditional resource management practices frequently lead systems in large-scale cloud environments to produce suboptimal results. This research presents a brand-new computational framework that unites Self-Organizing Neural Networks (SONN) with Artificial Fish Swarm Algorithm (AFSA) to enhance energy efficiency alongside optimized resource allocation and scheduling improvements. The SONN system groups workload information and automatically changes its structure to support fluctuating demand rates then the AFSA optimizes resource management through swarm-based intelligent protocols for high performance with scalable benefits. The SONN-AFSA model achieves substantial performance gains by analyzing real-world CPU usage statistics and memory usage behavior together with scheduling data from Google Cluster Data. The experimental findings show 20.83% lower energy utilization next to 98.8% prediction rates alongside 95% SLA maintenance and an outstanding 98% task execution rate. The proposed model delivers reliability outcomes superior to traditional approaches PSO and DRL and PSO-based neural networks which achieve accuracy rates above 88% and reach 92% accuracy. The adaptive platform delivers better power management to cloud computations yet preserves operational agility by adapting workload distributions. The learning ability of SONN joined with AFSA optimization segments produces superior resource direction capabilities which yield better service delivery quality. Research will proceed beyond its current scope to study real-time feedback structures as it evaluates multi-objective enhancement through large-scale dataset validation work to boost cloud computing sustainability across various platforms.

Cloud computing provides on-demand access to a shared pool of computing resources, which enables organizations to outsource their IT infrastructure. Cloud providers are building data centers to handle the continuous increase in cloud users’ demands. Consequently, these cloud data centers consume, and have the potential to waste, substantial amounts of energy. This energy consumption increases the operational cost and the CO 2 emissions. The goal of this paper is to develop an optimized energy and SLA-aware virtual machine (VM) placement strategy that dynamically assigns VMs to Physical Machines (PMs) in cloud data centers. This placement strategy co-optimizes energy consumption and service level agreement (SLA) violations. The proposed solution adopts utility functions to formulate the VM placement problem. A genetic algorithm searches the possible VMs-to-PMs assignments with a view to finding an assignment that maximizes utility. Simulation results using CloudSim show that the proposed utility-based approach reduced the average energy consumption by approximately 6 % and the overall SLA violations by more than 38 %, using fewer VM migrations and PM shutdowns, compared to a well-known heuristics-based approach.