A Recursive Ant Colony Optimization Algorithm for Energy Consumption in Cloud Computing

Virtual machine (VM) consolidation in Cloud computing environments provides a great opportunity for energy saving. However, the obligation of providing suitable quality of service to end users leads to the necessity in dealing with energy-performance tradeoff. In this paper, we propose a redesigned energy-aware heuristic framework for VM consolidation to achieve a better energy-performance tradeoff. The main contribution of us is a Service Level Agreement (SLA) violation decision algorithm, which is used to decide a host is overload with SLA violation or not. Finally, we have evaluated our framework through simulation on large-scale experiments driven by workload traces from more than a thousand VMs, and the results show that our framework outperforms previous work, which has 11.8%~27% decrease in energy consumption, 57.9%~78.4% decrease in SLA violation, 63.2%~84.1% decrease in energy-performance metric which is a product metric of the energy consumption and SLA violation.

With the fast development of cloud computing methods, exponential growth is faced by several users. It is complicated for traditional data centers to perform several jobs in real-time because of inadequate bandwidth resources. Therefore, the method of fog computing is recommended for supporting and providing fast cloud services. It is not a substitute but is a powerful complement to cloud computing. The reduction of energy consumption through the notion of fog computing has certainly been a challenge for current researchers, industries, and communities. Various industries, including finance and healthcare, require a rich resource-based platform for processing large amounts of data with cloud computing across fog architecture. The consumption of energy across fog servers relies on allocating techniques for services (user requests). It facilitates processing at the edge with the probability of interacting with the cloud. This article proposed energy-aware scheduling by using Artificial Neural Network (ANN) and Modified Multiobjective Job Scheduling (MMJS) techniques. The emphasis of the work is on the reduction of energy consumption rate with less Service Level Agreement (SLA) violation in fog computing for data centers. The result shows that there is a 3.9% reduction in SLA violation when a multiobjective function with Artificial Neural Network is applied.

Virtual Machine (VM) consolidation provides a promising approach to save energy and improve resource utilization in cloud data centers. However, aggressive consolidation of VMs may lead to performance degradation of data centers and even Service Level Agreement (SLA) violation. Therefore, it is very important to solve the tradeoff between reduction of SLA violation level and energy costs. In this paper, we propose a Host State 3-order Markov Chain prediction model for SLA-aware and energy efficient consolidation of VMs in cloud data centers. We propose two adaptive utilization thresholds based on a robust statistical method for host state estimate, and our model uses them to build a 3-order Markov Chain Transition Matrix manually for host state detection. We implement our algorithms on Cloudsim simulator for comparative study. The experimental results show that our model can significantly reduce SLA violation rates while keeping energy cost efficient, it can reduce the metric of SLA Violation by at most 98.69% and the metric of Energy by at most 21.88% for real world workload.

Cloud computing is popular among industries, academia, and government to supply reliable and scalable computational power. High-speed networks in cloud data centers connect Virtual machines with Physical Machines. Virtualization assists the cloud service providers to manage resources effectively but unoptimized and inefficient services degrade the performance of the system. The scheduling architecture of cloud computing includes Physical Machines (PMs), Virtual Machines (VMs) and the allocation and migration policy of the VMs over the PMs. The overutilized PMs get few migrations and this paper introduces novel behavior of VM selection from overutilized PMs using Swarm intelligence. The evaluation of the proposed algorithm architecture is compared with another state-of-the-art optimization algorithm from the same series. The evaluation has been done on the base of Quality of Service (QoS) parameters, such as Service Level Agreement (SLA) violation, and energy consumption against various load variation scenarios to support elasticity. The proposed algorithm has outperformed other techniques by considerable margin in terms of QoS, and the details are presented in the results section. The simulation results demonstrate that the proposed technique exhibits 6.3% and 6.7% enhancement in terms of reduced energy consumption compared to both Cuckoo Search (CS) and general Dragonfly (DF) techniques, and 3% decrease in SLA violations in comparison to current methods. Additionally, the results reveal an 11% enhancement in VM migration compared to existing approaches.

In mobile cloud computing, high energy consumption and service-level agreements (SLAs) violation are the challenging issues considering that the demand for computational power is growing rapidly, which requiring large-scale cloud data centers. The mobile cloud computing enables customers to outsource the large and complex tasks to the cloud data centers for the execution and result generations. To provide resources such as CPU, RAM, memory, and bandwidth cloud data centers host incoming tasks, due to the large data centers that provide the basic resources for the host operations, and it consumes a huge amount of energy, which results in very high operational costs. In order to improve the energy efficiency and SLA violation of a cloud data center, energy-aware methods for overloaded host detection and VM selection from an overloaded host are needed. Cloud computing research community felt the need to provide or improve the energy-efficient solutions that reduce the impact of the issues. In this paper, we have shown many existing algorithm for energy efficiency and SLA violation reduction.

Cloud computing providers have to deal with the energy-performance trade-off: minimizing energy consumption, while meeting service level agreement (SLA) requirements. This paper proposes a new heuristic approach for the dynamic consolidation of virtual machines (VMs) in cloud data centers. The fast best-fit decreasing (FBFD) algorithm for intelligent VMs allocating into hosts and dynamic utilization rate (DUR) algorithm for utilization space and VM migration are successfully proposed. We performed simulations using PlanetLab and GWDG data center workloads to compare our approach against the existing models. It has been observed that the FBFD heuristic algorithm produces better results compared to modified BFD algorithm in terms of energy consumption and SLA violation. Additionally, the time complexity of FBFD algorithm is significantly improved from the order of O(\(m\,*\,n\)) to O(\(m\,*\,\log _2{n}\)). Furthermore, leaving some rates of capacity in the physical machines by the proposed DUR algorithm for VMs to be extended reduces the number of migrations which in turn improves the energy consumption and SLA violation. Our heuristic approach is evaluated using CloudSim and the results show that it performs better than the current state-of-the-art approaches.

Cloud data centers present a challenge to environmental sustainability because of their significant energy consumption. Additionally, performance degradation resulting from energy management solutions, such as virtual machine (VM) consolidation, impacts service level agreements (SLAs) between cloud service providers and users. Thus, to achieve a balance between efficient energy consumption and avoiding SLA violations, we propose a novel VM consolidation algorithm. Conventional algorithms result in unnecessary migrations when improperly selecting VMs. Therefore, our proposed E2SVM algorithm addresses this issue by selecting VMs with high load fluctuations and minimal resource usage from overloaded servers. These selected VMs are then placed on normally loaded servers, considering their stability index. Moreover, our approach prevents server underutilization by either applying all or no VM migrations. Simulation results demonstrate a 12.9% decrease in maximum energy consumption compared with the minimum migration time VM selection policy. In addition, a 47% reduction in SLA violations was observed when using the medium absolute deviation as the overload detection policy. Therefore, this approach holds promise for real-world data centers because it minimizes energy waste and maintains low SLA violations.

Cloud computing provides utility oriented services and pay-as-you-go computing model for its users. Massive adoption of Cloud services leads to build a sustainable environment and also demands for green Cloud services. With the advancement in Cloud services, the issue of carbon dioxide emission becomes significant. There is also a challenge for Cloud provider to satisfy the customer by guaranteeing the services with performance and availability in Service Level Agreement (SLA). Hence, energy consumption and SLA violation become important issues for Cloud providers. These issues can be addressed in many facets viz. efficient provisioning of resources, reducing energy consumption in datacenters, server consolidation, virtual machine (VM) migration, minimizing SLA violation or also by providing SLA with green requirements. In this paper, we survey various techniques which address green SLA with Quality of Service (QoS) requirements by using different resource management and consolidation procedures. Further, we propose our work by considering the multi-criteria method for resource management which selects the host as probable migration destination having highest predicted utilization after allocating a VM on the host. Hence, we aim to address the issue of maintaining the trade-off between energy consumption and SLA violation.

Online service is used to be as Pay-Per-Use in Cloud computing. Service user need not be in a long time contract with cloud service providers. Service level agreements (SLAs) are understandings marked between a cloud service providers and others, for example, a service user, intermediary operator, or observing operators. Since cloud computing is an ongoing technology giving numerous services to basic business applications and adaptable systems to manage online agreements are significant. SLA maintains the quality-of-service to the cloud user. If service provider fails to maintain the required service SLA is considered to be SLA violated. The main aim is to minimize the SLA violations for maintain the QoS of their cloud users. In this research article, a toolbox is proposed to help the procedure of exchanging of a SLA with the service providers that will enable the cloud client in indicating service quality demands and an algorithm as well as Negotiation model is also proposed to negotiate the request with the service providers to produce a better agreement between service provider and cloud service consumer. Subsequently, the discussed framework can reduce SLA violations as well as negotiation disappointments and have expanded cost-adequacy. Moreover, the suggested SLA toolkit is additionally productive to clients so clients can secure a sensible value repayment for diminished QoS or conceding time. This research shows the assurance level in the cloud service providers can be kept up by as yet conveying the services with no interruption from the client's perspective

Service Level Agreements (SLA) represents the principal means to control the Quality of Service (QoS). In cloud computing, various advanced SLA strategies are used; some techniques among others try to avoid the SLA violations. Taking into account the cloud consumer contextual parameters is a promising way to predict and avoid costly SLA violations. Therefore, in this paper, we propose a context-aware system for the SLA in order to guarantee the QoS. We create a contextual ontology to introduce the semantic meaning of the cloud consumer contextual parameters. In addition, using reasoning techniques, we (1) predict SLA violations and we (2) choose and apply the corrective actions in autonomous manner. This allows getting a proactive adjustment of the cloud service execution and respecting the SLA parameters.

Cloud computing virtual machine consolidation based on stock trading forecast techniques

Cloud computing is currently playing an essential role in supporting emerging sectors such as smart energy, intelligent transportation, and large-scale distributed systems. The scalability and adaptability features of cloud computing are efficiently enable resource utilization and continuous data exchange in dynamic and heterogeneous environments. However, the increasing demand for cloud services has raised the energy consumption of cloud data centers, which has a critical environmental impact. Therefore, sustainable resource management tactics have become crucial in today's world. Dynamic Virtual Machine (VM) consolidation is one of the major tactics for sustainable resource management in the green cloud computing environment. Herein, a novel dynamic Energy-Aware Cosine Similarity Learning Network (ECSLN) is proposed to predict the overutilized host. Further, an Impact Factor-Based VM Selection (IFBVMS) method is proposed to select VMs for migration, and an ECSLN-Packed Placement method for VM placement into hosts. The main aim of the proposed VM consolidation is to maximize energy efficiency while preserving compliance with Service Level Agreements (SLAs) for sustainable environments. The experimental evaluation using real-world traces like PlanetLab, Bitbrains, and Alibaba Cluster 2020 workload validates that the proposed VM consolidation methods significantly reduce energy consumption and SLA violation compared to the existing methods for sustainable environments. The evaluation shows that the proposed ECSLN-based consolidation framework enables green cloud infrastructure by intelligently balancing energy consumption, SLA violation, and performance of cloud data centers.

The rapid growth in cloud computing encourages the cloud provider to create more reliable, scalable and efficient cloud environment. For providing such a scalable environment, cloud provider develops large-scale Cloud Data Centers (CDC). Such CDCs consume a large amount of electrical energy for operational purpose and cooling purpose to keep the machines operating at a right temperature, but it also emits a large amount of carbon-dioxide. Thus to decrease the energy consumption, Virtual Machines (VMs) are consolidated to fewer numbers of active servers through VM migration techniques, this leads to Service Level Agreement (SLA) violation and performance degradation. Therefore to retain the energy-performance tradeoff, we should perform an optimum number of VM migrations. There are two cases when the VM migration performed: First when a hot spot is found and Second when a cold spot is found. But in both the cases we do not directly migrate the VM instead first we check whether it is required to migrate the VM. After that, the algorithm finds an appropriate destination host on which the VM will be migrated. In this paper, we implement three approaches to select destination host: First-fit, Best-fit, and Worst-fit. Finally compare the result of a number of VM migration performed, energy consumption and SLA violations with existing one.

Cloud computing has recently emerged as an important service to manage applications efficiently over the Internet. Various cloud providers offer pay per use cloud services that requires Quality of Service (QoS) management to efficiently monitor and measure the delivered services through Internet of Things (IoT) and thus needs to follow Service Level Agreements (SLAs). However, providing dedicated cloud services that ensure user's dynamic QoS requirements by avoiding SLA violations is a big challenge in cloud computing. As dynamism, heterogeneity and complexity of cloud environment is increasing rapidly, it makes cloud systems insecure and unmanageable. To overcome these problems, cloud systems require self-management of services. Therefore, there is a need to develop a resource management technique that automatically manages QoS requirements of cloud users thus helping the cloud providers in achieving the SLAs and avoiding SLA violations. In this paper, we present SLA-aware autonomic resource management technique called STAR which mainly focuses on reducing SLA violation rate for the efficient delivery of cloud services. The performance of the proposed technique has been evaluated through cloud environment. The experimental results demonstrate that STAR is efficient in reducing SLA violation rate and in optimizing other QoS parameters which effect efficient cloud service delivery.

Cloud computing provides online services to users from anywhere in a pay‐as‐you‐go basis. This leads to the construction of different forms of data centers around the world, which usually consumes huge amount of energy and produce pollution (heat and carbon dioxide footprint). Many efforts have focused on managing cloud resources to decrease their energy consumption. Since servers are the highest energy consumers in data centers, most researchers have focused on optimizing their energy consumption while other resources such as network devices and cooling systems have received less attention. In this paper, we propose a network‐aware virtual machine (VM) relocation algorithm, which optimizes the distribution of the VMs running on the data center in order to conserve energy in both servers and network devices (switches). The proposed technique targets the communicated VMs and places them closer to each other. This would reduce the traffic overhead between any communicating VMs so that fewer network devices are involved in the communication process. This also increases the VMs consolidation to some servers while leaving other servers idle. Switching off the idle servers and network devices would significantly reduce the total energy consumed by the data center. The proposed algorithm is evaluated using simulation and the performance of the proposed algorithm is compared against different previously proposed algorithms in the literature. Performance metrics used are the energy consumed by the host and by the network switches, the service level agreement violations, and the number of VM migrations. The impact of different packet sizes, the efficiency of switching the network elements to sleep mode, and the impact of resource homogeneity and heterogeneity are also studied and discussed. Simulation results show that the proposed algorithm outperforms the existing algorithms in terms of all evaluation metrics.