VM Consolidation

Virtual Machine (VM) consolidation is an optimization approach for VM placement in cloud infrastructure, which is one of the effective ways to efficiently utilize cloud resources in order to optimize number of VM migrations, Service Level Agreement (SLA) violations and energy consumption problems. Static VM consolidation strategies have not been effective in handling variation of workloads in the cloud systems. Dynamic VM strategies have been proposed in the literatures. However, in the dynamic VM consolidation process, most existing algorithms consolidate active servers mainly based on the current resource requirements and forgo the demands of resources in the future during VM allocation. Thus, they result in needless VM migrations and cause high rate of SLA violations in data centers. This research proposed a prediction-based VM consolidation approach. The proposed method utilized a multi-resource utilization to predict the current and future CPU and memory utilization of active s...

As the demand for cloud computing services escalates, energy consumption in cloud data centers has surged, raising environmental and operational concerns. Efficient task scheduling stands as a critical technique for reducing energy consumption while maintaining Quality of Service (QoS). This paper investigates energy-efficient task scheduling algorithms with an emphasis on green computing. We examine and compare various strategies from existing literature and propose a structured methodology to enhance resource utilization and reduce carbon footprint. Key performance metrics include energy savings, load balancing, and execution time.

The rapid growth of cloud computing has fundamentally transformed how organizations access and leverage computing resources. Virtualization, a key enabling technology of cloud computing, allows for the efficient provisioning and utilization of computing resources. However, high-performance applications often require specialized optimizations or overprovisioning to meet their resource demands. This paper explores the integration of AI techniques within cloud hypervisors to enhance the performance and efficiency of virtualized environments. By leveraging machine learning and other AI algorithms, cloud hypervisors can dynamically adapt resource allocation, workload scheduling, and power management to better accommodate the needs of diverse applications. This paper presents a comprehensive review of the state-of-the-art in AI-integrated cloud hypervisors, highlighting the key challenges, techniques, and performance improvements achieved.

In today's world, where technology plays a crucial role in development, education is also benefiting from advancements in network infrastructure and virtualized services. Virtualization technology and highavailability (HA) infrastructures usage can transform how educational institutions manage and deliver their services. This is especially important for learning environments that require continuous access to critical systems such as data servers, online learning platforms, and student resources. Network and server administrators in educational institutions must ensure the availability and security of their systems. Building high-availability infrastructures enables the creation of more secure and stable environments, ensuring uninterrupted access to essential resources for students and educators. This paper addresses the use of virtualization technology in managing high-availability services using open-source tools. Educational services can be hosted on virtual machines that are automatically migrated between physical nodes of a cluster system in case of a network failure, ensuring a seamless experience for users. The project utilizes the open-source Heartbeat program to enable real-time migration of virtual machines between physical nodes in a cluster. A solution and algorithm (Perf+) will be developed to improve CPU performance and memory and reduce downtime during migration. The solution will be tested in an experimental HA system, measuring the impact of migration on performance. This approach helps educational institutions adopt innovative technologies to enhance the quality of education, ensuring continuous and reliable access to their services.

The growing demand for computational resources and IT technologies, including cloud services, data centers, and virtualization systems like VMware, has significantly increased energy consumption and environmental concerns. This research demonstrates a methodology for computing operational emissions, or carbon emissions due to energy consumption, as part of the Software Carbon Intensity (SCI) score. The method primarily focuses on the utilization of hardware resources, including CPU, memory, in virtual machine (VM) environments. With today technology of hypervisors, multiple virtual machines can be created on a single host machine. Each virtual machine has its own operating system and hardware resources. The study thus presents an energy consumption estimation model for VMs and resource-based power distribution method. Power consumption is estimated using boundary conditions, idle states, and trapezoidal integration. The resulting total energy consumption multiplied by the emission factor of electricity will contribute the value of operational emissions. The proposed framework would be considered as a preliminary step useful for data center operators and organizations aiming to achieve sustainable IT practices while maintaining operational efficiency.

Cloud Computing is a new era of remote computing / Internet based computing where one can access their personal resources easily from any computer through Internet. Cloud delivers computing as a utility as it is available to the cloud consumers on demand. It is a simple pay-per-use consumer-provider service model. It contains large number of shared resources. So Resource Management is always a major issue in cloud computing like any other computing paradigm. Due to the availability of finite resources it is very challenging for cloud providers to provide all the requested resources. From the cloud providers perspective cloud resources must be allocated in a fair and efficient manner. Research Survey is not available from the perspective of resource management as a process in cloud computing. So this research paper provides a detailed sequential view / steps on resource management in cloud computing. Firstly this research paper classifies various resources in cloud computing. It also...

With the rise of cloud computing, data centers have been called to play a main role in the Internet scenario nowadays. Despite this relevance, they are probably far from their zenith yet due to the ever increasing demand of contents to be stored in and distributed by the cloud, the need of computing power or the larger and larger amounts of data being analyzed by top companies such as Google, Microsoft or Amazon.

Placement of virtual sensors in servers of cloud environment is one of the most important issues in resource management of sensor-cloud networks. Virtual sensors allocate resources of cloud servers to themselves, run different applications and use equivalent physical sensors. Selecting an appropriate method for placement of virtual sensors on cloud servers has a significant impact on resource management and energy consumption. Also, because of the real-time requirements of sensor-cloud networks, trafficaware placement is necessary to minimize delay in a network and respond to user requests in the shortest possible time. A new method for traffic-aware placement and migration of virtual sensors in sensor-cloud networks is proposed using the unique characteristics of sensor-cloud networks such as grouping virtual sensors and sharing them among different applications. This approach tries to meet the needs of users and the quality of service expected by the applications. Also, the resources of cloud servers are managed properly in this approach. The results of simulation show an optimization in energy consumption and also a reduction in traffic costs and reduction in service level agreement violation.

Green cloud computing, an emerging computing paradigm in IT industry, is about providing efficient utilization of cloud's resources with minimal impact on the environment. The virtual machine placement has a considerable impact on a data center's energy and resource utilisation. The assignment of Virtual Machines (VMs) to Physical Machines (PMs) is an NP-hard problem to be solved using approximate algorithms at a reasonable computing time. In this paper, an energy-aware based algorithm is proposed for virtual machine placement. Our aim is to reduce the energy consumption and CO2 emission under a set of constraints. The performance and the efficiency of the proposed approach is tested on a cloud computing simulation toolkit.

Cloud computing data centers contain a large number of physical machines (PMs) and virtual machine (VMs). This number can increase the energy consumption of the data centers especially when the VMs placed inappropriately on the PMs. This paper presents a new VM placement approach with the objective of minimizing the total energy consumption of a data center. VM placement problem is formulated as a combinatorial optimization problem. Since this problem has been proven to be an NP hard problem, Ant Colony Optimization (ACO) algorithm is adopted to solve the formulated problem. Information heuristic of ACO is used differently based on PM energy efficiency. Experimental results show that the proposed approach scales well on large data centers and significantly outperforms selected benchmark (ACOVMP) in terms of energy consumption.

At a high level, data centres are large IT facilities hosting physical machines (servers) that often run a large number of virtual machines (VMs)but at a lower level, data centres are an intricate collection of interconnected and virtualised computers, connected services, complex service-level agreements. While data centre managers know that reassigning VMs to the servers that would best serve them and also minimise some cost for the company can potentially save a lot of money-the search space is large and constrained, and the decision complicated as they involve different dimensions. This paper consists of a comparative study of heuristics and exact algorithms for the Multi-objective Machine Reassignment problem. Given the common intuition that the problem is too complicated for exact resolutions, all previous works have focused on various (meta)heuristics such as First-Fit, GRASP, NSGA-II or PLS. In this paper, we show that the state-of-art solution to the single objective formulation of the problem (CBLNS) and the classical multi-objective solutions fail to bridge the gap between the number, quality and variety of

In recent years, vehicular cloud computing (VCC) has gained vast attention for providing a variety of services by creating virtual machines (VMs). These VMs use the resources that are present in modern smart vehicles. Many studies reported that some of these VMs hosted on the vehicles are overloaded, whereas others are underloaded. As a circumstance, the energy consumption of overloaded vehicles is drastically increased. On the other hand, underloaded vehicles are also drawing considerable energy in the underutilized situation. Therefore, minimizing the energy consumption of the VMs that are hosted by both overloaded and underloaded is a challenging issue in the VCC environment. The proper and ef cient utilization of the vehicle's resources can reduce energy consumption signi cantly. One of the solutions is to improve the resource utilization of underloaded vehicles by migrating the over-utilized VMs of overloaded vehicles. On the other hand, a large number of VM migrations can lead to wastage of energy and time, which ultimately degrades the performance of the VMs. This paper addresses the issues mentioned above by introducing a resource management algorithm, called resource utilization-aware VM migration (RU-VMM) algorithm, to distribute the loads among the overloaded and underloaded vehicles, such that energy consumption is minimized. RU-VMM monitors the trend of resource utilization to select the source and destination vehicles within a predetermined threshold for the process of VM migration. It ensures that any vehicles' resource utilization should not exceed the threshold before or after the migration. RU-VMM also tries to avoid unnecessary VM migrations between the vehicles. RU-VMM is extensively simulated and tested using nine datasets. The results are carried out using three performance metrics, namely number of nal source vehicles (nfsv), percentage of successful VM migrations (psvmm) and percentage of dropped VM migrations (pdvmm), and compared with This work is licensed under a Creative Commons Attribution 4.

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Cloud computing is a style of technology that is increasingly used every day. It requires the use of an important amount of resources that is dynamically provided as a service. The growth of energy consumption associated to the process of resource allocation implemented in the cloud computing is an important issue that needs to be taken into consideration. Better performance will be acquired by allowing the same required workload to be performed using a lower number of servers, which could bring to important energy savings. So it is a requirement to adopt efficient techniques in order to save and minimize energy consumed clouds such as virtual machines migration. This paper analyzes two algorithms: First Fit and Ant Colony which address the use of virtual machine migration approaches to improve the cloud performance in terms of reducing the consumed energy.

Virtual Machine (VM) consolidation in the cloud has received significant research interest. A large body of approaches for VM consolidation in data centers resort to variants of the bin packing problem which tries to minimize the number of deployed physical machines while meeting the Service-Level-Agreement (SLA) constraints. In this paper we introduce the concept of workload delay as a Quality-of-Service (QoS) metric that captures directly the resulting degradation that a cloud user would experience in the case where the SLA is violated. Our results, that are based on real-life trace-based simulations, show that consolidating VMs based on the level of utilization results in little control over the resulting delay, a particularly significant drawback when running jobs with deadline requirements, while we are able to control the delay much better if we take into account our suggested metric of the delay.

Cloud Computing is a promising approach to handle the growing needs for computation and storage in an efficient and cost-effective manner. Towards this end, characterizing workloads in the cloud infrastructure (e.g., a data center) is essential for performing cloud optimizations such as resource provisioning and energy minimization. However, there is a huge gap between the characteristics of actual workloads (e.g., they tend to be bursty and exhibit fractal behavior) and existing cloud optimization algorithms, which tend to rely on simplistic assumptions about the workloads. To close this gap, based on fractional calculus concepts, we present a fractal model to account for the complex dynamics of cloud computing workloads (i.e., the number of request arrivals or CPU/memory usage during each time interval). More precisely, we introduce a fractal operator to account for the time-varying fractal properties of the cloud workloads. In addition, we present an efficient (online) parameter estimation algorithm, an accurate forecasting strategy, and a novel fractal-based model predictive control approach for optimizing the CPU utilization, and hence, the overall energy consumption in the system while satisfying networked architecture performance constraints like queue capacities. We demonstrate advantages of our fractal model in forecasting the complex cloud computing dynamics over conventional (non-fractal) models by using real-world cloud (Google) traces. Unlike non-fractal models, which have very poor prediction capabilities under bursty workload conditions, our fractal model can accurately predict bursty request processes, which is crucial for cloud computing workload forecasting. Finally, experimental results demonstrate that the fractal model based optimization outperforms the non-fractal based ones in terms of minimizing the resource utilization by an average of 30%.

Green cloud computing, an emerging computing paradigm in IT industry, is about providing efficient utilization of cloud's resources with minimal impact on the environment. The virtual machine placement has a considerable impact on a data center's energy and resource utilisation. The assignment of Virtual Machines (VMs) to Physical Machines (PMs) is an NP-hard problem to be solved using approximate algorithms at a reasonable computing time. In this paper, an energy-aware based algorithm is proposed for virtual machine placement. Our aim is to reduce the energy consumption and CO2 emission under a set of constraints. The performance and the efficiency of the proposed approach is tested on a cloud computing simulation toolkit.

Unlike a traditional virtual machine (VM), a container is an emerging lightweight virtualization technology that operates at the operating system level to encapsulate a task and its library dependencies for execution. The Container as a Service (CaaS) strategy is gaining in popularity and is likely to become a prominent type of cloud service model. Placing container instances on virtual machine instances is a classical scheduling problem. Previous research has focused separately on either virtual machine placement on physical machines (PMs) or container, or only tasks without containerization, placement on virtual machines. However, this approach leads to underutilized or overutilized PMs as well as underutilized or overutilized VMs. Thus, there is a growing research interest in developing a container placement algorithm that considers the utilization of both instantiated VMs and used PMs simultaneously. The goal of this study is to improve resource utilization, in terms of number of CPU cores and memory size for both VMs and PMs, and to minimize the number of instantiated VMs and active PMs in a cloud environment. The proposed placement architecture employs scheduling heuristics, namely, Best Fit (BF) and Max Fit (MF), based on a fitness function that simultaneously evaluates the remaining resource waste of both PMs and VMs. In addition, another meta-heuristic placement algorithm is proposed that uses Ant Colony Optimization based on Best Fit (ACO-BF) with the proposed fitness function. Experimental results show that the proposed ACO-BF placement algorithm outperforms the BF and MF heuristics and maintains significant improvement of the resource utilization of both VMs and PMs.

Cloud platforms host several independent applications on a shared resource pool with the ability to allocate computing power to applications on a per-demand basis. The use of server virtualization techniques for such platforms provide great flexibility with the ability to consolidate several virtual machines on the same physical server, to resize a virtual machine capacity and to migrate virtual machine across physical servers. A key challenge for cloud providers is to automate the management of virtual servers while taking into account both high-level QoS requirements of hosted applications and resource management costs. This paper proposes an autonomic resource manager to control the virtualized environment which decouples the provisioning of resources from the dynamic placement of virtual machines. This manager aims to optimize a global utility function which integrates both the degree of SLA fulfillment and the operating costs. We resort to a Constraint Programming approach to formulate and solve the optimization problem. Results obtained through simulations validate our approach.

Cloud Computing delivers users a proficient way to dynamically allocate computing resources to meet demands. The use of Server virtualization techniques for Cloud Computing platforms provide great elasticity with the capability to consolidate several virtual machines on the same physical server, to resize a virtual machine capacity and to migrate virtual machine across physical servers. A key challenge for Cloud Service providers is proper resource management while taking into account of Service Level Agreement and Quality of Experience of users. Reserving resources would be beneficial to improve the quality of service, if the actual demand of the user is known in advance. However, in reality, the actual computing demand can be pragmatic only at the point of actual usage. In this paper, we propose a demand prediction method constructed on present usage of resources, aiming better provisioning of resource demand. Our proposed VM allocation is mapped into the multidimensional bin-packing problem, which is NP-complete. To solve this problem, we have designed heuristics for quantitatively optimizing the VM allocation. The simulation results show that our scheme performs better comparing to the existing VM allocation schemes in cloud computing environment, in terms of maintaining SLA and better QoE.

Ant Colony Optimization is an algorithm based on Swarm Intelligence (SI) and inspired by the actual ant's clustering behavior. Many styles of work have shown that ACO algorithms have been demonstrated to be substantially effective in reducing and addressing different complexities in real-life problems. This paper offers a brief review as an insight into the developments in ACO algorithms and the patterns in their publishing over the 2013-2019 review periods. This is intended to help inexperienced researchers and other readers who might need a bird-eye view of the general, local and global convergences of ACO algorithms.

Ant Colony Optimization is an algorithm based on Swarm Intelligence (SI) and inspired by the actual ant's clustering behavior. Many styles of work have shown that ACO algorithms have been demonstrated to be substantially effective in reducing and addressing different complexities in real-life problems. This paper offers a brief review as an insight into the developments in ACO algorithms and the patterns in their publishing over the 2013-2019 review periods. This is intended to help inexperienced researchers and other readers who might need a bird-eye view of the general, local and global convergences of ACO algorithms.

VM Migration for Autonomic Fault Tolerance provides the continuous availability for applications in the event of virtual machine failures along with the benefits of improved performance and manageability. Multipath correlation coefficient approach for VM migration has been proposed, which takes into account the overutilization of CPU without performance degradation based on work variability. The proposed approach has been validated through CloudSim toolkit. The experimental results demonstrate that the proposed approach is effective for handling VM migration or VM Reallocation. Further the proposed algorithm considerably reduces the VM Reallocation execution time, VM Reallocation time standard deviation and number of VM migrations. Keywords—VM Migration, autonomic, fault tolerance, cloud computing, overutilization.

Cloud computing is a computing model which uses network facilities to provision, use and deliver computing services. Nowadays, the issue of reducing energy consumption has become very important alongside the efficiency for Cloud service providers. Dynamic virtual machine (VM) consolidation is a technology that has been used for energy efficient computing in Cloud data centers. In this paper, we offer solutions to reduce overall costs, including energy consumption and service level agreement (SLA) violation. To consolidate VMs into a smaller number of physical machines, a novel SLA-aware VM placement method based on genetic algorithms is presented. In order to make the VM placement algorithm be SLA-aware, the proposed approach considers workloads as non-stationary stochastic processes, and automatically approximates them as stationary processes using a novel dynamic sliding window algorithm. Simulation results in the CloudSim toolkit confirms that the proposed virtual server consolidation algorithms in this paper provides significant total cost savings (evaluated by ESV metric), which is about 45% better than the best of the benchmark algorithms.

Resource management in data centres continues to be a critical problem due to increased infrastructure complexity and dynamic workload conditions. Workload and energy consumption prediction are crucial for efficient resource management decisions in cloud data centres. Existing solutions only consider forecasting the usage of virtual machine resources such as CPU and memory; they do not consider provisioned resources (CPU and memory) and disk, network transmission rates, which significantly affect the energy consumption of the host as well. VM-level energy consumption can be estimated for automated energy management decisions in modern data centres. However, it is not easy to measure energy for VM devices such as CPU, memory, and disk at the software level. In this way, we propose an ML-based model to predict load and energy to aid resource management decisions. For modelling workload predictions, we investigated several distinctive ML algorithms such as Linear Regression (LR), Ridge Regression (RR), ARD Regression (ARDR), ElasticNet (EN) and deep learning (DL) algorithm like Gated Recurrent Unit (GRU). The model's predictions are measured using standard evaluation metrics like root mean square error (RMSE). We have discovered that GRU has performed very well by accomplishing the most negligible RMSE value for all the workload performances based on experimental results. For energy state estimation, we propose four diverse clustering algorithms, including, semi-supervised affinity propagation based on transfer learning (TSSAP), CLA based on transfer learning (TCLA), kmeans based on transfer learning (TKmeans), P-teda based on transfer learning (TP-teda) to discover similar groups of VMs dependent on features that may influence energy consumption as opposed to estimating it for each VM. The TSSAP has acquired promising clustering accuracy with 87.48% and 53.80% in identifying the VM classes which have been calculated using standard metric such as micro-precision for the chosen workload in compassion to affinity propagation (AP) and the average of other proposed clustering algorithms respectively.

Virtual machines are deployed to ease the performance, management overhead and access regularities of applications on cloud platforms. Virtual machines are often susceptible to overloading burdens, delays and other hurdles during server consolidation and migration processes. In order to regulate the energy dissipation, monitor the overloading and under loading issues, dynamic consolidation processes are introduced to distribute loads across virtual machines substantially. Consolidation process demands additional computations and resources to reallocate the services from one virtual machine to another, subjected to adherence of Service Level Agreements. The proposed methodology advocates the implementation of a novel architecture for consolidating virtual machines and thus balance the energy and performance parameters. Overall resource requirements, Performance to Power Ratio (PPR) are primary factors in design of proposed Dynamic Weightage algorithm, including the clustering approac...

In the context of cloud computing, the energy consumed by the data center is higher because it contains a large number of physical machines, which in turn contain a number of virtual machines resulting in high power consumption. In addition, the cloud provider must provide a high quality of service (QoS) to its customers on the condition of not consuming a large amount of energy. Among the techniques of minimizing energy consumption is to turn down servers when the workload is low and relocate its virtual machines to another server. In this paper, we propose to combine this technique with another that uses a threshold ensuring the condition of not crossing a given level of use capacity of each server. We validate our model by numerical evaluation which demonstrates the effectiveness of the proposition in terms of energy efficiency and QoS improvement.

Philosophical methods of query process play a pivotal role in data retrieval from social networks linked to a graph NoSQL database that consolidate massive types of data. This massive data called as big data need to be distributed and sharded across many adjacent machines so that the queries when posted can be retrieved faster. An efficient storage mechanism for flexible retrieval of a query by the user needs to be established in Neo4j High Availability graph NoSQL database for less time overhead query process. The main focus of this paper is how to share the database constituting data across machines such that the storage of all related data comes in same or adjacent machines. This graph NoSQL database allocation problem referred as Neo4j High Availability big data allocation has been proved to be NP-Hard in this paper. To solve this hard problem, an optimization strategy by integrating Best Fit Decreasing with Ant Colony Optimization-based metaheuristic algorithm is suggested and implemented and results are analyzed. This data allocation in a distributed master-slave architecture of Neo4jHA is evaluated based on simulation, and performance is compared on the query efficiency of the proposed method to other best data allocation heuristic algorithms like First Fit, Best Fit, First Fit Decreasing and Best Fit Decreasing available in literature so far. The results exhibit how the proposed algorithm with replication and relation outperforms in query execution compared to other data allocation methods without relation and replication.

Nowadays data-intensive applications for processing big data are being hosted in the cloud. Since the cloud environment provides virtualized resources for computation, and data-intensive applications require communication between the computing nodes, the placement of Virtual Machines (VMs) and location of data affect the overall computation time. Majority of the research work reported in the current literature consider the selection of physical nodes for placing data and VMs as independent problems. This paper proposes an approach which considers VM placement and data placement hand in hand. The primary objective is to reduce cross network traffic and bandwidth usage, by placing required number of VMs and data in Physical Machines (PMs) which are physically closer. The VM and data placement problem (referred as MinDistVMDataPlacement problem) is defined in this paper and has been proved to be NP-Hard. This paper presents and evaluates a metaheuristic algorithm based on Ant Colony Optimization (ACO), which selects a set of adjacent PMs for placing data and VMs. Data is distributed in the physical storage devices of the selected PMs. According to the processing capacity of each PM, a set of VMs are placed on these PMs to process data stored in them. We use simulation to evaluate our algorithm. The results show that the proposed algorithm selects PMs in close proximity and the jobs executed in the VMs allocated by the proposed scheme outperforms other allocation schemes.

Cloud Computing has emerged as a computing paradigm where services are provided through the internet in recent years. Offering on-demand services has transformed the IT companies' working environment, leading to a linearly increasing trend of its usage. The provisioning of the Computing infrastructure is achieved with the help of virtual machines. A great figure of physical devices is required to satisfy the users' resource requirements. To meet the requirements of the submitted workloads that are usually dynamic, the cloud data centers cause the over-provisioning of cloud resources. The result of this over-provisioning is the resource wastage with an increase in the levels of energy consumption, causing a raised operational cost. High CO 2 emissions result from this huge energy consumption by data centers, posing a threat to environmental stability. The environmental concern demands for the controlled energy consumption, which can be attained by optimal usage of resources to achieve in the server load, by minimizing the number of active nodes, and by minimizing the frequency of switching between active and de-active server mode in the data center. Motivated by these actualities, we discuss numerous statistical, deterministic, probabilistic, machine learning and optimization based computational solutions for the cloud computing environment. A comparative analysis of the computational methods, on the basis of architecture, consolidation step involved, objectives achieved, simulators involved and resources utilized, has also been presented. A taxonomy for virtual machine (VM) consolidation has also been derived in this research article followed by emerging challenges and research gaps in the field of VM consolidation in cloud computing environment.

Recently, the networking industries have gone through tremendous changes. It demands high-speed operations and complex problem-solving abilities. To manage these evolutions Internet-of-Things (IoT) is a proposed solution from several technical corners. Numerous researchers and government organizations showing their interest to provide solutions with IoT implementation. Handling a huge amount of network data, its privacy and security, Quality of Service (QoS) requirements and heterogeneity of underlying networking components are the various challenges in IoT implementations. To provide the solution, Software Defined Networking (SDN) is becoming a bliss in managing such complex networking problems. The allocation of the Virtual Machines (VMs) into the end device is an NP-Hard combinatorial optimization problem. We formulate the problem by using simple Additive Weighing (SAW) or Weighted Sum Method (WSM) to allocate the VMs asymmetrically based on CPU Utilization and Memory usage to optimize the energy. The proposed algorithm ServerCons minimizes the number of live migrations and the number of nodes used as well as the energy usage is at par with the state of art algorithms such as First-Fit-Decreasing(FFD), Best-Fit-Decreasing (BFD), and Modified-Best-Fit-Decreasing (MBFD).

Cloud computing emerged as one of the leading computational paradigms due to elastic resource provisioning and pay-as-you-go model. Large data centers are used by the service providers to host the various services. These data centers consume enormous energy, which leads to increase in operating costs and carbon footprints. Therefore, green cloud computing is a necessity, which not only reduces energy consumption, but also affects the environment positively. In order to reduce the energy consumption, workload consolidation approach is used that consolidates the tasks in minimum possible servers. However, workload consolidation may lead to service level agreement (SLA) violations due to non-availability of resources on the server. Therefore, workload consolidation techniques should consider the aforementioned problem. In this paper, we present two consolidation based energy-efficient techniques that reduce energy consumption along with resultant SLA violations. In addition to that, we also enhanced the existing Enhanced-Conscious Task Consolidation (ECTC) and Maximum Utilization (MaxUtil) techniques that attempt to reduce energy consumption and SLA violations. Experimental results show that the proposed techniques perform better than the selected heuristic based techniques in terms of energy, SLA, and migrations.

Cloud computing services have been on the rise over the past few decades, which has led to an increase in the number of data centers worldwide which increasingly consume more and more amount of energy for their operation, leading to high carbon dioxide emissions and also high operation costs. Cloud computing infrastructures are designed to support the accessibility and deployment of various service oriented applications by the users. The resources are the major source of the power consumption in data centers along with air conditioning and cooling equipment. Moreover the energy consumption in the cloud is proportional to the resource utilization and data centers are almost the worlds highest consumers of electricity. It is therefore, the need of the hour to devise efficient consolidation schemes for the cloud model to minimize energy and increase Return of Investment(ROI) for the users by decreasing the operating costs. The consolidation problem is NP-complete in nature, which requi...

In the tremendous growth of the cloud computing, server consolidation plays a vital role. It gives more benefits also it gives the pollution towards the nature. Inspite of avoiding this, saving of energy in the data center or the cluster is more important. In this regard this paper proposes a method of which reduces the energy consumption. This is done by identifying the active physical machines and the remaining machines were kept in the sleep or off mode according to the user’s availability. Here Virtual Machines (VMs) are grouped under the particular types. Based on this type the customer’s resource request can be maintained.

In this paper, we present a novel multi-objective ant colony system algorithm for virtual machine (VM) consolidation in cloud data centers. The proposed algorithm builds VM migration plans, which are then used to minimize over-provisioning of physical machines (PMs) by consolidating VMs on under-utilized PMs. It optimizes two objectives that are ordered by their importance. The first and foremost objective in the proposed algorithm is to maximize the number of released PMs. Moreover, since VM migration is a resource-intensive operation, it also tries to minimize the number of VM migrations. The proposed algorithm is empirically evaluated in a series of experiments. The experimental results show that the proposed algorithm provides an efficient solution for VM consolidation in cloud data centers. Moreover, it outperforms two existing ant colony optimization based VM consolidation algorithms in terms of number of released PMs and number of VM migrations.

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Cloud Computing delivers users a proficient way to dynamically allocate computing resources to meet demands. The use of Server virtualization techniques for Cloud Computing platforms provide great elasticity with the capability to consolidate several virtual machines on the same physical server, to resize a virtual machine capacity and to migrate virtual machine across physical servers. A key challenge for Cloud Service providers is proper resource management while taking into account of Service Level Agreement and Quality of Experience of users. Reserving resources would be beneficial to improve the quality of service, if the actual demand of the user is known in advance. However, in reality, the actual computing demand can be pragmatic only at the point of actual usage. In this paper, we propose a demand prediction method constructed on present usage of resources, aiming better provisioning of resource demand. Our proposed VM allocation is mapped into the multidimensional bin-packing problem, which is NP-complete. To solve this problem, we have designed heuristics for quantitatively optimizing the VM allocation. The simulation results show that our scheme performs better comparing to the existing VM allocation schemes in cloud computing environment, in terms of maintaining SLA and better QoE.

With the rising demands for the services provided by cloud computing, virtual machine allocation (VMA) has become a tedious task due to the dynamic nature of the cloud. Millions of virtual machines (VMs) are allocated and de-allocated at every instant, so an efficient VMA has been a significant concern to enhance resource utilization and depreciate its wastage. Encouraged by the prodigious performance of the nature-inspired algorithm, the binary whale optimization approach has been eventuated to get to grips with the VMA issue with the focus on minimizing the resource waste and volume of servers working actively. The deliberate approach's accomplishment is assessed against the literature's well-known algorithms for VMA issues. The comparison results showed that the least resource wastage fitness of 15.68, minimum active servers of 216, and effective CPU and memory utilization of 88.31% and 88.79%, respectively, have been achieved.

Underutilization of computing resources and high power consumption are two primary challenges in the domain of Cloud resource management. This paper deals with these challenges through offline, migration impact-aware, multi-objective dynamic Virtual Machine (VM) consolidation in the context of large-scale virtualized data center environments. The problem is formulated as an NP-hard discrete combinatorial
optimization problem with simultaneous objectives of minimizing resource wastage, power consumption, and the associated VM migration overhead. Since dynamic VM consolidation through VM live migrations have negative impacts on hosted applications performance and data center components, a VM live migration overhead estimation technique is proposed, which takes into account pragmatic migration parameters and overhead factors. In order to tackle scalability issues, a hierarchical, decentralized dynamic VM consolidation framework is presented that helps to localize migration-related network traffic and reduce network cost. Moreover, a multi-objective, dynamic VM consolidation algorithm is proposed by utilizing the Ant Colony Optimization (ACO) metaheuristic, with integration of the proposed VM migration
overhead estimation technique. Comprehensive performance evaluation makes it evident that the proposed dynamic VM consolidation approach outpaces the state-of-the-art offline, migration-aware dynamic VM consolidation algorithm across all performance metrics by reducing the overall power consumption by up to 47%, resource wastage by up to 64%, and migration overhead by up to 83%.

Underutilization of computing resources and high power consumption are two primary challenges in the domain of Cloud resource management. This paper deals with these challenges through offline, migration impact-aware, multi-objective dynamic Virtual Machine (VM) consolidation in the context of large-scale virtualized data center environments. The problem is formulated as an NP-hard discrete combinatorial
optimization problem with simultaneous objectives of minimizing resource wastage, power consumption, and the associated VM migration overhead. Since dynamic VM consolidation through VM live migrations have negative impacts on hosted applications performance and data center components, a VM live migration overhead estimation technique is proposed, which takes into account pragmatic migration parameters and overhead factors. In order to tackle scalability issues, a hierarchical, decentralized dynamic VM consolidation framework is presented that helps to localize migration-related network traffic and reduce network cost. Moreover, a multi-objective, dynamic VM consolidation algorithm is proposed by utilizing the Ant Colony Optimization (ACO) metaheuristic, with integration of the proposed VM migration
overhead estimation technique. Comprehensive performance evaluation makes it evident that the proposed dynamic VM consolidation approach outpaces the state-of-the-art offline, migration-aware dynamic VM consolidation algorithm across all performance metrics by reducing the overall power consumption by up to 47%, resource wastage by up to 64%, and migration overhead by up to 83%.

Cloud Computing is a new paradigm of distributed computing that offers virtualized resources and services over the Internet. To offer Infrastructure-as-a-Service (IaaS) many Cloud providers uses a large data center which usage ranges 5% to 10% of capacity in average. In order to improve Cloud data center management and resources usage a Virtual Machine (VM) consolidation technique can be applied to increase workloads and save energy. Using VM consolidation, we introduce an artificial intelligence consolidation based in Pseudo-Boolean (PB) Constraints to find a optimal consolidation. To evaluate our PB consolidation approach we used the DInf-UFPR and Google Cluster scenario and the formulas are solved with two state-of-the-art solvers.

An agent-based simulation model was built for studying resource management in the clouds, focusing on how service placement strategies, service auto-migration, and server consolidation affect the overall performance of clouds of homogeneity and heterogeneity, with regard to power consumption, resource utilisation, server-level agreement violation. Major cloud elements were modelled as autonomous agents with their characteristics encapsulated. The complex relationships between components are no longer to be hardcoded rather than realised through asynchronous agent interactions. In the evaluation, performance of several resource management algorithms was studied using statistical methods, and accuracy of server power consumption models were evaluated.

Cloud computing is the stage for a choice of services like software, infrastructure as a cloud service and each person wants to have the benefit of that cloud services using the cloud computing concept, which ultimately increases the data size and loaded records on cloud servers. Due to increased number of files on the cloud database the retrieval of files becomes much more time consuming and complex. Also this file retrieval doesn’t ensure the exact retrieval of files from the storage. Besides, the isolation apprehension affect to the appropriate documents regained by the cloud user in the afterward phase in view of the fact that they may also enclose sensitive data and make known information about sensitive exploration words or phrase. Here in this paper an efficient approach of power consumption using scheduling of resources is implemented.

Cloud data centers always are facing challenges related to Virtual Machine (VM) placement operation, such as high violations in Service Level Agreement (SLA), high power consumption, and unfair load distribution among the physical servers. In this paper, in order to face these challenges, we first presented a multi-objective model for VM placement problem in the cloud data center, and then we formulate the VM placement problem as a multi-objective optimization problem. Finally, we proposed an improved VM placement algorithm, optimized by Particle Swarm Optimization (PSO). We have presented the specific design and implementation of the proposed algorithm such as the design of encoding scheme, fitness values, and mutations. The goal of the proposed algorithm to find the best solution for allocating a set of VMs to a set of physical servers without exceeding the capacity of the servers and considering a variety of objectives such as power consumption, violation of SLA and the degree of imbalance. The proposed algorithm is compared with different VM placement algorithms, such as First Fit Decreasing (FFD), Best Fit Decreasing (BFD) and Modified Best Fit Decreasing (MBFD). Simulation results show that the proposed algorithm leads to better results. The power consumption of the proposed algorithm achieves 36%, 24% decreases less than FDD and BFD respectively. Furthermore, the violation of SLA of the proposed algorithm achieves 31%, 21%, 15% decreasing compared with FDD, BFD and MBFD respectively.

Data centers are becoming considerably more significant and energy-intensive due to the exponential growth of cloud computing. Cloud computing allows people to access computer resources on demand. It provides amenities on the pay-as-you-go basis across the data center locations spread over the world. Consequently, cloud data centers consume a lot of electricity and leave a proportional carbon impact on the environment. There is a need to investigate efficient energy-saving approaches to reduce the massive energy usage in cloud servers. This review paper focuses on identifying the research done in the field of energy consumption (EC) using different techniques of machine learning, heuristics, metaheuristics, and statistical methods. Host CPU utilization prediction, underload/overload detection, virtual machine selection, migration, and placement have been performed to manage the resources and achieve efficient energy utilization. In this review, energy savings achieved by different t...

Cloud computing services have been on the rise over the past few decades, which has led to an increase in the number of data centers worldwide which increasingly consume more and more amount of energy for their operation, leading to high carbon dioxide emissions and also high operation costs. Cloud computing infrastructures are designed to support the accessibility and deployment of various service oriented applications by the users. The resources are the major source of the power consumption in data centers along with air conditioning and cooling equipment. Moreover the energy consumption in the cloud is proportional to the resource utilization and data centers are almost the worlds highest consumers of electricity. It is therefore, the need of the hour to devise efficient consolidation schemes for the cloud model to minimize energy and increase Return of Investment(ROI) for the users by decreasing the operating costs. The consolidation problem is NP-complete in nature, which requi...

Cloud Computing has rapidly emerged as a successful paradigm for providing Information and Communication Technology (ICT) infrastructure. Resource allocation is used to execute user applications in the form of requests for consolidated resources in order to minimize energy consumption and violation of the Service Level Agreement (SLA) for large-scale data centers resource utilization. The energy consumption is usually caused due to local entrapment and violation of SLA during resource assigning and execution. Several researchers have proposed solutions to reduce local entrapments and violations of SLA, to minimize the energy consumption of the entire data center. However, strategies employed in their solutions face entrapment in either local searches or at the global search level with a certain level of SLA violation. In this light, a Multi-Objective Hybrid Flower Pollination Resource Consolidation (MOH-FPRC) scheme for efficient and optimal resource consolidation of data center res...

Philosophical methods of query process play a pivotal role in data retrieval from social networks linked to a graph NoSQL database that consolidate massive types of data. This massive data called as big data need to be distributed and sharded across many adjacent machines so that the queries when posted can be retrieved faster. An efficient storage mechanism for flexible retrieval of a query by the user needs to be established in Neo4j High Availability graph NoSQL database for less time overhead query process. The main focus of this paper is how to share the database constituting data across machines such that the storage of all related data comes in same or adjacent machines. This graph NoSQL database allocation problem referred as Neo4j High Availability big data allocation has been proved to be NP-Hard in this paper. To solve this hard problem, an optimization strategy by integrating Best Fit Decreasing with Ant Colony Optimization-based metaheuristic algorithm is suggested and implemented and results are analyzed. This data allocation in a distributed master-slave architecture of Neo4jHA is evaluated based on simulation, and performance is compared on the query efficiency of the proposed method to other best data allocation heuristic algorithms like First Fit, Best Fit, First Fit Decreasing and Best Fit Decreasing available in literature so far. The results exhibit how the proposed algorithm with replication and relation outperforms in query execution compared to other data allocation methods without relation and replication.

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 computing is a model for providing computing resources as a utility which faces several challenges on management of virtualized resources. Accordingly, virtual machine placement and migration are crucial to achieve multiple and conflicting goals. Regarding the complexity of these tasks and plethora of existing proposals, this work surveys the state-of-the-art in the area. It presents a cloud computing background, a review of several proposals, a discussion of problem formulations, advantages and shortcomings of reviewed works. Furthermore, it highlights the challenges for new solutions and provides several open issues, showing the relevancy of the topic in an increasing and demanding market.