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Numerous data centers have adopted heterogeneous computing server to accelerate the processing speed with various applications. However, as the growth of efficiency, the issue of high power consumption is increasingly becoming crucial. Traditional researchers focus on the developing strategies for power efficiency that neglects the importance of power prediction that substantially impacts on energy planning on data center. Different to the previous work, this paper utilizes the advantage of Artificial Neural Network (ANN) to design and implement an improved algorithm of power consumption prediction on heterogeneous computing server with various types of jobs. First, the impact of multi-type of jobs has been taken into account, i.e., CPU-intensive, memory-intensive, I/O-intensive and GPU-intensive. We collect the data trace from these jobs by a set of benchmarks. Then, based on the collected data trace, power prediction algorithm has been established, so that developing an improved ANN-based power consumption prediction algorithm with CNN-BiLSTM neural network and attention mechanism. Specifically, one dimensional CNN is utilized to perform local feature extraction and dimension reduction on the input, the BiLSTM network is adopted to extract high-level features from the above results. The miscellaneous features extracted are filtered out by using attention mechanism. Last, the experiment has been conducted, and the peculiarity for four types of jobs has been discussed. Compared with the latest proposed ANN-based prediction power algorithms, our proposed algorithm has better performance in prediction accuracy on power consumption. [ABSTRACT FROM AUTHOR]

Recently, cloud computing infrastructure (CCI) models have received much attention for their exceptional scalability, dependability, Data Information Sharing (DIS), and low cost rate. There are many hardware and software elements that are accessed over the internet by cloud data centers. Modern data centers utilize Virtualization Techniques (VT) to offer a dispersed CI that employs Virtual Machines (VM) based on Physical Hosts (PH). With the increasing number of centers, optimizing energy consumption has become vital to saving costs due to DCC's high energy consumption. In our CPS algorithm, we combine the Cuckoo algorithm and the particle swarm optimization (PSO). It is determined which virtual machine can be assigned to each host, thus choosing the best virtual machine. As a result, if the selected host is overloaded, it is determined which virtual machines are generating high loads and migrated to another host, which is determined based on the cuckoo algorithm and PSO. In testing each algorithm separately, the combination method proved to consume less energy and execute faster than the other methods in the CloudSim simulation environment. Fault tolerance for our network and evaluation of VMs have also been emphasized in vSphereTM. [ABSTRACT FROM AUTHOR]

In the past decade, the cloud environment has emerged as a resource-enriched technology for computational modeling. Most cloud data centers are deployed far from the device layer, which may cause higher service delivery time. Therefore, Fog servers are implemented to retain the commitment of minimum service delivery time. The computational resources (processing unit, memory, storage) and network bandwidth are to be appropriately scheduled to make an efficient system. For delay and resource-constrained devices, the essential issues include minimum service latency and improved system performance by offloading non-sensitive devices to the cloud layer. In this study, an adaptive bandwidth adjustment technique is proposed considering the sensitivity of the applications and load on the multiple queues at the access point based on which, a compelling dynamic bandwidth ratio is calculated. Further, blocking probability of task and task dropping probability is used as task offloading probability to overcome the dropping ratio of type-II applications. The random early detection (RED) algorithm is implemented in queueing management to maximize the system performance. Extensive simulation results highlighted that the proposed model significantly improves the system performance in terms of network delay, queue delay, and overall response time over traditional algorithms. [ABSTRACT FROM AUTHOR]

A huge cloud data center makes it possible to offer computing as a utility to customers. However, the main challenge is to fulfill the customer's dynamic workload requirement seamlessly. Additionally, cloud data center consumes an enormous amount of power due to improper scheduling of virtual machines over the physical machines, which lead to inefficient usage of heterogeneous computing resources. So, to minimize energy consumption in the cloud data center, virtual machines should be scheduled in an energy-efficient way. In this paper, an Artificial Immune System based Virtual Machine Scheduling using Modified Clonal Selection Algorithm (VMS-MCSA) is proposed to schedule virtual machines energy efficiently. The classical Clonal Selection Algorithm(CSA) operators are modified such that they can be applied to the discrete optimization dynamic virtual machine scheduling problem. The randomized mutation operator is proposed, which reschedule VMs at each scheduling interval to handle the dynamicity of workload with minimum virtual machine migrations. Additionally, the VM-consolidation model was proposed for constraint-based virtual machine migration. The proposed VMS-MCSA algorithm is implemented on a cloudsim simulator, and the results show that the VM scheduling using VMS-MCSA is energy-efficient compared to other recent approaches. [ABSTRACT FROM AUTHOR]

Nowadays, cloud computing is known as an internet-based modern area among emerging technologies that brings up an environment, in which computing resources such as hardware, software, storage, etc. can be rented by cloud users based on a pay per use model. Since the size of cloud computing is widely expanding and the number of cloud users is also increasing day by day, high energy consumption becomes a serious concern in the operation of complex cloud data centers. In this regards, Virtual Machine (VM) consolidation plays a vital role in utilizing cloud resources in an efficient manner. It migrates the running VMs from overloaded Physical Machines (PMs) to other PMs considering multiple factors, such as migration overhead, energy consumption, resource utilization, and migration time. Since the VM consolidation issue is known as an NP-hard problem, various nature‐inspired meta-heuristic algorithms aiming to solve this problem have been utilized in recent years. However, a lack of systematic and detailed survey study in this field is obvious. Therefore, this gap motivated us to provide the current paper aiming to highlight the role of nature-inspired meta-heuristic algorithms in the VM consolidation problem, review the existing approaches, offer a detailed comparison of approaches based on important factors, and finally, outline the future directions. [ABSTRACT FROM AUTHOR]

Traditional data centers are shifted toward the cloud computing paradigm. These data centers support the increasing demand for computational and data storage that consumes a massive amount of energy at a huge cost to the cloud service provider and the environment. Considerable energy is wasted to constantly operate idle virtual machines (VMs) on hosts during periods of low load. Dynamic consolidation of VMs from overloaded or underloaded hosts is an effective strategy for improving energy consumption and resource utilization in cloud data centers. The dynamic consolidation of VM from an overloaded host directly influences the service level agreements (SLAs), utilization of resources, and quality of service (QoS) delivered by the system. We proposed an algorithm, namely, GradCent, based on the Stochastic Gradient Descent technique. This algorithm is used to develop an upper CPU utilization threshold for detecting overloaded hosts by using a real CPU workload. Moreover, we proposed a dynamic VM selection algorithm called Minimum Size Utilization (MSU) for selecting the VMs from an overloaded host for VM consolidation. GradCent and MSU maintain the trade-off between energy consumption minimization and QoS maximization under specified SLA goal. We used the CloudSim simulations with real-world workload traces from more than a thousand PlanetLab VMs. The proposed algorithms minimized energy consumption and SLA violation by 23% and 27.5% on average, compared with baseline schemes, respectively. [ABSTRACT FROM AUTHOR]

Efficiency in cloud servers' power consumption is of paramount importance. Power efficiency makes the reduction in greenhouse gases establishing the concept of green computing. One of the beneficial ways is to apply power-aware methods to decide where to allocate virtual machines (VMs) in data center physical resources. Virtualization is utilized as a promising technology for power-aware VM allocation methods. Since the VM allocation is an NP-complete problem, we use of evolutionary algorithms to solve it. This paper presents an effective micro-genetic algorithm in order to choose suitable destinations between physical hosts for VMs. Our evaluations in simulation environment show that micro-genetic approach provides invaluable improvements in terms of power consumption compared with other methods. [ABSTRACT FROM AUTHOR]

Cloud computing has developed as a high-performance computing environment with a huge set of virtualized, abstracted, and flexible resource. It provides service to the user with high-performance. In a large-scale cloud computing environment, the cloud data centers and users are distributed physically across the globe. In a distributed environment, the arrangement of scientific workflow is considered as a popular NP-complete problem and they prevails to be intractable. An extra-ordinary issue in the distributed environment is scientific workflow scheduling and it is difficult to track the exact solution. It becomes even more challenging in the cloud computing platform due to its dynamic and heterogeneous nature. The biggest challenge for cloud data centers is how to handle and service the millions of requests that are arriving very frequently from end users efficiently and correctly. The aim of this study is to obtain an efficient load-balancing in the large-scale platform of cloud computing based on the proposed Meta-heuristic based multi objective optimisation. The main contributions of this paper are related to the scheduling of tasks to the resource groups using multi-objective memetic algorithm (MOMA), it uses a local search technique to reduce the likelihood of the premature convergence. To reschedule the failed workload to achieve fault tolerance an adaptive plant intelligent behavior optimization (APIBO) is proposed. The experiments using different scientific workflow applications highlight the effectiveness, usefulness, and better performance of the proposed approach and the Performances are evaluated in terms of resource contention, response time, execution time, throughput, and resource utilization. [ABSTRACT FROM AUTHOR]

Virtualization provides the flexibility to distribute the workload among physical servers to reduce overall electrical power consumption, through the consolidation of Virtual Machines (VMs). Many research projects have been done on VM migration to reduce energy consumption in data centers while ensuring a high level of adherence to the Service Level Agreements (SLA). Service levels of running applications are likely to be negatively affected during a live VM migration. For this reason, in this paper, we propose a new intelligent VM migration approach, called CLANFIC, which utilizes modified Cellular Learning Automata based Evolutionary Computing (CLA-EC) and neuro-fuzzy to minimize the number of VM migrations and improve energy consumption. This goal is achieved by utilizing an optimized placement method and delaying migration time based on future resource demand prediction. This algorithm reduces the number of migrations in two steps (i) finding the optimal virtual machine placement and replacement on physical servers by using modified CLA-EC (ii) predicting future resource usage in each host by a neuro-fuzzy algorithm to prevent unnecessary migrations. The experimental results on the real workload traces from PlanetLab show that the proposed method reduces the mean migration number, energy consumption, and SLA violation of the data center by 59.05%, 8.5%, and 70.76%, respectively. [ABSTRACT FROM AUTHOR]

The unprecedented growth of energy consumption in data centers created critical concern in recent years for both the research community and industry. Besides its direct associated cost; high energy consumption also results in a large amount of CO2 emission and incurs extra cooling expenditure. The foremost reason for overly energy consumption is the underutilization of data center resources. In modern data centers, virtualization provides a promising approach to improve the hardware utilization level. Virtual machine placement is a process of mapping a group of virtual machines (VMs) onto a set of physical machines (PMs) in a data center with the aim of maximizing resource utilization and minimizing the total power consumption by PMs. An optimal virtual machine placement algorithm substantially contributes to cutting down the power consumption through assigning the input VMs to a minimum number of PMs and allowing the dispensable PMs to be turned off. However, VM Placement Problem is a complex combinatorial optimization problem and known to be NP-Hard problem. This paper presents an extensive review of virtual machine placement problem along with an overview of different approaches for solving virtual machine placement problem. The aim of this paper is to illuminate challenges and issues for current virtual machine placement techniques. Furthermore, we present a taxonomy of virtual machine placement based on various aspects such as methodology, number of objectives, operation mode, problem objectives, resource demand type and number of clouds. The state-of-the-art VM Placement techniques are classified in single objectives and multi-objective groups and a number of prominent works are reviewed in each group. Eventually, some open issues and future trends are discussed which serve as a platform for future research work in this domain. [ABSTRACT FROM AUTHOR]

Emerging persistent file systems are designed to achieve high-performance data processing by effectively exploiting the advanced features of Non-volatile Memory (NVM). Non-uniform memory access (NUMA) architectures are universally used in high-performance computing and data centers due to its scalability. However, existing NVM-based in-memory file systems are all designed for uniformed memory access systems. Their performance is not satisfactory on NUMA machine as they do not consider the architecture of multiple nodes and the asymmetric memory access speed. In this paper, we design an efficient NUMA-aware in-memory file system which distributes file data on all nodes to effectively balance the loads of file requests. Three approaches for improving the performance of the file system on NUMA machine are proposed, including Node-oriented File Creation algorithm to dispatch files over multiple nodes, File-oriented Thread Binding algorithm to bind threads to the gainful nodes and a buffer assignment technique to allocate the user buffer from the proper node. Further, based on the new design, we implement a functional NUMA-aware in-memory file system, HydraFS, in Linux kernel. Extensive experiments show that HydraFS significantly outperforms existing representative in-memory file systems on NUMA machine. The average performance of HydraFS is 76.6%, 91.9%, 26.7% higher than EXT4-DAX, PMFS, and SIMFS, respectively. [ABSTRACT FROM AUTHOR]

A cloud service provider (CP) offers computing resources with their own interface type and pricing policies besides other services such as storage on a pay-per-use model. Client's requests should be processed in an appropriate CP datacenters in a trade-off relation between price and performance. The appropriate choice of a CP datacenters is the responsibility of the cloud-based service broker routing policy which acts as an intermediate between the users and the CP's datacenters. However, due to the distribution nature of the CP's datacenters, these datacenters can be overloaded with the increasing number of users and their requests being served at the same time if the datacenters are unwisely chosen. Therefore, choosing the appropriate datacenter is significant to the overall performance of the cloud computing systems. This paper aims to propose an optimized service broker routing policy based on different parameters that aims to achieve minimum processing time, minimum response time and minimum cost through employing a searching algorithm to search for the optimal solution from a possible solution space. A simulation-based deployment of the proposed algorithm along with a comparison study with other known algorithms form the field, confirms the ability of the proposed algorithm to minimize the load on service provider datacenters with minimum processing time, response time and overall cost. [ABSTRACT FROM AUTHOR]