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To augment situational awareness capabilities for high-frequency disturbances, the modern power system necessitates the implementation of high-sampling rates for real-time Synchro-Waveform (SW) measurements. Nevertheless, the nonlinear nature of data collected from SW measurement devices poses significant challenges to the communication and storage capacities of data centers. To effectively tackle this issue, this paper proposes a Multi-stage Hybrid Coding (MHC) method for the lossless compression of SW data. The MHC method utilizes a well-structured two-stage approach to achieve efficient compression. In the first stage, a variable frame-based method is designed to reduce redundant information and achieve initial compression using multiple periodic recursive compression. Subsequently, the dictionary-based method, specifically the Lempel–Ziv–Markov chain algorithm, is seamlessly integrated to further compress the SW data. Experimental and numerical analyses are conducted using both simulated and real-world source data, with real-time performance validation in SW measurement units. The obtained results convincingly demonstrate that the proposed method enables online compression of both event and non-event SW data, achieving an impressive reduction of over 73.4% in data space, while maintaining a data loss ratio lower than 0.4% for high-density SW data. • Multiple periodic recursive method is proposed to eliminate repetitive information. • A multi-stage hybrid coding method is implemented for the lossless compression. • A series of synchro-waveform events are collected to evaluate the performance. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel clustering approach that eliminates the need for predefined cluster centres and cluster counts, addressing common limitations in traditional clustering algorithms. We achieve this by allowing each data point within the problem space to serve as a potential cluster centre, determined by the presence of nearby data points. Three distinct algorithms are introduced: i) Identifying valid data points for clustering by assessing their presence within the specified vicinity of each potential cluster centre, allowing data points to belong to multiple clusters, ii) Calculating the fuzzy membership of shared data points among the clusters and iii) Retaining shared data points in the cluster with the highest membership and removing them from others. A comprehensive evaluation of this method using synthetic data and benchmark datasets from well-known problems demonstrates promising performance. Our approach offers increased flexibility, ease of use, and enhances the fidelity of cluster centres to the underlying data distribution. [ABSTRACT FROM AUTHOR]

Cloud Computing is a robust model that allows users and organizations to purchase required services per their needs. The model offers many services such as storage, platforms for deployment, convenient access to web services, and so on. Load Balancing is a common issue in the cloud that makes it hard to maintain the performance of the applications adjacent to the Quality of Service (QoS) measurement and following the Service Level Agreement (SLA) document as required from the cloud providers to enterprises. Cloud providers struggle to distribute equal workload among the servers. An efficient LB technique should optimize and ensure high user satisfaction by utilizing the resources of VMs efficiently. This paper presents a comprehensive review of various Load Balancing techniques in a static, dynamic, and nature-inspired cloud environment to address the Data Center Response Time and overall performance. An analytical review of the algorithms is provided, and a research gap is concluded for the future research perspective in this domain. This research also provides a graphical representation of reviewed algorithms to highlight the operational flow. Additionally, this review presents a fault-tolerant framework and explores the other existing frameworks in the recent literature. [ABSTRACT FROM AUTHOR]

Load balancing of tasks on the cloud environment is an important aspect of distributing resources from a data centre. Due to the dynamic computing through the internet; cloud computing agonizes from overloading of requests. Load balancing has to be carried out in such a manner that all virtual machines (VM) should have balanced load to achieve optimal utilization of its capabilities. This paper proposes a novel methodology of dynamic balancing of load among the virtual machines using hybridization of modified Particle swarm optimization (MPSO) and improved Q-learning algorithm named as QMPSO. The hybridization process is carried out to adjust the velocity of the MPSO through the gbest and pbest based on the best action generated through the improved Q-learning. The aim of hybridization is to enhance the performance of the machine by balancing the load among the VMs, maximize the throughput of VMs and maintain the balance between priorities of tasks by optimizing the waiting time of tasks. The robustness of the algorithm has been validated by comparing the results of the QMPSO obtained from the simulation process with the existing load balancing and scheduling algorithm. The comparison of the simulation and real platform result shows our proposed algorithm is outperforming its competitor. [ABSTRACT FROM AUTHOR]

We propose a parallel multi delay (PMD) carrier-assisted differential detection (CADD) receiver for the first time, including PMD asymmetric-CADD (PMD A-CADD) and PMD symmetric-CADD (PMD S-CADD). The transfer function of the PMD CADD receiver is optimized by expanding the number of optical delays to a general selection, thereby suppressing the signal-signal beat interference (SSBI). As a result, the required carrier-to-signal power ratio (CSPR) is reduced, and the optical signal-to-noise ratio (OSNR) sensitivity is drastically improved. Two schemes for further mitigation of residual SSBI are introduced, namely SSBI iterative cancellation algorithm and the increased guard band mitigation method. For SSBI iterative cancellation algorithm, by optimizing the number of optical delays, the optimal CSPR for the P6D A-CADD and the P5D S-CADD can be reduced to −1 dB, the required number of iterations is only 3, and compared with the parallel dual delay-based asymmetric-CADD (PDD A-CADD) receiver, the OSNR sensitivity is improved by about 4.5 dB. For the increased guard band mitigation method, 2.34 GHz and 3.16 GHz guard band are required for the P6D A-CADD and the P5D S-CADD to meet the 7% FEC threshold at 0 dB CSPR. • A parallel multi delay CADD receiver is proposed for data center interconnect applications. • The proposed receiver suppresses SSBI by modifying the transfer function, thereby reducing the optimal CSPR to less than 0 dB. • Two SSBI iteration cancellation algorithms are used to improve the system performance, and the SSBI simplified iteration cancellation algorithm had lower complexity and better performance. [ABSTRACT FROM AUTHOR]

With the deployment of workflow and other applications, cloud computing is accessible and offers assistance for optimizing workflow execution and enhancing performance. Existing research, however, tends to disregard the influence of dataset migration on workflow execution and focuses more on task execution time. This study suggests a new model for the problem of data-intensive workflow execution. Firstly, according to the structure of the workflow scheduling problem, it is divided into two sub-problems: data placement and task scheduling. The two sub-problems interact with each other and a bi-level optimum model is established. By seeking a better allocation strategy for the dataset placement and then seeking the best task-scheduling solution. Secondly, an improved multitasking bi-level evolutionary algorithm (IM-BLEA) is proposed. When dealing with the lower-level optimization problem (LLOP), offspring are selected by sorting individuals by their performance and overall performance in the population, and this environmental selection enhances the diversity and searchability of the population. Finally, compared with the other multitasking algorithm, IM-BLEA has good performance. Simulation results based on real scientific workflows show that the algorithm improves the values of transfer time and number of selected data centers by 56% and 10% compared to the comparison algorithm. • A bi-level optimization model in data-intensive workflows is proposed. • The upper-level and lower-level problems are data placement and task scheduling. • Multitasking evolutionary algorithms on bi-level optimization model. • Based on the modeling of the problem, an algorithm (IM-BLEA) is proposed. • Simulation experiments based on scientific workflows to validate algorithms. [ABSTRACT FROM AUTHOR]

• Right-sizing of heterogeneous data centers with d different server types. • Discrete setting, time- and load-independent operating cost. • 2d-competitive deterministic online algorithm. • 1.582d-competitive randomized online algorithm. • Lower bound of 2d for deterministic online algorithms. Power consumption is the major cost factor in data centers. It can be reduced by dynamically right-sizing the data center according to the currently arriving jobs. If there is a long period with low load, servers can be powered down to save energy. For identical machines, the problem has already been solved optimally by Lin et al. (2013) and Albers and Quedenfeld (2018). In this paper, we study how a data-center with heterogeneous servers can dynamically be right-sized to minimize the energy consumption. There are d different server types with various operating and switching costs. We present a deterministic online algorithm that achieves a competitive ratio of 2 d as well as a randomized version that is 1.58 d -competitive. Furthermore, we show that there is no deterministic online algorithm that attains a competitive ratio smaller than 2 d. Hence our deterministic algorithm is optimal. In contrast to related problems like convex body chasing and convex function chasing, we investigate the discrete setting where the number of active servers must be integral, so we gain truly feasible solutions. [ABSTRACT FROM AUTHOR]

Cloud computing applications have grown rapidly in the last decade, where many organizations are migrating their applications to cloud data center as they expected high performance, reliability, and the best quality of service. Data centers deploy a variety of technologies, such as software-defined networks (SDN), to effectively manage their resources. The SDN approach is a highly flexible network architecture that automates network configuration using a centralized controller to overcome traditional network limitations. This paper proposes an SDN-based monitoring algorithm to detect the performance anomaly and identify the bottleneck of the distributed application in the cloud data center using the support vector machine algorithm. It collects the data from the network devices and calculates the performance metrics for the distributed application components that are used to train the SVM algorithm and build a baseline model of the normal behavior of the distributed application. The SVM model detects performance anomaly behavior and identifies the root cause of bottlenecks using one-class support vector machine (OCSVM) and multi-class support vector machine (MCSVM) algorithms. The proposed method does not require any knowledge about the running applications or depends on static threshold values for performance measurements. Simulation results show that the proposed method can detect and locate the failure occurrences efficiently with high precision and low overhead compared to statistical methods, Naive Bayes Classifier and the decision tree machine learning method. [ABSTRACT FROM AUTHOR]

The Resource Provisioning (RP) and Task Scheduling (TS) issues has become an attractive paradigms in cloud industry, this is due to the increasing demand for the services provided by virtual machines that are structured by physical servers owned by the data centers of cloud service providers (CSPs). In this paper, we propose a new model based on multi-agent system for the RP and TS reducing the cost of energy using Deep Reinforcement Learning DRL. A Quantile Regression Deep Q Network (QR-DQN) algorithm generates an appropriate policy and the optimal long-term decisions. A set of experiments show the efficiency of our proposed scheduling approach and the performance of our task allocation method.. [ABSTRACT FROM AUTHOR]

The n -dimensional HSDC with the logic graph H n is one of the most attractive server-centric data center networks for high incremental scalability. The routing design in network topology is very important for information transmission. In particular, the application of disjoint path covers can solve various problems such as program code optimization and mapping parallel programs to parallel structures. H n consists of 2 n cliques and each two vertices in a clique are adjacent. For any vertex sets { x , u } and { y , v } in W and B , respectively, and any two disjoint vertex sets A 1 , A 2 in some clique, where (W , B) is a bipartition of H n , in this paper, we prove that, when | A i | ≠ 1 and A i ∩ { x , y , u , v } = ∅ for i = 1 , 2 , if | A 1 ∪ A 2 | ≤ n − 3 , then there exists an (x , y) -path P 1 and a (u , v) -path P 2 in H n passing through A 1 and A 2 , respectively, such that P 1 and P 2 have no common vertices and P 1 ∪ P 2 contains all vertices of H n. Then P 1 ∪ P 2 is called the 2-disjoint path cover in H n and we propose the efficient algorithms for the path design. Furthermore, we prove that our main result is optimal. • In the problem of data harvest and information transmission, it is very important to design the disjoint path cover in the logic graph of Data Center Network. • We consider the construction of 2-disjoint path cover such that each path should contain all prescribe vertices which have low probability of failure. • The efficient algorithms have been given and we prove that the number of prescribed vertices is not more than n − 3 and explain that this upper bound is sharp. [ABSTRACT FROM AUTHOR]

• A deep neural network model based on incremental updating was constructed. • Under-sampling preprocessing was carried out. • The clustering center was set. In order to address the clustering problem of intelligent medical data, the data sets were not preprocessed using the traditional method, leading to a large amount of calculation, low efficiency, and large data cluster center offset distance. We proposed a balanced clustering algorithm for intelligent medical heterogeneous big data set using deep learning. Firstly, a deep neural network model based on incremental updating was constructed, and adaptive training and adjustment were made according to data scale, and the multi-layer feature learning of heterogeneous big data sets of intelligent medical care. Secondly, under-sampling preprocessing was carried out on the data set so that the data of the heterogeneous big data set was in a balanced state, and on this basis, clustering calculation of the heterogeneous big data was conducted. Then, the clustering center was set according to the kernel density estimation results, and the data cluster center was updated iteratively until convergence by combining the data features obtained from deep learning and euclidean distance calculation, so as to complete the balanced clustering of the heterogeneous big data set of intelligent medical treatment. The results show that the proposed algorithm has the advantages of small data cluster center offset distance, short clustering time, low energy consumption, high Macro-F1 value and NMI value, and the accuracy of clustering can be as high as 95%, the calculational cost is low, which has certain advantages. 2020 Elsevier Ltd. All rights reserved. [ABSTRACT FROM AUTHOR]

Cloud Computing has emerged as a new paradigm that permits to deliver Information Technology resources on demand and outsource software and hardware infrastructure. To support dynamic load, the system should select the best location for serving the request to achieve best performance. Selection of data center plays an important role to improve the performance as well as to reduce the cost. Service broker controls the traffic routing between user bases and data centers based on different service broker policies. In this paper we propose a new service broker algorithm which combines the cost and efficiency for optimized the cost and response time. [ABSTRACT FROM AUTHOR]

In data centers, data replication is the primary method used to ensure availability of customer data. To avoid correlated failure, cloud storage infrastructure providers model hierarchical failure domains using a tree, and avoid placing a large number of data replicas within the same failure domain (i.e. on the same branch of the tree). Typical best practices ensure that replicas are distributed across failure domains, but relatively little is known concerning optimization algorithms for distributing data replicas. Using a hierarchical model, we answer how to distribute replicas across failure domains optimally. We formulate a novel optimization problem for replica placement in data centers. As part of our problem, we formalize and present a new criterion for optimizing a replica placement. Our overall goal is to choose placements in which correlated failures disable as few replicas as possible. In this work, we provide two optimization algorithms for dependency models represented by trees. We first present an O (n + ρ log ⁡ ρ) time dynamic programming algorithm for optimally placing ρ replicas of a single block on the leaves (representing servers) of a tree with n vertices. We next consider the problem of optimally placing replicas of multiple blocks of data, where every block may have a different replication factor. For this problem, we give a dynamic programming algorithm that runs in O (n ρ max 3 δ 2 m poly (δ)) , where m denotes the number of blocks, ρ max denotes the maximum replication factor of a block, and δ denotes the maximum difference in the replication factors of any two blocks. The running time of the algorithm is polynomial when the δ , which we refer to as the skew , is a constant. [ABSTRACT FROM AUTHOR]

• A hybrid cloud can be built across clouds, CDNs and private data centers. • We formulate the problem of delivering mixture media content using the platform. • The NP-hard optimization problem can be transformed into a Nash bargaining problem. • The Nash bargaining problem can be solved efficiently from a geometrical perspective. • The algorithm can reduce cost and improve QoS significantly. With the increase in the amount of dynamic and personalized content in media streaming systems, a solution that is based on only the cloud or content delivery network (CDN) cannot satisfy all application-specific requirements. Thus integrating the cloud with CDNs and private data centers can be advantageous. To efficiently deliver a huge amount of media content using a geographically distributed hybrid cloud, we propose a Multi-Resource Scheduling Algorithm for Hybrid Cloud-Based Large-Scale Media Streaming(MHLMS). The algorithm transforms resource scheduling into a Nash bargaining problem and solves it efficiently from a geometrical perspective. The simulation results show that compared to algorithms that use only the cloud or CDN, MHLMS can reduce the cost by approximately 60% and reduce the average delivery distance by up to 70%. Thus, compared with the existing video delivery methods, MHLMS can handle millions of channels effectively with much lower time complexity. [ABSTRACT FROM AUTHOR]

Abstract Energy related costs and environmental sustainability present a significant challenge for cloud computing practitioners and the development of next generation data centers. Virtual Machine (VM) placement provides a promising technique to save energy and improve resource management which is one of the greatest causes of high energy consumption in the operation of data centers today. A key challenge for VM placement algorithms is the ability to accurately forecast future resource demands due to the dynamic nature of cloud applications. Furthermore, the literature rarely considers placement strategies based on co-located resource consumption which has the potential to improve allocation decisions. Using real workload traces this work presents a comparative study of the most widely used prediction models and introduces our novel Predictive Anti-Correlated Placement Algorithm (PACPA) which considers both CPU and bandwidth resource consumption. Our empirical results demonstrate how the proposed approach reduces energy by 18% while also reducing service violations by over 34% compared to some of the most commonly used placement algorithms. [ABSTRACT FROM AUTHOR]

Abstract The area of sustainable green smart computing highlights key challenges towards reducing cost and carbon dioxide emissions due to the high-energy consumption of Cloud data centres. Here, we focus on the Cloud virtual machine (VM) scheduling that is usually based on simple algorithms, e.g. VM placement on nodes with low memory usage. This approach fails to consider the actual configuration of nodes inside the server rack resulting in local overheating of Cloud data centres. To solve this, we propose a VM scheduling algorithm based on the gravitational effect, called VMSAGE , to optimize energy efficiency of Cloud computing systems. Inspired by the physical gravitation model, we define the thermal repulsion and logical gravitation factors between physical nodes and VMs. To achieve optimized VM scheduling, we propose a gravitation function that refers to the calculation of the logical quality of each VM, host and rack through the algorithm, so as to draw the attractiveness between them. Based on the concept of dimension reduction, VMSAGE conducts the two-dimensional plane target selection twice to reduce the computational cost. Additionally, VMSAGE evaluates attributes of the computer room to carry out the VM deployment. To demonstrate the effectiveness of our solution, we compare it with the Best Fit Heuristic (BFH) and the dynamic voltage and frequency scaling (DVFS) algorithms. The results indicate that our algorithm achieves 10% and 20% optimized energy consumption respectively. The experimental results highlight our contribution, in where VMSAGE can significantly reduce energy consumption rates and VM migration times. [ABSTRACT FROM AUTHOR]

Abstract Recently, lots of datacenters (DCs) have been built to enhance reliability and expand processing ability for computing service in cloud computing. Furthermore, spatial division multiplexing (SDM) technology and elastic optical network (EON) are also considered as the candidate solutions to solve the requirements of huge bandwidth and flexible connections. However, it is much more difficult to use few mode fiber (FMF) in DCs-based EON due to the existing modal crosstalk among different modes. Faced with the challenges, we propose a routing, spectrum, mode and time assignment algorithm to decrease the blocking probability and increase the spectrum utilization. To deal with the problem more efficiently, we propose a method called flexible sliding three-dimensional block (3D block) which represents the dimensions of spectrum, space and time in our algorithm. 3D block slides in the resource space to find out proper resources. The simulations are conducted on datacenter networks (DCNs) and the results show that lower blocking probability and higher spectrum utilization are achieved by efficiently utilizing the spectrum, space and time domains. [ABSTRACT FROM AUTHOR]

Cloud Computing has emerged as a new paradigm that permits to deliver Information Technology resources on demand and outsource software and hardware infrastructure. To support dynamic load, the system should select the best location for serving the request to achieve best performance. Selection of data center plays an important role to improve the performance as well as to reduce the cost. Service broker controls the traffic routing between user bases and data centers based on different service broker policies. In this paper we propose a new service broker algorithm which combines the cost and efficiency for optimized the cost and response time. [ABSTRACT FROM AUTHOR]

Abstract Cloud computing uses scheduling and load balancing for virtualized file sharing in cloud infrastructure. These two have to be performed in an optimized manner in cloud computing environment to achieve optimal file sharing. Recently, Scalable traffic management has been developed in cloud data centers for traffic load balancing and quality of service provisioning. However, latency reducing during multidimensional resource allocation still remains a challenge. Hence, there necessitates efficient resource scheduling for ensuring load optimization in cloud. The objective of this work is to introduce an integrated resource scheduling and load balancing algorithm for efficient cloud service provisioning. The method constructs a Fuzzy-based Multidimensional Resource Scheduling model to obtain resource scheduling efficiency in cloud infrastructure. Increasing utilization of Virtual Machines through effective and fair load balancing is then achieved by dynamically selecting a request from a class using Multidimensional Queuing Load Optimization algorithm. A load balancing algorithm is then implemented to avoid underutilization and overutilization of resources, improving latency time for each class of request. Simulations were conducted to evaluate the effectiveness using Cloudsim simulator in cloud data centers and results shows that the proposed method achieves better performance in terms of average success rate, resource scheduling efficiency and response time. Simulation analysis shows that the method improves the resource scheduling efficiency by 7% and also reduces the response time by 35.5 % when compared to the state-of-the-art works. Graphical abstract Highlights • FMRS algorithm for minimizing response time on handling complex query. • Multidimensional queuing network model achieves load balancing in cloud after scheduling the resource. • Average success rate is improved with the aid of MQLO algorithm. • Both FMRSQN helps for efficient data sharing in cloud environment with low computational complexity. [ABSTRACT FROM AUTHOR]

The pervasive adoption of cloud computing services and applications at a rapid rate makes the underlying data centers exacerbate the problems like carbon footprint and the operational cost, caused by the energy consumption. Various hardware-centric and software-centric approaches are proposed in the literature to reduce the energy consumption of the cloud data centers. Task scheduling algorithms are software-centric approaches to reduce the energy consumption in cloud computing systems. The majority of these algorithms focus on server consolidation leading to idle servers that reduce energy efficiency optimization. In this paper, we propose an Energy-Aware Task Scheduling algorithm on cloud Virtual Machines (EATSVM) that assigns a task to the VM where the increase in energy consumption is the least, considering both active and idle VMs. The algorithm also takes into consideration the increase in the energy consumption of the already running tasks on the VM due to increase in their execution time, while assigning a new task to that VM. We analyze the performance of our algorithm in a heterogeneous cloud environment with increasing number of tasks and compare the energy-savings of our algorithm with that of Energy Conscious Task Consolidation (ECTC) algorithm. Our experimental results demonstrate that EATSVM achieves energy-saving in a heterogeneous cloud-computing environment. [ABSTRACT FROM AUTHOR]

With the increasing consumption of energy in data centers, the demand for energy efficient multiprocessor job scheduling is growing dramatically. Besides flow time, energy conservation has become a significant issue and drawn enormous interest. In this paper, an online non-clairvoyant scheduling algorithm Significance-based Multiprocessor Round Robin (SbMRR) is proposed. SbMRR utilizes the unbounded speed model, where the range of the speed of any processor is from zero to infinity. To validate the effectiveness of the algorithm, mathematical and simulation-based analysis are conducted which demonstrates that SbMRR provides the minimum sum of significance-based flow time and energy consumed. SbMRR is O(α)-competitive, more precisely ((α + 1)⁄((1–1⁄αβ)))-competitive, where β ≥ 4 a constant. The competitive ratio of SbMRR is least to date. SbMRR provides the minimum sum of energy consumed and significance-based flow-time. [ABSTRACT FROM AUTHOR]

Resource sharing is an inherent characteristic of cloud data centers. Virtual Machines (VMs) and/or Containers that are co-located in the same physical server often compete for resources leading to interference. The noisy neighbor’s effect refers to an anomaly caused by a VM/container limiting resources accessed by another one. Our main contribution is an online, lightweight and application-agnostic solution for anomaly detection, that follows an unsupervised approach. It is based on comparing models for different lags: Dirichlet Process Gaussian Mixture Models to characterize the resource usage profile of the application, and distance measures to score the similarity among models. An alarm is raised when there is an abrupt change in short-term lag (i.e. high distance score for short-term models), while the long-term state remains constant. We test the algorithm for different cloud workloads: websites, periodic batch applications, Spark-based applications, and Memcached server. We are able to detect anomalies in the CPU and memory resource usage with up to 82–96% accuracy (recall) depending on the scenario. Compared to other baseline methods, our approach is able to detect anomalies successfully, while raising low number of false positives, even in the case of applications with unusual normal behavior (e.g. periodic). Experiments show that our proposed algorithm is a lightweight and effective solution to detect noisy neighbor effect without any historical info about the application, that could also be potentially applied to other kind of anomalies. [ABSTRACT FROM AUTHOR]

Virtualization technology represented through Virtual Machines (VMs) is recognized as a key infrastructure in cloud computing. This technology is developing rapidly and cloud data centers face challenges such as Virtual Machine Placement (VMP) for energy efficiency. VMP is defined as the efficient allocation of VMs to Host Machines (HMs) to achieve various objectives such as reducing energy consumption, load balancing and avoid Service Level Agreement Violations (SLAV). In this paper, VMP is addressed using a Deep Reinforcement Learning (DRL) based strategy to determine the best mapping between VMs and HMs. We present VMP-A3C, an effective strategy to solve VMP using Asynchronous Advantage Actor-Critic (A3C) algorithm as a new DRL approach. VMP-A3C aims at load balancing in HMs without SLAV, where energy consumption is reduced as much as possible. VMP-A3C learns to dynamically consolidate VMs using migration techniques to a minimum number of HMs. We believe that there is scope for improvements in shutting down little-workload HMs through VMs migration. The effectiveness of the proposed algorithm has been evaluated from various aspects such as the deployment rate, energy consumption, SLAV, the number of shutdown HMs and the number of migrated VMs. The main difference in terms of energy consumption and the number of required HMs between VMP-A3C and the best existing state-of-the-art method is 2.54% and 7.14%, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of wide-sense nonblocking operation of the elastic optical switch. The switch consists of waveband-converting switches in input and output stages, while in the center stage there is only one wavelength-selective space switch without wavelength-conversion capabilities. Two control algorithms are proposed, namely, 2-split and 3-split. For these algorithms we derived wide-sense nonblocking conditions and compared them with strict-sense nonblocking ones. The results show that the 3-split algorithm reduces the required number of FSUs to up to almost 70% of the FSUs required in the strict-sense nonblocking switching fabrics. Application of the proposed elastic optical switch in optical datacenter networks is also proposed. [ABSTRACT FROM AUTHOR]

Advances in wireless communication and ubiquitous mobile networks have resulted in location-based services (LBSs) contributing significantly to entertainment services for mobile users by utilizing their location data. However, for the request of a mobile user with a limited time frame, such as "where is the gas station nearest to me? The anonymous server should return the optimal result to me within a few minutes," the anonymous server immediately generates the cloaking region based on the user's current location information and the k-anonymity requirement privacy level to ensure location privacy protection when receiving the LBS request in most existing approaches. Unfortunately, mobility by the mobile user could cause the smaller cloaking region generated by the server. The existing algorithm would return the sub-optimal cloaking region. This problem prompted us to develop a novel probability-based prediction query (PBPQ) algorithm. On the server side, our method firstly predicts the probability of the mobile user being on each road, followed the final probability of the user entering the cloaking region. Our proposed algorithm can generate the optimal cloaking region generated within the limited time required by users. The experimental results show that our PBPQ algorithm can predict the optimal cloaking region effectively at the highly successful rate of more than 90%. Thus, the PBPQ algorithm can decrease the size of the cloaking region considerably while maintaining good QoS. [ABSTRACT FROM AUTHOR]

Cluster analysis is a broadly used unsupervised data analysis technique for finding groups of homogeneous units in a data set. Probabilistic distance clustering adjusted for cluster size (PDQ), discussed in this contribution, falls within the broad category of clustering methods initially developed to deal with continuous data; it has the advantage of fuzzy membership and robustness. However, a common issue in clustering deals with treating mixed-type data: continuous and categorical, which are among the most common types of data. This paper extends PDQ for mixed-type data using different dissimilarities for different kinds of variables. At first, the PDQ for mixed-type data is defined, then a simulation design shows its advantages compared to some state of the art techniques, and ultimately, it is used on a real data set. The conclusion includes some future developments. • We extend probabilistic distance clustering adjusted for cluster size (PDQ) for mixed-type data. • We define a dissimilarity measure for mixed-type data. • We define cluster centers for mixed-type data. • We include the R code and an example on the star classification data set. [ABSTRACT FROM AUTHOR]

In Recent days, cloud computing technology is used as a remote-based virtual computer resource utilization to offer consumers quick and accurate massive data services. In the cloud, infrastructure as services have a significant impact on computing efficiency by Infrastructure as a services (IaaS). Cloud computing uses on-demand resource provisioning. In the cloud, a variety of metaheuristic algorithms are helpful for process allocation. Large-scale datacenters utilise a lot of electricity, which has an influence on the environment and the economy. Using a Micro-Genetic Algorithm, a stable combined process workload allocation method with Cat Swarm Optimization (MG-CSO) is introduced by addressing pre-convergence problems and optimal resource management. For optimal computing efficiency, the resources are dynamically consolidated and clustered. Additionally, the MG-CSO Algorithm produces positive outcomes when compared to other well-known algorithms, such as the Genetic Algorithm (GA) and the Bat Algorithm (BA).This research's focus is to reduce cost, time, Service Level Agreement(SLA), energy usage, and afford good Quality of Service(QoS). Our final scientific results were achieved with 91%. [Display omitted] [ABSTRACT FROM AUTHOR]

This study aimed to develop an adaptive artificial neural network model (AAM) for the prediction of the rack inlet temperature and cooling system energy, and the optimal control algorithm for cooling system of a containment-type data center. A cyber-physical system (CPS) framework, that incorporated the AAM and control algorithm, was also proposed for the precise control of the data center cooling system. To develop the AAM model and control algorithm, mathematical modeling of a reference physical model was conducted, and training data were acquired from this model. The performance of the proposed AAM and control algorithm was then compared with that of a non-adaptive ANN model (NAAM) in terms of prediction accuracy and control stability. The analysis results indicated that the optimal control algorithm with the AAM exhibited superior prediction accuracy and control stability than the algorithm with the NAAM. In particular, for the AAM-based algorithm under conditions representing a novel data center environment, the root mean square error (RMSE) and coefficient of variation of the RMSE (CV(RMSE)) for the predicted and actual values were 0.22 °C and 1.02%, respectively, for the inlet rack temperature and 0.19 kW and 0.76% for the cooling system energy. The control was also stable, with an MAE of 0.08 °C and a maximum error of 1.17 °C. Based on this analysis, a CPS-based control strategy incorporating an ANN-based optimal control algorithm is expected to be an effective energy efficiency solution for existing data center without changing IT equipment or cooling systems. • An adaptive ANN model and optimal control algorithm for data centers were proposed. • A CPS framework was also proposed for advanced data center control. • Comparison simulations were conducted for the performance evaluation. • The proposed work outperformed non-adaptive ANN model due to real-time training. [ABSTRACT FROM AUTHOR]

• We prove that a highly scalable data center network (HSDC) is vertex-symmetric. • We investigate the independent spanning trees (IST) problem on HSDC. • We propose an algorithm to construct n ISTs on the n -dimensional HSDC, denoted as H S n. • The algorithm can be parallelized to run in O (n) time using N = 2 n processors. • As a by-product, we obtain the connectivity of H S n to be n. An emerging datacenter network (DCN) with high scalability called HSDC is a server-centric DCN that can help cloud computing in supporting many inherent cloud services. For example, a server-centric DCN can initiate routing for data transmission. This paper investigates the construction of independent spanning trees (ISTs for short), a set of the rooted spanning trees associated with the disjoint-path property, in HSDC. Regarding multiple spanning trees as routing protocol, ISTs have applications in data transmission, e.g., fault-tolerant broadcasting and secure message distribution. We first establish the vertex-symmetry of HSDC. Then, by the structure that n -dimensional HSDC is a compound graph of an n -dimensional hypercube Q n and n -clique K n , we amend the algorithm constructing ISTs for Q n to obtain the algorithm required by HSDC. Unlike most algorithms of recursively constructing tree structures, our algorithm can find every node's parent in each spanning tree directly via an easy computation relied upon only the node address and tree index. Consequently, we can implement the algorithm for constructing n ISTs in O (n N) time, where N = n 2 n is the number of vertices of n -dimensional HSDC; or parallelize the algorithm in O (n) time using N processors. Remarkably, the diameter of the constructed ISTs is about twice the diameter of Q n. [ABSTRACT FROM AUTHOR]

• We develop a joint energy efficiency optimization scheme to coordinately optimize the energy of servers and cooling systems. • l An online task scheduling algorithm is developed using marginal cost evaluation. • l We design a resource management strategy integrated workload prediction technique. • l We develop a task migration strategy with marginal cost evaluation and prediction results. • l We validate the designed optimization scheme though experiments based on real traces. With the widespread development of cloud computing, the dramatic increase in the number and size of data centers (DCs) has resulted in substantial energy consumption and serious environmental problems. This creates a challenge for the further development of DCs. It is imperative to improve the overall energy efficiency of DCs. According to the statistics, servers and cooling systems are the main energy-consuming components of DCs. Recently, several energy-efficient strategies have been developed to address these problems. However, most of these works only consider the energy optimization of servers or cooling systems separately. Therefore, a Jo int E nergy E fficiency Optimization S cheme (JEES) is proposed in this paper, where the energy consumed by servers and the cooling system is jointly considered, and coordinately optimized. JEES includes a dynamic online task scheduling algorithm based on marginal cost evaluation, a resource management strategy that integrates the workload prediction technique to manage resources, and a task migration method using marginal cost evaluation. By using the proposed techniques, the total energy consumption of DCs can be reduced. Extensive experiments have been conducted based on real-world workload traces, and the results demonstrate that compared with other techniques, the proposed scheme effectively improves the overall resource utilization and reduces the total energy consumption of DCs. [ABSTRACT FROM AUTHOR]

• Hybrid ant genetic algorithm for efficient task scheduling in cloud datacenter is proposed. • Reduces scheduling solution space. • Minimizes workflow execution time by load balancing. • Less response time than genetic algorithm. Cloud computing is a computing paradigm which meets the computational and storage demands of end users. Cloud-based data centers need to continually improve their performance due to exponential increase in service demands. Efficient task scheduling is essential part of cloud computing to achieve maximum throughput, minimum response time, reduced energy consumption and optimal utilization of resources. Bio-inspired algorithms can solve task scheduling difficulties effectively, but they need a lot of computational power and time due to high workload and complexity of the cloud environment. In this research work, Hybrid ant genetic algorithm for task scheduling is proposed. The proposed algorithm adopts features of genetic algorithm and ant colony algorithm and divides tasks and virtual machines into smaller groups. After allocation of tasks, pheromone is added to virtual machines. The proposed algorithm effectively reduces solution space by dividing tasks into groups and by detecting loaded virtual machines. Due to the minimum solution space of proposed algorithm, convergence and response time is significantly decreased. It finds a feasible scheduling solution to minimize the running time of workflows and tasks. The proposed algorithm achieved 64% decrease in execution time and 11% decrease in overall data center costs. [Display omitted] [ABSTRACT FROM AUTHOR]

Reconfigurable optical networks have emerged as a promising technology to efficiently serve the fast-growing traffic produced by the digital society. This paper provides a survey of the field. We first review enabling optical hardware technologies in general and then consider technologies that are specific to data center networks and wide-area networks in more detail. We further provide an overview of the cost models used in the literature as well as the algorithmic problems introduced by these technologies, their first solutions, and discuss systems and implementation aspects. We conclude with a discussion of open challenges. [ABSTRACT FROM AUTHOR]

Association rule mining has been adopted to medical fields to discover prescribing patterns or relationships among diseases and/or medications; however, it has generated unreasonable associations among these entities. This study aims to identify the real-world profile of disease-medication (DM) associations using the modified mining algorithm and assess its performance in reducing DM pseudo-associations. We retrieved data from outpatient records between January 2011 and December 2015 in claims databases maintained by the Health and Welfare Data Science Center, Ministry of Health and Welfare, Taiwan. The association rule mining's lift (Q-value) was adopted to quantify DM associations, referred to as Q 1 for the original algorithm and as Q 2 for the modified algorithm. One thousand DM pairs with positive Q 1 -values (Q 1 +) and negative or no Q 2 -values (Q 2 − or Q 2 ∅) were selected as the validation dataset, in which two pharmacists assessed the DM associations. A total of 3,120,449 unique DM pairs were identified, of which there were 333,347 Q 1 + Q 2 − pairs and 429,931 Q 1 + Q 2 ∅ pairs. Q 1 + Q 2 − rates were relatively high in ATC classes C (29.91%) and R (30.24%). Classes L (69.91%) and V (52.52%) demonstrated remarkably high Q 1 + Q 2 ∅ rates. For the 1000 pairs in the validation, 93.7% of the Q 1 + Q 2 − or Q 1 + Q 2 ∅ DM pairs were assessed as pseudo-associations. However, classes M (5.3%), H (4.5%), and B (4.1%) showed the highest rates of plausible associations falsely given Q 2 − or Q 2 ∅ by the modified algorithm. The modified algorithm demonstrated high accuracy to identify pseudo-associations regarded as positive associations by the original algorithm and would potentially be applied to improve secondary databases to facilitate research on real-world prescribing patterns and further enhance drug safety. • Association rule mining's lift was used to measure disease-medication associations. • Over one billion prescriptions retrieved from claims databases were analyzed. • Our modified algorithm showed high accuracy to reduce pseudo-associations. [ABSTRACT FROM AUTHOR]

In the modern era of digitalization, many applications exist that consume a huge computational processing power. These applications due to massive costs involved in procurement and maintenance, looks at resource abundant cloud environments as effective solution. However, cloud environments often suffer from challenges that threaten the performance due to inefficient resource utilization. Load balancing across multiple virtual machines in cloud deployment is one of the major issue that leads to under-utilization of cloud resources. In this research work, the problem of task to resource mapping is addressed using crow search based load balancing algorithm for greater optimization as solution. The solution uses parameters such as average power consumption of data center, average cost of the data center and data center loading for allocating the best resource to the submitted tasks. The proposed solution is validated against standard ant colony optimization based load balancing algorithm. It has been found that the proposed algorithm has outperformed standard algorithm and emerges out as a most optimal load balancing algorithm. [Display omitted] • CSLBA is suggested as a solution to load balancing problem in a cloud environment. • Performance metrics for dispersing the load among resources have been identified. • The proposed algorithm is validated in terms of load mapping ability. • CSLBA proved to be effective as it evenly distributed load among all data centers. [ABSTRACT FROM AUTHOR]

As server densities increase to support the rising demand for high density computing, traditional room cooling infrastructure is struggling to keep up. Rack-mounted cooling units are increasingly being deployed in data centers. These cooling units are located closer to the heat sources (the servers) which allow them to cool more efficiently than traditional cooling infrastructure. In this work, a Multi-Input Single-Output (MISO) Adaptive Predictive Controller (APC) for rack-mounted cooling units is investigated and implemented using a low-cost general-purpose microcontroller. The proposed APC is implemented using Weighted Recursive Least Squares (WRLS) and a sub-optimal but fast algorithm based on the General Predictive Controller (GPC) approach. These are combined with a variable forgetting factor and variable prediction horizon algorithms. In addition, methods are proposed to handle stability issues arising due to practical hardware limitations. The controller is implemented on a real single rack system and challenges for practical implementation are addressed and illustrated. The proposed APC is also compared (via simulation) to a standard APC with equivalent complexity and a split-range PI controller. The results show that the proposed controller outperforms both the standard APC and the split-range PI controllers with respect to Mean Squared Error (MSE). • A fast and sub-optimal Adaptive Predictive Controller is designed and implemented. • Variable forgetting factor and variable prediction horizon allow higher flexibility. • Implementation can be done in a low-cost and general-purpose microcontroller. • Capabilities to appropriately control a Multi-Input Single-Output system was shown. • Competitive performance is obtained when compared to a standard APC and PI with split range control. [ABSTRACT FROM AUTHOR]

• Integrated planning of Internet data centers and battery energy storage systems. • Coupled cyber-physical modelling of Internet data centers and a smart grid. • Multi-objective modelling of the planning task. Modern power grids have been becoming complex cyber-physical systems integrated with distributed energy sources and information and communication facilities. With prevalence of cloud computing, geo-distributed, networked data centers have become an integrated part of modern grids. The coupling impact between data centers and smart grids thus becomes an important consideration. This paper proposes an integrated planning scheme that optimally determines the locations and capacities of interconnected Internet data centers and battery energy storage systems in a smart grid. The model is formulated as a multi-objective optimization problem, in which both computational performance metrics of Internet data centers and operational criteria of the grid are coordinately considered as three inter-related but conflict objectives; the coupling impact between the cyber and energy resources are modelled. An advanced evolutionary algorithm – Multi-Objective Natural Aggregation Algorithm is used to solve the model. Extensive case studies are conducted to demonstrate the reasonability and effectiveness of the proposed integrated planning method. [ABSTRACT FROM AUTHOR]

• Proposing the notion of joint VM and container consolidation in cloud data centers • Proposing a new architecture and flowchart for joint VM and container consolidation • Proposing a new algorithm (JVCMMD) for joint VM and container consolidation • Simultaneous optimization of energy consumption, migration counts, and SLA violation Cloud computing is being rapidly adopted for managing IT services as a notable solution due to diverse beneficiaries such as automatically optimized resource management as well as modern service delivery models. The container as a service has been recently introduced by cloud providers as a new service apart from traditional cloud services. Containers enable applications to run and deploy on isolated virtual space, and the operating system kernel is shared among them. Also, containerization has some attributes such as scalability, highly portable properties, and lightweight, for those reasons, it is applied for running isolated applications. Reducing energy consumption, as well as their CO 2 emissions, are great deals for cloud providers. In this direction, consolidation is recommended as a vital energy-aware approach in cloud data centers. Previously, independent virtual machine migration or container migration was proposed in the literature for green computing in cloud data centers. However, this paper proposes a new cloud resource management procedure based on a multi-criteria decision-making method that takes advantage of a joint virtual machine and container migration approach concurrently. The results of simulations using ContainerCloudsim simulator validates the applicability of the proposed approach which shows notable reductions in energy consumption, SLA violation, and number of migrations in comparison with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Based on software defined optical network and machine learning, the flexible resource management mechanism (ML-FRM) is proposed, which meets the resource requirements of different services in the optical data center networks. The machine learning is integrated to the SDON controller, which accomplishes the resource allocation algorithms according to the classification and clustering results. ML-FRM firstly utilizes unsupervised learning K-means algorithm to cluster traffic flows, and uses supervised learning support vector machine (SVM) algorithm to realize hierarchical classification of channel qualities. Fragmentation-Function-Fit algorithm is proposed to reduce the blocking probability, the results show that it has the lower blocking probability than First-Fit and Exact-First-Fit algorithms. ML-FRM allocates the required resources through different algorithms based on different traffic flow clustering results, and uses different modulation methods for different channel qualities. The analysis results show that ML-FRM has lower blocking probability, acceptable complexity level, and higher spectrum resource utilization efficiency than other algorithms under different offered load level. • A mechanism called ML-FRM is proposed to achieve flexible resource management. • It utilizes K-means to cluster flows, and uses SVM to classify channel qualities. • It allocates resources through different algorithms based on flows clustering results. • It uses BPSK, QPSK and 16QAM modulations in different channel qualities. • It has lower blocking probability, higher spectrum resource utilization efficiency. [ABSTRACT FROM AUTHOR]

Task scheduling in heterogeneous environments such as cloud data centers is considered to be an NP-complete problem. Efficient task scheduling will lead to balance the load on the virtual machines (VMs) thereby achieving effective resource utilization. Hence there is a need for a new scheduling framework to perform load balancing amid considering multiple quality of service (QoS) metrics such as makespan, response time, execution time, and task priority. Multi-core Web server is difficult to achieve dynamic balance in the process of remote dynamic request scheduling, so it is necessary to improve it based on the traditional scheduling algorithm to enhance the actual effect of the algorithm. This article do research on the multi-core Web server, Focusing on multi-core Web server queuing model. On this basis, the author draws the drawbacks of the multi-core Web server in the remote dynamic request scheduling algorithm, and improves the traditional algorithm with the demand analysis. Not only it overcomes the drawbacks of traditional algorithms, but also promotes the system threads carrying the same amount of tasks, and promotes the server being always in a dynamic balance. On the basis of this, it achieves an effective solution to customer requests. [ABSTRACT FROM AUTHOR]

• An improved genetic algorithm is proposed for permutation-based problems. • A hybrid virtual machine placement algorithm is proposed for cloud data centers. • The proposed hybrid algorithm significantly reduces the power consumption. • The proposed hybrid algorithm achieves high resources utilization rates. The high energy consumption of cloud data centers presents a significant challenge from both economic and environmental perspectives. Server consolidation using virtualization technology is widely used to reduce the energy consumption rates of data centers. Efficient Virtual Machine Placement (VMP) plays an important role in server consolidation technology. VMP is an NP-hard problem for which optimal solutions are not possible, even for small-scale data centers. In this paper, a hybrid VMP algorithm is proposed based on another proposed improved permutation-based genetic algorithm and multidimensional resource-aware best fit allocation strategy. The proposed VMP algorithm aims to improve the energy consumption rate of cloud data centers through minimizing the number of active servers that host Virtual Machines (VMs). Additionally, the proposed VMP algorithm attempts to achieve balanced usage of the multidimensional resources (CPU, RAM, and Bandwidth) of active servers, which in turn, reduces resource wastage. The performance of both proposed algorithms are validated through intensive experiments. The obtained results show that the proposed improved permutation-based genetic algorithm outperforms several other permutation-based algorithms on two classical problems (the Traveling Salesman Problem and the Flow Shop Scheduling Problem) using various standard datasets. Additionally, this study shows that the proposed hybrid VMP algorithm has promising energy saving and resource wastage performance compared to other heuristics and metaheuristics. Moreover, this study reveals that the proposed VMP algorithm achieves a balanced usage of the multidimensional resources of active servers while others cannot. [ABSTRACT FROM AUTHOR]