Computing Data Centers Research Articles

Clonal selection algorithm for energy minimization in software defined networks

With the advancements of Information and Communication Technologies (ICT), large scale distributed computing and massive data center infrastructures are becoming more common these days. Such trends have drastically put a lot of load on the volumes of data transferred over networks, thus necessitating close to capacity link utilizations flexible forwarding decision-making. Software-defined networking (SDN), with its inherent segregation of control and data planes, provides flexible decision making that can leverage the global network information available to the SDN controller for dynamic and accurate solutions. However, contemporary researchers have focused on the flexibility and security aspects of SDN, widely ignoring energy consumption strategies in next-generation IP networks, which otherwise is a crucial driver in any research field. The scanty existing energy minimization strategies are mostly based on aggregate traffic, which leads to imbalanced utilization of links and affects the quality of service (QoS) adversely. In this paper, we leverage SDN’s key benefits for reducing energy network consumption while realizing dynamic load balance with a few QoS constraints. To this end, a multiobjective optimization problem (MOOP) is formulated that attempts to minimize power consumption and link utilization. With different capacities of switches and links, finding optimal configurations and deciding best paths, even for relatively small networks, become computationally challenging and is, in fact, an NP-hard problem. In this paper, we propose to employ the Clonal Selection Algorithm (CSA), a discrete, metaheuristic solution to find out optimal solutions for this MOOP, namely a Clonal Selection based Energy Minimization (CSEM). Simulations have been carried out for testing the efficacy of the proposed CSEM using real-life network topologies and link-traffic data. The results obtained by the proposed CSEM prove to be efficacious, and the same have also been validated with three different benchmark functions to test the suitability of SDN for CSA.

RETRACTED ARTICLE: A novel scheduling approach to improve the energy efficiency in cloud computing data centers

Cloud computing is the combination of grid computing, distributed computing and utility computing. Cloud computing provides various types of services (servers and storage facility) in the on demand basis. The main goal of the cloud computing is to preserve and organize the very huge data center or data forms. The data forms are composed of thousands of servers that absorb the giant (ample) amount of electricity in the word of energy. The decreasing the energy consumption in datacenter is the major challenge in cloud computing now a day. This research article is going to address the problem of high energy consumption at datacenter. Concentrate on virtual machine scheduling in cloud datacenter with Dynamic Voltage Frequency Scaling (DVFS) approach. We have combined shortest job first and Round Robin algorithms with Vibrant Quantum. This combination of algorithm is considered as shortest round vibrant queue (SRVQ) algorithm. SRVQ reduces the waiting time of the scheduling process and minimize the starvation. The DVFS and SRVQ worked together and produced fruitful results in the final experiments. This work reduced the server’s energy consumption in the cloud data center. In the final results, our proposed framework exhibits 45% of energy efficiency compare to other previously proposed algorithms. 33% of QoS performance were enhanced by our framework.

Fog Based Three Layer Architecture for Privacy Preserving

Fog computing is one of the most latest technology used by the cloud providers to safe guard the user data and service provider’s data servers. Fog computing acts as mediator between hardware and remote servers or cloud servers. Cloud computing still has the lot of vulnerabilities. Privacy to the users data is main issue in the present cloud computing. Whenever users uploads data into cloud server then user will lose their right on their own data because users don’t know about, what cloud providers do with users data, they can sell the users data for their own profit without knowing to users. Fog computing provides lot of services like operation of computer, storage and networking services between users and cloud computing data centers. With the networking services users can lose their data privacy or leakage without knowing to user. Because public clouds are not secure enough and users doesn’t know where data is storing in cloud servers. Breaking the data into small parts can lead to loss of data and which it can create way to attackers to steal data. Even data might be changed instated of one data with another. Intelligence can be applied in the fog computing technology to use of computing resources and security reasons. Applying multiple layers of security features by using kubernets can improve better service to user and user’s data can be safe from the attackers. Whenever user lost connection with the server kubernets establishes reconnection between user and server. RSA256 encryption is applied to users data with this we can provide better security between cloud server and users.

An edge streaming data processing framework for autonomous driving

In recent years, with the rapid development of sensing technology and the Internet of Things (IoT), sensors play increasingly important roles in traffic control, medical monitoring, industrial production and etc. They generated high volume of data in a streaming way that often need to be processed in real time. Therefore, streaming data computing technology plays an indispensable role in the real-time processing of sensor data in high throughput but low latency. However, there are two problems in deploying streaming data process ability in cloud computing data centre. Firstly, massive sensor nodes simultaneously upload data to the remote cloud computing data centre, which requires a large number of bandwidth resources supports. The existing network infrastructure cannot provide enough bandwidth at a reasonable price. Secondly, due to the geographical distribution characteristics of the cloud computing data centre, there will inevitably be large transmission delay during the process of data transmission. Such end-to-end delay is intolerable to mobile applications especially for those latency sensitive tasks. In view of the above problems, this paper proposes an autonomous driving oriented edge streaming data processing framework, which migrates the computing and storage capability from the remote cloud data centre to the edge data centre. It focuses on the change of vehicle flow in a specific geographical area, and uses the computing power sunk to edge node to process the massive streaming data generated by autonomous vehicles nearby. The proposed framework is implemented on top of Spark Streaming, which builds up a gray model based traffic flow monitor, a traffic prediction orientated prediction layer and a fuzzy control based Batch Interval dynamic adjustment layer for Spark Streaming. It could forecast the variation of sensors data arrive rate, make streaming Batch Interval adjustment in advance and implement real-time streaming process by edge. Therefore, it can realise the monitor and prediction of the data flow changes of the autonomous driving vehicle sensor data in geographical coverage of edge computing node area, meanwhile minimise the end-to-end latency but satisfy the application throughput requirements. The experiments show that it can predict short-term traffic with no more than 4% relative error in a whole day. By making batch consuming rate close to data generating rate, it can maintain system stability well even when arrival data rate changes rapidly. The Batch Interval can be converged to a suitable value in two minutes when data arrival rate is doubled. Compared with vanilla version Spark Streaming, where there has serious task accumulation and introduces large delay, it can reduce 35% latency by squeezing Batch Interval when data arrival rate is low; it also can significantly improve system throughput by only at most 25% Batch Interval increase when data arrival rate is high.

Application of network virtual cloud computing data center based on fuzzy algorithm

Application of network virtual cloud computing data center based on fuzzy algorithm

Load Predicting Model of Mobile Cloud Computing Based on Glowworm Swarm Optimization LSTM Network

Focusing on the issue of host load estimating in mobile cloud computing, the Long Short Term Memory networks (LSTM)is introduced, which is appropriate for the intricate and long-term arrangement information of the cloud condition and a heap determining calculation dependent on Glowworm Swarm Optimization LSTM neural system is proposed. Specifically, we build a mobile cloud load forecasting model using LSTM neural network, and the Glowworm Swarm Optimization Algorithm (GSO) is used to search for the optimal LSTM parameters based on the research and analysis of host load data in the mobile cloud computing data center. Finally, the simulation experiments are implemented and similar prediction algorithms are compared. The experimental results show that the prediction algorithms proposed in this paper are in prediction accuracy higher than equivalent prediction algorithms.

Potential for waste heat utilization of hot‐water‐cooled data centers: A case study

AbstractThe electric energy demand of data centers in Germany has grown rapidly from 10.5 TWh/a in 2010 to 13.2 TWh/a in 2017, an average of 25% of which are used to fulfill the data centers' cooling demand. In order to increase its energy efficiency, TU Darmstadt applies a new cooling concept in the next generation of its high‐performance computing data center “Lichtenberg II.” Instead of the current air‐cooled servers with water‐cooled rear doors at 17‐24°C, the new data center will be equipped with direct hot‐water cooling for the high‐performance computer, supplying heat at a temperature of 45°C. The high‐temperature waste heat is used for heating purposes on the university's campus Lichtwiese. For waste heat utilization, two concepts are presented, either integrating the heat in the return line of the district heating network or using it locally in buildings located near the data center. Reductions in CO2 emission and annuity are generated both by decreased compression cooling demand for the data center and by decreased heat generation due to waste heat utilization. Depending on the scenario, a total of 20%‐50% of the waste heat emitted by the high‐performance computer can be used for heating purposes, while the remaining heat is dissipated efficiently via free cooling without additional energy demand for mechanical chillers. CO2 emission can be decreased by up to 720 , representing a reduction of about 4% of the total emission at campus Lichtwiese. TU Darmstadt is currently implementing the waste heat integration into its district heating network and will benefit from this concept starting in 2020.

The High Performance of a Task Scheduling Algorithm Using Reference Queues for Cloud- Computing Data Centers

As the network technology continues to grow at a high rate of speed, the traditional network topology is improved with novel distributed topologies such as the Cloud computing network. A cloud computing environment consists of a huge number of processors and memories, high-speed networks, and various application services to provide a lot of services over the Internet for users. However, many services need to search for suitable service nodes, and the workload of each node can be unbalanced. Based on the reason above, the Reference Queue based Cloud Service Architecture (RQCSA) and Fitness Service Queue Selection Mechanism (FSQSM) are proposed to handle more tasks, lower the makespan and queue waiting time, and improve efficiency. Moreover, the tasks can be distributed more evenly to avoid overloading cluster managers and lower the efficiency of the system.

Load Balancing Scheduling of Power Network in Cloud Computing Environment

In the traditional cloud computing environment, the power network load balancing scheduling method cannot adaptively track the time-varying coupling characteristics of load data, and the power data center is not efficient in processing massive data, so it is increasingly unable to meet the multi-service quality requirements of power users. The purpose of this paper is to improve the load balancing of power network, improve the efficiency of power network balance scheduling and power resource balance allocation in cloud computing environment. At the research method level, this paper analyzes the application status of data center and cloud computing task scheduling, and starts from the task scheduling demand analysis of cloud computing data center to determine load balancing and QoS as the technical goals of task scheduling. According to the parallel model framework of MapReduce, the task execution process is studied, and the design scheme of power cloud data center is given in view of the problems of low resource utilization, poor scalability and high energy consumption cost faced by the existing power data center. The experimental results show that the network load balancing scheduling method proposed in this paper can meet the multi-qos demand of power users to the maximum extent, effectively improve the efficiency of power data center operation, and achieve better load balancing effect than the traditional scheme.

Nash equilibrium based replacement of virtual machines for efficient utilization of cloud data centers

Workload uncertainty has been increased with the integration of the Internet of Things to the computing grid i.e. edge computing and cloud data centers. Therefore, efficient resource utilization in cloud data centers become more challenging. Dynamic consolidation of virtual machines on optimal number of processing machines can increase the efficiency of resource utilization in cloud data centers. This process requires the migration of virtual machines from the under-utilized and over-utilized processing machines to other suitable machines. In this work, the problem of efficient replacement of virtual machines is solved using a game theory based well known technique, Nash Equilibrium (NE). We designed a nash equilibrium based dual on two players, over-load manager and under-load manager, to deduce the dominant strategy profiles for various scenarios during consolidation cycles. Dominant strategy profile is the set of strategies where every player has no incentive in deviation, thus leading to equilibrium position. A virtual machines redeployment algorithm, Nash Equilibrium based Virtual Machines Replacement (NE-VMR), has been proposed on the basis of the dominant strategy profiles for efficient consolidation. Experiment results show that NE-VMR is a more efficient server consolidation technique, saved 30% energy and improved 35% quality of service as compared to baselines.

A multi-objective trade-off framework for cloud resource scheduling based on the Deep Q-network algorithm

Cloud computing, now a mature computing service, provides an efficient and economical solution for processing big data. As such, it attracts a lot of attention in academia and plays an important role in industrial applications. With the recent increase in the scale of cloud computing data centers, and improvements in user service quality requirements, the structure of the whole cloud system has become more complex, which has also made the resource scheduling management of these systems more challenging. Thus, the goal of this research was to resolve the conflict between cloud service providers (CSPs) who aim to minimize energy costs and those who seek to optimize service quality. Based on the excellent environmental awareness and online adaptive decision-making ability of deep reinforcement learning (DRL), we proposed an online resource scheduling framework based on the Deep Q-network (DQN) algorithm. The framework could make a trade-off of the two optimization objectives of energy consumption and task makespan by adjusting the proportion of the reward of different optimization objectives. Experimental results showed that this framework could effectively be used to make a trade-off of the two optimization objectives of energy consumption and task makespan, and exhibited obvious optimization effects compared with the baseline algorithm. Therefore, our proposed framework can dynamically adjust the optimization objective of the system according to the different requirements of the cloud system.

Silicon photonics for telecom and data-com applications

In recent decades, silicon photonics has attracted much attention in telecom and data-com areas. Constituted of high refractive-index contrast waveguides on silicon-on-insulator (SOI), a variety of integrated photonic passive and active devices have been implemented supported by excellent optical properties of silicon in the mid-infrared spectrum. The main advantage of the silicon photonics is the ability to use complementary metal oxide semiconductor (CMOS) process-compatible fabrication technologies, resulting in high-volume production at low cost. On the other hand, explosively growing traffic in the telecom, data center and high-performance computer demands the data flow to have high speed, wide bandwidth, low cost, and high energy-efficiency, as well as the photonics and electronics to be integrated for ultra-fast data transfer in networks. In practical applications, silicon photonics started with optical interconnect transceivers in the data-com first, and has been now extended to innovative applications such as multi-port optical switches in the telecom network node and integrated optical phased arrays (OPAs) in light detection and ranging (LiDAR). This paper overviews the progresses of silicon photonics from four points reflecting the recent advances mentioned above. CMOS-based silicon photonic platform technologies, applications to optical transceiver in the data-com network, applications to multi-port optical switches in the telecom network and applications to OPA in LiDAR system.

Comparative analysis of VM consolidation algorithms for cloud computing

Virtual machine consolidation is a major solution for addressing the issue of increasing energy consumption by cloud computing data centers. A lot of work is done on developing algorithms for detecting underloaded, overloaded hosts, selection of virtual machines and their placement to perform the consolidation. These algorithms are usually tested on publicly available Planet lab workload. There is a need to know how benchmarks algorithms used in consolidation of virtual machines respond to other workloads. This paper is an attempt to evaluate these algorithms on Google workload trace. An importer is made to use this dataset by extending the CloudSim toolkit. The comparison of results using Planet lab and Google workload traces is made which shows the difference of 46.41%, 14.84%, 12.86% and 44.83% respectively in terms of number of virtual machine migrations, service level agreement violation time per active host, number of hosts shutdown, and energy consumption. The objective comparison of results illustrated that there is a need to test the proposed algorithms on multiple datasets in order to be assessed as optimal.

High Performance Computing (HPC) Data Center for Information as a Service (IaaS) Security Checklist: Cloud Data Governance

High Performance Computing (HPC) Data Center for Information as a Service (IaaS) Security Checklist: Cloud Data Governance

Energy Efficiency Analysis Between Green Computing and Cloud Computing

Usually cloud computing is based under effective data computing but green cloud computing focus on energy efficiency of device and computing mainly based on computing architecture. Here cloud computing is also known as green computing due its better effective energy. The purpose of green computing is to decrease the usage of harmful resources, maximizing energy efficiency throughout the living span of the good and also to promote the recyclability or bio degradability of useless goods plus misuse materials of the factory. In order to simulate the hardware by utilizing software, green computing plays a major role for various technologies like virtualization, green data center, cloud and grid computing and power optimization. Here computer resources are effectively utilized by replacing the data center standalone server system by artificial server so as to run the software by less number for large computers called as virtualized. Finally this paper is fulfilled by the advancement of recognized energy efficient computing

Research on Software Architecture Optimization of Cloud Computing Data Center Based on Hadoop

This paper introduces the architecture optimization design of high performance cloud computing data center (HCDC), and proposes an efficient parallel algorithm based on Hadoop system and MapReduce framework. The performance of the algorithm in big data analysis and processing, and the impact of the algorithm on energy efficiency are studied. Finally, the challenges of future work in this area of research are discussed.

Role of Different Integer Virtual Machine in Cloud Data Center

In modern data centres of cloud computing contains virtualization system. In order to improve network stability, energy efficiency, and makespan proper virtualization need. The virtual machine is one of the examples of virtualizations. Cloud computing data centres consist of millions of virtual machine to manage load balancing. In this study check the different number of virtual machine role in data centres, for that purpose, we established a network with the help of cloudsim and compare different data centres at each zones taking a different number of the virtual machine with different paramater and network banwith.After the simulation the result shows that increasning in the number of VM can affect the netwok accuracy in term of energy ,processing time ,coast and network stabality .Â

Research on Internet of Things security architecture based on fog computing

The traditional data processing of the Internet of Things is concentrated in cloud computing, and its huge number of devices and massive real-time data transmission are extremely stressful on network bandwidth and cloud computing data centers. Fog computing is the infrastructure that can use processing power anywhere in the cloud. Virtual computing extends the power of cloud computing to the edge of the network, enabling any computing device to host and process software services, analyzing and storing data closer to where data are generated. The architecture of the fog computing brings enormous processing power. Since its processing power is often located near the required equipment, the distance of data transmission is reduced and the delay is reduced. This article explores how to use the fog computing layer between the cloud data center and the end node layer to store and process large amounts of local data in a timely manner, speeding decision making and enabling Internet of Things manufacturers and software developers to limit their ability to send data. They reduced cloud computing costs and built a reasonable security architecture.

Resonant converter for Data Center and Super Computer applications

In this article proposes the load sharing performance of converters in supercomputers. A new control method is proposed for dc to dc switch controlled capacitor (SCC) - LLC converter. The switching frequency is utilized for controlling the regulation of output voltage. It can give the good frequency variation range and peak gain range compared to conventional converters. To attain load sharing the half wave switch controlled capacitor (SCC) is used to control the resonant frequency of each LLC stage. The simulation results are compared with experimental results. A 600w prototype model is developed to prove the feasibility.

Randomized routing of virtual machines in IaaS data centers.

Cloud computing technology has been a game changer in recent years. Cloud computing providers promise cost-effective and on-demand resource computing for their users. Cloud computing providers are running the workloads of users as virtual machines (VMs) in a large-scale data center consisting a few thousands physical servers. Cloud data centers face highly dynamic workloads varying over time and many short tasks that demand quick resource management decisions. These data centers are large scale and the behavior of workload is unpredictable. The incoming VM must be assigned onto the proper physical machine (PM) in order to keep a balance between power consumption and quality of service. The scale and agility of cloud computing data centers are unprecedented so the previous approaches are fruitless. We suggest an analytical model for cloud computing data centers when the number of PMs in the data center is large. In particular, we focus on the assignment of VM onto PMs regardless of their current load. For exponential VM arrival with general distribution sojourn time, the mean power consumption is calculated. Then, we show the minimum power consumption under quality of service constraint will be achieved with randomize assignment of incoming VMs onto PMs. Extensive simulation supports the validity of our analytical model.