Data Center Locations Research Articles

On the Dynamic Maintenance of Data Replicas based on Access Patterns in A Multi-Cloud Environment

Cloud computing provides services and infrastructures to enable end-users to access, modify and share massive geographically distributed data. There are increasing interests in developing data-intensive (big data) applications in this computing environment that need to access huge datasets. Accessing such data in an efficient way is deterred with factors such as dynamic changes in resource availability, provision of diverse service quality by different cloud providers. Data replication has already been proven to be an effective technique to overcome these challenges. Replication offers reduced response time in data access, higher data availability and improved system load balancing. Once the replicas are created in a multi-cloud environment, it is of utmost importance to continuously support maintenance of these replicas dynamically. This is to ensure that replicas are located in optimal data center locations to minimize replication cost and to meet specific user and system requirements. First, this paper proposes a novel approach to distributed placement of static replicas in appropriate data center locations. Secondly, motivated by the fact that a multi-cloud environment is highly dynamic, the paper presents a dynamic replica maintenance technique that re-allocates replicas to new data center locations upon significant performance degradation. The evaluation results demonstrate the effectiveness of the presented dynamic maintenance technique with static placement decisions in a multi-cloud environment

Steady and transient behavior of data centers with variations in thermal load and environmental conditions

Data centers are extensively used for the storage and retrieval of large amounts of data. Efficient removal of the energy dissipated by the electronic circuitry is a major concern since the temperature must not rise beyond acceptable levels that affect the performance and since the energy required for cooling is generally quite substantial, with typical data centers requiring hundreds for megawatts for heat removal. Though steady operation has been of interest in most studies on data center cooling, transient behavior is also of considerable interest since the load is generally not a constant but varies with time. Also, distribution of load between various data centers may be used effectively to reduce the overall power consumption. The environmental conditions may be used advantageously in the selection of a particular data center in order to reduce the cooling load. All these considerations make it imperative to study both the steady and time-dependent behavior of data centers and to obtain results that may be used to optimize the cooling systems and reduce the overall energy consumption. This paper presents an analytical and numerical study that focuses on load distribution, as well as data center location that determines the relevant environment, in order to obtain characteristic results on the resulting temperature distributions for a range of operating conditions. Such results could form the basis for optimizing the cooling strategy and the system.

Virtual-MIMO feedback and transmission for wireless ad hoc networks

Ina wireless ad hoc network, single-antenna nodes can cooperate in their transmissions to create a virtual multiple-input multiple-output (MIMO) channel for maximizing the spatial multiplexing gain. However, implementing virtual-MIMO transmission can result in overwhelming overhead, such as channel state information (CSI) feedback and local data exchange, as the number of cooperating nodes increases. To suppress this overhead, we apply the principle of virtual-MIMO transmission to CSI feedback and design a novel framework for supporting parallel feedback streams, called virtual-MIMO feedback. Building on this feedback framework, we optimize virtual-MIMO transmission techniques and quantify their throughput gains in the presence of CSI and local-exchange overhead. To be specific, the outage capacities of these virtual-MIMO techniques are analyzed, and the corresponding bandwidth allocation for local data exchange and long-range transmission is optimized in a way to maximize overall spectral efficiency. Finally, the improvement of spectral efficiency in proposed virtual-MIMO feedback and transmission is demonstrated by the simulation results.

Enhanced evolutionary heuristic approaches for remote metering smart grid networks

For a smart grid network, the communication infrastructure is an important part to provide the stability and reliability. To build an efficient communication network is crucial for the success of a smart grid network. This study improves the previous published work by proposing two novel evolutionary heuristic approaches (discrete differential evolutionary clustering approach and particle swarm optimisation clustering approach) to obtain the minimum number and location of the powerful nodes to fulfil the connectivity requirement of a smart meters network. The contribution of this study includes the designing and implementing of the two novel evolutionary heuristic approaches which could significantly improve the optimal solution [the least number of the local data centres (powerful nodes)] for a smart meter network which has arbitrary number of smart meters (ordinary nodes) with arbitrary transmission range. Comprehensive case studies and numerical statistical analysis demonstrate that the approaches could efficiently obtain better optimal results, and the connectivity of the smart meter network could be fulfilled with minimum number of the powerful nodes.

A novel and secure IoT based cloud centric architecture to perform predictive analysis of users activities in sustainable health centres

Diabetes, blood pressure, heart, and kidney, some of the diseases common across the world, are termed ’silent killers’. More than 50 % of the world’s population are affected by these diseases. If suitable steps are not taken during the early stages then severe complications occur from these diseases. In the work proposed, we have discussed the manner in which the Internet-of-Things based Cloud centric architecture is used for predictive analysis of physical activities of the users in sustainable health centers. The architecture proposed is based on the embedded sensors of the equipment rather than using wearable sensors or Smartphone sensors to store the value of the basic health-related parameters. Cloud centric architecture is composed of a Cloud data center, Public cloud, Private cloud, and uses the XML Web services for secure and fast communication of information. The architecture proposed here is evaluated for its adoption, prediction analysis of physical activities, efficiency, and security. From the results obtained it can be seen that the overall response between the local database server and Cloud data center remains almost constant with the rise in the number of users. For prediction analysis, If the results collected in real time for the analysis of physical activities exceed any of the parameter limits of the defined threshold value then an alert is sent to the health care personnel. Security analysis also shows the effective encryption and decryption of information. The architecture presented is effective and reduces the proliferation of information. It is also suggested, that a person suffering from any of the diseases mentioned above can defer the onset of complications by doing regular physical activities.

Cloud geographies

The architecture of cloud computing is becoming ever more closely intertwined with geopolitics – from the sharing of intelligence data, to border controls, immigration decisions, and drone strikes. Developing an analogy with the cloud chamber of early twentieth century particle physics, this paper explores the geography of the cloud in cloud computing. It addresses the geographical character of cloud computing across two distinct paradigms. The first, ‘ Cloud I’ or a geography of cloud forms, is concerned with the identification and spatial location of data centres where the cloud is thought to materialize. Here the cloud is understood within a particular history of observation, one where the apparently abstract and obscure world can be brought into vision and rendered intelligible. In the second variant, ‘ Cloud II’ or the geography of a cloud analytic, the cloud is a bundle of experimental algorithmic techniques acting upon the threshold of perception itself. Like the cloud chamber of the twentieth century, contemporary cloud computing is concerned with rendering perceptible and actionable that which would otherwise be beyond the threshold of human observation. The paper proposes three elements of correlative cloud reasoning, suggesting their significance for our geopolitical present: condensing traces; discovering patterns; and archiving the future.

Eavesdropping Prevention for Network Coding Encrypted Cloud Storage Systems

Network coding is an important cloud storage technique, which can recover data with small repair bandwidth and high reliability compared to the existing erasure coding and replication methods. However, regardless of which data recovery technique is used, the repaired data in a geographically distributed cloud storage system are easy to be eavesdropped at the transmission link between the local datacenter and its remote backup site. This kind of network security issue is called link eavesdropping in this paper. For a network coded cloud storage system, we propose a systematic design methodology to determine the important data recovery system parameters for any specified security level. Through analysis, we present the performance curves to relate the remote repair bandwidth and the number of coded data fragments. Consequently, all the important system parameters of a network coded data recovery system, including the number of storage nodes and the link capacity between the datacenter and the backup site, can be precisely designed for satisfying different security level requirements.

Multidimensional cloud latency monitoring and evaluation

Measuring or evaluating performance of a Cloud service is a non-trivial and highly ambiguous task. We focus on Cloud-service latency from the user’s point of view, and, to this end, utilize the multidimensional latency measurements obtained using an in-house designed active-probing platform, CLAudit, deployed across PlanetLab and Microsoft Azure datacenters. The multiple geographic Vantage Points, multiple protocol layers and multiple datacenter locations of CLAudit measurements allow us to pinpoint with great precision if, where and what kind of a particular latency-generating event has happened. We analyze and interpret measurements over two one-month time-intervals, one in 2013 and one in 2016. As these traces are large, an automated interpretation has been appended to the data-capture process. In summary, we demonstrate the utility of the multidimensional approach and document the differences in the measured Cloud-services latency over time. Our measurements data is publicly available and we encourage the research community to use it for verification and further studies.

Modeling and optimization of data center location and routing and spectrum allocation in survivable elastic optical networks

In this paper, we focus on the problem of locating data centers (DCs) and routing traffic demands in survivable elastic optical network (EON). We consider and compare two different modeling and optimization approaches: joint optimization of both issues at the same time (problem DC-RSA) and decomposition method, which divides problem into two subsequent subproblems of DC location and demands routing with a given DC locations. For both approaches we formulate integer linear programming (ILP) models and propose heuristic algorithms to solve related problem instances. In case of the decomposition method, we propose to use topology-based (that follows directly from the network graph structure) and demographic-economical (characteristics of the cities that correspond to network nodes) data to define DC location policies. We define seven different DC location polices, which can be classified as topology-based, demographic-economical, and hybrid (use both groups of information). Next, we perform extensive numerical experiments on realistic network topologies, in order to evaluate efficiency of the proposed optimization methods and compare different optimization approaches in terms of the computational complexity, spectrum usage, and survivability provisioning in case of a random large-scale disaster. The results of our investigation show that the joint optimization problem (DC-RSA) is much more complex than the decomposition approach. However, the corresponding results bring better network performance according to the spectrum usage and provide good level of network survivability at the same time. Regarding different DC location policies applied with decomposition method, the investigation proves that DC location policy has a crucial influence on the network performance (in terms of the spectrum usage and survivability provisioning), especially when the number of available DCs is relatively small. According to the results, topology-based DC location policies significantly outperform demographic-economical methods in terms of both—spectrum usage and survivability provisioning.

Key based data analytics across data centers considering bi-level resource provision in cloud computing

Due to the distribution characteristic of the data source, such as astronomy and sales, or the legal prohibition, it is not always practical to store the world-wide data in only one data center (DC). Hadoop is a commonly accepted framework for big data analytics. But it can only deal with data within one DC. The distribution of data necessitates the study of Hadoop across DCs. In this situation, though, we can place mappers in the local DCs, where to place reducers is a great challenge, since each reducer needs to process almost all map output across all involved DCs. In this paper, a novel architecture and a key based scheme are proposed which can respect the locality principle of traditional Hadoop as much as possible while realizing deployment of reducers with lower costs. Considering both the DC level and the server level resource provision, bi-level programming is used to formalize the problem and it is solved by a tailored two level group genetic algorithm (TLGGA). The final results, which may be dispersed in several DCs, can be aggregated to a designative DC or the DC with the minimum transfer and storage cost. Extensive simulations demonstrate the effectiveness of TLGGA. It can outperform both the baseline and the state-of-the-art mechanisms by 49% and 40%, respectively.

A Holistic Evaluation of Data Center Water Cooling Total Cost of Ownership

The generation-to-generation information technology (IT) performance and density demands continue to drive innovation in data center cooling technologies. For many applications, the ability to efficiently deliver cooling via traditional chilled air cooling approaches has become inadequate. Water cooling has been used in data centers for more than 50 years to improve heat dissipation, boost performance, and increase efficiency. While water cooling can undoubtedly have a higher initial capital cost, water cooling can be very cost effective when looking at the true life cycle cost of a water-cooled data center. This study aims at addressing how one should evaluate the true total cost of ownership (TCO) for water-cooled data centers by considering the combined capital and operational cost for both the IT systems and the data center facility. It compares several metrics, including return-on-investment for three cooling technologies: traditional air cooling, rack-level cooling using rear door heat exchangers, and direct water cooling (DWC) via cold plates. The results highlight several important variables, namely, IT power, data center location, site electric utility cost, and construction costs and how each of these influences the TCO of water cooling. The study further looks at implementing water cooling as part of a new data center construction project versus a retrofit or upgrade into an existing data center facility.

BGP Design for the Separation of the Local Big Data Traffic in Data Center

As the service for big data increases, the number of analysis servers for big data grows rapidly. For example, the scale of analysis servers for the big data from Large Hadron Collier in Swiss CERN is generally beyond tens of thousands of servers. Therefore, tasks for the selection of the appropriate local routing protocol and the efficient configuration of the routing protocol for those servers are getting more important portion in the local network administration in big data center. In this paper, we introduce BGP as a routing protocol for the local network of big data center even though it is developed for WAN. The concept we introduced in this paper for the separation of big data traffic in local data center network is created based on the concept of the partition of the conference room in convention center. The main conference room can be partitioned by multiple small meeting rooms according to users’ requests. Each small room can be protected from the noise of the neighbor meeting rooms by partition walls. In the same way, the huge number of servers can be grouped into various server farms and then each of server farms can be identified Autonomous System number that is used for the BGP routing domain for network administrator. Using the techniques of AS numbering we can control the big data traffic more efficiently than ever in data center. We introduced new application method of AS number for a service to recognize certain traffic. Therefore, we can separate and control big data traffic more in detail. We tried to prove that BGP is superior to IGP and overlay networking with respect to the routing control in big data center.

Numerical investigation and parametric study for thermal and energy management enhancements in data centers' buildings

A CFD numerical investigation of air flow and temperature distribution in data centers is presented for different operating and geometric conditions. Numerical experiments were conducted to parametrically study the effects of perforated tiles opening ratio, data center power density and locations of Computer Room Air Conditioning (CRAC) on the data center performance. Temperature distribution especially hot spot points, air flow characteristics particularly air recirculation and bypass and thermal management in data centers are evaluated in terms of the measureable overall performance parameters: supply/return heat indices (SHI/RHI), return temperature indices (RTI) and return cooling indices (RCI). Techniques of using cold aisle containments for enhancing the cooling effectiveness of data centers are investigated and evaluated. The results showed that (i) increasing the perforation ratio of perforated tails decreases the recirculation and bypass of air flow around the rack, (ii) increasing data centers' power density and supplied air flow rates increases the air bypass and air re-circulation, (iii) changing the spacing between the CRAC units and cold aisle slightly changes of air recirculation and bypass around the racks, and (iv) using appropriate cold aisle containment improves the performance of cooling of data centers, especially at high power density.

Understanding Power Requirements, Energy Costs, and Incentives for Data Centers

More than any other industry, electric supply is the most important attribute necessary for siting and operating a data center or mission‐critical facility. The following article takes a look at how utilities can affect the location and operation of data centers by understanding specific power requirements and costs, and reviews incentives offered by utilities in each state for data centers.

Data Center Energy and Cost Saving Evaluation

In data centers, about 40% of the total energy is consumed for cooling the IT equipment. Cooling costs are thus one of the major contributors to the total electricity bill of large data centers. This paper studies two factors affecting data center cooling energy consumption, namely air flow management and data center location selection. A unique rack layout with a vertically cooling air flow is proposed. Two cooling systems, computer room air conditioning (CRAC) cooling system and airside economizer (ASE), have been studied. Based on these two cooling systems, four cities have been selected from the worldwide data center locations. A number of energy efficiency metrics are explored for data center cooling, such as power usage effectiveness (PUE), coefficient of performance (COP) and chiller hours. By analyzing the effects of chiller hours and economizer hours, comparative economic results of cooling power consumption are provided in both systems. The results show that the cooling efficiency and operating costs vary significantly with different climate conditions, energy prices and cooling technologies. As climate condition is the major factor which affects the airside economizer, employing the airside economizer in the cold climate yields much lower energy consumption and operation costs.

Cloud-Enabled Data Center Organization using K-D Tree

An efficient load balancing algorithm in the field of cloud computing is absolutely essential in order to have a cloud network with graceful performance satisfying user expectation. Inspite of the existence of handful lightly loaded data centers, numerous heavily overloaded data centers may lead to a performance degradation of overall cloud network. Proper workload distribution may improve the overall performance of the cloud system. Now a day’s eminent cloud division rules are highly demanding algorithm for distributing workloads among various cloud server nodes deployed in cloud-enabled data centers scattered over the geographical regions. For the researcher, cloud division rule and optimal cloud server node searching are the most demanding jobs in load balancing leading towards more efficient cloud network and improve users’ satisfaction. This paper presents an expeditious cloud division rule based on geographical location of the cloudenabled data centers distributed over earth surface and builds a two-dimensional space partition k-d tree to partition them in order to search intended cloud server node efficiently. Our proposed organization scheme can be utilized for active monitoring load balancing algorithm to improve the resource utilization for high performance in present cloud computing environment.

Agile-SD: A Linux-based TCP congestion control algorithm for supporting high-speed and short-distance networks

Recently, high-speed and short-distance networks are widely deployed and their necessity is rapidly increasing everyday. This type of networks is used in several network applications; such as Local Area Networks (LAN) and Data Center Networks (DCN). In LANs and DCNs, high-speed and short-distance networks are commonly deployed to connect between computing and storage elements in order to provide rapid services. Indeed, the overall performance of such networks is significantly influenced by the Congestion Control Algorithm (CCA) which suffers from the problem of bandwidth under-utilization, especially if the applied buffer regime is very small. In this paper, a novel loss-based CCA tailored for high-speed and Short-Distance (SD) networks, namely Agile-SD, has been proposed. The main contribution of the proposed CCA is to implement the mechanism of agility factor. Further, intensive simulation experiments have been carried out to evaluate the performance of Agile-SD compared to Compound and Cubic which are the default CCAs of the most commonly used operating systems. The results of the simulation experiments show that the proposed CCA outperforms the compared CCAs in terms of average throughput, loss ratio and fairness, especially when a small buffer is applied. Moreover, Agile-SD shows lower sensitivity to the buffer size change and packet error rate variation which increases its efficiency.

Information Ownership and Materiality in an Age of Big Data Surveillance

Abstract Can private data “havens,” localized servers, and offline infrastructure empower secure and private user control over data? Contrasting Sealand's counter-surveillance policies with the NSA's Utah Data Center mass surveillance apparatus, the authors analyze how control of the physical location of data centers shapes the possibilities of data agency and ownership. They note that to whom data belongs has become regulated asymmetrically through technological capacity rather than through socially articulated norms or deliberative legal processes. They offer a number of policy approaches to enhance the democratic potential of information stewardship, but question whether any are sufficient to subvert the power of agencies such as the NSA.

Information Ownership and Materiality in an Age of Big Data Surveillance

Abstract Can private data “havens,” localized servers, and offline infrastructure empower secure and private user control over data? Contrasting Sealand's counter-surveillance policies with the NSA's Utah Data Center mass surveillance apparatus, the authors analyze how control of the physical location of data centers shapes the possibilities of data agency and ownership. They note that to whom data belongs has become regulated asymmetrically through technological capacity rather than through socially articulated norms or deliberative legal processes. They offer a number of policy approaches to enhance the democratic potential of information stewardship, but question whether any are sufficient to subvert the power of agencies such as the NSA.

Datacenter location for Internet services in the province of Quebec

This paper introduces an efficient algorithm based on mixed integer linear programming (MILP) for the “datacenter location“ problem. This model defines the datacenter locations under Internet services demands and, at the same time, ensures a low energy consumption of information technology (IT) equipment. The data center location has a direct impact on the Internet services response time and costs (fixed and variable). In the end of this paper, model validation and results are proved via experimentation example and numerical study.