Data-centric Model Research Articles

Incremental Innovation towards Business Performance: Data Management Challenges in Healthcare Industry in Indonesia

The Industry 4.0 indicate the increases in competitive pressures, the margins reduction, the availability of new technology and the marketing development techniques suggest making more complex decisions to make and sustain the success. Data management is essential for organizations because the administrative process, which data is acquired, validated, stored, protected and processed through its accessibility, reliability and timeliness ensures that the needs of data are met. It is also a process of developing data architectures, practices and procedures that address the data and then implement these aspects on a regular basis. All these process will ease and smoothen the business flow. Therefore, there are number of data management challenges to maintain sheer volume of data, taking reactive approach, lack of process and data handling, fragmented data ownership and driving a data culture. This study investigate the current issues in health industry to overcome through recognizing both the importance of quality data and having more sophisticated approach to manage data as the organization begin shifting to be more data centric model.

A Comprehensive Model of Data Center: From CPU to Cooling Tower

Aiming at addressing environmental challenges, large data centers, such as Facebook, Google, and Yahoo, are increasing share of green power in their daily energy consumption. Such trends drive research into new directions, e.g., sustainable data centers. The research often relies on expressive models that provide sufficient details, however, practical to re-use and expand. There is a lack of available data center models that capture internal operating states of the facility from the CPU to the cooling tower. It is a challenge to develop a model that allows to describe complete data center of any scale including its connection to the grid. This paper proposes such a model building on the existing work. The challenge was to put the pieces of data center together and model behavior of each element so that interdependencies between components and parameters and operating states are captured correctly and in sufficient details. The proposed model was used in the project “Data center microgrid integration” and proven to be adequate and important to support such study.

Performance Modeling and Suitability Assessment of Data Center Based on Fog Computing in Smart Systems

In human-centered smart systems, traditional cloud computing has been well adopted for several years for supporting IoT applications of smart systems, however, the requirements of the IoT applications vary due to different tasks in the smart systems. In order to offer location-aware, latency-sensitive monitoring, and intelligent control for IoT applications in the smart systems like smart home or smart grid, we introduce fog computing and discuss the combination of fog computing and data center hoping to improve the quality of experience of users in human-centered smart systems. In this paper, we propose a three-layer architecture of the data center by introducing the fog computing layer between the traditional centralized data center and terminal nodes. Performance related service latency of different cloud services is modeled and evaluated based on it. It is verified that the new architecture has a great improvement in terms of service latency and network load by simulation experiments.

Enhancing Security and Trust in Named Data Networking using Hierarchical Identity Based Cryptography

Named data networking (NDN) represents a promising clean slate for future internet architecture. It adopts the information-centric networking (ICN) approach that treats named data as the central element, leverages in-network caching, and uses a data-centric security model. This model is built mainly in the addition of a signature to each of the recovered data. However, the signature verification requires the appropriate public key. To trust this key, multiple models were proposed. In this article, the authors analyze security and trust in NDN, to deduct the limits of the already proposed solutions. They propose a security extension that strengthens security and builds trust in used keys. The main idea of this extension is the derivation of these keys from data name, by using hierarchical identity-based cryptography (HIBC). To confirm the safety of the new proposal, a formal security analysis is provided. To evaluate its efficiency, a performance evaluation is performed. It proves that by adopting the proposed extension, performance is comparable, even better in some cases than plain NDN.

Eventual Consistency: Origin and Support

Eventual consistency is demanded nowadays in geo-replicated services that need to be highly scalable and available. According to the CAP constraints, when network partitions may arise, a distributed service should choose between being strongly consistent or being highly available. Since scalable services should be available, a relaxed consistency (while the network is partitioned) is the preferred choice. Eventual consistency is not a common data-centric consistency model, but only a state convergence condition to be added to a relaxed consistency model. There are still several aspects of eventual consistency that have not been analysed in depth in previous works: 1. which are the oldest replication proposals providing eventual consistency, 2. which replica consistency models provide the best basis for building eventually consistent services, 3. which mechanisms should be considered for implementing an eventually consistent service, and 4. which are the best combinations of those mechanisms for achieving different concrete goals. This paper provides some notes on these important topics.

Model Predictive Control for Energy-Efficient Operations of Data Centers with Cold Aisle Containments

In this paper, we study a problem of controlling cooling facilities and computational equipments for energy-efficient operations of data centers. Although a plethora of approaches have been proposed in previous literatures, there is a lack of rigorous methodologies for effectively addressing the problem. To bridge this gap, we propose MPC frameworks for jointly and optimally tuning the Computer Room Air Conditioner (CRAC) supplying air temperature as well as the number of active servers for minimizing the overall energy consumption. We specifically find that, when a standard model of data centers with contained cold aisles and cooling facilities are used, the optimization problems arising from the MPC framework can be transformed to convex optimization problems that can be solved efficiently. We present several numerical simulations to illustrate the effectiveness of our theoretical results.

Towards a Business Process Modeling Technique for Agile Development of Case Management Systems

A modern organization needs to adapt its behavior to changes in the business environment by changing its Business Processes (BP) and corresponding Business Process Support (BPS) systems. One way of achieving such adaptability is via separation of the system code from the process description/model by applying the concept of executable process models. Furthermore, to ease introduction of changes, such process model should separate different perspectives, for example, control-flow, human resources, and data perspectives, from each other. In addition, for developing a completely new process, it should be possible to start with a reduced process model to get a BPS system quickly running, and then continue to develop it in an agile manner. This article consists of two parts, the first sets requirements on modeling techniques that could be used in the tools that supports agile development of BPs and BPS systems. The second part suggests a business process modeling technique that allows to start modeling with the data/information perspective which would be appropriate for processes supported by Case or Adaptive Case Management (CM/ACM) systems. In a model produced by this technique, called data-centric business process model, a process instance/case is defined as sequence of states in a specially designed instance database, while the process model is defined as a set of rules that set restrictions on allowed states and transitions between them. The article details the background for the project of developing the data-centric process modeling technique, presents the outline of the structure of the model, and gives formal definitions for a substantial part of the model.

Development of a simple power consumption model of information technology (IT) equipment for building simulation

The IT equipment power consumption estimated by building simulation software is much higher than the actual case, but there are no simple solutions for building engineers to reduce the bias in data center models in building simulation. This paper address this issue by proposing a simple model of IT equipment load. The model estimates the IT equipment load based on critical control inputs of the IT equipment such as its processor utilization rate and on/off status, and a survey of literature for the ordinary values of these IT equipment control variables is conducted to facilitate its uses by building engineers. A case study was conducted with building models of large offices with lots of IT equipment, and the estimated annual building energy use was reduced by more than 30% in all three cases. This shows that the conventional constant thermal load model overestimates the IT equipment load and the IT equipment model improves the accuracy of modeling of data centers in building simulation by modeling the changes of the load with the status of the servers.

Models and algorithm of optimization launch and deployment of virtual network functions in the virtual data center

The goal of our investigation is optimization of network work in virtual data center. The advantage of modern infrastructure virtualization lies in the possibility to use software-defined networks. However, the existing optimization of algorithmic solutions does not take into account specific features working with multiple classes of virtual network functions. The current paper describes models characterizing the basic structures of object of virtual data center. They including: a level distribution model of software-defined infrastructure virtual data center, a generalized model of a virtual network function, a neural network model of the identification of virtual network functions. We also developed an efficient algorithm for the optimization technology of containerization of virtual network functions in virtual data center. We propose an efficient algorithm for placing virtual network functions. In our investigation we also generalize the well renowned heuristic and deterministic algorithms of Karmakar-Karp.

Energy and Exergy Analysis of Modular Data Centers

The data center industry focuses on initiatives to reduce its enormous energy consumption and to minimize its adverse environmental impact. Modular data centers provide considerable operational flexibility in that they are mobile and are manufactured using standard containers. This paper develops steady-state energy and exergy destruction models for modular data centers with the open-source EnergyPlus software package. Three different cooling approaches are examined: direct expansion (DX) cooling, evaporative cooling (direct evaporative cooling, DEC, in this study), and free air cooling (air-side economization in this study). This paper shows that for hot and arid climates like those in the southwestern U.S., augmenting DX cooling with evaporative and free air cooling can result in energy savings of up to 38% and 36%, respectively. This paper also applies exergy analysis to suggest that the Energy Reuse Effectiveness of the data center increases with decreasing ambient (outdoor) temperature and increasing server inlet–outlet temperature difference. Furthermore, simulations indicate that the use of passive cooling techniques (e.g., DEC and free air cooling) decrease data center heating, ventilation, and air-conditioning energy consumption, except in extremely hot and humid climates.

CloudNetSim++: A GUI Based Framework for Modeling and Simulation of Data Centers in OMNeT++

State-of-the-art cloud simulators in use today are limited in the number of features they provide, lack real network communication models, and do not provide extensive Graphical User Interface (GUI) to support developers and researchers to extend the behavior of the cloud environment. We propose CloudNetSim++, a comprehensive packet level simulator that enables simulation of cloud environments. CloudNetSim++ can be used to evaluate a wide spectrum of cloud components, such as processing elements, storage, networking, Service Level Agreement (SLA), scheduling algorithms, fine grained energy consumption, and VM consolidation algorithms. CloudNetSim++ offers extendibility, which means that the developers and researchers can easily incorporate own algorithms for scheduling, workload consolidation, VM migration, and SLA agreement. The simulation environment of CloudNetSim++ offers a rich GUI that provides a high level view of distributed data centers connected with various network topologies. The package also includes an energy computation module that provides a fine grained analysis of energy consumed by each component. This paper shows the flexibility and effectiveness of CloudNetSim++ through experimental results demonstrated using real-world data center workloads. Moreover, to demonstrate the correctness of CloudNetSim++, we performed formal modeling, analysis, and verification using High-level Petri Nets, Satisfiability Modulo Theories (SMT), and Z3 solver.

Energy-efficient and QoS-aware model based resource consolidation in cloud data centers

High energy consumption in data center has become a key problem, and the Carbon (CO $$_2$$ ) emissions from data center have serious impacts on environment. To improve traditional performance-energy model and minimize energy consumption in cloud data center, an energy-efficient and QoS-aware (EEQoS) model is developed for virtual resources consolidation. The EEQoS model is designed to investigate the trade-off between energy and QoS. The user’s satisfaction degree and Euclidean distance of resources are also respectively applied to the QoS model and power model. In addition, this EEQoS model is combined with the particle swarm optimization algorithm by setting the power consumption per QoS value as the objective function to consolidate virtual resources in data center. Compared with traditional power model, the proposed model reduces response time by 27.2%, cost by 31.4%, SLA violations by 40.5% and improves throughput by 13.5% on average while increasing less than 3.8% energy consumption during the whole test period.

Cooperative Downlink Listening for Low-Power Long-Range Wide-Area Network

Recently, the development of the Internet of Things (IoT) applications has become more active with the emergence of low-power wide-area network (LPWAN), which has the advantages of low-power and long communication distance. Among the various LPWAN technologies, long-range wide-area network (LoRaWAN, or LoRa) is considered as the most mature technology. However, since LoRa performs uplink-oriented communication to increase energy efficiency, there is a restriction on the downlink function from the network server to the end devices. In this paper, we propose cooperative downlink listening to solve the fundamental problem of LoRa. In particular, the proposed scheme can be extended to various communication models such as groupcasting and geocasting by combining with the data-centric model. Experiments also show that the proposed technology not only significantly reduces network traffic compared to the LoRa standard, but also guarantees maximum energy efficiency of the LoRa.

Tandem Equipment Arranged Architecture with Exhaust Heat Reuse System for Software-Defined Data Center Infrastructure

In this paper, we propose a novel energy-efficient architecture for software-defined data center infrastructures. In our proposed data center architecture, we include an exhaust heat reuse system that utilizes high-temperature exhaust heat from servers in conditioning humidity and air temperature of office space near the data center. To obtain high-temperature exhaust heat, equipment such as server racks and air conditioners are deployed in tandem so that the aisles are divided into three types: cold, hot, and super-hot. In this paper, to investigate the fundamental characteristics of our proposed data center architecture, we consider various types of data center models and conduct numerical simulations that use results obtained by experiments at an actual data center. Through simulation, we show that the total power consumption by a data center with our proposed architecture is 27 percent lower than that by data center with a conventional architecture. In addition, it is also shown that the proposed tandem equipment arrangement is suitable for obtaining high-temperature exhaust heat and decreasing the total power consumption significantly under a wider range of conditions than in the conventional equipment arrangement.

Service reliability modeling and evaluation of active-active cloud data center based on the IT infrastructure

As cloud data center has caught the eye of the information-intensive society since its birth, it keeps on a flourishing development due to its advantages such as high availability and resource utilization, rapid elasticity and disaster recovery. However, as a complex system, it means the centralization of failures and risks, which exerts great influence on the service that cloud data center provides to customers. Thus, the service reliability of the cloud data center is always a key concern. In order to evaluate and further improve the service reliability of the cloud data center, modeling analysis is absolutely necessary but difficult to apply because of the complicated cloud control flows, massive-scale service sharing and complex real-world infrastructures. Focused on the active-active data center, which is a typical mode of the cloud data center, a methodology of the service reliability modeling and analysis based on IT infrastructure is proposed. In this methodology, the queuing theory and graph theory are used to formulate the service reliability model, but the Monte Carlo simulation is used for statistical evaluation. During the modeling, the operational process of the active-active data center is divided into two parts—the request stage and execution stage, which are modeled respectively. Then, the modeling and evaluation approaches are applied in a use case, which verifies the applicability and creditability of our approach. Meanwhile, by sensitivity analysis considering the variation of uncertain or key factors both internal and external, several parameters are identified as high-sensitive factors, which can enlighten service providers on service reliability improvement.

Optimal virtual network function placement in multi-cloud service function chaining architecture

Service Function Chaining (SFC) is the problem of deploying various network service instances over geographically distributed data centers and providing inter-connectivity among them. The goal is to enable the network traffic to flow smoothly through the underlying network, resulting in an optimal quality of experience to the end-users. Proper chaining of network functions leads to optimal utilization of distributed resources. This has been a de-facto model in the telecom industry with network functions deployed over underlying hardware. Though this model has served the telecom industry well so far, it has been adapted mostly to suit the static behavior of network services and service demands due to the deployment of the services directly over physical resources. This results in network ossification with larger delays to the end-users, especially with the data-centric model in which the computational resources are moving closer to end users. A novel networking paradigm, Network Function Virtualization (NFV), meets the user demands dynamically and reduces operational expenses (OpEx) and capital expenditures (CapEx), by implementing network functions in the software layer known as virtual network functions (VNFs). VNFs are then interconnected to form a complete end-to-end service, also known as service function chains (SFCs). In this work, we study the problem of deploying service function chains over network function virtualized architecture. Specifically, we study virtual network function placement problem for the optimal SFC formation across geographically distributed clouds. We set up the problem of minimizing inter-cloud traffic and response time in a multi-cloud scenario as an ILP optimization problem, along with important constraints such as total deployment costs and service level agreements (SLAs). We consider link delays and computational delays in our model. The link queues are modeled as M/D/1 (single server/Poisson arrival/deterministic service times) and server queues as M/M/1 (single server/Poisson arrival/exponential service times) based on the statistical analysis. In addition, we present a novel affinity-based approach (ABA) to solve the problem for larger networks. We provide a performance comparison between the proposed heuristic and simple greedy approach (SGA) used in the state-of-the-art systems. Greedy approach has already been widely studied in the literature for the VM placement problem. Especially we compare our proposed heuristic with a greedy approach using first-fit decreasing (FFD) method. By observing the results, we conclude that the affinity-based approach for placing the service functions in the network produces better results compared against the simple greedy (FFD) approach in terms of both, total delays and total resource cost. We observe that with a little compromise (gap of less than 10% of the optimal) in the solution quality (total delays and cost), affinity-based heuristic can solve the larger problem more quickly than ILP.

Experimental parametric study of servers cooling management in data centers buildings

A parametric study of air flow and cooling management of data centers servers is experimentally conducted for different design conditions. A physical scale model of data center accommodating one rack of four servers was designed and constructed for testing purposes. Front and rear rack and server’s temperatures distributions and supply/return heat indices (SHI/RHI) are used to evaluate data center thermal performance. Experiments were conducted to parametrically study the effects of perforated tiles opening ratio, servers power load variation and rack power density. The results showed that (1) perforated tile of 25% opening ratio provides the best results among the other opening ratios, (2) optimum benefit of cold air in servers cooling is obtained at uniformly power loading of servers (3) increasing power density decrease air re-circulation but increase air bypass and servers temperature. The present results are compared with previous experimental and CFD results and fair agreement was found.

Ant Colony Optimization Algorithm to Dynamic Energy Management in Cloud Data Center

With the wide deployment of cloud computing data centers, the problems of power consumption have become increasingly prominent. The dynamic energy management problem in pursuit of energy‐efficiency in cloud data centers is investigated. Specifically, a dynamic energy management system model for cloud data centers is built, and this system is composed of DVS Management Module, Load Balancing Module, and Task Scheduling Module. According to Task Scheduling Module, the scheduling process is analyzed by Stochastic Petri Net, and a task‐oriented resource allocation method (LET‐ACO) is proposed, which optimizes the running time of the system and the energy consumption by scheduling tasks. Simulation studies confirm the effectiveness of the proposed system model. And the simulation results also show that, compared to ACO, Min‐Min, and RR scheduling strategy, the proposed LET‐ACO method can save up to 28%, 31%, and 40% energy consumption while meeting performance constraints.

Facilitating agronomic mobile data management in a crop field

One of the enterprises that has heavily adopted mobile technology today is the agriculture sector. Farmers and agro specialists can use their smartphones and tablets to easily communicate, advertise goods and services, as well as access agronomic data in soft-real time. The palpable barrier however is the mode of communication of these mobile devices, which is the use of wireless channels orthodoxly. As a result, agronomic mobile applications can experience bandwidth fluctuations and inefficient propagation of data in mobile cloud computing architectures. To address this issue, some existing works proposed a P2P synchronisation technique that is based on three algorithms namely: bloom filters, whole state data transfer, and exchange of deltas only. In this paper, we have totally redesigned and replaced the P2P synchronisations with the data-centric publish-subscribe model that improves the efficiency of the agronomic data synchronisation. As part of this research, a real-world agronomic mobile app is developed and tested where empirical evaluations show that the data-centric publish-subscribe approach is better in terms of latency optimisation in agronomic data management. The well-researched app is called FlagThis and it is available in the App store for free download by farmers and agro-experts.

Air-conditioning system with underfloor air distribution integrated solar chimney in data center

Data centers have the characteristics of high heat production from equipment and uneven temperature distribution. Thus, in order to improve the thermal environment in data centers, this paper puts forward an air-conditioning system with under-floor integrated solar chimney. The improved system can also reduce energy consumption due to annual cooling. A typical data center model is built and verified with experiments. Based on this model, the new model integrated solar chimney is built and simulated. The results show that after integrating the solar chimney, the heat island effect impairs and the temperature of all compared points have a decline, which reached an average decline of about 4.4 ℃. In addition, integrated solar chimney system improves the mixing of cold air and hot air effectively.