Data Center Providers Research Articles

Downtime Data Center: Memahami Penyebab, Dampak, dan Solusi Efektif

This research focuses on the management of downtime risks in Data Centers from a service provider perspective. Utilizing the systematic literature review method, it investigates the impacts of small yet significant downtimes in Data Centers, along with the strategies employed by service providers to address these issues. Key findings highlight that even brief downtimes can lead to substantial financial losses and operational disruptions. Strategies for downtime prevention and recovery are explored, including multi-server optimization, early risk detection, and efficient resource management. This study offers crucial insights for Indonesian Data Center providers to enhance their services and security, aiding them in competing in the global market.

Resilience enhancement in open network–cloud ecosystems through disaggregation and cooperation [Invited

To accommodate the growing demand for cloud services, telecom carriers’ networks and datacenter (DC) facilities form large network–cloud ecosystems (ecosystems for short) physically supporting these services. These large-scale ecosystems are continuously evolving and must be highly resilient to support critical services. Open and disaggregated optical-networking technologies promise to enhance the interoperability across telecom carriers and DC operators, thanks to their open interfaces in both the data plane and control/management plane. In the first part of this paper, we focus on a single entity (e.g., a telecom carrier or an emerging telecom/DC partnership company) that owns both the network and DC infrastructures in the ecosystem. We introduce a solution by leveraging open and disaggregated technologies to enhance the resilience of the optical networks within a multi-vendor and multi-domain ecosystem. In the second part of this paper, we consider the case when the networks and DCs are owned by different entities. Also, in this case, cooperation among datacenter providers (DCPs) and carriers is crucial to provide failure/disaster resilience to today’s cloud services. However, such cooperation is more challenging since DCPs and carriers, being different entities, may not disclose confidential information, e.g., detailed resource availability. Hence, we introduce a solution to enhance the resilience of such multi-entity ecosystems through cooperation between DCPs and carriers without violating confidentiality.

Mitigating Carbon Footprint through Green Software: A Comprehensive Framework for Energy-Efficient Data Centers and User-Friendly Carbon Footprint Calculation

The carbon footprint of the world is increasing day by day by the activities caused by every citizen. This is because the demand for energy increases and due to the lack of awareness.The average carbon emission is around 4.72 tonnes per person. There are some free online tools which are available for calculating the carbon footprint of each citizen. But those work as a datacentre in which data must be entered manually by the user which makes it quite inefficient. The existing methods do not meet the requirement of ease of handling the data.This software and the datacentres were developed which includes the availability of a user-friendly interface.Data centers also play a major role in carbon footprint, they emit CO2 and consume more energy. Data centers and new technology adoptions are mainly causing this carbon emission.These Data Centres use cloud energy to serve the user-generated requests and this energy consumption is the basic cause of carbon emission. Therefore, the energy efficiency of the data center must be governed. To reduce it, the concept of green software is introduced. Though hardware is seen as the main culprit for the consumption of energy, software plays a tremendous role in determining the efficiency of the hardware. Therefore, software must be energy efficient to handle the data center in low cost, low consumption, and low pressure.This led to the development of GREEN Software. The implementation of green software involves various factors.To reduce it, data center simulators are being used. This simulator calculates the amount of energy consumed and the amount of CO2 emitted, which as a result computes the change in carbon footprint due to it. This green software design framework provides a separate track session on energy consumption by the data center and cloud service provider along with the acquired energy efficiency level by the specific center.

Dynamic Optimization of Energy Efficiency and Service Level Agreement violation in Cloud Data Centers via Virtual Machine Consolidation

With the escalating proliferation of cloud computing services, cloud data center providers are actively in pursuit of solutions not only to curtail energy consumption but also to efficiently cater to the unique demands of their users, thereby enhancing user satisfaction. The process of consolidating virtual machines emerges as a viable strategy for striking a balance between energy consumption and Service Level Agreement violation (SLAv), potentially surmounting this predicament faced by cloud service providers. This study aims to minimize energy consumption and SLAv to their utmost feasible extent by utilizing the First Median Threshold (FMT) technique for physical host selection and virtual machine allocation, in conjunction with the Maximum Ratio of CPU utilization to memory Utilization (MRCU) method for virtual machine selection within the framework of virtual machine consolidation. This dual-pronged approach not only translates into cost savings for cloud providers but also ensures the delivery of optimal services to users. Furthermore, a novel method has been introduced for selecting virtual machines for migration and appropriately choosing physical hosts to achieve equilibrium between two critical parameters: energy consumption and SLAv. The proposed methodology is implemented and evaluated using the CloudSim simulator, utilizing real-world PlanetLab data. The comprehensive analysis of the simulation results reveals that the proposed approach can significantly reduce energy consumption while effectively mitigating SLAv for users.

An Artificial Intelligent Mechanism for Future Networks using Mininet Wi-Fi

In this paper, Mininet Wi-Fi was used to simulate a Software Defined Network to demonstrate Mininet Wi-Fi’s s ability to simulate their post and wireless dimension by assigning site to other site or access points and revoking these wireless associations which can also be integrated into the existing network. Need of networks continuously growing with more clients, more base points, and more network traffic. The security of these networks and network topologies becomes the most crucial part. The discovered mechanism will work in the network for security purposes. Mininetwi-fi will run the topology and parallellyWireshark that start capturing the network packet and protocol-like information. The extracted information will load in the CIC flow meter to make that readable. Further by processing that data using AIML algorithms data will be shuffled to avoid overfitting&underfitting then will be classified and labeled as normal data and attacked data. These results benefit modern and current networkline-up as the live network devices can also interact with the testing space for the data center, cloud, and mobile providers. The proposed framework can correctly enhance the overall performance of the synchrophasor based adaptive dependability/ security bias scheme in the course of DoS assaults and keep away from maloperation of the security devices, which enhances the strength system's balance.

Guest Editors Introduction: Special Issue on Advanced Management of Softwarized Networks

The Softwarization of networks is enabled by the SDN (Software-Defined Networking), NV (Network Virtualization), and NFV (Network Function Virtualization) paradigms, and offers many advantages for network operators, service providers and data-center providers. Given the strong interest in both industry and academia in the softwarization of telecommunication networks and cloud computing infrastructures, a series of special issues was established in IEEE Transactions on Network and Service Management, which aims at the timely publication of recent innovative research results on the management of softwarized networks.

Running Industrial Workflow Applications in a Software-Defined Multicloud Environment Using Green Energy Aware Scheduling Algorithm

Industry 4.0 have automated the entire manufacturing sector (including technologies and processes) by adopting Internet of Things and cloud computing. To handle the workflows from Industrial Cyber-Physical systems, more and more data centers have been built across the globe to serve the growing needs of computing and storage. This has led to an enormous increase in energy usage by cloud data centers, which is not only a financial burden but also increases their carbon footprint. The private software defined wide area network (SDWAN) connects a cloud provider's data centers across the planet. This gives the opportunity to develop new scheduling strategies to manage cloud providers workload in a more energy-efficient manner. In this context, this article addresses the problem of scheduling data-driven industrial workflow applications over a set of private SDWAN connected data centers in an energy-efficient manner while managing tradeoff of a cloud provider' revenue. Our proposed algorithm aims to minimize the cloud provider's revenue and the usage of nonrenewable energy by utilizing the real-world electricity prices with the availability of green energy on different cloud data centers, where the energy consumption consists of the usage of running application over multiple data centers and transferring the data among them through SDWAN. The evaluation shows that our proposed method can increase usage of green energy for the execution of industrial workflow up to 3× times with a slight increase in the cost when compared to cost-based workflow scheduling methods.

Joint Optimization for Bandwidth Utilization and Delay Based on Particle Swarm Optimization

One of the most significant problems in large-scale applications is how to allocate requests from end-users to datacenters. Most existing studies focus only on high bandwidth utilization of datacenters or low request delay of end-users, lacking the consideration of both at the same time. To achieve the joint optimization for bandwidth utilization of datacenter providers and delay of end-users, this paper proposes a novel Particle Swarm Optimization (PSO) based request allocation algorithm, based on the construction of a mathematical model for joint optimization. The algorithm adapts the coding scheme and redefines the operator to make PSO suitable for discrete optimization problems and use fitness function to get a global optimal solution. Through extensive simulations, it is demonstrated that the PSO based request allocation algorithm proposed in this study can improve the bandwidth utilization and keep the average request delay within acceptable limits or even better.

Anomaly traffic detection and correlation in Smart Home automation IoT systems

AbstractSmart building automation systems are increasingly the target of hacking attacks. Moreover, they may be used as a tool for attacks against targets located out of the native Home Area Network (HAN). These attacks are often resulted in changes in traffic volume, damaged packets, increased message traffic, and so on. Symptoms of attacks can be detected as anomalies in traffic model and recognized by a software agent run on Home Gateway. Although these anomalies are detected locally, it may help network provider to protect his resources as well as other resources of his clients. For that purpose, network operator should be able to recognize anomalies and correlate them on the network level. In this way, the network operator has the ability to protect both its own network and HANs of its clients. This article shows that Smart Home security might be coupled with the providers' network security policy. For that reason, security tasks should be performed both in HAN and providers' data center. This article describes a novel strategy for anomaly detection that provides shared responsibility between a service client and the network provider. It uses a machine learning approach for classifying the monitoring data and correlation in searching suspicious behavior across the network resources at the service provider's data center.

Revenue and Energy Cost-Optimized Biobjective Task Scheduling for Green Cloud Data Centers

The significant growth in the number and types of tasks of heterogeneous applications in green cloud data centers (GCDCs) dramatically increases their providers' revenue from users as well as energy consumption. It is a big challenge to maximize such revenue, while minimizing energy cost in a market where prices of electricity, availability of renewable power generation, and behind-the-meter renewable generation contract models differ among the geographical sites of the GCDCs. A multiobjective optimization method that investigates such spatial differences in the GCDCs is for the first time proposed to trade off such two objectives by cost-effectively executing all tasks while meeting their delay constraints. In each time slot, a constrained biobjective optimization problem is formulated and solved by an improved multiobjective evolutionary algorithm based on decomposition. Realistic data-based simulations prove that the proposed method achieves a larger total profit in faster convergence speed than the two state-of-the-art algorithms. Note to Practitioners-This article considers the tradeoff between profit maximization and energy cost minimization for the green cloud data center (GCDC) providers while meeting the delay constraints of all tasks. Current task-scheduling methods fail to take the advantage of spatial variations in many factors, e.g., prices of electricity and availability of renewable power generation at geographically distributed GCDC locations. As a result, they fail to execute all tasks of heterogeneous applications within their delay bounds in a high-revenue and low-energy-cost manner. In this article, a multiobjective optimization method that addresses the disadvantages of the existing methods is proposed. It is realized by a proposed intelligent optimization algorithm. Simulations demonstrate that in comparison with the two state-of-the-art scheduling algorithms, the proposed one increases the profit and reduces the convergence time. It can be readily implemented and integrated into actual industrial GCDCs.

DrawerPipe: A Reconfigurable Pipeline for Network Processing on FPGA-Based SmartNIC

In the public cloud, FPGA-based SmartNICs are widely deployed to accelerate network functions (NFs) for datacenter operators. We argue that with the trend of network as a service (NaaS) in the cloud is also meaningful to accelerate tenant NFs to meet performance requirements. However, in pursuit of high performance, existing work such as AccelNet is carefully designed to accelerate specific NFs for datacenter providers, which sacrifices the flexibility of rapidly deploying new NFs. For most tenants with limited hardware design ability, it is time-consuming to develop NFs from scratch due to the lack of a rapidly reconfigurable framework. In this paper, we present a reconfigurable network processing pipeline, i.e., DrawerPipe, which abstracts packet processing into multiple “drawers” connected by the same interface. NF developers can easily share existing modules with other NFs and simply load core application logic in the appropriate “drawer” to implement new NFs. Furthermore, we propose a programmable module indexing mechanism, namely PMI, which can connect “drawers” in any logical order, to perform distinct NFs for different tenants or flows. Finally, we implemented several highly reusable modules for low-level packet processing, and extended four example NFs (firewall, stateful firewall, load balancer, IDS) based on DrawerPipe. Our evaluation shows that DrawerPipe can easily offload customized packet processing to FPGA with high performance up to 100 Mpps and ultra-low latency (<10 µs). Moreover, DrawerPipe enables modular development of NFs, which is suitable for rapid deployment of NFs. Compared with individual NF development, DrawerPipe reduces the line of code (LoC) of the four NFs above by 68%.

Guest Editorial: Special Issue on Latest Developments for the Management of Softwarized Networks

The softwarization of networks is enabled by the SDN (Software-Defined Networking), NV (Network Virtualization), and NFV (Network Function Virtualization) paradigms, and offers many advantages for network operators, service providers and datacenter providers. Given the strong interest in both industry and academia in the softwarization of telecommunication networks and cloud computing infrastructures, a series of special issues was established in IEEE Transactions on Network and Service Management, which aims at the timely publication of recent innovative research results on management of softwarized networks.

A survey on resource scheduling for data transfers in inter-datacenter WANs

Today, datacenter providers spend a lot of money on inter-datacenter WANs to transmit traffic flows between geographically distributed datacenter sites. It is very important for datacenter providers to design resource scheduling schemes to schedule inter-datacenter networking resources to satisfy data transfer requests and achieve maximum performance or economic benefits. In this article, we conduct a review study on research efforts being conducted on the inter-datacenter networking resource scheduling problem. Our survey study is presented in three parts. The first part focuses on formulating the problem theoretically, including the definition and features of a data transfer, network models of inter-DC WANs, scheduling objectives, and scheduling dimensions. We also summarize a conceptual implementation architecture of scheduling systems. Second, we classify existing schemes according to their objectives and scheduling dimensions and then explain how they formulate and solve their own problems. In the third part, we examine practical challenges in developing scheduling systems and also point out some future directions. Since we do not see any survey focusing on the scheduling problem of inter-DC WANs, we believe this article can provide some help for research developments in this important field by organizing logically the recent research efforts from industry and academia.

Cost-Aware Traffic Management Under Demand Uncertainty from a Colocation Data Center User’s Perspective

Burstable billing is widely adopted by colocation data center providers to charge their users for data transferring. This paper proposes a cost-aware traffic management approach for a colocation data center user under burstable billing where it is charged based on the 95th percentile bandwidth usage. To do this, we first develop a tractable mathematical expression to calculate the 95th percentile usage of a user. Then, we develop an optimization problem to maximize the user’s surplus based on both deterministic and stochastic predictions of the user’s demand. We show that the resulted optimization problem, while non-convex by nature, can be efficiently solved or approximated using a convex program. We also show that the proposed approach can also be applied in a more general scenario where the user gets services from multiple service providers. Using real-world workload traces, we show that the proposed approach can reduce a colocation data center user’s IP transit cost by 26 percent and increase its total surplus by 23 percent, compared to the current practice of allocating bandwidth on-demand.

Two-tier anomaly detection based on traffic profiling of the home automation system

Smart building equipment and automation systems often become a target of attacks and are used for attacking other targets located out of the Home Area Network. Attacks are often related to changes in traffic volume, disturbed packet flow or excessive energy consumption. Their symptoms can be recognized and interpreted locally, using software agent at Home Gateway. Although anomalies are detected locally at the Home Gateway, they can be exploited globally. Thus, it is significantly important to detect global attack attempts through anomalies correlation.Our proposal in this paper is the involvement of the Network Operator in Home Area Network security. Our paper describes a novel strategy for anomaly detection that consists of shared responsibilities between user and network provider. The proposed two-tier Intrusion Detection System uses a machine learning method for classifying the monitoring records and searching suspicious anomalies across the network at the service provider's data center. Result show that local anomaly detection combined with anomaly correlation at the service providers level can provide reliable information on the most frequent IoT devices misbehavior which may be caused by infection.

Modeling and deploying hybrid tenant requests with shared networklets

In recent years, IaaS providers are trying to cater to application layer development, PaaS providers are exposing more infrastructure APIs to customers, and SaaS providers start to allow tenants to build their apps within their software. All of these indicate that, the popular wisdom that cloud computing comes in three flavors—IaaS, PaaS, and SaaS—no longer describes reality. The lines between IaaS, PaaS, and SaaS are becoming blurred as datacenter providers seek to create cloud platforms that can satisfy the needs of enterprises and widen their appeal to developers. In hybrid datacenters, resource requests from tenants are increasingly transforming into hybrid requests that may simultaneously demand IaaS, PaaS, and SaaS resources. This paper tackles the challenge of modeling and deploying hybrid tenant resource requests in datacenter networks, for which we coin “networklet” to represent a set of VMs that collaboratively provide some PaaS or SaaS service. Through extracting networklets from tenant requests and thus sharing them between multiple tenants, we can achieve a win-win situation for datacenter providers and tenants. Extensive evaluations show that, the proposed model and deployment algorithm indeed reduce the completion time of tenant requests while maintaining performance guarantee.

Guest Editors’ Introduction: Special Section on Novel Techniques for Managing Softwarized Networks

The softwarization of networks is enabled by the SDN (Software-Defined Networking), NV (Network Virtualization), and NFV (Network Function Virtualization) paradigms, and offers many advantages for network operators, service providers and datacenter providers. Given the strong interest in both industry and academia in the softwarization of telecommunication networks and cloud computing infrastructures, a series of special section was established in IEEE Transactions on Network and Service Management, which aims at the timely publication of recent innovative research results on management of softwarized networks.

Preface to the Special Issue on Data Center Communications

Data Center networking requirements have evolved rapidly in recent years. Driven by services that include the Internet of Things, Big Data processing, Cloud Services and Social Media, large-scale Data Center providers are in urgent need to develop scalable solutions that will accommodate the ever growing capacity demands in a cost- and energy-efficient manner and provide simplified data center management and automation. As a result, we have recently seen several important developments in the implementation of interconnects offering ever broader bandwidths, which have attracted fevered discussions on what may be the most effective route forward towards their adoption. Advances in silicon technology have allowed for more compact and affordable hardware implementations. However, the limits to the capabilities of current transceiver and switching technologies are already in sight, forcing Data Center providers to upgrade their hardware every few years. The wide adoption of pluggable 100 Gb/s systems and the technology push towards small form-factor transceivers with capacities that are several multiples of this, triggers the development of a whole new technological ecosystem, including new fibre and device technologies as well as networking concepts and architectures. In this rapidly evolving environment, this Special Issue offers a timely insight to some of the latest research developments in areas including: – Integrated photonic technologies, including sources, switches, silicon photonic integrated circuits and their packaging – Novel fibres to serve Data Center Interconnects – Data Center Communication links – Advanced Data Center networking concepts We hope that this collection of invited papers from some of the leading research groups working on Data Center Communications will provide the readers with a detailed appreciation of the state-of-the-art in this hot application area of optical communications, and that it will inspire further cutting-edge research in the field. We would like to thank all of the authors, as well as the anonymous reviewers of the papers for their hard work towards ensuring a high quality Special Issue. This Issue would not have been realised without the initiative, guidance and patience of Editor-in-Chief Bertrand Desthieux, whose leadership and contribution we gratefully appreciate.

A Comprehensive Survey on SLA Compliant Energy Aware Resource Allocation in Cloud Datacenters

Cloud Computing has achieved immense popularity due to its unmatched benefits and characteristics. With its increasing popularity and round the clock demand, cloud based data centers often suffer with problems due to over-usage of resources or under-usage of capable servers that ultimately leads to wastage of energy and overall elevated cost of operation. Virtualization plays a key role in providing cost effective solution to service users. But on datacenters, load balancing and scheduling techniques remain inevitable to provide better Quality of Service to the service users and maintenance of energy efficient operations in datacenters. Energy-Aware resource allocation and job scheduling mechanisms in VMs has helped datacenter providers to reduce their cost incurrence through predictive job scheduling and load balancing. But it is quite difficult for any SLA oriented systems to maintain equilibrium between QoS and cost incurrence while considering their legal assurance of quality, as there should not be any violations in their service agreement. This paper presents some state-of-the-art works by various researchers and experts in the arena of cloud computing systems and particularly emphasizes on energy aware resource allocations, job scheduling techniques, load balancing and price prediction methods. Comparisons are made to demonstrate usefulness of the mechanisms in different scenarios.

Drawing the Cloud Map: Virtual Network Provisioning in Distributed Cloud Computing Data Centers

Efficient virtualization methodologies constitute the core of cloud computing data center implementation. Clients are attracted to the cloud model by its ability to scale the resources dynamically and the flexibility in payment options that it offers. However, performance hiccups may push them to go back to the buy-and-maintain model. Virtualization plays a key role in the synchronous management of the thousands of servers along with clients' data living on them. To achieve seamless virtualization, cloud providers require a system that performs the function of virtual network provisioning. This includes receiving the cloud client requests and allocating their computational and network resources in a way that guarantees the quality-of-service conditions for clients while maximizing the data center resource utilization and providers' revenue. We introduce a comprehensive system to solve the problem of virtual network mapping for a set of connection requests sent by cloud clients. Connections are collected in time intervals called windows. Consequently, node and link provisioning is performed. Different window size selection schemes are introduced and evaluated. Three schemes to prioritize connections are used, and their effect is assessed. Moreover, a technique dealing with connections spanning over more than a window is introduced. The proposed algorithm is compared with previous work well known in the literature. Simulation results show that the dynamic window size algorithm achieves cloud service providers' objectives in terms of generated revenue, served-connection ratio, resource utilization, and computational overhead. In addition, experimental results show that handling spanning connections independently improves the performance of the system.