Scalable Services Research Articles

Empowering the Internet of Vehicles with Multi-RAT 5G Network Slicing.

Internet of Vehicles (IoV) is a hot research niche exploiting the synergy between Cooperative Intelligent Transportation Systems (C-ITS) and the Internet of Things (IoT), which can greatly benefit of the upcoming development of 5G technologies. The variety of end-devices, applications, and Radio Access Technologies (RATs) in IoV calls for new networking schemes that assure the Quality of Service (QoS) demanded by the users. To this end, network slicing techniques enable traffic differentiation with the aim of ensuring flow isolation, resource assignment, and network scalability. This work fills the gap of 5G network slicing for IoV and validates it in a realistic vehicular scenario. It offers an accurate bandwidth control with a full flow-isolation, which is essential for vehicular critical systems. The development is based on a distributed Multi-Access Edge Computing (MEC) architecture, which provides flexibility for the dynamic placement of the Virtualized Network Functions (VNFs) in charge of managing network traffic. The solution is able to integrate heterogeneous radio technologies such as cellular networks and specific IoT communications with potential in the vehicular sector, creating isolated network slices without risking the Core Network (CN) scalability. The validation results demonstrate the framework capabilities of short and predictable slice-creation time, performance/QoS assurance and service scalability of up to one million connected devices.

A comprehensive and systematic review of the load balancing mechanisms in the Internet of Things

The Internet of Things (IoT) is a network of different objects that refers to an environment in which intelligent devices around us can connect to the Internet and exchange information together. A large number of generated events from IoT objects causes overhead on the network. Therefore, to optimize the usage of IoT network, it is essential to provide solutions for network problems including scalability, routing, reliability, security, energy conservation, network lifetime, congestion, heterogeneity, and quality of service (QoS). In this regard, load balancing as an efficient method takes a significant role in distributing loads among different routes. Imbalance traffic load across the network causes high latency in some routes and loss of data packets and decreases packet delivery ratio. Although load balancing has a critical importance in the IoT, there is still a lack of an organized and comprehensive review about analyzing and examining its remarkable methods. Therefore, this paper by adopting a systematic manner aims to address this gap. In this research, the load balancing methods are categorized into two main categories including centralized and distributed and their merits and demerits are specified. Moreover, vital parameters, the challenges, and open issues in this scope are also discussed. Thus, future authors will be able to develop more effective load balancing mechanisms.

Cloud-based software services delivery from the perspective of scalability

Measuring and testing the scalability and performance of cloud-based software services is critical for the delivery of such services, and the development of cloud computing. There are three interconnected Cloud-based software services’ performance aspects; both elasticity and efficiency are depending on the delivery of a sufficient level of scalability performance. In this work, we focused on testing and measuring the cloud-based software services scalability from a technical perspective. This paper uses technical scalability metrics that address both volume and quality scaling, inspired by earlier technical metrics of elasticity. We demonstrate the application of the metrics using a practical example and three demand scenarios and discuss the importance of these metrics. We show how our technical scalability metrics integrated into an earlier utility-oriented metric of scalability, in order to enable the scalability analysis from technical and production-driven perspectives. This work is related to the delivery of cloud-based software services from scalability perspective. We demonstrate our metrics by testing a cloud-based software service connecting with both AWS auto-scaling and load-balancing services. Measurements have been collected and analysis, and then been used to calculate the values of our scalability metrics.

Dynamic Resource Provisioning on Fog Landscapes

A huge amount of smart devices which have capacity of computing, storage, and communication to each other brings forth fog computing paradigm. Fog computing is a model in which the system tries to push data processing from cloud servers to “near” IoT devices in order to reduce latency time. The execution orderings and the deployed places of services make significant effect on the overall response time of an application. Beside new research directions in fog computing, e.g., fog-cloud collaboration, service scalability, fog scalability, mobile fog computing, fog federation, trade-off between energy consumption and communication efficiency, duration of storing data locally, storage security and communication security, and semantic-aware fog computing, the service deployment problem is one of the attractive research fields of fog computing. The service deployment is a multiobjective optimization problem; there are so many proposed solutions for various targets, such as response time, communication cost, and energy consumption. In this paper, we focus on the optimization problem which minimizes the overall response time of an application with awareness of network usage and server usage. Then, we have conducted experiments on two service deployment strategies, called cloudy and foggy strategies. We analyze numerically the overall response time, network usage, and server usage of those two strategies in order to prove the effectiveness of our proposed foggy service deployment strategy.

Privacy-aware cross-cloud service recommendations based on Boolean historical invocation records

In the age of big data, service recommendation has provided an effective manner to filter valuable information from massive data. Generally, by observing the past service invocation records (Boolean values) distributed across different cloud platforms, a recommender system can infer personalized preferences of a user and recommend him/her new services to gain more profits. However, the historical service invocation records are a kind of private information for users. Therefore, how to protect sensitive user data distributed across multiple cloud platforms is becoming a necessity for successful service recommendations. Additionally, the historical service invocation records often update with time, which call for an efficient and scalable service recommendation method. In view of these challenges, we introduce the multi-probe Simhash technique in information retrieval domain into the recommendation process and further put forward a privacy-preserving recommendation method based on historical service invocation records. At last, we design several experiments on the real-world service quality data in set WS-DREAM. Experimental results show the feasibility of the proposal in terms of producing accurate recommended results while protecting users’ private information contained in historical service invocation records.

Hybrid Telecoaching for Corporate Speech Training and Potential Applications to Clinical Practice

Purpose The purpose of this article was to describe a model for “hybrid speech telecoaching” developed for a Fortune 100 organization and offer a “thought starter” on how clinicians might think of applying these corporate strategies within future clinical practice. Conclusion The author contends in this article that corporate telecommunications and best practices gleaned from software development engineering teams can lend credibility to e-mail, messaging apps, phone calls, or other emerging technology as viable means of hybrid telepractice delivery models and offer ideas about the future of more scalable speech-language pathology services.

Editorial: IEEE Transactions on Sustainable Computing, Special Issue on Secure Sustainable Green Smart Computing

The booming development of cloud computing has resulted in a remarkable growth of multiple industries in which dramatic demands of green computing and sustainability are addressed. The platform of smart computing has provided an efficient approach for connecting various infrastructure such that many new technologies are eventually formed, such as Internet-of-Thing and ubiquitous computing. The concept of sustainable green smart computing has become a significant issue for those enterprises or practitioners who are engaging the implementations of smart computing aiming at a longer-term strategy. Considering the achievement of the real sustainability, one of the crucial values is to ensure all operations across different computing sources are under a secure executive environment. For reaching a high performance of securing sustainable green smart computing, many problems need to be solved. For example, one of the main challenges is to balance the costs among security, energy, performance, and sustainable requirements. The distribution of the computing resources in this issue is a great challenge because the real-time executions are usually constrained by multiple elements. An efficient approach of providing an adaptive and scalable service as well as addressing sustainability is an urgent research direction for current advanced cloud computing applications. Thus, this special issue aims at collecting updated outstanding papers that illustrate the latest achievements and development updates concerning the security solutions, issues, applications, trends, and implementations in sustainable green smart computing.

Flexible deployment of component-based distributed applications on the Cloud and beyond

In an effort to minimize operational expenses and supply users with more scalable services, distributed applications are actually going towards the Cloud. These applications, sent out over multiple environments and machines, are composed by inter-connecting independently developed services and components. The implementation of such programs on the Cloud is difficult and generally carried out either by hand or perhaps by composing personalized scripts. This is extremely error prone plus it has been found that misconfiguration may be the root of huge mistakes. We introduce AutoBot, a flexible platform for modeling, installing and (re)configuring complex distributed cloud-based applications which evolve dynamically in time. AutoBot includes three modules: A simple and new model describing the configuration properties and interdependencies of components; a dynamic protocol for the deployment and configuration ensuring appropriate resolution of these interdependencies; a runtime system that guarantee the proper configuration of the program on many virtual machines and, if necessary, the reconfiguration of the deployed system. This reduces the manual application deployment process that is monotonous and prone to errors. Some validation experiments were conducted on AutoBot in order to ensure that the proposed system works as expected. We also discuss the opportunity of reusing the platform in the transition of applications from Cloud to Fog computing.

CAP Theorem: Revision of Its Related Consistency Models

The CAP theorem states that only two of these properties can be simultaneously guaranteed in a distributed service: (i) consistency, (ii) availability and (iii) network partition tolerance. This theorem was stated and proved assuming that ‘consistency’ refers to atomic consistency. However, multiple consistency models exist and atomic consistency is located at the strongest edge of that spectrum. Many distributed services deployed in cloud platforms should be highly available and scalable. Network partitions may arise in those deployments and should be tolerated. One way of dealing with CAP constraints consists in relaxing consistency. Therefore, it is interesting to explore the set of consistency models not supported in an available and partition-tolerant service (CAP-constrained models). Other weaker consistency models could be maintained when scalable services are deployed in partitionable systems (CAP-free models). Three contributions arise: (i) multiple other CAP-constrained models are identified, (ii) a borderline between CAP-constrained and CAP-free models is set and (iii) a hierarchy of consistency models depending on their strength and convergence is built.

Shared Virtual Function Orchestration Technique

Network function virtualization (NFV) is a promising technique of high quality, flexible and scalable service for telecommunication companies clients and for enterprise data center clients. One of the important capabilities of this technique is providing a virtual service as a combination of multiple virtual functions. There are two types of virtual functions: those intended for a single customer (su-VF) and those that can serve multiple users (mu-VF). In case when output of mu-VF is chained with inputs of several different su-VFs, there is a need for a mechanism of identification and separation of users network flows passing through mu-VF to allocate them correctly between inputs of su-VFs in the NFV infrastructure. In the cloud environment, it is not always possible to use VLAN tags, IP and MAC addresses for that. In this paper, we consider the problem of identification of network traffic coming from a certain user inside an NFV platform and present a solution implemented in C2 MANO-platform.

Multi-Factorial Energy Aware Resource Management in Edge Networks

Edge networks deliver computing services close to the user, unlike centralized clouds. This improves service scalability and delay-sensitive functions can be offloaded to the edge, when the latency incurred by cloud services is too high. Since services in edge networks, by their nature, are not centralized, careful design is required to achieve efficient resource utilization and low power consumption. These issues are addressed in this paper. A network device power model is formulated to explore the power dissipation characteristics of frequency scalable CMOS devices (as measured using a NetFPGA testbed). An on-demand energy-efficient resource allocation model (OERA) is designed based on this model. OERA features acceptance ratios that are 11%–17% higher than existing solutions and 9% lower power consumption. A novel algorithm is presented for resource placement in edge networks, which can accommodate higher traffic flow demands and distribution distance than existing solutions. This uses mixed integer linear programming to simultaneously maximize the aggregate flow demands and to minimize the network energy consumption. An iterative algorithm and a heuristic greedy edge network device placement algorithm are implemented that not only solve this NP-Hard problem but also significantly reduce the network energy consumption.

Secure user authentication scheme with novel server mutual verification for multiserver environments

SummaryThe fast growth of mobile services and devices has made the conventional single‐server architecture ineffective from the point of its functional requirements. To extend the scalability and availability of mobile services to various applications, it is required to deploy multiserver architecture. In 2016, Moon et al insisted that Lu et al's scheme is weak to insiders and impersonation attack, then they proposed a biometric‐based scheme for authentication and key agreement of users in multiserver environments. Unfortunately, we analyze Moon et al's scheme and demonstrate that their scheme does not withstand various attacks from a malicious registered server. We propose a user authentication scheme with server mutual verification to overcome these security drawbacks. The proposed scheme withstands an attack from malicious insiders in multiserver environments. We use a threshold cryptography to strengthen the process of server authorization and to provide better security functionalities. We then prove the authentication and session key of the proposed scheme using Burrows‐Abadi‐Needham (BAN) logic and show that our proposed scheme is secure against various attacks.

Implementation and Utility of an Automated Text Messaging System to Facilitate Symptom Self-Monitoring and Identify Risk for Post-Traumatic Stress Disorder and Depression in Trauma Center Patients

Background and Introduction:Comprehensive monitoring and follow-up after traumatic injury is important for psychological recovery. However, scalable services to facilitate this are limited. Automated text message-based symptom self-monitoring (SSM) may be a feasible approach. This study examined its implementation and utility in identifying patients at risk for mental health difficulties after traumatic injury. Materials and Methods:Five hundred two patients admitted to a Level I trauma center between June 20, 2016 and July 31, 2017 were offered enrollment in a text message-based SSM service. Patients who enrolled received daily text message prompts over 30 days and most participated in a mental health screening 30 days postbaseline. Results:Approximately 67% of patients enrolled in the service; of these, 58% responded to the text messages, with an average response rate of 53%. Younger patients and those with elevated peritraumatic distress were more likely to enroll. Patients with higher levels of mental health stigma, who were White, or had been in a motor vehicle collision were more likely to enroll and respond to text messages once enrolled. Patients' daily ratings of distress detected clinically elevated 30-day mental health screens with high sensitivity (83%) and specificity (70%). Discussion and Conclusions:Text message-based SSM can be implemented as a clinical service in Level I trauma centers, and patient participation may increase engagement in mental health follow-up. Further, it can inform the use of risk assessments in practice, which can be used to identify patients with poor psychological recovery who require additional screening.

Meeting the Needs of Evolving and Growing Elder Population: A Framework for Life-Long Learning and Program Development

Background: The growing population of older adults will require specialized, adaptable and scalable services in order to maintain a quality of life. We offer an intergenerational and lifelong long learning model and provide guidelines for developing senior programming with relevance, accessibility and sustainability. Purpose: The aim of this work is to provide a framework for expanding and/or studying senior services. We also report on a preliminary needs assessment survey of older adults for the senior centers in their community. Method: A needs assessment questionnaire was developed and distributed to 56 men and women aged 50+ attending senior and/or community activities. Results: The vast majority (82%) were satisfied with the activities and services offered by their local senior or community center and most (74%) reported the cost was acceptable. Requested new activities included all-ages events, ukulele, scrap-booking, arts, and book clubs. Discussion: Centers looking to improve or increase senior programs should assess their local elders interests and needs. Delivering more varied activities to seniors could lead to numerous benefits, however, a number of real-world obstacles limit expanding senior services without increased cost. We recommend “senior centers” consider adding peer-mentors, and branching out to other ages, especially college students who have relevent skills and may need to volunteer for coursework or to explore career options.

Microservices Model to Enhance the Availability of Data for Buildings Energy Efficiency Management Services

In the Internet of Things (IoT)-supported energy data management infrastructure, objects from various energy generation and consumption terminals in buildings produce a tremendous amount of data. However, this data is not useful unless it is available on-time for services that discover meaningful information in order to provide intelligent decisions. The microservices-based data caching, data virtualization, data processing, data analysis, and data ingestion methods can be applied to enhance the data availability for energy efficiency management services provision across buildings. To foster building energy efficiency management services (BEEMS), Web of Objects (WoO) provides data abstraction, aggregation, and ingestion mechanism with virtual objects (VOs) and composite virtual objects (CVOs) by using ontologies and availability and scalability of services with microservices. This article proposes the use of data processing microservices modeling to enhance data availability and expose services capabilities with microservices for BEEMS. We present a semantic web agent based on an ontology for linking, enhancement, reusability, and availability of data-objects, services, and microservices. For the evaluation, we present a use case, which includes heterogeneous data collection and processing and provision of various BEEMS. A prototype for the use case scenario has been built and the results have been evaluated in the laboratory to mimic the enhanced data availability for BEEMS.

AAAS - Framework in Large Virtualized Environment

Objective: To define template architecture for large virtualized environment automation ecosystem. Methods/Statistical Analysis: This study proposes self-sustaining, highly scalable automation architecture with multiple services components supporting each other’s to deliver a reliable and scalable service in self-contained virtualized environment. This new proposed template architecture has been evaluated by building Proof of concept built using few readily available tools and few property application developed for this project. Finally we evaluated the architecture with varying load of storage, SAN networking and Backup and recovery workflow tasks. Stability, sustainability and self-load balancing capabilities are measure over time, by injecting dynamic work task to this framework. Findings: Large virtualized environment gives unlimited access to compute and storage resource on demand. One of the key enabler for this functionality is the robust automation framework. Due to limited technical expertise and lack any open source standard shinder migration of any existing mid-size virtualized IT assets into fully automated eco-system. This study tries to address this challenge by proposing new template open-ended architecture. This takes care of any organizational scalability, security, and compliance and maintainability issues. Application/Improvements: The proposed architecture can be used for implementing highly automated virtualized ecosystem in large virtualized environment. Keywords: Automation, Integrated Automation Framework, Orchestration, Scalable Architecture, Virtualized Environment

Scalability assurance process in replication and migration using cloud simulator

In service-oriented computing, service customers call up services deployed by service providers and service providers are responsible to deal with the excellence of their services including scalability, accessibility and performance. In particularly, scalability is tough to get, mainly due to the unidentified nature of service customers and changeable volumes of service invocations. Services with underprivileged scalability would be defeated the consumer basis, resultant in small revenues. Predictable approach to scalability of network, database and scattered computing are hardware-oriented solutions. In this paper, we present software-oriented approaches to guarantee elevated scalability of services in cloud computing. We first identify scalability in service-oriented computing, present suitable foundation of services scalability. Then, we present two efficient scalabilities assure scheme; service replication and migration. The future schemes are well-known from predictable scalability schemes in three ways. To give you an idea about the applicability of planned schemes, we demonstrate a result of implementing the scheme and experiments with them. Through means of the methods, services scalability can be to the highest degree improved enthusiastically by using software solutions. Growth of large network infrastructures have led to several systems that design large-scale distributed systems supporting efficient, secure and available services.

MONIT: Monitoring the CERN Data Centres and the WLCG Infrastructure

The new unified monitoring architecture (MONIT) for the CERN Data Centres and for the WLCG Infrastructure is based on established open source technologies to collect, stream, store and access monitoring data. The previous solutions, based on in-house development and commercial software, have been replaced with widely- recognized technologies such as Collectd, Kafka, Spark, Elasticsearch, InfluxDB, Grafana and others. The monitoring infrastructure, fully based on CERN cloud resources, covers the whole workflow of the monitoring data: from collecting and validating metrics and logs to making them available for dashboards, reports and alarms. The deployment in production of this new DC and WLCG monitoring is well under way and this contribution provides a summary of the progress, hurdles met and lessons learned in using these open source technologies. It also focuses on the choices made to achieve the required levels of stability, scalability and performance of the MONIT monitoring service.

Improvement of Privacy and Security in Hybrid Cloud with Attribute Group Based Access Control

Cloud computing enables users to accumulate their sensitive data into cloud service providers to achieve scalable services on-demand. Outstanding security requirements arising from this means of data storage and management include data security and privacy. Attribute-based Encryption (ABE) is an efficient encryption system with fine-grained access control for encrypting out-sourced data in cloud computing. Since data outsourcing systems require flexible access control approach Problems arises when sharing confidential corporate data in cloud computing. User-Identity needs to be managed globally and access policies can be defined by several authorities. Data is dual encrypted for more security and to maintain De-Centralization in Multi-Authority environment.

Improving NoSQL Storage Schema Based on Z-Curve for Spatial Vector Data

NoSQL database can provide massive, high concurrency, and scalable services for storing different types of data. HBase, a type of NoSQL database, in which columns are grouped into column families, is very suitable for storing semi-structured or unstructured spatial vector data. However, since there are few rules and constraints to be followed for the NoSQL database, the design of storage schema for spatial data based on NoSQL is difficult. In this paper, based on our early work, an improved Z-curve storage schema is proposed for spatial vector data. According to our new schema, row key of a geometric object is the Z-curve code of the spatial grids intersected with the geometric object. Moreover, geometric objects with the same row key are stored in a column family. Our proposed method has two features. First, geometric objects adjacent in the location are adjacent in physical storage. Second, redundant exists in storage for improving query accuracy. In our experiments, we compare the improved Z-curve storage schema with a Quadtree storage schema, an R-tree storage schema, and the previous Z-curve storage schema. Query response time, memory usage, and the query accuracy of spatial query on point and range are used to verify the validity of our proposed method. The experimental results show that the two storage schemas based on Z-curve achieve higher query efficiency than the two storage schemas based on tree-the Quadtree storage schema and the R-tree storage schema. More importantly, the query results of the improved Z-curve schema are completely correct, while the query results of the previous Z-curve schema are not.