Service Instances Research Articles

Trusted Service Provider Discovery Based on Data, Information, Knowledge, and Wisdom

Data, information, knowledge, and wisdom forms a progressive relationship. Information is formed by data collation. Knowledge is filtered, refined, and processed from relevant information. Wisdom is based on knowledge and is accumulated through experience. This paper uses the progressive relationship of service data, information, knowledge, and wisdom to explain the expression of service knowledge graph. It is an increasingly challenging demand to discover trusted Cloud service providers with service data, information, and knowledge. We propose an efficient method of trusted service provider discovery based on service knowledge graphs, called PDG (Provider Discovery based on Graphs), to ensure that each service instance of composite services in Cloud systems is trustworthy. PDG evaluates the outputs of service providers in service classes with the help of additional service information. According to the additional service information, service knowledge is generated and trusted service providers can be found easily. PDG improves the accuracy of processing results by automatically replacing data provided by untrusted service providers with results provided by trusted service providers.

Virtualization in Distributed System: A Brief Overview

Virtual machines are popular because of their efficiency, ease of use & flexibility. There has been a growing need for the development of a resilient distributed network in order to maximise system performance while lowering infrastructure costs. The creation of a robust distributed network has become more important in order to improve system performance while minimising infrastructure costs. The paper gives an overview of various types of virtualization techniques & their benefits. For eg: Server virtualization helps to create multiple server instances from one physical server. Such techniques will decrease the infrastructure cost, make the system more scalable & help in full utilization of available resources.

Virtualization in Distributed System: A Brief Overview

Virtual machines are popular because of their efficiency, ease of use and flexibility. There has been an increasing demand for deployment of a robust distributed network for maximizing the performance of such systems and minimizing the infrastructural cost. In this paper we have discussed various levels at which virtualization can be implemented for distributed computing which can contribute to increased efficiency and performance of distributed computing. The paper gives an overview of various types of virtualization techniques and their benefits. For eg: Server virtualization helps to create multiple server instances from one physical server. Such techniques will decrease the infrastructure cost, make the system more scalable and help in full utilization of available resources.

Autoscalabile ditributed anti-spam SMTP system based on kubernetes

Due to the increasing amount of spam email traffic, email users are in increasing danger, while email server resources are becoming overloaded. Therefore, it is necessary to protect email users, but also to prevent SMTP system overload during spam attacks. The aim of this paper is to design and implement an autoscalable distributed anti-spam SMTP system based on a Proof of work concept. The proposed solution extends SMTP protocol in order to enable the evaluation of the client?s credibility using the Proof of work algorithm. In order to prevent resource overload during spam attacks, the anti-spam SMTP system is implemented in a distributed environment, as a group of multiple anti-spam SMTP server instances. Kubernetes architecture is used for system distribution, configured with the possibility of autoscaling the number of antispam SMTP server instances depending on the system load. The implemented system is evaluated during a distributed spam attempt, simulated by a custom-made traffic generator tool. Various performance tests are given: (1) The proposed system?s impact on client?s behaviour and the overall amount of spam messages, (2) The performance of the undistributed anti-spam SMTP server during spam attack, in terms of resource load analysis (3) Autoscaling demonstration and evaluation of proposed distributed system?s performance during a spam attack. It is shown that the proposed solution has the possibility of reducing the amount of spam traffic, while processing tens of thousands of simultaneous SMTP client requests in a distributed environment.

Behavior-Driven Quality First Agile Testing for Cloud Service

In a recent testing software system, with cloud implementation a cloud service abstraction allows providing high-level ubiquitous language (UL) compositions through behavior-driven step-wise agile cycles. By adapting this UL the instantiated behaviors simplify the demand in software quality through mechanisms to cope with complex digital transformation and evolution. However, in such a context, testing becomes challenging quality engineering. As a result, it poses a threat to the services on a cloud as the access to its source codes rely on these abstractions. The aim is to introduce a way by strictly focusing on a black box approach. One way is to realize the client-side continuous Quality First (QF)-Test behavior-driven development (BDD). On this point, a meta-model helps to transform the RESTful cloud specification through domain specific language (DSL) while accommodating a low-level details on a test coverage report. By using features from the user stories the Gherkin BDD styles enable the meta-model which creates the instances of DSL to implement the runnable test steps on a cucumber framework. Each step is designed in a GraphWalker through modeling a context finite machine via a model visual editor, and generates the dependency test model paths. As an evaluation, the QF-Test executes the required steps given by data-driven elements for creating run-log trace analysis. As a comparison, the Jenkins framework is configured to build the QF-Test node of test suites for generating the behavior-driven and continuous integration test report. Moreover, the steps with the keywords are automated to verify the GraphWalker test cases through traversing the paths generated. As a case application, a sample REST API mobility service instance is considered.

AI-enabled mobile multimedia service instance placement scheme in mobile edge computing

Leveraging cloud infrastructure to the mobile edge computing helps the mobile users to get real time multimedia services in Fifth Generation (5G) network system. To ensure higher Quality-of-Experience (QoE), faster migration of mobile multimedia service instances is required to cope up with user mobility. By deploying the mobile multimedia service instances proactively in multiple edge nodes (ENs) helps the users to get higher QoE. However, excessive deployment of service replicas might increase the cost of the overall network. To establish trade-off between these two conflicting objectives, we have formulated the problem as a Multi-objective Integer Linear Programming (MILP) by integrating the users’ path prediction model. This problem is proven to be an NP-hard one for large networks, thus we develop an artificial intelligence (AI) based meta-heuristic Binary Particle Swarm Optimization (BPSO) algorithm to achieve near-optimal solution within polynomial time. The performance analysis results show the significant performance improvement in terms of QoE and user satisfaction as compared to other state-of-the-art works.

A Taxonomy and Survey of Power Models and Power Modeling for Cloud Servers

Due to the increasing demand of cloud resources, the ever-increasing number and scale of cloud data centers make their massive power consumption a prominent issue today. Evidence reveals that the behaviors of cloud servers make the major impact on data centers’ power consumption. Although extensive research can be found in this context, a systematic review of the models and modeling methods for the entire hierarchy (from underlying hardware components to the upper-layer applications) of the cloud server is still missing, which is supposed to cover the relevant studies on physical and virtual cloud server instances, server components, and cloud applications. In this article, we summarize a broad range of relevant studies from three perspectives: power data acquisition, power models, and power modeling methods for cloud servers (including bare-metal, virtual machine (VM), and container instances). We present a comprehensive taxonomy on the collection methods of server-level power data, the existing mainstream power models at multiple levels from hardware to software and application, and commonly used methods for modeling power consumption including classical regression analysis and emerging methods like reinforcement learning. Throughout the work, we introduce a variety of models and methods, illustrating their implementation, usability, and applicability while discussing the limitations of existing approaches and possible ways of improvement. Apart from reviewing existing studies on server power models and modeling methods, we further figure out several open challenges and possible research directions, such as the study on modeling the power consumption of lightweight virtual units like unikernel and the necessity of further explorations toward empowering server power estimation/prediction with machine learning. As power monitoring is drawing increasing attention from cloud service providers (CSPs), this survey provides useful guidelines on server power modeling and can be inspiring for further research on energy-efficient data centers.

Spotlight: Scalable Transport Layer Load Balancing for Data Center Networks

Load Balancing plays a vital role in cloud data centers to distribute traffic among instances of network functions or services. State-of-the-art load balancers dispatch traffic obliviously without considering the real-time utilization of service instances and therefore can lead to uneven load distribution and sub-optimal performance. In this article, we design and implement Spotlight, a scalable and distributed load balancing architecture that maintains connection-to-instance mapping consistency at the edge of data center networks. Spotlight uses a new stateful flow dispatcher which periodically polls instances’ load and dispatches incoming connections to instances in proportion to their available capacity. Our design utilizes a distributed control plane and in-band flow dispatching; thus, it scales horizontally in data center networks. Through extensive flow-level simulation and packet-level experiments on a testbed with HTTP traffic on unmodified Linux kernel, we demonstrate that compared to existing methods Spotlight distributes traffic more efficiently and has near-optimum performance in terms of overall service utilization. Compared to existing solutions, Spotlight improves aggregated throughput and average flow completion time by at least 20 percent with infrequent control plane updates. Moreover, we show that Spotlight scales horizontally as it updates the switches at O(100ms) and is resilient to lack of control plane convergence.

CDN Slicing over a Multi-Domain Edge Cloud

We present an architecture for the provision of video Content Delivery Network (CDN) functionality as a service over a multi-domain cloud. We introduce the concept of a CDN slice, that is, a CDN service instance which is created upon a content provider's request, is autonomously managed, and spans multiple, potentially heterogeneous, edge cloud infrastructures. Our design is tailored to a 5G mobile network context, building on its inherent programmability, management flexibility, and the availability of cloud resources at the mobile edge level, thus close to end users. We exploit Network Functions Virtualization (NFV) and Multi-access Edge Computing (MEC) technologies, proposing a system which is aligned with the recent NFV and MEC standards. To deliver a Quality-of-Experience (QoE) optimized video service, we derive empirical models of video QoE as a function of service workload, which, coupled with multi-level service monitoring, drive our slice resource allocation and elastic management mechanisms. These management schemes feature autonomic compute resource scaling, and on-the-fly transcoding to adapt video bit-rate to the current network conditions. Their effectiveness is demonstrated via testbed experiments.

Rule based auto-scalability of IoT services for efficient edge device resource utilization

Conveying the workload of IoT systems from the cloud to edge nodes have been widely adopted by industrial and academic sectors. This tendency is generally promoted to meet the requirements of some time-sensitive use cases such as IoT healthcare applications. However, IoT devices at the edge network are likely to be resource-limited, as well as, they perform under an extremely heterogeneous environment in terms of the connected devices and the deployed software modules. Thus, both of the aforementioned concerns have considerably led to hindering the deployment process of services on IoT edge devices. In this paper, we propose an approach to facilitate a scalable and lightweight solution for service deployment for efficient resource utilization on IoT edge nodes. Our solution is based on the container concept, and we adopt the cluster concept to define a group of IoT edge devices. Containers are lightweight virtualization technique that enables services to be packaged and deployed with their dependencies regardless of the hosts infrastructure, as well as, they facilitate the service communication and the update process. Furthermore, containers are supported by some means of orchestration such as swarm. These orchestration tools can be configured to enable services deployment and resources sharing among IoT edge devices falling within the same cluster. However, they lack elasticity in terms of auto-scaling up/down of services instances in corresponding to the resource utilization of all cluster elements, as well as, service performance metrics. Our approach overcomes these limitations by following an auto-scaling process based on MAPE-K loop, which is based on our proposed rule model to generate a scaling plan by analyzing collected performance metrics of a cluster. Our evaluation shows the efficiency of the proposed approach in adapting the system performance to meet service performance requirements and the availability of system resources.

Smart City IoT Services Creation Through Large-Scale Collaboration

Smart cities solutions are often monolithically implemented, from sensors data handling through to the provided services. The same challenges are regularly faced by different developers, for every new solution in a new city. Expertise and know-how can be re-used and the effort shared. In this article we present the methodologies to minimize the efforts of implementing new smart city solutions and maximizing the sharing of components. The final target is to have a live technical community of smart city application developers. The results of this activity comes from the implementation of 35 city services in 27 cities between Europe and South Korea. To share efforts, we encourage developers to devise applications using a modular approach. Single-function components that are re-usable by other city services are packaged and published as standalone components, named Atomic Services. We identify 15 atomic services addressing smart city challenges in data analytics, data evaluation, data integration, data validation, and visualization. 38 instances of the atomic services are already operational in several smart city services. We detail in this article, as atomic service examples, some data predictor components. Furthermore, we describe real-world atomic services usage in the scenarios of Santander and three Danish cities. The resulting atomic services also generate a side market for smart city solutions, allowing expertise and know-how to be re-used by different stakeholders.

An Ant Colony Optimization-Based Multiobjective Service Replicas Placement Strategy for Fog Computing.

In recent years, fog computing has emerged as a new paradigm for the future Internet-of-Things (IoT) applications, but at the same time, ensuing new challenges. The geographically vast-distributed architecture in fog computing renders us almost infinite choices in terms of service orchestration. How to properly arrange the service replicas (or service instances) among the nodes remains a critical problem. To be specific, in this article, we investigate a generalized service replicas placement problem that has the potential to be applied to various industrial scenarios. We formulate the problem into a multiobjective model with two scheduling objectives, involving deployment cost and service latency. For problem solving, we propose an ant colony optimization-based solution, called multireplicas Pareto ant colony optimization (MRPACO). We have conducted extensive experiments on MRPACO. The experimental results show that the solutions obtained by our strategy are qualified in terms of both diversity and accuracy, which are the main evaluation metrics of a multiobjective algorithm.

Microscaler: Cost-Effective Scaling for Microservice Applications in the Cloud With an Online Learning Approach

Recently, the microservice becomes a popular architecture to construct cloud native systems due to its agility. In cloud native systems, autoscaling is a key enabling technique to adapt to workload changes by acquiring or releasing the right amount of computing resources. However, it becomes a challenging problem in microservice applications, since such an application usually comprises a large number of different microservices with complex interactions. When the performance decreases due to an unpredictable workload peak, it is difficult to pinpoint the scaling-needed services which need to scale out and evaluate how many resources they need. In this article, we present a novel system named <i>Microscaler</i> to automatically identify the scaling-needed services and scale them to meet the Service Level Agreement (SLA) with an optimal cost for microservice applications. <i>Microscaler</i> first collects the quality of service (QoS) metrics in the service mesh enabled microservice infrastructure. Then, it determines under-provisioning or over-provisioning service instances along the service dependency graph with a novel scaling-needed service criterion named <i>service power</i>. The service dependency graph could be obtained by correlating each request flow in the service mesh. By combining an online learning approach and a step-by-step heuristic approach, <i>Microscaler</i> can precisely reach the optimal service scale meeting the SLA requirements. The experimental evaluations in a microservice benchmark show that <i>Microscaler</i> achieves an average 93 percent precision in scaling-needed service determination and converges to the optimal service scale faster than several state-of-the-art methods. Moreover, <i>Microscaler</i> is lightweight and flexible enough to work in a large-scale microservice system.

Efficient Horizontal Scaling of Databases using Data Sharding Technique

As the new era of computing technology begins with the start of the 21st century, the data produced by each person are increasing enormously day-by-day. Companies started to track one’s personal details from applications like media sharing, social networks, eCommerce, etc. since they have started to generate a lot of data traffic. The Database, where these heavy loads of generated data have been stored, had to scale out to handle the network traffic. Cloud computing arrival carves a solution for this trafficking problem by making data sharding a suitable option for scaling the data. This paper discusses about the importance of database sharding and the distribution of the database over the different server and also how sharding is helpful to scale the incoming amount of enormous data to control the data trafficking issues in the cloud database. Also compares the difference in the performance of database with and without sharding techniques using SQL server instance.

Scheduling Workflows With Composite Tasks: A Nested Particle Swarm Optimization Approach

Scientific cloud workflows enable the access to distributed computing resources in cloud environments for executing scientific computing applications. In the literature, most workflow scheduling models assume that each workflow task is mapped to only one service instance. But in computation and data-intensive applications, it is common that the computation resources provided by a single service instance are insufficient for some complicated tasks which contain several closely correlated sub-tasks. To manage such complicated workflows, this article devises a novel workflow model with composite tasks (cWFS). The model views a complicated task as a composite task and allows mapping multiple service instances to a composite task. The data transmission among sub-tasks of a composite task can also be addressed by the proposed model. To solve cWFS problem, we devise a nested particle swarm optimization (N-PSO) that utilizes two kinds of populations, i.e., the outer population and inner population. Since N-PSO is a bit time-consuming, we further devise a Fast version of N-PSO (FN-PSO), which can save more than 60 percent of running time compared with N-PSO. The proposed approaches are evaluated on five real-world workflow types. The experimental results verify that the proposed approaches can solve the new workflow model effectively.

Illicit to legal: marijuana as a de-stigmatising product and the role of social acceptability in new product adoption

ABSTRACT History has shown instances of products and services, which have, and in some cases still are, moving from illicit to legal by their associated regulatory systems. While not ‘new’ in the sense that these are entirely new to consumers, these products are ‘new’ to the markets they become a part of. These products and services operate in peculiar ways as they transition to established and/or licit markets and have unique adoption hurdles to overcome because of the negative stigma that typically surrounds them. Using the U.S. marijuana market as an empirical setting, this research examines a novel type of products, which we term de-stigmatising products. We introduce the concept of de-stigmatising products to new product and innovation literature and examine the de-stigmatisation of a product in regulatory flux. While results show that traditional new product constructs (e.g., product quality and consumer opinion leadership) influence the adoption of de-stigmatising products, we find that the perception of the social acceptability of de-stigmatising products – a new construct developed for this study – provides additional explanatory power on adoption of de-stigmatising products and in turn, the adoption of really new products. We also find that social acceptability of de-stigmatising products moderates traditional product adoption constructs. Important theoretical and managerial implications of our findings are also discussed.

Dynamic Provisioning of Service Composition in a Multi-Tenant SaaS Environment

Multi-tenant service composition has become a common delivery model for business processes in cloud computing. To dynamically support the workload tenant variation, elasticity holds the promise of ensuring the quality of service (QoS) of the business process by providing the involved service instances at a low cost. However, integrating both of multi-tenancy and elasticity during service composition is a key problem for serving multiple tenants from a single process instance. Nowadays, existing approaches in the field of cloud service composition, although numerous, still fall short since they cannot adequately address issues related to supporting the scalability of the composed service and adapting it to the workload fluctuation. In this paper, we propose a holistic approach which makes the dynamic multi-tenant services matching and manages their elasticity in distributed business processes. This approach is based on a generic service pattern that integrates multi-tenancy property and handles elasticity at the process and service levels. Furthermore, we present elastic composition algorithms to compose multi-tenant cloud services and perform their elasticity through the proposed service pattern. The evaluation of our approach, compared to the baseline approach, proves that the latency taken to provide an elastic multi-tenant service composition and detect its SLA (Service Level Agreements) violation are reasonably short. We also show that the CPU overhead of using our approach is negligible. Furthermore, experimental results demonstrate the merits of our approach in terms of minimizing the memory consumption through the deployed service instances.

The influence of internet architecture on centralised versus distributed internet services

ABSTRACT The internet evolves rapidly as innovations in technology, applications and business emerge. In some cases, the changes have also given rise to the creation of centralised service deployment models and industry consolidation. This paper focuses on the question of how internet technology and open interfaces may affect the ability to deploy services in a collaborative fashion vs in a centralised fashion. The paper presents a categorisation of factors influencing these choices and discusses these factors in the context of several case studies. Some aspects of centralisation and consolidation are direct consequences of physics. A large organisation can provide a short round-trip time to users around the globe, due to service instances in many locations. Other aspects are due to economics. For instance, network effects cause the value of a service to grow per Metcalfe’s law. But technology and deployment choices also have an effect. Federated, collaborative networks with open standardised interfaces (such as email) allow multiple service providers to interact with each other. Closed systems may not allow this. Many popular social networks fall in this category. While technology is not the main driving force behind what are often business and economic decisions, awareness of technology choices makes it easier to understand the likely impacts of a chosen model.

The design and application of landslide monitoring and early warning system based on microservice architecture

The establishment of monitoring and warning systems for landslide with potential instability risk is an economical and effective way to ensure life safety and reduce property losses. However, as the number of landslides and sensors increases, the amount of monitoring data generated is close to massive, and the traditional monitoring systems are gradually unable to manage and analyze the acquired data. In this study, an intelligent monitoring and early warning system and its application were developed based on microservice architecture. In this system, the functions of landslide’s project management, data reception and parsing, data analysis and early warning are distributed to multiple service instances in the form of microservice, thereby achieving the purpose of decentralizing service pressure. The application results of the system indicated that the microservice architecture could decouple components and balance distributed loads in the system, thereby reducing the difficulties of algorithm development, decentralizing calculation and service pressures and improving the service capacity of the monitoring system.

Development of tunnel intelligent monitoring and early warning system based on micro-service architecture: the case of AnPing tunnel

Along with the development of computer science and communication techniques, tunnel monitoring has been digital and intellectualized gradually. In recent years, traditional tunnel monitoring systems have been becoming powerless to deal with huge amounts of data as the number of tunnels and monitoring sensors surge. In this study, an innovative and comprehensive intelligent tunnel monitoring system was designed based on micro-service architecture. Various function modules were distributed to multiple service instances to get the computing pressure decentralized. The system offered powerful massive data processing support, flexibility, and extensibility. For a certain tunnel, the system could acquire the monitoring data in real-time using wireless sensors deployed on its surface. When data reached the early warning threshold, the system would immediately transmit the warning message to users. Besides, an app was developed to offer support for wireless communication between the monitoring apparatus and watchers. The application of the Anping case, one of the monitoring targets, proved the high efficiency of data transmitting and effective emergency response of the developed system. The results indicated that the microservice architecture could facilitate algorithm development, decentralize calculation pressure, and promote the service capacity by decoupling components and distributing loads.