Multiple Tenants Research Articles

Customization of multi-tenant learning process as a service with Business Process Feature Model

Cloud computing has a model for utility computing that promotes on-demand scalability, flexible application deployment and reuse. It also promotes multi-tenancy for efficient resource utilization by sharing hardware and software infrastructure among multiple users. Multi-tenant applications running on a cloud infrastructure are provided to clients as Software-as-a-Service (SaaS) over the network. In this context, Software product lines (SPL) promote reusable application development for product families. On the other hand, business processes of most organizations are automated to support the advanced workflow management systems. In the context of the e-learning applications, it is perfectly suitable to manage the e-learning application by automating the e-learning process to effectively manage multiple tenants’ (institutions/teachers) ever changing requirements. As the available LMSs such as Moodle, Claroline and WebCT do not satisfy all the needs of different institutions/teachers, their demand is to develop their own systems. Cloud-based applications can be modeled as Business Process-as-a-Service (BPaas) families similar to the SPL products. As SPL development techniques rely on feature models to describe the commonality and variability of family member applications, such techniques can be used to model variability in BPaas. This paper proposes a generative multi-tenant e-learning process, that utilizes Business Process Feature Model (BPFM) approach which is a combination of SPL approach and BPM approach for modeling the reconfigurable e-learning process with multi-tenancy capabilities in cloud computing (BPaas).

Dynamic Slicing Approach for Multi-Tenant 5G Transport Networks [Invited

Software defined networking allows network providers to share their physical network (PN) among multiple tenants by means of network slicing, where several virtual networks (VNs) are provisioned on top of the physical one. In this scenario, PN resource utilization can be improved by introducing advanced orchestration functionalities that can intelligently assign and redistribute resources among the slices of different tenants according to the temporal variation of the VN resource requirements. This is a concept known as dynamic slicing. This paper presents a solution for the dynamic slicing problem in terms of both mixed integer linear programming formulations and heuristic algorithms. The benefits of dynamic slicing are compared against static slicing, i.e., an approach without intelligent adaptation of the amount of resources allocated to each VN. Simulation results show that dynamic slicing can reduce the VN rejection probability by more than 1 order of magnitude compared to static slicing. This can help network providers accept more VNs into their infrastructure and potentially increase their revenues. The benefits of dynamic slicing come at a cost in terms of service degradation (i.e., when not all the resources required by a VN can be provided), but the paper shows that the service degradation level introduced by the proposed solutions is very small.

ONVisor: Towards a scalable and flexible SDN‐based network virtualization platform on ONOS

SummaryNetwork virtualization (NV) technologies have attracted a lot of attention as an essential solution for future networking infrastructure. The NV enables multiple tenants to share the same physical infrastructure and to create independent virtual networks (VNs) by decoupling the physical network in terms of topology, address, and control functions. One feasible way to realize full NV involves considering solutions based on the software‐defined networking (SDN) paradigm using its programmability. The SDN contributes many benefits to both network operations and management including programmability, agility, elasticity, and flexibility. There are several SDN‐based NV solutions; however, they suffered from a lack of scalability, high availability. Also, they have high latency between control and data plane because of proxy‐based architecture. In this thesis, we introduce a new NV platform, named Open Network Hypervisor (ONVisor). The design objectives include, among the features, (1) multitenancy, (2) scalability, (3) flexibility, (4) isolated VNs, and (5) VN federation. ONVisor was designed and implemented by extending Open Network Operating System, an open‐source SDN controller. The main features of ONVisor are (1) isolated control and data plane per VN, (2) support of distributed operations, (3) extensible translators, (4) on‐platform VN application development and execution, and (5) support of heterogenous SDN data‐plane implementations. Several experiments are conducted on various test scenarios in different test environments in terms of control and data plane performance compared to nonvirtualized SDN network. The results show that ONVisor can provide VNs a little bit lower control plane performance and similar data plane performance.

IAACaaS: IoT Application-Scoped Access Control as a Service

access control is a key element when guaranteeing the security of online services. However, devices that make the Internet of Things have some special requirements that foster new approaches to access control mechanisms. Their low computing capabilities impose limitations that make traditional paradigms not directly applicable to sensors and actuators. In this paper, we propose a dynamic, scalable, IoT-ready model that is based on the OAuth 2.0 protocol and that allows the complete delegation of authorization, so that an as a service access control mechanism is provided. Multiple tenants are also supported by means of application-scoped authorization policies, whose roles and permissions are fine-grained enough to provide the desired flexibility of configuration. Besides, OAuth 2.0 ensures interoperability with the rest of the Internet, yet preserving the computing constraints of IoT devices, because its tokens provide all the necessary information to perform authorization. The proposed model has been fully implemented in an open-source solution and also deeply validated in the scope of FIWARE, a European project with thousands of users, the goal of which is to provide a framework for developing smart applications and services for the future Internet. We provide the details of the deployed infrastructure and offer the analysis of a sample smart city setup that takes advantage of the model. We conclude that the proposed solution enables a new access control as a service paradigm that satisfies the special requirements of IoT devices in terms of performance, scalability and interoperability.

Multi-Tenant Radio Access Network Slicing: Statistical Multiplexing of Spatial Loads

This paper addresses the slicing of Radio Access Network (RAN) resources by multiple tenants, e.g., virtual wireless operators and service providers. We consider a criterion for dynamic resource allocation amongst tenants, based on a weighted proportionally fair objective, which achieves desirable fairness/protection across the network slices of the different tenants and their associated users. Several key properties are established, including: the Pareto optimality of user association to base stations, the fair allocation of base stations resources, and the gains resulting from dynamic resource sharing across slices, both in terms of utility gains and capacity savings. We then address algorithmic and practical challenges in realizing the proposed criterion. We show that the objective is NP-hard, making an exact solution impractical, and design a distributed semi-online algorithm which meets performance guarantees in equilibrium and can be shown to quickly converge to a region around the equilibrium point. Building on this algorithm, we devise a practical approach with limited computational, information, and handoff overheads. We use detailed simulations to show that our approach is indeed near-optimal and provides substantial gains both to tenants (in terms of capacity savings) and end-users (in terms of improved performance).

Feedback for increased robustness of forwarding graphs in the cloud

Abstract Cloud computing technology provides the means to share physical resources among multiple users and data center tenants by exposing them as virtual resources. There is a strong industrial drive to use similar technology and concepts to provide timing sensitive services. One such domain is a chain of connected virtual network functions. This allows the capacity of each function to be scaled up and down by adding or removing virtual resources. In this work, we develop a model of such service chain and pose the dynamic allocation of resources as an optimization problem. We design and present a set of strategies to allow virtual network nodes to be controlled in an optimal fashion subject to latency and buffer constraints. Furthermore, we derive a feedback-law for dynamically adjusting the amount of resources given to each functions in order to ensure that the system remains in the desired state even if there are modeling errors or for a stochastic input.

Line: Evaluating Software Applications in Unreliable Environments

Cloud computing has paved the way to the flexible deployment of software applications. This flexibility offers service providers a number of options to tailor their deployments to the observed and foreseen customer workloads, without incurring in large capital costs. However, cloud deployments pose novel challenges regarding application reliability and performance. Examples include managing the reliability of deployments that make use of spot instances, or coping with the performance variability caused by multiple tenants in a virtualized environment. In this paper, we introduce L ine , a tool for performance and reliability analysis of software applications. L ine solves layered queueing network (LQN) models, a popular class of stochastic models in software performance engineering, by setting up and solving an associated system of ordinary differential equations. A key differentiator of L ine compared to existing solvers for LQNs is that L ine incorporates a model of the environment the application operates in. This enables the modeling of reliability and performance issues such as resource failures, server breakdowns and repairs, slow start-up times, resource interference due to multitenancy, among others. This paper describes the L ine tool, its support for performance and reliability modeling, and illustrates its potential by comparing L ine predictions against data obtained from a cloud deployment. We also illustrate the applicability of L ine with a case study on reliability-aware resource provisioning.

Mitigating incast-TCP congestion in data centers with SDN

In data center networks (DCNs), the presence of long-lived TCP flows tends to bloat the switch buffers. As a consequence, short-lived TCP-incast traffic suffers repeated losses that often lead to loss recovery via timeout. Because the minimum retransmission timeout (minRTO) in most TCP implementations is fixed to around 200 ms, interactive applications that often generate short-lived incast traffic tend to suffer unnecessarily long delays waiting for the timeout to elapse. The best and most direct solution to such problem would be to customize the minRTO to match DCNs delays; however, this is not always possible; in particular in public data centers where multiple tenants, with various versions of TCP, co-exist. In this paper, we propose to achieve the same result by using techniques and technologies that are already available in most commodity switches and data centers and that do not interfere with the tenant’s virtual machines or TCP protocol. In this approach, we rely on the programmable nature of SDN switches and design a SDN-based incast congestion control (SICC) framework, that uses a SDN network application in the controller and a shim-layer in the host hypervisor, to mitigate incast congestion. We demonstrate the performance gains of the proposed scheme via real deployment in a small-scale testbed as well as ns2 simulation experiments in networks of various sizes and settings.

5G-Crosshaul Network Slicing: Enabling Multi-Tenancy in Mobile Transport Networks

5G requires a redesign of transport networks in order to feed the increasingly bandwidth hungry radio access networks and to benefit from the performance/cost efficiency provided by the integration of both backhaul and fronthaul segments over the same transport substrate as well as the incorporation of cloud RAN architectures. In addition, to increase its usage and cost efficiency, this new transport network should allow simultaneous use by different tenants (e.g. MVNOs, OTTs, and vertical industries). This article presents the 5G transport network architecture designed in the 5G-Crosshaul project to address this challenge. An SDN/NFV-based control plane has been designed that enables multi-tenancy through network slicing. The proposed solution allows for flexible and efficient allocation of transport network resources (networking and computing) to multiple tenants by leveraging on widespread architectural frameworks for NFV (ETSI NFV) and SDN (e.g., Open Daylight and ONOS).

A Spot Capacity Market to Increase Power Infrastructure Utilization in Multi-Tenant Data Centers

Despite the common practice of oversubscription, power capacity is largely under-utilized in data centers. A significant factor driving this under-utilization is fluctuation of the aggregate power demand, resulting in unused "spot (power) capacity". In this paper, we tap into spot capacity for improving power infrastructure utilization in multi-tenant data centers, an important but under-explored type of data center where multiple tenants house their own physical servers. We propose a novel spot capacity market, called SpotDC, to allocate spot capacity to tenants on demand. Specifically, SpotDC extracts tenants' rack-level spot capacity demand through an elastic demand function, based on which the operator sets the market price for spot capacity allocation. We evaluate SpotDC using both testbed experiments and simulations, demonstrating that SpotDC improves power infrastructure utilization and creates a "win-win" situation: the data center operator increases its profit (by nearly 10%), while tenants improve their performance (by 1.2{1.8x on average, yet at a marginal cost).

Optimizing Differentiated Latency in Multi-Tenant, Erasure-Coded Storage

Erasure codes are widely used in distributed storage systems since they provide space-optimal data redundancy to protect against data loss. Despite recent progress on quantifying average service latency when erasure codes are employed, there is very little work on providing differentiated latency among multiple tenants that may have different latency requirements. This paper proposes a novel framework for providing and optimizing differentiated latency in erasure-coded storage by investigating two policies, weighted queue and priority queue, for scheduling tenant requests. For both policies, we quantify service latency for different tenant classes for homogeneous files with arbitrary placement and service time distributions. We develop an optimization framework that jointly minimizes differentiated latency over three decision spaces: 1) data placement; 2) request scheduling; and 3) resource management. Efficient algorithms harnessing bipartite matching and convex optimization techniques are developed to solve the proposed optimization. Our solution enables elastic service-level agreements to meet heterogeneous application requirements. We further prototype our solution with both queuing models applied in an open-source, cloud storage deployment that simulates three geographically distributed data centers through bandwidth reservations. Experimental results validate our theoretical delay analysis and show significant joint latency reduction for different classes of files, providing valuable insights into service differentiation and elastic quality of service in erasure-coded storage systems.

Efficient QoS-Aware Service Recommendation for Multi-Tenant Service-Based Systems in Cloud

The popularity of cloud computing has fueled the growth in multi-tenant service-based systems (SBSs) that are composed of selected cloud services. In the cloud environment, a multi-tenant SBS simultaneously serves multiple tenants that usually have differentiated QoS requirements. This unique characteristic further complicates the problems of QoS-aware service selection at build-time and system adaptation at runtime, and renders conventional approaches obsolete and inefficient. In the dynamic and volatile cloud environment, the efficiency of building and adapting a multi-tenant SBS is of paramount importance. In this paper, we present two service recommendation approaches for multi-tenant SBSs, one for build-time and one for runtime, based on K-Means clustering and Locality-Sensitive Hashing (LSH) techniques respectively, aiming at finding appropriate services efficiently. Extensive experimental results demonstrate that our approaches can facilitate fast multi-tenant SBS construction and rapid system adaptation.

F2C: Enabling Fair and Fine-Grained Resource Sharing in Multi-Tenant IaaS Clouds

This paper presents F2C , a cooperative resource management system for Infrastructure-as-a-Service (IaaS) clouds. Inspired by group-buying mechanisms in real product and service markets, F2C advocates a group of cloud tenants (called tenant coalition ) to buy resource capacity in bulk and share the resource pool in the form of virtual machines (VMs). Tenant coalitions leads to vast opportunities for fine-grained resource sharing among multiple tenants. However, resource sharing, especially for multiple resource types, poses several challenging problems in pay-as-you-use cloud environments, such as sharing incentive, free-riding, lying and economic fairness. To address those problems, we propose Reciprocal Resource Fairness (RRF) , a novel resource allocation mechanism to enable fair sharing on multiple resource types within a tenant coalition. RRF is implemented in two complementary and hierarchical mechanisms: inter-tenant resource trading and intra-tenant weight adjustment. RRF satisfies several highly desirable properties to ensure fairness. We implement F2C in Xen platform. The experimental results show F2C is promising for both cloud providers and tenants. For cloud providers, F2C improves VM density and cloud providers’ revenue by 2.2X compared to the current IaaS cloud models. For tenants, F2C improves application performance by 45 percent and guarantees 95 percent economic fairness among multiple tenants.

SDVN: enabling rapid network innovation for heterogeneous vehicular communication

With the advances in telecommunications, more and more devices are connected to the Internet and getting smart. As a promising application scenario for carrier networks, vehicular communication has enabled many traffic-related applications. However, the heterogeneity of wireless infrastructures and the inflexibility in protocol deployment hinder the real world application of vehicular communications. SDN is promising to bridge the gaps through unified network abstraction and programmability. In this research, we propose an SDN-based architecture to enable rapid network innovation for vehicular communications. Under this architecture, heterogeneous wireless devices, including vehicles and roadside units, are abstracted as SDN switches with a unified interface. In addition, network resources such as bandwidth and spectrum can also be allocated and assigned by the logically centralized control plane, which provides a far more agile configuration capability. Besides, we also study several cases to highlight the advantages of the architecture, such as adaptive protocol deployment and multiple tenants isolation. Finally, the feasibility and effectiveness of the proposed architecture and cases are validated through traffic-trace-based simulation.

A Proposed Software as a Service (SaaS) Toolkit for Cloud Multi- Tenancy

Cloud computing is a general term for anything that involves delivering hosted services over the Internet. It is a construct that allows you to access applications that actually reside at locations other than your computer or other Internet-connected devices. Software as a Service (SaaS) is a software delivery model in which software resources are accessed remotely by users over the internet. Enterprises prefer using SaaS because of its low cost. SaaS requires sharing of application servers among multiple tenants for low operational costs. Several SaaS Toolkits are available and used to develop cloud SaaS applications. However, most of these Toolkits are very complex. This paper aims to build a new simple and easy SaaS Toolkit to develop SaaS applications and to avoid the difficulties and complexity in the current cloud SaaS Toolkits. The proposed Toolkit is based on java virtual machine and the popular web programming languages HTML and PHP. To evaluate the proposed Toolkit an empirical study has been conducted. The result of the empirical study revealed that the proposed Toolkit outperforms the current Toolkits in terms of complexity, understandability and learnability.

A Systematic Approach to Deal with Noisy Neighbour in Cloud Infrastructure

Background/Objectives: One of the major challenges of the multitenant cloud model is performance unpredictability because of resource contention. The objective of this paper is to propose an approach to deal with noisy neighbours in a shared cloud infrastructure and reduce their affect on the other tenant applications. Methods/Statistical Analysis: Multiple tenant applications are deployed on cloud VMs which share the underlying system resources. Noisy neighbour applications take up more resources and leave other applications in turmoil state that leads to lack of predictability of the performance of other applications. The proposed system actively monitors the resource consumption of the tenant applications based on some identified parameters. Findings: Monitoring the resource consumption of the applications helps categorize the resource greedy applications as noisy neighbours. The other tenant applications which do not get their fair share of the resources are victim applications. Once the noisy neighbours have been identified, the next step is to deal with noisy neighbours by either migration of victim applications on a separate node on the cloud or borrowing resources from other nodes or implementing a quota system for resource allocation in cloud. Applications/Improvements: The pragmatic study of the behaviour of tenant applications based on their resource consumption pattern would help researchers to better focus on techniques to improve the Quality of Service of a cloud.

Virtualizing vehicular node resources: Feasibility study of virtual machine migration

With emerging geo-distributed services, there is a need to coordinate the use of resources offered by field-area networks. In the case of vehicular networks, such resources include the processing, sensing, and storage capabilities offered to service providers for urban sensing or intelligent transportation. In this paper, we propose to virtualize the resources embedded on the vehicular nodes to allow multiple tenants to coexist and deploy their services on the same underlying mobile substrate. Virtualization is the task of an infrastructure provider that controls the mobile substrate and allocates sliced resources to the tenants. A service results from a collection of virtual machines hosted on the mobile nodes allocated by the infrastructure provider. Efficient utilization of the node resources may trigger virtual machine migrations. We study the problem of virtual machine migrations through V2V communications between mobile nodes. To evaluate the impact of such migrations on the resource allocation process, we use the real traces of a bus transit system to simulate a vehicular network where virtual machines migrate via V2V communications. Our results show that virtual machines of several hundreds of Megabytes can migrate between moving buses. We then discuss design principles and research issues toward the full virtualization of opportunistic networks.

Tenant-centric Sub-Tenancy Architecture in Software-as-a-Service

Multi-tenancy architecture (MTA) is often used in Software-as-a-Service (SaaS) and the central idea is that multiple tenant applications can be developed using components stored in the SaaS infrastructure. Recently, MTA has been extended to allow a tenant application to have its own sub-tenants, where the tenant application acts like a SaaS infrastructure. In other words, MTA is extended to STA (Sub-Tenancy Architecture). In STA, each tenant application needs not only to develop its own functionalities, but also to prepare an infrastructure to allow its sub-tenants to develop customized applications. This paper applies Crowdsourcing as the core to STA component in the development life cycle. In addition, to discovering adequate fit tenant developers or components to help build and compose new components, dynamic and static ranking models are proposed. Furthermore, rank computation architecture is presented to deal with the case when the number of tenants and components becomes huge. Finally, experiments are performed to demonstrate that the ranking models and the rank computation architecture work as design.

A technique for full flow virtualization of multi-tenant OpenFlow networks

OpenFlow provides new capabilities for wide-area network applications such as traffic engineering and security applications with many implementations having been proposed. OpenFlow allows flows to be described using arbitrary values or wildcards in the header fields. This functionality enables a controller to manage flow entries using only a limited number of the entries. In particular, in the case of wide-area networks, the sharing of the infrastructure among tenants (e.g., application service providers and their customers) is necessary, owing to the high capital outlay and operational costs. A technique for multi-tenancy creates logically isolated virtual OpenFlow networks so that multiple tenants can handle flows independently on a single physical OpenFlow network. Of the proposed techniques, OpenVirteX virtualizes the header address space (called the flow space) for tenants. This encourages tenants to participate because, differing from some other implementations, negotiations by tenants to divide the flow space are not required. However, OpenVirteX forces tenant controllers to set either exact Media Access Control (MAC) or Internet Protocol (IP) address in the matching fields because matching fields in the physical OpenFlow switches must have either exact MAC or IP addresses to isolate flows of the different tenants. Although prefix or wildcard matching is widely used to handle a large number of flows with a small number of flow setting by an OpenFlow controller, there is no available virtualization technique that enables prefix or wildcard matching with flow space virtualization. In this paper, we propose an OpenFlow network virtualization technique that allows prefix or wildcard matching with flow space virtualization. The proposed technique translates flow entries set by tenant controllers using prefix or wildcard matching to exact-match flow entries through lazy evaluation of the actual data packets. Evaluation results obtained using a prototype of the proposed technique show that the overhead of virtualization has an acceptable impact.

Industrial symbiosis in the Taranto industrial district: current level, constraints and potential new synergies

The environmental crisis affecting the Taranto provincial industrial district in Italy, centred around the Ilva steelworks, has made its existence within the surrounding contexts unsustainable. The application of industrial symbiosis principles in this district can certainly represent a purposeful means for a move towards a more sustainable industrial system. This work presents the results of a research project, funded by the Caripuglia Foundation, for a study of industrial symbiosis in the Taranto industrial district. The study of the district, which is characterized by the presence of heavy industries such as steelworks, oil refinery, cement industry and power stations with an energy consumption of 182.4 PJ/year, identified the industrial sectors and firms that could be involved in symbiotic activities and the spontaneous existing synergic interactions taking place among the largest firms of the district. The study has also quantified and illustrated the useful recycling, within various industrial sectors, of inefficiently used and disposed waste energy (44.6 PJ/year of heat) and material waste and by-products (3.28 Mt/year). Among the latter are steelworks slag, mill scale, spent refractories and coal fly ash. However, the study has revealed that, despite the presence of multiple anchor tenants on the territory, the industrial symbiosis is still implemented at a basic level. There are different constraints on the full development of the industrial symbiosis in the industrial district of Taranto. Among these, the focus of the entrepreneurs on their core business was the main one, while additional uses of the current waste are seen as a diversion of human resources and capital.