Multi-tenant Applications Research Articles

A systematic review of in-memory database over multi-tenancy

The significant cost and time are essential to obtain a comprehensive response, the response time to a query across a peer-to-peer database is one of the most challenging issues. This is particularly exact when dealing with large-scale data processing, where the traditional approach of processing data on a single machine may not be sufficient. The need for a scalable, reliable, and secure data processing system is becoming increasingly important. Managing a single in-memory database instance for multiple tenants is often easier than managing separate databases for each tenant. The research work is focused on scalability with multi-tenancy and more efficiency with a faster querying performance using in-memory database approach. We compare the performance of a row-oriented approach and column-oriented approach on our benchmark human resources (HR) schema using Oracle TimesTen in-memory database. Also, we captured some of the key advantages on optimization dimension(s) are the traditional approach, late-materialization, compression and invisible join on column-store (c-store) and row-base. When compression and late materialization are enabled in a query set; it improves the overall performance of query sets. In particular, the paper aims to elucidate the motivations behind multi-tenant application requirements concerning the database engine and highlight major designs over in-memory database for the tenancy approach on cloud.

AroMa : Evaluating Deep Learning Systems for Stealthy Integrity Attacks on Multi-tenant Accelerators

Multi-tenant applications have been proliferating in recent years, supported by the emergence of computing-as-service paradigms. Unfortunately, multi-tenancy induces new security vulnerabilities due to spatial or temporal co-location of applications with possibly malicious intent. In this article, we consider a special class of stealthy integrity attacks on multi-tenant deep learning accelerators. One interesting conclusion is that it is possible to perform targeted integrity attacks on kernel weights of deep learning systems such that it remains functional but mis-labels specific categories of input data through standard RowHammer attacks by only changing 0.0009% of the total weights. We develop an automated framework, AroMa , to evaluate the impact of multi-tenancy on security of deep learning accelerators against integrity attacks on memory systems. We present extensive evaluations on AroMa to demonstrate its effectiveness.

Business-IT Alignment in Cloud environment Proposed Framework

Nowadays, business process reuse is becoming a trendy topic in the enterprise as well as in scientific research to reduce the cost and time of process development for the benefit of the enterprise. Moreover, with the emergence of cloud environments and the various advantageous and competitive aspects of cloud computing, it becomes necessary to develop non-installable or so-called multi-tenant applications. In this sense, BPM researchers are working on improving the BPaaS (Business Process as Service) model, which enables business processes to be managed in the context of the cloud. However, the problem of aligning IT operations in the cloud, bridging the gap between business requirements and technical cloud solutions, remains an emerging issue in digital transformation, as cloud solutions are purely technical and do not allow stakeholders, who are usually not fluent in the language of business, to properly evaluate or choose the most appropriate cloud solution. Identifying the most appropriate cloud solutions in this context, therefore, requires specifying service requirements and capabilities at both the business and IT levels.

A Survey of Distributed Data Storage in the Cloud for Multitenant Applications

A Survey of Distributed Data Storage in the Cloud for Multitenant Applications

Software-defined networking enabled optical data center network with flexible QoS provisioning

Switching the traffic in optical domain has the potential to build the optical data center networks (DCNs) with fast switching and high capacity, overcoming thus the bandwidth-bottleneck of conventional electrically switched DCNs. The rapidly increasing multi-tenant applications in DCNs with heterogeneous traffic pattern requires flexible quality of service (QoS) to the network infrastructure. Network Slices (NS) is a promising solution to automatically slice the optical DCNs to support dynamic slicing interconnections in provision of flexible QoS. In this work, a Software-defined networking (SDN) enabled optical DCN is proposed and experimentally investigated with flexible QoS provisioning for the deployed multi-tenant applications. The proposed SDN enabled optical DCN, based on the monitored network topology and statistics, can be automatically sliced and reconfigured under the management of SDN controller. Experimental result validates the flexible priority allocation to NS traffic flows can provide dynamic QoS. In addition, the automatic operation of load balancing based on the monitored network statistics further improves the network performance with high QoS. At a load of 0.5, zero packet loss and lower than 4.8 μs end-to-end latency have been demonstrated for the NS with highest priority.

ReSAW: a reconfigurable and picosecond-synchronized optical data center network based on an AWGR and the WR protocol

The explosive growth of data center (DC) traffic imposes unprecedented challenges on the current electrical switch-based data center networks (DCNs) with the bottleneck of limited bandwidth and high latency. Benefitting from transparency to the data rate and format, optical switching with theoretically infinite bandwidth could overcome the bandwidth bottleneck of electrical switching DCNs. However, DCNs normally deploy multitenant applications with a variety of DC traffic. It is hard to reconfigure the optical interconnections in real-time to provide adaptable bandwidth to the traffic with heterogeneous characteristics. Moreover, to improve the bandwidth utilization, statistical multiplexing is generally deployed in optical DCNs to forward the traffic flow in a time slot, which requires the network time to be precisely synchronized to all network nodes. For fast optical switches with a nanosecond switching configuration time, overall end node times must be synchronized at subnanosecond magnitude. In this paper, we propose and experimentally investigate a reconfigurable and picosecond-synchronized optical DCN (ReSAW) based on an arrayed waveguide grating router (AWGR) and the White Rabbit (WR) protocol. A scheduler based on a distributed field-programmable gate array is implemented in the proposed ReSAW to realize flexible wavelength configuration by controlling the fast laser array based on semiconductor optical amplifiers (SOAs) according to time slot and traffic priority. Moreover, the WR protocol is implemented in optical DCNs for what we believe is the first time to synchronize the time of the distributed top of racks (ToRs). The experimental demonstration validates that ReSAW achieves an average end-to-end latency of 317.44 ns and a precise synchronized time with an average 386 ps skew. When the load is 0.4, the packet loss after ReSAW reconfiguration is less than 1.83 × 10 − 6 , and the network latency is less than 1.73 µs. Based on the experimental parameters and results, the OMNeT++ simulation model is built to further verify the reconfigurability and scalability of the ReSAW network. Results show that the packet loss rate and latency performance increase 8.24% and 12.47%, respectively, at a load of 0.6 as the ReSAW network scales from 2560 to 40,960 servers compared to before the reconfiguration.

Cloud-Edge Orchestration for Smart Cities: A Review of Kubernetes-based Orchestration Architectures

Edge computing offers computational resources near data-generating devices to enable low-latency access. Especially for smart city contexts, edge computing becomes inevitable for providing real-time services, like air quality monitoring systems. Kubernetes, a popular container orchestration platform, is often used to efficiently manage containerized applications in smart cities. Although it misses essential requirements of edge computing, like network-related metrics for scheduling decisions, it is still considered. This paper analyzes custom cloud-edge architectures implemented with Kubernetes. Specifically, we analyze how essential requirements of edge orchestration in smart cities are solved. Also, shortcomings are identified in these architectures based on the fundamental requirements of edge orchestration. We conduct a literature review to obtain the general requirements of edge computing and edge orchestration for our analysis. We map these requirements to the capabilities of Kubernetes-based cloud-edge architectures to assess their level of achievement. Issues like using network-related metrics and the missing topology-awareness of networks are partially solved. However, requirements like real-time resource utilization, fault-tolerance, and the placement of container registries are in the early stages. We conclude that Kubernetes is an eligible candidate for cloudedge orchestration. When the formerly mentioned issues are solved, Kubernetes can successfully contribute latency-critical, large-scale, and multi-tenant application deployments for smart cities.

Architectural Design and Life Cycle Management of Network Slicing for Software-Defined Optical Access Networks

Optical access networks (OANs) with passive optical network technology have been globally deployed on a massive scale, serving as the fundamental information infrastructure of teleCommunication networks. Some telecom service providers are re-architecting their networks based on cloud central office with software-defined networking and network functions virtualization capabilities to provide service agility, intelligence, and scalability. In this article, we present a synthesized analysis of the inherent value and practical use cases of access network (AN) slicing in the context of software-defined OANs. A novel hierarchical abstraction model and a workflow of end-to-end network slicing are presented to illustrate the support of multi-service and multi-tenancy applications over a converged OAN platform by enabling automatic life cycle management of AN slicing. Two successful trials utilizing the model and the workflow are also demonstrated.

Cover Image

The cover image is based on the Original Article Verification Algebra for Multi-Tenant Applications in VaaS Architecture by Kan Luo et al., https://doi.org/10.1002/stvr.1763.

TOSDS: Tenant-centric Object-based Software Defined Storage for Multitenant SaaS Applications.

Enormous amounts of unstructured data such as images, videos, emails, sensors’ data and documents of multiple types are being generated daily by varied applications. Apart from the challenges related to collection or processing of this data, its efficient storage is also a significant challenge since this data do not conform to any predefined storage model. Therefore, any enterprise dealing with huge unstructured data requires a scalable storage system that can provide data durability and availability at a low cost. The paper proposes a tenant-centric approach to develop an object-based software defined storage system for SaaS multi-tenant applications. We present TOSDS (Tenant-centric Object-based Software Defined Storage), a system that can efficiently meet the storage requirements of users or tenants with diverse needs who are using a multitenant SaaS application. The experimental verification of TOSDS illustrates its effectiveness in storage utilization as well as tenant isolation.

Resource-Aware Collaborative Allocation for CPU-FPGA Cloud Environments

Cloud Warehouses have been exploiting CPU-FPGA environments to accelerate multi-tenant applications to achieve scalability and maximize resource utilization. In this scenario, kernels are sent to CPU and FPGA concurrently, considering available resources and workload characteristics, which are highly variant. Therefore, we propose a multi-objective optimization strategy to improve resource provisioning in CPU-FPGA environments. It is based on the Genetic Multidimensional Knapsack solution and can be tuned to minimize makespan or energy. Our strategy provides similar results as the optimal Exhaustive Search, but with feasible execution time, while presenting 77% energy savings with 39% lower makespan than the commonly-used First-Fit strategy.

Design Patterns and Electric Vehicle Charging Software

The development and maintenance of complex software systems, with ever-changing requirements has benefited from the implementation of design patterns, which ensure a higher degree of maintainability. The present article illustrates the essential role of design patterns in sketching the software architecture for an electric vehicle charging management platform. We have integrated a series of design patterns to create a solid base structure for the API (Application Programming Interface). Furthermore, we have explored cloud design patterns in the deployment process to ensure a proper multi-tenant cloud application with the best possible tenant isolation for the cost. The aim of this paper is to offer readers an introduction in the case study theme, to describe a complex platform development through design patterns. With proper examples from real projects debated in the industry. The paper demonstrates the design patterns applicability from software level to cloud resources plan and advocates for a high-quality solution in every segment of project development. In conclusion, the results are promising, and the functionality of the established methods on this type of platforms will be evaluated during the implementation period. Overall, design patterns have proven to be essential in the development process, ensuring effective team communication and the delivery of qualitative software solutions.

Automatic Evolution of Eco-Efficient Software Architectures with CVL Models

Resource sharing and mass storage in server farms provided by cloud platforms save huge amounts of energy. However, optimizing energy consumption at the server room is not enough, being desirable to perform energy optimization of cloud services at the application level. In cloud computing a tailored configuration of services is deployed for each client (tenant), requiring different energy consumption optimizations. Indeed, energy consumption of cloud services depends on several factors determined by the context and usage of the applications. So, to evolve a cloud application to new requirements of energy efficiency implies to perform custom-made adaptations for each tenant. Thus, managing the evolution of a multi-tenant application with hundreds of tenants and thousands of different valid architectural configurations can become intractable if performed manually. This paper proposes a product line architecture approach that: (1) uses cardinality-based variability models to model each tenant as a clonable feature, and (2) automatizes the process of evolving the multi-tenant application architecture when the energy requirements change. The implemented process is efficient for a high number of tenants in a reasonable time.

SDN-Controlled and Orchestrated OPSquare DCN Enabling Automatic Network Slicing With Differentiated QoS Provisioning

In this work, we propose and experimentally assess the automatic and flexible NSs configurations of optical OPSquare DCN controlled and orchestrated by an extended SDN control plane for multi-tenant applications with differentiated QoS provisioning. Optical Flow Control (OFC) protocol has been developed to prevent packet losses at switch sides caused by packet contentions.Based on the collected resource topology of data plane, the optical network slices can be dynamically provisioned and automatically reconfigured by the SDN control plane. Meanwhile, experimental results validate that the priority assignment of application flows supplies dynamic QoS performance to various slices running applications with specific requirements in terms of packet loss and transmission latency. In addition, the capability of exposing traffic statistics information of data plane to SDN control plane enables the implementation of load balancing algorithms further improving the network performance with high QoS. No packet loss and less than 4.8 us server-to-server latency can be guaranteed for the sliced network with highest priority at a load of 0.5.

Virtual DBA: virtualizing passive optical networks to enable multi-service operation in true multi-tenant environments

This paper presents the concept of virtual dynamic bandwidth allocation (DBA), a method we propose to virtualize upstream capacity scheduling in passive optical networks (PONs), which provides multiple independent virtual network operators with the ability to precisely schedule their upstream traffic allocation. After a brief introduction on the evolution of access network sharing, we present our virtual DBA architecture, detailing its main components. We then provide a summary of the work done in this area from both theoretical and practical implementation perspectives. In this paper, we propose a novel stateless algorithm for merging multiple independent virtual bandwidth maps based on priority classes and analyze its performance in terms of efficiency of capacity allocation and latency. Through our results, we discuss the existence of a trade-off between traffic load and grant size distribution versus efficiency and latency. We find that, different from a residential single-tenant application, when PONs are used for low-latency and multi-tenant applications, the system has better overall performance if grants are allocated in small size. In addition, our analysis shows that for high-priority, strict latency services, our proposed merging algorithm presents delay performance that is independent of the traffic distribution considered.

Подход к проектированию мультитенантного приложения

Подход к проектированию мультитенантного приложения

AdaptiveSLA: A Two-Stage Scheduling Framework for SLA Profit Maximization in Multi-tenant Database

The requirement of multi-tenant applications is continuously increasing in cloud computing epoch, and they usually have larger data volume compared with traditional applications. Considering the quality of managing these data, SLAs (Service Level Agreement) are usually defined between multi-tenant database service providers and tenants. The providers will get revenues if SLAs are met, otherwise they will pay penalties. In order to maximize the profit, this paper presents AdaptiveSLA, a two-stage scheduling policy for multi-tenant database. In the first stage, AdaptiveSLA detects performance crises and leverages sliding window algorithm to mitigate crises in distributed multitenant database. The crises may derive from heterogeneity of requests or performance degradation of nodes. In the second stage, AdaptiveSLA makes requests’ execution sequence. This stage aims at completing requests as many as possible. The executing process is constrained by SLA, which is modeled as slack time. Extensive experimental results demonstrate the effectiveness and efficiency of AdaptiveSLA scheduling policy.

Theoretical and Pragmatic Cases of a Rich-Variant Approach for a User-Aware Multi-Tenant SaaS Applications

Software as a Service cloud computing model favorites the Multi-Tenancy as a key factor to exploit economies of scale. However Multi-Tenancy present several disadvantages. Therein, our approach comes to optimize instances assigned to multi-tenants with a solution using rich-variant components while ensuring more economies of scale and avoiding tenants hesitation to share resources. The paper present the theoretical and pragmatic cases of a user-aware multi-tenancy SaaS approach focused on graph-based algorithms. The theoretical case consists in having a set of tenants while the pragmatic case consists in adding a new tenants to a set of tenants.

Support mechanisms for cloud configuration using XML filtering techniques: A case study in SaaS

Software-as-a-service (SaaS) has attracted substantial attention as a software delivery and service model in a cloud computing environment. SaaS delivery can help organizations significantly reduce the cost of using software, because the resources for running SaaS applications are shared among tenants (end users or organizations). However, multi-tenancy can considerably complicate SaaS development, deployment, and maintenance as a result of a large number of co-existing tenant-specific constraints or features. Manually configuring and maintaining tenant-specific features will increase the cost, introduce possible errors, and limit scalability and flexibility. The paper addresses the problem of large variations and complex configurations. Specifically, the objective is to develop mechanisms to support automatic multi-tenant software features analysis and matching for the purposes of efficient deployment and operations in the cloud. The emphasis of this paper is on the matching between the tenant-specific requirements and the SaaS features managed by the cloud provider. This paper proposes a novel approach for cloud feature matching using XML filtering techniques to support the process of multi-tenant SaaS deployment and management. Feature modeling has been widely used to capture requirements and constraints. On the other hand, XML filtering techniques are mature and have been adopted in various problem domains. We used Yfilter, a proven and XML filtering technique, to support two multi-tenant applications: (i) Identifying SaaS configurations (in XPath representations) that satisfy tenant-specific requirements and constraints (in XML notation); and (ii) Identifying tenants that have subscribed to a specific set of SaaS features. The applications can effectively facilitate SaaS subsequent management and operations due to various changes, e.g., functionalities, constraints, cost, etc. The experimental results demonstrate that the proposed approach can automatically and correctly identify cloud system configurations that match tenant-specific requirements or identify the group of tenants that have subscribed to a particular set of cloud features. In addition, the execution time of our proposed approach is only a small fraction compared to the existing approach using the formal method, e.g., FaMa, and the configuration space is also much smaller.

Software-Defined Edge Cloud Framework for Resilient Multitenant Applications

Edge application’s distributed nature presents significant challenges for developers in orchestrating and managing the multitenant applications. In this paper, we propose a practical edge cloud software framework for deploying multitenant distributed smart applications. Here we exploit commodity, a low cost embedded board to form distributed edge clusters. The cluster of geo-distributed and wireless edge nodes not only power multitenant IoT applications that are closer to the data source and the user, but also enable developers to remotely deploy and orchestrate application containers over the cloud. Specifically, we propose building a software platform to manage the distributed edge nodes along with support services to deploy and launch isolated and multitenant user applications through a lightweight container. In particular, we propose an architectural solution to improve the resilience of edge cloud services through peer collaborated service migration when the failures happen or when resources are overburdened. We focus on giving the developers a single point control of the infrastructure over the intermittent and lossy wide area networks (WANs) and enabling the remote deployment of multitenant applications.