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Modern computing infrastructures are evolving due to virtualisation, especially with the advent of 5G and future technologies. While this transition offers numerous benefits, it also presents challenges. Consequently, understanding these complex systems, including networks, services, and their interconnections, is crucial. This paper introduces ServiceNet, a groundbreaking architecture that accurately performs the important task of providing understanding of a multi-tenant architecture by discovering the complete topology, crucial in the realm of high-performance distributed computing. Experimental results have been carried out in different scenarios in order to validate our approach, demonstrating the effectiveness of our approach in comprehensive multi-tenant topology discovery. The experiments, involving up to forty tenant, highlight the adaptability of ServiceNet as a valuable tool for real-time monitoring in topology discovery purposes, even in challenging scenarios. [ABSTRACT FROM AUTHOR]

In the envisioned 6G landscape, data sharing is expected to become increasingly prevalent, giving rise to digital marketplaces that foster cooperation among organizations for collecting, sharing, and processing data for analysis. These marketplaces serve as connectors between data producers and consumers, empowering multi-tenancy scenarios for seamless and secure data sharing both within and outside organizations. Given that 6G networks promise ultra-low latency, enhanced connectivity, and massive data throughput, the need for robust data access control mechanisms becomes imperative. These mechanisms ensure security and trust among entities, particularly in multi-tenant environments where multiple organizations share infrastructure and data resources. In this paper, we have designed and implemented a novel access control mechanism tailored for a distributed data streaming system developed by Nokia Bell Labs. Our approach leverages fine-grained policies, dynamic enforcement, and transparency mechanisms to enhance trust between data owners and consumers. By facilitating secure multi-tenancy data sharing, our solution contributes to the seamless exchange of data across diverse entities within the next-generation communication ecosystem. We demonstrate that our proposed access control mechanism incurs minimal overhead while ensuring data confidentiality and integrity. The introduction of such advancements in data sharing markets strengthens the overall ecosystem by providing heightened transparency and enhanced control over data, promoting collaboration and innovation in the 6G era. [ABSTRACT FROM AUTHOR]

Abstract In the envisioned 6G landscape, data sharing is expected to become increasingly prevalent, giving rise to digital marketplaces that foster cooperation among organizations for collecting, sharing, and processing data for analysis. These marketplaces serve as connectors between data producers and consumers, empowering multi-tenancy scenarios for seamless and secure data sharing both within and outside organizations. Given that 6G networks promise ultra-low latency, enhanced connectivity, and massive data throughput, the need for robust data access control mechanisms becomes imperative. These mechanisms ensure security and trust among entities, particularly in multi-tenant environments where multiple organizations share infrastructure and data resources. In this paper, we have designed and implemented a novel access control mechanism tailored for a distributed data streaming system developed by Nokia Bell Labs. Our approach leverages fine-grained policies, dynamic enforcement, and transparency mechanisms to enhance trust between data owners and consumers. By facilitating secure multi-tenancy data sharing, our solution contributes to the seamless exchange of data across diverse entities within the next-generation communication ecosystem. We demonstrate that our proposed access control mechanism incurs minimal overhead while ensuring data confidentiality and integrity. The introduction of such advancements in data sharing markets strengthens the overall ecosystem by providing heightened transparency and enhanced control over data, promoting collaboration and innovation in the 6G era.

Companies increasingly adopt software as a Service (SaaS) as a project management tool. SaaS offers greater flexibility, availability, and accessibility than traditional information system software. In this study, SaaS is used as the basis for creating project management applications, including recording work plans, the progress of each user's performance, and meeting documentation. The stages of the literature study are carried out by looking at journals and books. The trial was carried out using black-box testing. Verification trials are carried out by involving a team of programmers to see the flow of the system algorithm. Validation trials are carried out by asking various users involved in project implementation to try the system and asking users to fill out questionnaires related to the ease of use of project management features. The two stages of the trial showed good results, as evidenced by 77.8% of users stating that the SaaS concept really helped them with the flexibility of system installation with a short waiting time. Meanwhile, 76.16% of users stated that the features provided and their configuration could help them in project management. The research results show that SaaS has great potential to help companies to manage projects effectively. In future research, various factors in different project management can be explored deeper, so that SaaS becomes more configurable and used by a wider variety of users.

Aiming at the problem that the existing trusted container framework lacks key protection, a trusted container hierarchical key management (TCKM) method based on the trusted platform module is proposed. The hardware TPM and the container key block are bound by the hierarchical key management mechanism to protect the security of the generation and storage stage of the container key block. When the key is used, the container attribute is verified through the key authorization value, so that only the specified container that meets certain attributes can obtain and use the key, preventing the theft of the container key block, and realizing the secure use of the key. Finally, the encryption method combining kernel and hardware TPM is adopted to improve the efficiency of data protection based on TCKM key management method. The TCKM key management method is implemented and evaluated based on Docker18.09. The results show that the mechanism can effectively protect the security of the key and the confidentiality of the file data in the container. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper describes the development activity that has been carried out for a living laboratory for the city of Cagliari aimed at functioning as a learning center for local SMEs willing to improve their skills in IoT and create applications that will be integrated in an open innovation ecosystem. The many users belonging to the various SMEs involved in the project required an ICT laboratory with a platform that could manage them and provide a multi-tenant environment for the development of IoT applications. The architecture also had to be scalable and interoperable, and the resulting platform had to collect many kinds of data from sensors or other data sources, elaborate them, and show georeferenced information on a 3D satellite interactive view along with statistics on side panels. This work was based on a platform already developed by CRS4 for a previous project. Preserving the concept of the decision-making tool for Smart Cities, almost every component was redesigned, and, in this paper, we describe the new solutions that have been implemented. Starting from the former structure, further features were added in a novel way in order to offer an enhanced framework that can deal with the activities of the laboratory, exploiting the scalability of the open-source systems involved, their robustness and flexibility, and leveraging domain standards. In this article, the main challenges involved in the development of the platform are described, as well as the solutions that have been implemented so far. [ABSTRACT FROM AUTHOR]

Low-Rank Adaptation (LoRA) has garnered increasing attention for effectively fine-tuning large language models (LLMs) with limited resources. Nonetheless, conventional approaches that cater to multiple LoRA models independently lead to redundant computations and suboptimal GPU utilization. This study tackles these inefficiencies by presenting Dynamic Operator Optimization, a sophisticated automated optimization methodology crafted to dynamically enhance the Segmented Gather Matrix-Vector Multiplication (SGMV) operator according to specific contexts. The distinctive design of SGMV facilitates the batching of GPU operations for diverse LoRA models, resulting in a notable enhancement in computational efficiency. The strategy exploits a Search Space Constructor to construct a structured search space, segmenting the program space into overarching structural outlines and intricate implementation particulars to ensure a varied and adaptable operator implementation. Moreover, an Optimization Engine fine-tunes these implementations through evolutionary search driven by a performance estimation cost model. This progressive optimization procedure ensures that SGMV implementations can dynamically adjust to varying scenarios to uphold superior performance. The findings illustrate that our design can elevate throughput by up to 1.46 times in cutting-edge multi-tenant LoRA deployments.

This paper describes the development activity that has been carried out for a living laboratory for the city of Cagliari aimed at functioning as a learning center for local SMEs willing to improve their skills in IoT and create applications that will be integrated in an open innovation ecosystem. The many users belonging to the various SMEs involved in the project required an ICT laboratory with a platform that could manage them and provide a multi-tenant environment for the development of IoT applications. The architecture also had to be scalable and interoperable, and the resulting platform had to collect many kinds of data from sensors or other data sources, elaborate them, and show georeferenced information on a 3D satellite interactive view along with statistics on side panels. This work was based on a platform already developed by CRS4 for a previous project. Preserving the concept of the decision-making tool for Smart Cities, almost every component was redesigned, and, in this paper, we describe the new solutions that have been implemented. Starting from the former structure, further features were added in a novel way in order to offer an enhanced framework that can deal with the activities of the laboratory, exploiting the scalability of the open-source systems involved, their robustness and flexibility, and leveraging domain standards. In this article, the main challenges involved in the development of the platform are described, as well as the solutions that have been implemented so far.

A multi‐tenant microservice architecture involving components with asynchronous interactions and batch jobs requires efficient strategies for managing asynchronous workloads. This article addresses this issue in the context of a leading company developing tax software solutions for many national and multi‐national corporations in Brazil. A critical process provided by the company's cloud‐based solutions encompasses tax integration, which includes coordinating complex tax calculation tasks and needs to be supported by asynchronous operations using a message broker to guarantee order correctness. We explored and implemented two approaches for managing asynchronous workloads related to tax integration within a multi‐tenant microservice architecture in the company's context: (i) a polling‐based approach that employs a queue as a distributed lock (DL) and (ii) a push‐based approach named single active consumer (SAC) that relies on the message broker's logic to deliver messages. These approaches aim to achieve efficient resource allocation when dealing with a growing number of container replicas and tenants. In this article, we evaluate the correctness and performance of the DL and SAC approaches to shed light on how asynchronous workloads impact the management of multi‐tenant microservice architectures from delivery and deployment perspectives. [ABSTRACT FROM AUTHOR]

Security in multi-tenant databases in cloud computing is a critical aspect, as it ensures that sensitive data is protected and isolated from other tenants. In a multi-tenant environment, multiple organizations or customers share the same infrastructure, making it challenging to ensure the security of their data. In this study, several papers have been analyzed from journals and conference papers, covering a variety of works on Security Multitenant databases in cloud computing. The articles in the literature used for the review were published between 2012 and 2022. This abstract presents a Hybrid approach for securing multi-tenant databases in cloud computing. This paper illustrates the approach which uses a combination of encryption and virtualization techniques to secure data and provide isolation between tenants. The main idea behind this approach is to balance the trade-off between security and performance, which are crucial aspects in multi-tenant environments. This approach's use of encryption ensures the security of data both in transit and at rest, and the virtualization process adds a layer of protection by isolating residents from one another. This survey shows that it is a viable solution for securing multi-tenant databases in the cloud, providing an acceptable level of security and performance. [ABSTRACT FROM AUTHOR]

We present SensiX++, a multi-tenant runtime for adaptive model execution with integrated MLOps on edge devices, e.g., a camera, a microphone, or IoT sensors. SensiX++ operates on two fundamental principles: highly modular componentisation to externalise data operations with clear abstractions and document-centric manifestation for system-wide orchestration. First, a data coordinator manages the lifecycle of sensors and serves models with correct data through automated transformations. Next, a resource-aware model server executes multiple models in isolation through model abstraction, pipeline automation, and feature sharing. An adaptive scheduler then orchestrates the best-effort executions of multiple models across heterogeneous accelerators, balancing latency and throughput. Finally, microservices with REST APIs serve synthesised model predictions, system statistics, and continuous deployment. Collectively, these components enable SensiX++ to serve multiple models efficiently with fine-grained control on edge devices while minimising data operation redundancy, managing data and device heterogeneity, and reducing resource contention.We benchmark SensiX++ with 10 different vision and acoustics models across various multi-tenant configurations on different edge accelerators (Jetson AGX and Coral TPU) designed for sensory devices. We report on the overall throughput and quantified benefits of various automation components of SensiX++ and demonstrate its effi- cacy in significantly reducing operational complexity and lowering the effort to deploy, upgrade, reconfigure, and serve embedded models on edge devices. [ABSTRACT FROM AUTHOR]

Cloud warehouses have been exploiting multi-tenancy in CPU-FPGA collaborative environments, so clients can share the same infrastructure, achieving scalability and maximizing resource utilization. Therefore, the distribution of tasks across CPU and FPGA must be well-balanced so performance and energy are optimized in a highly variant workload scenario. In this paper, we take a step further and, in contrast to existing approaches, exploit DVFS (Dynamic Voltage and Frequency Scaling) on the CPU, together with an intelligent CPU-FPGA resource provisioning mechanism, to further improve energy. For that, we propose EASER, an end user-transparent framework that employs multiple strategies and dynamically selects the most appropriate one to optimize resource provisioning and DVFS according to the warehouse needs, workload properties, and target architecture. Our synergistic DVFS optimization brings up to 22% additional energy gains over our dynamic provisioning alone. Compared to fixed single strategies with DVFS, EASER brings, on average, 71% of energy gains. • We explore voltage/frequency scaling and provisioning in multi-tenant CPU-FPGA Cloud. • Different strategies are needed depending on the architecture and workload properties. • EASER is end-user transparent and fully-adaptive. • EASER achieves energy improvements without harming makespan. [ABSTRACT FROM AUTHOR]

Over the past decade, network operators worldwide have regarded passive optical networks (PONs) as a successful access network system and the preferred technology for fiber-to-the-home/building/curb/distribution-point (FTTx) architecture. However, implementing the state-of-the-art PONs requires a high initial capital expenditure (CapEx) to deploy fiber in the field and acquires the necessary active devices. Thus, new architectures need to be developed to share the physical architecture and enable the multi-tenancy capability in a flexible way to satisfy different requirements from the operators and users. This research proposes to integrate the next-generation TWDM-PON with software-defined network (SDN) and the network function virtualization (NFV) to provide highly enhanced form of virtual unbundling of broadband access networks, enabling cost sharing and the dynamics of a competitive landscape. The simulation results show that the proposed architecture and mechanism can provide better performance in multi-tenancy condition, while guaranteeing the quality-of-service requirements in terms of the mean packet delay, jitter, throughput, and packet loss ratio.

As part of 3GPP releases 15 and 16 Wi-Fi networks have been integrated with the 5G Core, which is a key feature in private network scenarios. Another important feature for private networks is multi-tenancy, whereby an infrastructure provider shares a common radio access network among several tenants subject to service level agreements (SLAs). 3GPP has defined network slicing on the radio access segment supporting multi-tenancy for 5GNR, but a similar feature is lacking in Wi-Fi networks. In this paper we present G-ADRR, which, to be best of our knowledge, is the first global slicing policy for Wi-Fi that delivers per-tenant radio level SLAs over a given geographical area. We extensively evaluate the performance of G-ADRR by means of an experimental prototype and packet level simulations, and demonstrate its advantages as compared to two alternative global scheduling strategies and a static slice configuration policy commonly used in the state-of-the-art. [ABSTRACT FROM AUTHOR]

Since the world is getting digitalized, cloud computing has become a core part of it. Massive data on a daily basis is processed, stored, and transferred over the internet. Cloud computing has become quite popular because of its superlative quality and enhanced capability to improvise data management, offering better computing resources and data to its user bases (UBs). However, there are many issues in the existing cloud traffic management approaches and how to manage data during service execution. The study introduces two distinct research models: data center virtualization framework under multi-tenant cloud-ecosystem (DCVF-MT) and collaborative workflow of multi-tenant load balancing (CW-MTLB) with analytical research modeling. The sequence of execution flow considers a set of algorithms for both models that address the core problem of load balancing and resource allocation in the cloud computing (CC) ecosystem. The research outcome illustrates that DCVF-MT, outperforms the one-toone approach by approximately 24.778% performance improvement in traffic scheduling. It also yields a 40.33% performance improvement in managing cloudlet handling time. Moreover, it attains an overall 8.5133% performance improvement in resource cost optimization, which is significant to ensure the adaptability of the frameworks into futuristic cloud applications where adequate virtualization and resource mapping will be required. [ABSTRACT FROM AUTHOR]

Resource reliability is crucial in scheduling workflow instances for different tenants. Both cloud resource reliability and precedence constraints in workflows bring about great challenges for these kinds of scheduling problems. In this paper, we construct a hybrid resource reliability model which is adaptively evaluated in every time window. The objective is to optimize the QoS (quality of service) for tenants which is measured by the introduced AISE (all instance success entropy) index. A scheduling algorithmic framework is proposed for the studied workflows which consider cloud resource reliability. Deadline and budget division (BD) methods are presented to divide deadlines and budgets of instances into those of tasks. A tenant sequence method is developed to determine the order of tenants. A task allocation strategy is investigated to schedule tasks that are ready to appropriate available resources. Parameters and algorithm component candidates are statistically calibrated over a comprehensive set of random instances using the analysis of variance technique. The performance of the proposed algorithm is also evaluated in practical instances. [ABSTRACT FROM AUTHOR]

Workflow management system (WfMS) in cloud always works as platform as a service to manage customized business processes for massive enterprises. In big data era, non‐functional guarantees of such systems are significant when facing a large number of users and concurrent requests. It is not trivial to support multi‐tenancy and hold high‐availability, because traditional architecture cannot simultaneously satisfy requirements about data isolation and runtime efficiency. In this paper, a modularized distributed workflow management system is proposed, which considers both multi‐tenancy and high‐availability in storage and engine parts of the system. A multiple‐worker‐with‐separate‐schema mechanism is defined to jointly manage the data for tenants, and a proactive strategy is presented to intelligently dispatch large concurrent requests from users to engine workers. After extensive case studies and experiments in practical scenes, our system deployed on modest machines is proved to support tens of thousands of tenants, second‐level response time for 10 K concurrency, and no‐human‐intervened failure recovery for a fail‐stop system node. [ABSTRACT FROM AUTHOR]

Abstract Local 5G networks are emerging as a new form for 5G deployment, targeting service delivery for vertical-specific purposes and other local users. These networks are also known as micro-operator networks for which prior work has established different deployment scenarios, namely Closed, Open and Mixed Networks. To achieve network flexibility, customization and privacy required by various vertical sectors, such as industry, health and energy, it is essential to have a well-defined network slicing architecture and adequate implementation procedure. In this paper, a sophisticated end-to-end network slicing architecture is proposed for different deployment scenarios of the local 5G micro-operator concept. The proposed architecture incorporates a broad four-layer concept, leveraging a multi-tenancy layer for different tenants and their end users, a descriptive service layer, a multi-domain slicing management and orchestration layer, and a resource layer. We further propose a network slice instance (NSI) communication service distribution technique for local 5G micro-operators. This is achieved by expanding/leveraging the communication service management function in the multi-tenant layer into a multi-tenant manager and an orchestrator of communication services. In addition, we describe how the communication service orchestrator will address all the possible multitenant-slice situations during the distribution of a network slice instance to multiple tenants. The novel methods described in the paper present a solution for not only network slice communication service distribution across different micro-operator’s tenants but also for future use cases, especially, when the allocated slice is responsible for multiple tenants or when a tenant requests multiple NSIs.

Different applications have different access characteristics and various performance requirements. Thus, the shared cloud object store entails providing tenant-specific policies. However, the limited configurability of existing storage policies makes it difficult to provide efficient and flexible policies to meet tenants' evolving needs. First, existing policies that only control request forwarding cannot provide sufficient optimizations for workload performance. Second, those policies lack the flexibility to adapt to the possible workload changes during runtime. In this paper, we propose Mass, a programmable framework to provide the enhanced storage policies for diverse workloads based on their access characteristics. We also design its enhancements, C-Mass, extending Mass's capabilities through container-based policy deployment to efficiently handle workload changes. Compared with existing storage policies, the latency and throughput of workloads under Mass are improved by up to 81.6% and 231.5%, respectively. Further, the workload performance under C-Mass is optimized by up to 40%. • We actively detect the workload characteristics and track their changes. • We propose Mass to provide workload-aware storage policies. • We set up prioritized queues to coordinate workloads. • We further build C-Mass over Mass to efficiently handle workload changes. [ABSTRACT FROM AUTHOR]

The multi-tenancy concept in cloud data center (DC) networks paves the way towards advancements and innovation in the underlying infrastructure such as network virtualization. Multicast routing is essential in leveraging multi-tenancy to its full potential. However, traditional IP multicast routing is not suitable for DC networks due to the need to support a massive amount of multicast groups and hosts. State-of-the-art DC multicast routing approaches aim to overcome these scalability issues by, for instance, taking advantage of the symmetry of DC topologies and the programmability of DC switches to compactly encode multicast group information inside packets, thereby reducing the overhead resulting from the need to store the states of flows at the network switches. Although these approaches scale well with the number of multicast groups, they do not perform well with group sizes and, as a result, yield substantial traffic control overhead and network congestion. In this article, we present Bert, a scalable source-initiated DC multicast routing approach that scales well with both the number and size of multicast groups through the clustering of multicast group members where each cluster employs its own forwarding rules. Compared to the state-of-the-art approach, Bert yields much less traffic control overhead by significantly reducing packet header sizes and eliminating switch memory usage across the switches.

Around 40% of the world’s population is currently without access to the Internet. The digital divide is due to the high cost of provisioning these services and the low return on investment for network operators. We propose using 5G network slicing with multi-tenancy (also known as neutral host networks (NHN)) for macro-cells and small cells in rural areas to reduce the costs. This paper investigates the techno-economic feasibility of using rural 5G NHN to minimise the digital divide. A generic model is developed to analyse the techno-economic analysis of 5G NHN deployment around the world, with a special focus on rural areas where no MNO is interested in providing services. To understand the application, it is applied to the Indian scenario. First, a discussion on existing infrastructure, competition and statistics for Indian telecommunications is presented. Next, the technical requirements are analysed using the key performance indicators (KPI) required for the rural 5G NHN for the Indian scenario. The study also analyses the relationship between coverage, investment in the network, the number of subscribers, investment time, demand, the investment per user and sensitivity analysis to understand the feasibility of the proposed solution for Indian villages with different input conditions. Later, a case study is carried out based on the proposed approach, along with coverage modelling for a few Indian villages having different topologies. The results show that 5G NHN using network slicing can significantly reduce the total investment required for providing 5G services in rural areas. Furthermore, the study shows that rural 5G NHN is a viable investment and a key enabler for Internet connectivity for villages with 10-year investment, having a subscribers’ base as low as 100 with a customer growth rate of 7%.

Nowadays, most applications hosted on public cloud data centers (DCs) disseminate data from a single source to a group of receivers for service deployment, data replication, software upgrade, etc. For such one-to-many data communication paradigm, multicast routing is the natural choice as it reduces network traffic and improves application throughput. Unfortunately, recent approaches adopting IP multicast routing suffer from scalability and load balancing issues, and do not scale well with the number of supported multicast groups when used for cloud DC networks. Furthermore, IP multicast does not exploit the topological properties of DCs, such as the presence of multiple parallel paths between end hosts. Despite the recent efforts aimed at addressing these challenges, there is still a need for multicast routing protocol designs that are both scalable and load-balancing aware. This paper proposes $\mathsf {Ernie}$ , a scalable load-balanced multicast source routing for large-scale DCs. At its heart, $\mathsf {Ernie}$ further exploits DC network structural properties and switch programmability capabilities to encode and organize multicast group information inside packets in a way that minimizes downstream header sizes significantly, thereby reducing overall network traffic. Additionally, $\mathsf {Ernie}$ introduces an efficient load balancing strategy, where multicast traffic is adequately distributed at downstream layers. To study the effectiveness of $\mathsf {Ernie}$ , we extensively evaluate $\mathsf {Ernie}$ ’s scalability behavior (i.e., switch memory, packet size overheads, and CPU overheads), and load balancing ability through a mix of simulation and analysis of its performances. For example, experiments of large-scale DCs with 27k+ servers show that $\mathsf {Ernie}$ requires a downstream header sizes that are $10\times $ smaller than those needed under state-of-the-art schemes while keeping end-host overheads at low levels. Our simulation results also indicate that at highly congested links, $\mathsf {Ernie}$ can achieve a better multicast load balancing than other existing schemes.

In 5G slice networks, the multi-tenant, multi-tier heterogeneous network will be critical in meeting the quality of service (QoS) requirement of the different slice use cases and in reduction of the capital expenditure (CAPEX) and operational expenditure (OPEX) of mobile network operators. Hence, 5G slice networks should be as flexible as possible to accommodate different network dynamics such as user location and distribution, different slice use case QoS requirements, cell load, intra-cluster interference, delay bound, packet loss probability, and service level agreement (SLA) of mobile virtual network operators (MVNO). Motivated by this condition, this paper addresses a latency-aware dynamic resource allocation problem for 5G slice networks in a multi-tenant, multi-tier heterogeneous environment, for efficient radio resource management. The latency-aware dynamic resource allocation problem is formulated as a maximum utility optimisation problem. The optimisation problem is transformed and the hierarchical decomposition technique is adopted to reduce the complexities in solving the optimisation problem. Furthermore, we propose a genetic algorithm (GA) intelligent latency-aware resource allocation scheme (GI-LARE). We compare GI-LARE with the static slicing (SS) resource allocation; the spatial branch and bound-based scheme; and, an optimal resource allocation algorithm (ORA) via Monte Carlo simulation. Our findings reveal that GI-LARE outperformed these other schemes.

The new fifth generation (5G) era has transformed previous mobile generations into fast, smart networks that will be more responsive and customizable. With network slicing, 5G networks can be dynamically adapted to the different needs of specific vertical industries. This capability has opened the opportunity to new business models whereby infrastructure owners can monetize their investment by leasing resources to third parties (i.e. tenants). In this respect, a challenging task for the owner of the radio access network infrastructure (i.e. the network provider) is the spectrum planning of multi-tenant scenarios. This paper proposes different alternatives of hiring capacity to the provider as well as a set of spectrum planning strategies, each giving a certain degree of flexibility to allocate resources per tenant. These strategies are evaluated in a 5G small cell multi-tenant network through snapshot-based simulations. The performance of the strategies is assessed in terms of scalability, spectrum isolation, utilization and efficiency.

The recent advancement in cloud computing enabled technologies to offer scalable and elastic computing components at a reduced cost to manage the IT infrastructure organizations effectively. It addresses the cost optimization problem, which arises in the context of traditional web-enabled computing models where bottle-neck conditions arise owing to managing high-scale dynamic clients along with the flexible infrastructure to map heterogeneous/complex data traffic with storage elements associated with appropriate server units. The trend of research evolution has given birth to the conceptualization of cloud computing, which offers storage, infrastructure, and platform as services with a virtualized mechanism to deal with uncertain complex and high-scaled data traffic irrespective of application domains. It has got a wide range of applications starting from health-care to service industries. Hence, it has become de-facto for any digital and information technology (IT) oriented application design and deployment aspects. Managing complex data flow in the context of the cloud is a highly challenging task, and the principle of multi-tenancy has come up as a solution approach to enhance resource sharing among cloud clients with isolated virtualization and application instances with services. The prime agenda here is to reduce the cost of computation and cloudlet handling time. The study introduces a unique load-balancing approach, namely, multi-tenant load balance (MTLBP), to cope with the service-oriented architecture of multi-tenancy in the cloud environment. It also ensures that the end client user can perform their task execution efficiently on the multi-tenant environment. The experimental approach adopts a behavioral traffic analysis, where the formulated approach's performance is compared with the traditional baseline approaches. The outcome obtained for the proposed approach found quite promising in terms of resource utilization and task execution in comparison with the other models. It shows that the formulated MTLBP approach outperforms the baseline approaches with approximately 26.83% when small value cloudlet processing time is concerned and 33.625% when medium volume cloudlet processing time is concerned and approximately 62% when large volume cloudlet handling time is concerned. It is also observed that for small volume cloudlets, MTLBP attains approximately 12.55% performance improvement on average when resource utilization cost is concerned. For medium and large volume cloudlets, it attains approximately the performance improvement of 9.85% and 3.14%, respectively. [ABSTRACT FROM AUTHOR]

Mobile cloud computing augments smart-phones with computation capabilities by offloading computations to the cloud. Recent works only consider the energy savings of mobile devices while neglecting the cost incurred to the tasks which are offloaded. We might offload several tasks to minimize the total energy consumption of mobile devices; however, this could incur a huge monetary cost. Furthermore, these issues become more complex in considering the multi-tenant cloud, which is not addressed in literature adequately. Thus, to balance the trade-off between monetary cost and energy consumption of the mobile devices, we need to decide whether to offload the task to the cloud or run it locally. In this article, first, we have formulated a 'MinEMC' optimization problem to minimize both the energy as well as the monetary cost of the mobile devices. The 'MinEMC' problem is proven to be NP-hard. We formulate a special case with an equal amount of resource requirement by each task for which a polynomial-time solution is presented. Further various policies are proposed, the cloud can employ to solve the general case. Then we proposed an efficient heuristic named 'Off-Mat' based on distributed stable matching, the solution for which determines whether the tasks are to be offloaded or not under multi-constraints. We also analyze the complexity of this proposed heuristic algorithm. Finally, performance evaluation through simulation results demonstrates that the Off-Mat algorithm attains high-performance in computational tasks offloading and scale well as the number of tenants increases. [ABSTRACT FROM AUTHOR]

The service level agreement (SLA) is an agreement between clients and the service provider, which defines the minimum performance and availability requirements for software. The service provider should have effective strategies for placement, migration, and replication of the tenants to reduce their operational costs, maximize the utilization of their hardware and software resources and accordingly meet the SLA requirements with slight SLA violations. In this research, a clustered‐based multi‐tenant database management system (CB‐MT DBMS) is proposed. Additionally, a dynamic proactive provisioning technique is built using three different prediction models: The Recursive Window Forecasting Autoregressive Integrated Moving Average (ARIMA) model, Exponential Moving Average (EMA) model, and the proposed Recurrent Neural Network (RNN) with Long Short‐Term Memory (LSTM) cells model. Various experimental scenarios with different datasets have been conducted to prove that the RNN model with LSTM cells is a promising solution in multi‐tenant environments, where the tenants have irregular workload patterns. Experimental results firstly show that the RNN model accuracy is superior to their counterparts (i.e., ARIMA and EMA models) when applied to multi‐tenant database workloads generated using TPC benchmarks, as it reduces the prediction error value which is computed using the Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE) metrics. Secondly, Experimental results prove that the RNN prediction model accuracy is superior to their counterparts for detecting SLA violation values and windows using different SLA values. [ABSTRACT FROM AUTHOR]

Cloud Computing has become a reliable solution for outsourcing business data and operation with its cost-effective and resource-efficient services. A key part of the success of the cloud is the multi-tenancy architecture, where a single instance of a service can be shared between a large number of users, also known as tenants. Service selection for multiple tenants presents a real challenge that has not been properly addressed in the literature so far. Most of the existing cloud services selection approaches are designed for a single-user, and hence are inefficient when applied to the case of a large group of users with different, and often, conflicting requirements. In this paper, we propose a multi-tenant cloud services evaluation framework, whereby service selection is carried out per group of tenants that can belong to different service classes, rather than per a single user. We formulate the cloud services selection for multi-tenants as a complex multi-attribute large-group decision-making (CMALGDM) problem. Skyline method is initially applied to reduce the search space by eliminating the dominated services regardless of tenants' requirements. Tenants are clustered based on their profiles characterized by different personal, service, and environmental features. Each tenant is assigned a weight to reflect its importance in the decision-making. The weight of a tenant is determined locally based on its closeness to the group decision and globally by combining its local weight with its corresponding cluster weight to reflect its total contribution to the overall decision-making. The final ranking of the alternatives is guided by a dynamic consensus process to reach a final solution with the highest level of agreement. The proposed framework supports multiple types of information, including deterministic data, interval numbers, and fuzzy numbers, to realistically represent the heterogeneity and uncertainty of security information. [ABSTRACT FROM AUTHOR]