Explore the vast range of titles available.

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.

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.

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.

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-to-one 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.

The next generation of wireless and mobile networks will have to handle a significant increase in traffic load compared to the current ones. This situation calls for novel ways to increase the spectral efficiency. Therefore, in this paper, we propose a wireless spectrum hypervisor architecture that abstracts a radio frequency (RF) front-end into a configurable number of virtual RF front ends. The proposed architecture has the ability to enable flexible spectrum access in existing wireless and mobile networks, which is a challenging task due to the limited spectrum programmability, i.e., the capability a system has to change the spectral properties of a given signal to fit an arbitrary frequency allocation. The proposed architecture is a non-intrusive and highly optimized wireless hypervisor that multiplexes the signals of several different and concurrent multi-carrier-based radio access technologies with numerologies that are multiple integers of one another, which are also referred in our work as radio access technologies with correlated numerology. For example, the proposed architecture can multiplex the signals of several Wi-Fi access points, several LTE base stations, several WiMAX base stations, etc. As it able to multiplex the signals of radio access technologies with correlated numerology, it can, for instance, multiplex the signals of LTE, 5G-NR and NB-IoT base stations. It abstracts a radio frequency front-end into a configurable number of virtual RF front ends, making it possible for such different technologies to share the same RF front-end and consequently reduce the costs and increasing the spectral efficiency by employing densification, once several networks share the same infrastructure or by dynamically accessing free chunks of spectrum. Therefore, the main goal of the proposed approach is to improve spectral efficiency by efficiently using vacant gaps in congested spectrum bandwidths or adopting network densification through infrastructure sharing. We demonstrate mathematically how our proposed approach works and present several simulation results proving its functionality and efficiency. Additionally, we designed and implemented an open-source and free proof of concept prototype of the proposed architecture, which can be used by researchers and developers to run experiments or extend the concept to other applications. We present several experimental results used to validate the proposed prototype. We demonstrate that the prototype can easily handle up to 12 concurrent physical layers.

Technologies like Internet of Things (IoT), cloud, artificial intelligence, blockchain etc. have become a perceptible part of our lives resulting in the generation of enormous amounts of data. Consequently, the systems used for storage and processing of this data are required to be scalable for handling the huge volumes of data. A shared, multitenant system such as a cloud-based storage-as-a-service provides scalability of storage as well as economics of sharing. However, there is a risk of data tampering when multiple tenants work in a shared environment. The benefits of a multitenant solution can be leveraged only if tenants’ data is isolated from each other. Further, prevention of data tampering from malicious tenant nodes is also required. Therefore, the paper proposes the use of a private blockchain for an implementation of a multi-tenant-based storage system. The objective is to develop a scalable system where tenants’ data is not at a risk of tampering. The efficacy of the proposed system has been demonstrated with synthetic data of multiple tenants using a Software as a Service (SaaS) healthcare application.

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.

Software-as-a-service (SaaS) has received significant attention recently as one of three principal components of cloud computing, and it often deals with applications that run on top of a platform-as-a-service (PaaS) that in turn runs on top of infrastructure-as-a-service (IaaS). This paper provides an overview of SaaS including its architecture and major technical issues such as customization, multi-tenancy architecture, redundancy and recovery mechanisms, and scalability. Specifically, a SaaS system can have architecture relating to a database-oriented approach, middleware-oriented approach, service-oriented approach, or PaaS-oriented approach. Various SaaS customization strategies can be used from light customization with manual coding to heavy customization where the SaaS system and its underlying PaaS systems are customized together. Multi-tenancy architecture is an important feature of a SaaS and various trade-offs including security isolation, performance, and engineering effort need to be considered. It is important for a SaaS system to have multi-level redundancy and recovery mechanisms, and the SaaS system needs to coordinate these with the underlying PaaS system. Finally, SaaS scalability mechanisms include a multi-level architecture with load balancers, automated data migration, and software design strategies.

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.