Heterogeneous Application Requirements Research Articles

D-RAN: A DRL-Based Demand-Driven Elastic User-Centric RAN Optimization for 6G & Beyond

With highly heterogeneous application requirements, 6G and beyond cellular networks are expected to be demand-driven, elastic, user-centric, and capable of supporting multiple services. A redesign of the one-size-fits-all cellular architecture is needed to support heterogeneous application needs. While several recent works have proposed user-centric cloud radio access network (UCRAN) architectures, these works do not consider the heterogeneity of application requirements or the mobility of users. Even though significant gains in performance have been reported, the inherent rigidity of these methods limits their ability to meet the quality of service (QoS) expected from future cellular networks. This paper addresses this need by proposing an intelligent, demand-driven, elastic UCRAN architecture capable of providing services to a diverse set of use cases including augmented/virtual reality, high-speed rails, industrial robots, E-health, and more applications. The proposed framework leverages deep reinforcement learning to adjust the size of a user-centered virtual cell based on each application’s heterogeneous requirements. Furthermore, the proposed architecture is adaptable to varying user demands and mobility while performing multi-objective optimization of key network performance indicators (KPIs). Finally, numerical results are presented to validate the convergence, adaptability, and performance of the proposed approach against meta-heuristics and brute-force methods.

Adaptively prioritizing candidate forwarding set in opportunistic routing in VANETs

Opportunistic routing (OR) paradigms in vehicular networks via opportunistic candidate set selection have a better performance over the pre-defined next-hop node in terms of packet delivery rate. Nevertheless, uneven vehicle distribution, highly dynamic topology, varying link interference, and bandwidth restrictions impose drastic challenges for satisfying applications requirements in static prioritizing candidate set selection policies. Besides, fulfilling quality of service (QoS) requirements of heterogeneous applications, due to the limited local topology perspective and many involving conflicting routing criteria, will be further compromised. Considering the candidate set selection policy as a Multi-Criteria Decision Making (MCDM) task, an adaptive two-hop cooperative forwarding set selection strategy called APFS is proposed in this paper. By developing a local topology perspective to the two-hop neighbor information, the APFS scheme jointly considers indicators of link stability, delay assessment, and link good-put as routing criteria. Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods are employed in two different scenarios in prioritizing candidate forwarding sets. First, classical AHP-TOPSIS technique with a mutable pair-wise comparison matrix is applied, and then Neutrosophic Set (NS) is combined with the classical AHP-TOPSIS method to handle ambiguous vehicular environments. The simulation results show that the APFS strategy using both prioritization policies have significantly improved the performance of packet delivery ratio, average end-to-end delay, throughput, and hop counts as compared to other schemes.

Slicing a FANET for heterogeneous delay-constrained applications

Today’s networks use a “one size fits all” approach that makes them unsuitable to satisfy the performance requirements of heterogeneous applications in terms of latency, scalability, availability and reliability. To this purpose, the concept of network slices has been recently introduced to meet heterogeneous requirements of vertical applications deployed on top of common network infrastructures.The objective of this paper is to define a 6G zero-touch management framework based on a Flying Aerial Network (FANET) made by a set of UAVs, which is able to provide ground devices with network slices that are able to guarantee edge computing to remote areas according to heterogeneous requirements in terms of mean delay and jitter. An inter-slice orchestrator is introduced to split the computation power of the Computing Element of each UAV between the different slices, while an intra-slice orchestrator is in charge of managing horizontal offload among UAVs to obtain load balancing, so decreasing mean delay and delay jitter. An optimization problem based on Reinforcement Learning (RL) is defined to optimize the horizontal-offload decision process of the Intra-slice Orchestrators at run-time. An extensive simulation campaign is used to evaluate the performance of the system and to demonstrate the ability of the proposed framework to achieve energy efficiency, therefore increasing the flight autonomy of the FANET.

Design and Implementation of Novel BRISI Lightweight Cipher for Resource Constrained Devices

The Internet of Things (IoT) and cyber-physical systems (CPS) has grown exponentially over the recent years, has motivated the development and deployment of the low resource devices for a wide range of applications in the IoT. Many such resource constrained devices are deployed to match the heterogeneous application requirements of IoT and CPS systems, wherein privacy and security have emerged, as the most difficult challenges, as the constrained devices are not been designed to have security features. This paper presents a lightweight cipher, based on ARX (Addition-Modulo, Rotation and XOR) operations, Fiestel structure, an amalgamation of BRIGHT and SIMON structure, hence the name BRISI. The cipher encrypts 32-bit plaintext using 64-bit key. The software implementation is performed using MATLAB tool and it fulfils the Avalanche criterion, Key-sensitivity, correlation coefficient, entropy and histogram. The proposed design is simulated using Xilinx Vivado and is implemented on Nexys-4 DDR Artix-7 and Basys-3 Artix-7 FPGA family and is evaluated for (LUT and register) power and timing

Orchestrating virtual slices in data centre infrastructures with optical DCN

Abstract The emergence of new paradigms and services is pushing the limits of nowadays cloud infrastructures. It is a fact that current solutions lack in the flexibility and configurability to adapt to heterogeneous requirements coming from the applications/services to be supported over them. This results most of the time in severe underutilization of the underlying physical substrate. In light of this, newer approaches on resource provisioning and infrastructure management are needed. The Infrastructure as a Service (IaaS) paradigm is proposed as a solution to overcome these limitations. Thanks to IaaS, the physical infrastructure is partitioned onto virtual slices, encompassing heterogeneous resources (e.g. network, computing). Such a concept is expected to be harnessed by future data center (DC) infrastructures in order to cope efficiently with multi-tenancy as well as heterogeneous application requirements. However, current DC networks (DCNs) impose sever limitations onto traffic handling to fully exploit this vision. In light of this, optical technologies are seen as prime candidates for realizing the high performance network fabrics that future DC architectures will need. Under such an umbrella, it becomes primordial to develop specific provisioning solutions that account for the particularities of the optical medium, while providing the means to efficiently slice the DC infrastructure. With all of these in mind, in this article we present a solution for orchestrating and controlling virtual slices in a DC scenario with optical intra-DCN, with the scope of optimizing the underlying physical infrastructure utilization. The benefits of the presented solution are demonstrated against legacy architectures through exhaustive experiments and simulations.

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.

Blind Spectrum Selection in a Decentralized Cognitive Radio Networks with Heterogeneous Applications

In this paper, we consider a cognitive radio network in which the sensing ability of cognitive radio is limited and the channel statistics are not known as a priori information in the opportunistic spectrum access(OSA) framework. It is a special challenge to design a joint spectrum sensing and access strategy for secondary users with diverse service requirements of heterogeneous applications, i.e. the real-time applications and best-effort applications. We formulate the spectrum decision problem as a decentralized multi-armed bandit problem and propose slot structures for cognitive radio network to cope with collisions between heterogeneous applications. The proposed scheme is proved achieving logarithmic regrets in time asymptotically and simulation results show that each user orthogonalizes into their rank-optimal channels according to their pre-allocated priorities, which indicates efficient spectrum utilization while satisfying service requirements.

SensoTrust: Trustworthy Domains in Wireless Sensor Networks

Wireless sensor networks (WSNs) based on wearable devices are being used in a growing variety of applications, many of them with strict privacy requirements: medical, surveillance, e-Health, and so forth. Since private data is being shared (physiological measures, medical records, etc.), implementing security mechanisms in these networks has become a major challenge. The objective of deploying a trustworthy domain is achieving a nonspecific security mechanism that can be used in a plethora of network topologies and with heterogeneous application requirements. Another very important challenge is resilience. In fact, if a stand-alone and self-configuring WSN is required, an autosetup mechanism is necessary in order to maintain an acceptable level of service in the face of security issues or faulty hardware. This paper presents SensoTrust, a novel security model for WSN based on the definition of trustworthy domains, which is adaptable to a wide range of applications and scenarios where services are published as a way to distribute the acquired data. Security domains can be deployed as an add-on service to merge with any service already deployed, obtaining a new secured service.

A case for core-assisted bottleneck acceleration in GPUs

Modern Graphics Processing Units (GPUs) are well provisioned to support the concurrent execution of thousands of threads. Unfortunately, different bottlenecks during execution and heterogeneous application requirements create imbalances in utilization of resources in the cores. For example, when a GPU is bottlenecked by the available off-chip memory bandwidth, its computational resources are often overwhelmingly idle, waiting for data from memory to arrive. This paper introduces the Core-Assisted Bottleneck Acceleration (CABA) framework that employs idle on-chip resources to alleviate different bottlenecks in GPU execution. CABA provides flexible mechanisms to automatically generate "assist warps" that execute on GPU cores to perform specific tasks that can improve GPU performance and efficiency. CABA enables the use of idle computational units and pipelines to alleviate the memory bandwidth bottleneck, e.g., by using assist warps to perform data compression to transfer less data from memory. Conversely, the same framework can be employed to handle cases where the GPU is bottlenecked by the available computational units, in which case the memory pipelines are idle and can be used by CABA to speed up computation, e.g., by performing memoization using assist warps. We provide a comprehensive design and evaluation of CABA to perform effective and flexible data compression in the GPU memory hierarchy to alleviate the memory bandwidth bottleneck. Our extensive evaluations show that CABA, when used to implement data compression, provides an average performance improvement of 41.7% (as high as 2.6X) across a variety of memory-bandwidth-sensitive GPGPU applications.

A Survey on DHT-Based Routing for Large-Scale Mobile Ad Hoc Networks

Mobile ad hoc networks (MANETs) are infrastructureless and distributed communication systems that require sophisticated approaches to routing to cope with node mobility and heterogeneous application requirements. In the past few years, distributed hash table (DHT) has come forth as a useful additional technique to the design and specification of spontaneous and self-organized networks. Researchers have exploited its advantages by implementing it at the network layer and developing scalable routing protocols for MANETs. The implementation of DHT-based routing in a MANET requires different algorithms and specifications compared to routing in the Internet because a MANET has its unique characteristics, such as node mobility, spontaneous networking, decentralized architecture, limited transmission range, dynamic topology, and frequent network partitioning/merging. In this article, we present a comprehensive survey of research related to DHT-based routing that aims at enhancing the scalability of MANETs. We present a vivid taxonomy of DHT-based routing protocols and the guidelines to design such protocols for MANETs. We compare the features, strengths, and weaknesses of existing DHT-based routing protocols and highlight key research challenges that are vital to address. The outcome of the analysis serves as a guide for anyone willing to delve into research on DHT-based routing in MANETs.

INDUSTRY AND PRACTICE

In a previous JDM issue, one of the Associate Editors issued a challenge to database researchers to “...encourage us to think about innovation and point out areas that ... are not only important but also open to innovation.†(Pirkul, 1995, p.33). In this editorial, I address one area that Pirkul explicitly identified as a topic for innovation, namely, Heterogeneous Databases. Below I present a novel framework for understanding Heterogeneous Databases and Database Systems. This framework identifies three dimensions of heterogeneity: the “Why†?, “How†?, and “What?†of heterogeneous database systems. I claim that exploring answers to these three questions will help practitioners (1) identify heterogeneous application requirements and map them to appropriate database capabilities, (2) evaluate heterogeneous application techniques that fulfill these necessary requirements, and (3) select products that support the required techniques.