Low-latency Access Research Articles

Delay-based I/O request scheduling in SSDs

Abstract To pave the way for fast data access, multiple parallel components (e.g., multiple dies) in SSDs are crafted. Therefore, how to fully utilize the parallel resources has become a challenging issue. To address this issue, we first design an SSD model, which not only considers the structure of the parallel components in SSDs but also investigates the utilization of these components. Then, a novel delay-based request scheduling strategy is devised based on the proposed model. The proposed scheduling strategy can predict the resource utilization and intelligently allocate requests to the parallel components (i.e., channels) to achieve the low storage access latency. We integrate the delay-based request scheduling with the proposed SSD model into a trace-driven simulator and evaluate the effectiveness of the proposed scheme on the Financial and WebSearch data sets. The experimental results show that the IOPS and request latency are improved by 16.5% and 14.2% respectively compared with the state-of-the-art scheduling strategy.

Deep Learning Aided Grant-Free NOMA Toward Reliable Low-Latency Access in Tactile Internet of Things

Tactile Internet of Things (IoT) requires ultraresponsive and ultrareliable connections for massive IoT devices. As a promising enabler of tactile IoT, grant-free nonorthogonal multiple access (NOMA) exploits the joint benefit of grant-free access and nonorthogonal transmissions to achieve low latency massive access. However, it suffers from the reduced reliability caused by random interference. Hence, we formulate a variational optimization problem to improve the reliability of grant-free NOMA. Due to the intractability of this problem, we resort to deep learning by parameterizing the intractable variational function with a specially designed deep neural network, which incorporates random user activation and symbol spreading. The network is trained according to a novel multiloss function where a confidence penalty based on the user activation probability is considered. The spreading signatures are automatically generated while training, which matches the highly automatic applications in tactile IoT. The significant reliability gain of our scheme is validated by simulations.

Tiered Cloud Storage via Two-Stage, Latency-Aware Bidding

In cloud storage, the digital data is stored in logical storage pools, backed by heterogeneous physical storage media and computing infrastructure that are managed by a cloud service provider (CSP). One of the key advantages of cloud storage is its elastic pricing mechanism, in which the users need only pay for the resources/services they actually use, e.g., depending on the storage capacity consumed, the number of file accesses per month, and the negotiated service level agreement. To balance the tradeoff between service performance and cost, CSPs often employ different storage tiers, for instance, cold storage and hot storage. Storing data in hot storage incurs high storage cost yet delivers low access latency, whereas cold storage is able to inexpensively store massive amounts of data and thus provides lower cost with higher latency. In this paper, we address a major challenge confronting the CSPs utilizing such tiered storage architecture—how to maximize their overall profit over a variety of storage tiers that offer distinct characteristics, as well as file placement and access request scheduling policies. To this end, we propose a scheme where the CSP offers a two-stage auction process for: 1) requesting storage capacity and 2) requesting accesses with latency requirements. Our two-stage bidding scheme provides a hybrid storage and access optimization framework with the objective of maximizing the CSP’s total net profit over four dimensions: file acceptance decision, placement of accepted files, file access decision and access request scheduling policy. The proposed optimization is a mixed-integer nonlinear program that is hard to solve. We propose an efficient heuristic to relax the integer optimization and to solve the resulting nonlinear stochastic programs. The algorithm is evaluated under different scenarios and with different storage system parameters, and insightful numerical results are reported by comparing the proposed approach with other profit-maximization models. We see a profit increase of over 60% of our proposed method compared to other baseline algorithms in certain simulation scenarios.

Low-Latency Oriented Network Planning for MEC-Enabled WDM-PON Based Fiber-Wireless Access Networks

WDM-PON-based mobile edge computing (MEC)-enabled fiber wireless access networks (MFWAN) have been identified as a promising technology for next-generation broadband access. Low-latency oriented network planning of the WDM-PON-based MFWAN would be required for low-latency access to newly emerging latency-sensitive applications in the fifth-generation (5G) era. However, this would require a low-latency oriented network design in the network planning phase and has thus become a crucial challenge. In this paper, we investigate low-latency oriented network planning of the WDM-PON-based MFWAN under physical and management constraints. To this end, we develop a mathematical model to minimize total transmission latency for all latency-sensitive services. Our model is composed of the propagation latency on the paths and the processing latency on the network equipment and is subject to constraints of maximal transmission distance, maximal PON power budget, bandwidth requests, and the fronthaul latency limit under some functional split options. Given the model’s complexity, we also propose a heuristic algorithm called latency-minimized integrated multi-associated positioning and routing algorithm (LMI-MAPRA). The simulation results show that the proposed algorithm outperforms the benchmark algorithm with more total transmission latency reductions in both sparse and dense networks. We also analyze the impact of key parameters on comparisons of different approaches in terms of low-latency optimal performances.

Optimal LQG Control Under Delay-Dependent Costly Information

In the design of closed-loop networked control systems (NCSs), induced transmission delay between sensors and the control station is an often-present issue which compromises control performance and may even cause instability. A very relevant scenario in which network-induced delay needs to be investigated is costly usage of communication resources. More precisely, advanced communication technologies, e.g. 5G, are capable of offering latency-varying information exchange for different prices. Therefore, induced delay becomes a decision variable. It is then the matter of decision maker's willingness to either pay the required cost to have low-latency access to the communication resource, or delay the access at a reduced price. In this article, we consider optimal price-based bi-variable decision making problem for single-loop NCS with a stochastic linear time-invariant system. Assuming that communication incurs cost such that transmission with shorter delay is more costly, a decision maker determines the switching strategy between communication links of different delays such that an optimal balance between the control performance and the communication cost is maintained. In this article, we show that, under mild assumptions on the available information for decision makers, the separation property holds between the optimal link selecting and control policies. As the cost function is decomposable, the optimal policies are efficiently computed.

CGAcc: A Compressed Sparse Row Representation-Based BFS Graph Traversal Accelerator on Hybrid Memory Cube

Graph traversal is widely used in map routing, social network analysis, causal discovery and many more applications. Because it is a memory-bound process, graph traversal puts significant pressure on the memory subsystem. Due to poor spatial locality and the increasing size of today’s datasets, graph traversal consumes an ever-larger part of application execution time. One way to mitigate this cost is memory prefetching, which issues requests from the processor to the memory in anticipation of needing certain data. However, traditional prefetching does not work well for graph traversal due to data dependencies, the parallel nature of graphs and the need to move vast amounts of data from memory to the caches. In this paper, we propose a compressed sparse row representation-based graph accelerator on the Hybrid Memory Cube (HMC), called CGAcc. CGAcc combines Compressed Sparse Row (CSR) graph representation with in-memory prefetching and processing to improve the performance of graph traversal. Our approach integrates the prefetching and processing in the logic layer of a 3D stacked Dynamic Random-Access Memory (DRAM) architecture, based on Micron’s HMC. We selected HMC to implement CGAcc because it can provide quite high bandwidth and low access latency. Furthermore, this device has multiple DRAM layers connected to internal logic to control memory access and perform rudimentary computation. Using the CSR representation, CGAcc deploys prefetchers in the HMC to exploit the short transaction latency between the logic and DRAM layers. By doing this, it can also avoid large data movement costs. In the runtime, CGAcc pipelines the prefetching to fetch data from DRAM arrays to improve memory-level parallelism. To further reduce the access latency, several optimized internal caches are also introduced to hold the prefetched data to be Processed In-Memory (PIM). A comprehensive evaluation shows the effectiveness of CGAcc. Experimental results showed that, compared to a conventional HMC main memory equipped with a stream prefetcher, CGAcc achieved an average 3.51× speedup with moderate hardware cost.

A novel non-volatile memory storage system for I/O-intensive applications

The emerging memory technologies, such as phase change memory (PCM), provide chances for highperformance storage of I/O-intensive applications. However, traditional software stack and hardware architecture need to be optimized to enhance I/O efficiency. In addition, narrowing the distance between computation and storage reduces the number of I/O requests and has become a popular research direction. This paper presents a novel PCMbased storage system. It consists of the in-storage processing enabled file system (ISPFS) and the configurable parallel computation fabric in storage, which is called an in-storage processing (ISP) engine. On one hand, ISPFS takes full advantage of non-volatile memory (NVM)’s characteristics, and reduces software overhead and data copies to provide low-latency high-performance random access. On the other hand, ISPFS passes ISP instructions through a command file and invokes the ISP engine to deal with I/O-intensive tasks. Extensive experiments are performed on the prototype system. The results indicate that ISPFS achieves 2 to 10 times throughput compared to EXT4. Our ISP solution also reduces the number of I/O requests by 97% and is 19 times more efficient than software implementation for I/O-intensive applications.

Opportunistic Edge Computing: Concepts, opportunities and research challenges

The growing need for low-latency access to computing resources has motivated the introduction of edge computing, where resources are strategically placed at the access networks. Unfortunately, edge computing infrastructures like fogs and cloudlets have limited scalability and may be prohibitively expensive to install given the vast edge of the Internet. In this paper, we present Opportunistic Edge Computing (OEC), a new computing paradigm that provides a framework to create scalable infrastructures at the edge using end-user contributed resources. One of the goals of OEC is to place resources where there is high demand for them by incentivizing people to share their resources. This paper defines the OEC paradigm and the involved stakeholders and puts forward a management framework to build, manage and monitor scalable edge infrastructures. It also highlights the key differences between the OEC computing models and the existing computing models and shed the light on early works on the topic. The paper also presents preliminary experimental results that highlight the benefits and the limitations of OEC compared to the regular cloud computing deployment and the Fog deployment. It finally summarizes key research directions pertaining to resource management in OEC environments.

On Enabling 5G Automotive Systems Using Follow Me Edge-Cloud Concept

One of the key targets of the upcoming 5G system is to build a mobile network architecture that supports not only classical mobile broadband applications (i.e., Internet and IMS), but also vertical industry services, such as those of automotive systems, e-health, public safety, and smart grid. Vertical industry is known to have specific needs that cannot be sustained by the current cellular networks. More notably, automotive systems require strict quality of service in terms of ultrashort latency for vehicle-to-infrastructure/network (V2I/N) communications. In this paper, we introduce the Follow Me edge-Cloud (FMeC) concept, leveraging the mobile edge computing (MEC) architecture to sustain requirements of the 5G automotive systems. Assuming that automotive services are deployed on MEC entities, FMeC ensures low-latency access to these services by guaranteeing that vehicles (i.e., as well as user equipment on board vehicles) always connect to nearest automotive service. Besides the FMeC architecture, our contribution in this paper consists in presenting a projection of the FMeC solution on an automated driving use case that integrates automotive and Telco infrastructures, to realize the vision of future 5G automotive systems. We introduce the envisioned software defined networking/OpenFlow-based architecture and our mobility-aware framework based on a set of building blocks that permit achieving the automated driving requirements within 5G network. The evaluation results, obtained conjointly through theoretical analysis and computer simulation, show that our proposed solution outperforms baseline approaches in meeting the automated driving latency requirement and minimizing the incurred global cost.

Multi-domain SDN controller federation in hybrid FiWi-MANET networks

Traditionally, mobile ad hoc networks (MANET) and hybrid optical-wireless networks, also known as fiber-wireless (FiWi), have been considered disjointly and independently, with no synergic management solutions. The former has primarily focused on dispatching packets among mobile nodes in infrastructure-less and very dynamic environments, the latter on offering high-bandwidth and low-latency access to cellular-equipped mobile nodes. The recent advancements and penetration of software-defined networking (SDN) techniques have stimulated the adoption of SDN-based flexible monitoring and control also for the MANET and FiWi domains. In this perspective, the paper originally proposes a model and an architecture that loosely federate MANET and FiWi domains based on the interaction of MANET and FiWi controllers: the primary idea is that our MANET and FiWi federated controllers can seldom exchange monitoring data and control hints the one with the other, thus mutually enhancing their capability of packet management over hybrid FiWi-MANET networks. Our model is applied to several relevant use cases to practically point out the benefits of the proposal in terms of both load balancing and fairness improvements.

Energy Management for EV Charging in Software-Defined Green Vehicle-to-Grid Network

Vehicle-to-grid (V2G) networks are expected to balance the supply and demand in smart grid by reducing the peak-to-average ratio of power grid load curve. We are entering the era of wireless communication, where we can enjoy various advantages such as lower cost, lower battery consumption, and lower access latency. We believe advanced wireless communication techniques have great potential to further promote the economical deployment of V2G network and lower energy consumption. In this article, we address the green V2G network for efficient energy management. However, we still face many challenging issues even if we exploit the promising wireless communication technique in green V2G networks. For example, it becomes more and more challenging to achieve the efficiency and economy of renewable energy resource allocation due to the increasing number of electric vehicles and limited capacity of local aggregators (LAGs). To address the issues, we consider a software-defined green V2G network for energy management, which consists of three planes: management plane, control plane, and data plane. Specifically, the control plane is aimed at guiding both data flow and energy flow to implement an efficient and economic strategy for energy scheduling, while the data plane collects the information through LAGs for the customized services in the management plane. Additionally, we present an energy management scheme of charging stations as a case study. Simulation results reveal that our proposals could achieve delightful performance on global optimization in the software-defined green V2G network.

Kogge-Stone Adder Realization using 1S1R Resistive Switching Crossbar Arrays

Low operating voltage, high storage density, non-volatile storage capabilities, and relative low access latencies have popularized memristive devices as storage devices. Memristors can be ideally used for in-memory computing in the form of hybrid CMOS nano-crossbar arrays. In-memory serial adders have been theoretically and experimentally proven for crossbar arrays. To harness the parallelism of memristive arrays, parallel-prefix adders can be effective. In this work, a novel mapping scheme for in-memory Kogge-Stone adder has been presented. The number of cycles increases logarithmically with the bit width N of the operands, i.e., O ( log 2 N ), and the device count is 5 N . We verify the correctness of the proposed scheme by means of TaO × device model-based memristive simulations. We compare the proposed scheme with other proposed schemes in terms of number of cycle and number of devices.

Contention-Based Access for Ultra-Reliable Low Latency Uplink Transmissions

We consider a sporadic ultra-reliable and low latency communications in the uplink 5G cellular systems. Reliable low latency access for randomly emerging packet transmission cannot be guaranteed in current wireless systems. To achieve the goal of low latency and high reliability simultaneously, we propose a contention-based transmission scheme aimed at users with small payloads. We seek to reduce collision probability by considering multiple transmissions for the same packet for reliable reception. We find the optimal number of consecutive multiple transmissions that reduces collisions and achieves target reliability within the latency window. Performance is analyzed with a frame structure planned for 5G cellular systems. Results are compared with default multi-channel slotted ALOHA access scheme.

SGMA: Semi-granted multiple access for non-orthogonal multiple access (NOMA) in 5G networking

With the rapid prosperity of mobile Internet, proliferating smart terminals, and deep penetration of Internet of things (IoTs), the fourth generation mobile communication system (4G) has become increasingly unable to meet the human needs. Therefore, the fifth generation mobile communication system (5G), being studied world-widely, needs to directly face to ultra-huge network capacity and the massive wireless connectivity. Non-orthogonal multiple access (NOMA), enabling different user equipments (UEs) sharing overlapping wireless resources, is regarded as one of the most promising key technologies of 5G, and has attracted great attention in both industry and academia in recent years. In order to guarantee the quality of service (QoS) and achieve low access latency and signaling overhead, NOMA needs to support two kinds of access methods: scheduling access and random access, named granted access and grant-free access respectively. However, how to efficiently support both granted and grant-free access simultaneously becomes a tricky challenge. In this article, we propose semi-granted multiple access, named SGMA. Other than strictly separating the wireless resources between granted and grant-free access, SGMA allows both of them to share the same resources. SGMA is a general method that can be used in several potential NOMA solutions. We also model SGMA and analyze the performance. Moreover, a heuristic resource allocation algorithm for SGMA is proposed. The simulation results show that SGMA further improves the network capacity and user connectivity of NOMA in 5G networking, and also adapts to the dynamic time-varying services. To the best of our knowledge, this is the first work to propose a multiple access method to allow the granted and grant-free access sharing the same wireless spectrum and further design the resource allocation algorithm in 5G NOMA system.

Overview and History of Statistics for Equity Markets

This article surveys the evolution of stock market trading over a 60-year period. It begins before 1960, when there was no database widely available to conduct a statistical analysis of stock price movements. This changed in the 1960s with the introduction of the Center for Research in Security Prices database. A major finding was the heavy-tailed nature of stock returns. The 1960s also brought major theoretical developments, including the martingale theory of stock price processes and the efficient market hypothesis. This hypothesis prevailed until the 1990s, when the discovery of market anomalies led to statistical arbitrage strategies. We describe the use of modern machine learning methods, such as AdaBoost and random forests, which can combine some of these strategies into an improved trading strategy. The twenty-first century was marked by the rapid evolution of electronic markets and the rise of computer-driven high-frequency trading based on computing technology, low latency access, and limit order book modeling.

A Software Defined Fog Node Based Distributed Blockchain Cloud Architecture for IoT

The recent expansion of the Internet of Things (IoT) and the consequent explosion in the volume of data produced by smart devices have led to the outsourcing of data to designated data centers. However, to manage these huge data stores, centralized data centers, such as cloud storage cannot afford auspicious way. There are many challenges that must be addressed in the traditional network architecture due to the rapid growth in the diversity and number of devices connected to the internet, which is not designed to provide high availability, real-time data delivery, scalability, security, resilience, and low latency. To address these issues, this paper proposes a novel blockchain-based distributed cloud architecture with a software defined networking (SDN) enable controller fog nodes at the edge of the network to meet the required design principles. The proposed model is a distributed cloud architecture based on blockchain technology, which provides low-cost, secure, and on-demand access to the most competitive computing infrastructures in an IoT network. By creating a distributed cloud infrastructure, the proposed model enables cost-effective high-performance computing. Furthermore, to bring computing resources to the edge of the IoT network and allow low latency access to large amounts of data in a secure manner, we provide a secure distributed fog node architecture that uses SDN and blockchain techniques. Fog nodes are distributed fog computing entities that allow the deployment of fog services, and are formed by multiple computing resources at the edge of the IoT network. We evaluated the performance of our proposed architecture and compared it with the existing models using various performance measures. The results of our evaluation show that performance is improved by reducing the induced delay, reducing the response time, increasing throughput, and the ability to detect real-time attacks in the IoT network with low performance overheads.

Performance Evaluation of Integrated Multi-Access Edge Computing and Fiber-Wireless Access Networks

With the widespread use of smart mobile devices, the exponential growth of mobile Internet traffic and newly emerging services, such as Internet of Things, virtual reality/augmented reality, and serious games, the network performance requirements for delay and bandwidth are increasing. The inherent long-distance propagation and possible network congestion of mobile cloud computing may lead to excessive latency, which cannot satisfy the new delay-sensitive mobile applications. The proximity of edge computing provides the possibility of low-latency access and raises increasing interest from non-mobile operators; therefore, edge computing faces a variety of access network technologies, including wired (fixed) and wireless (mobile) access. In this paper, we propose an integrated heterogeneous networking scheme for multi-access edge computing and fiber-wireless access networks that uses network virtualization to achieve the dynamic orchestration of the network, storage, and computing resources to meet diverse application demands. The global view and centralized control of the entire network and the unified scheduling of the resources in the scheme anticipate the convergence of various types of access networks and the edge cloud. The multipath transmission of the service flows is further combined as an instance of integrated edge cloud networking. An experimental testbed is established in the laboratory, and the performance of the multi-access edge computing and networking is evaluated to verify the feasibility and effectiveness of the scheme. The results demonstrate that the scheme can effectively improve the network performance.

Survey on Multi-Access Edge Computing for Internet of Things Realization

The Internet of Things (IoT) has recently advanced from an experimental technology to what will become the backbone of future customer value for both product and service sector businesses. This underscores the cardinal role of IoT on the journey towards the fifth generation (5G) of wireless communication systems. IoT technologies augmented with intelligent and big data analytics are expected to rapidly change the landscape of myriads of application domains ranging from health care to smart cities and industrial automations. The emergence of Multi-Access Edge Computing (MEC) technology aims at extending cloud computing capabilities to the edge of the radio access network, hence providing real-time, high-bandwidth, low-latency access to radio network resources. IoT is identified as a key use case of MEC, given MEC's ability to provide cloud platform and gateway services at the network edge. MEC will inspire the development of myriads of applications and services with demand for ultra low latency and high Quality of Service (QoS) due to its dense geographical distribution and wide support for mobility. MEC is therefore an important enabler of IoT applications and services which require real-time operations. In this survey, we provide a holistic overview on the exploitation of MEC technology for the realization of IoT applications and their synergies. We further discuss the technical aspects of enabling MEC in IoT and provide some insight into various other integration technologies therein.

Quality Management of Surveillance Multimedia Streams Via Federated SDN Controllers in Fiwi-Iot Integrated Deployment Environments

Traditionally, hybrid optical-wireless networks (Fiber-Wireless - FiWi domain) and last-mile Internet of Things edge networks (Edge IoT domain) have been considered independently, with no synergic management solutions. On the one hand, FiWi has primarily focused on high-bandwidth and low-latency access to cellular-equipped nodes. On the other hand, Edge IoT has mainly aimed at effective dispatching of sensor/actuator data among (possibly opportunistic) nodes, by using direct peer-to-peer and base station (BS)-assisted Internet communications. The paper originally proposes a model and an architecture that loosely federate FiWi and Edge IoT domains based on the interaction of FiWi and Edge IoT software defined networking controllers: the primary idea is that our federated controllers can seldom exchange monitoring data and control hints the one with the other, thus mutually enhancing their capability of end-to-end quality-aware packet management. To show the applicability and the effectiveness of the approach, our original proposal is applied to the notable example of multimedia stream provisioning from surveillance cameras deployed in the Edge IoT domain to both an infrastructure-side server and spontaneously interconnected mobile smartphones; our solution is able to tune the BS behavior of the FiWi domain and to reroute/prioritize traffic in the Edge IoT domain, with the final goal to reduce latency. In addition, the reported application case shows the capability of our solution of joint and coordinated exploitation of resources in FiWi and Edge IoT domains, with performance results that highlight its benefits in terms of efficiency and responsiveness.

GDS-LC

Successfully integrating cloud storage as a primary storage layer in the I/O stack is highly challenging. This is essentially due to two inherent critical issues: the high and variant cloud I/O latency and the per-I/O pricing model of cloud storage. To minimize the associated latency and monetary cost with cloud I/Os, caching is a crucial technology, as it directly influences how frequently the client has to communicate with the cloud. Unfortunately, current cloud caching schemes are mostly designed to optimize miss reduction as the sole objective and only focus on improving system performance while ignoring the fact that various cache misses could have completely distinct effects in terms of latency and monetary cost. In this article, we present a cost-aware caching scheme, called GDS-LC , which is highly optimized for cloud storage caching. Different from traditional caching schemes that merely focus on improving cache hit ratios and the classic cost-aware schemes that can only achieve a single optimization target, GDS-LC offers a comprehensive cache design by considering not only the access locality but also the object size, associated latency, and price, aiming at enhancing the user experience with cloud storage from two aspects: access latency and monetary cost. To achieve this, GDS-LC virtually partitions the cache space into two regions: a high-priority latency-aware region and a low-priority price-aware region. Each region is managed by a cost-aware caching scheme, which is based on GreedyDual-Size (GDS) and designed for a cloud storage scenario by adopting clean-dirty differentiation and latency normalization. The GDS-LC framework is highly flexible, and we present a further enhanced algorithm, called GDS-LCF , by incorporating access frequency in caching decisions. We have built a prototype to emulate a typical cloud client cache and evaluate GDS-LC and GDS-LCF with Amazon Simple Storage Services (S3) in three different scenarios: local cloud, Internet cloud, and heterogeneous cloud. Our experimental results show that our caching schemes can effectively achieve both optimization goals: low access latency and low monetary cost. It is our hope that this work can inspire the community to reconsider the cache design in the cloud environment, especially for the purpose of integrating cloud storage into the current storage stack as a primary layer.