High Communication Latency Research Articles

A Distributed Truthful Auction Mechanism for Task Allocation in Mobile Cloud Computing

In mobile cloud computing, offloading resource-demanded applications from mobile devices to remote cloud servers can alleviate the resource scarcity of mobile devices, whereas long distance communication may incur high communication latency and energy consumption. As an alternative, fortunately, recent studies show that exploiting the unused resources of the nearby mobile devices for task execution can reduce the energy consumption and communication latency. Nevertheless, it is non-trivial to encourage mobile devices to share their resources or execute tasks for others. To address this issue, we construct an auction model to facilitate the resource trading between the owner of the tasks and the mobile devices participating in task execution. Specifically, the owners of the tasks act as bidders by submitting bids to compete for the resources available at mobile devices. We design a distributed auction mechanism to fairly allocate the tasks, and determine the trading prices of the resources. Moreover, an efficient payment evaluation process is proposed to prevent against the possible dishonest activity of the seller on the payment decision, through the collaboration of the buyers. We prove that the proposed auction mechanism can achieve certain desirable properties, such as computational efficiency, individual rationality, truthfulness guarantee of the bidders, and budget balance. Simulation results validate the performance of the proposed auction mechanism.

Improving latency in Internet-of-Things and cloud computing for real-time data transmission: a systematic literature review (SLR)

To store, analyse and process the large volume of data generated by IoT traditional cloud computing, is used everywhere. However, the traditional cloud data centres have their limitations to handle high latency issues in time-critical applications of IoT and cloud. Their applications are computer gaming, e-healthcare, telemedicine and robot surgery. The high latency in IoTs and cloud includes high computational, communication latency (service) and network latencies. The vital requirement of IoT is to have minimum network, service and computation latencies for real-time applications. Network latency causes a delay in transmitting a message or communication from one location to another. Services that require data in real-time are almost impossible to access the data via the cloud. Traditional cloud computing approaches are unable to fulfil the quality-of-service (QoS) requirements in IoT devices. Researches related to latency reduction techniques are still in infancy. Some new approaches to minimize the latency for transmitting time-sensitive data in real-time are discussed in this paper for cloud and IoT devices. This research will help the researchers and industries to identify the techniques and technologies to minimize the latencies in IoT and cloud. The paper also discusses the research trends and the technical differences between the various technologies and techniques. With the increasing interest in the literature on latency minimization and its requirements for time-sensitive applications; it is important to systematically review and synthesize the approaches, tools, challenges and techniques to minimize latencies in IoT and cloud. This paper aims at systematically reviewing the state of the art of latency minimization to classify approaches, and techniques. The paper uses a PRISMA technique for a systematic review. The paper further identifies challenges and gaps in this regard for future research. We have identified 23 approaches and 32 technologies associated with latencies in the cloud and IoT. A total of 112 papers on latency reduction have been examined under this study. The existing research gaps and works for latency reduction in IoTs are discussed in detail. There are several challenges and gaps, which requires future research work for improving the latency minimization techniques and technologies. Finally, we present some open issues which will determine the future research direction.

Evaluation of Clustering Algorithms on GPU-Based Edge Computing Platforms.

Internet of Things (IoT) is becoming a new socioeconomic revolution in which data and immediacy are the main ingredients. IoT generates large datasets on a daily basis but it is currently considered as “dark data”, i.e., data generated but never analyzed. The efficient analysis of this data is mandatory to create intelligent applications for the next generation of IoT applications that benefits society. Artificial Intelligence (AI) techniques are very well suited to identifying hidden patterns and correlations in this data deluge. In particular, clustering algorithms are of the utmost importance for performing exploratory data analysis to identify a set (a.k.a., cluster) of similar objects. Clustering algorithms are computationally heavy workloads and require to be executed on high-performance computing clusters, especially to deal with large datasets. This execution on HPC infrastructures is an energy hungry procedure with additional issues, such as high-latency communications or privacy. Edge computing is a paradigm to enable light-weight computations at the edge of the network that has been proposed recently to solve these issues. In this paper, we provide an in-depth analysis of emergent edge computing architectures that include low-power Graphics Processing Units (GPUs) to speed-up these workloads. Our analysis includes performance and power consumption figures of the latest Nvidia’s AGX Xavier to compare the energy-performance ratio of these low-cost platforms with a high-performance cloud-based counterpart version. Three different clustering algorithms (i.e., k-means, Fuzzy Minimals (FM), and Fuzzy C-Means (FCM)) are designed to be optimally executed on edge and cloud platforms, showing a speed-up factor of up to for the GPU code compared to sequential counterpart versions in the edge platforms and energy savings of up to 150% between the edge computing and HPC platforms.

An Asymmetric, One-To-Many Traffic-Aware mm-Wave Wireless Interconnection Architecture for Multichip Systems

Platform based computing modules such as embedded systems and micro-servers are multichip systems with in-package memory and processing chips. Such systems consist of both one-to-one (unicast) and one-to-many (broadcast/ multicast) traffic patterns. State-of-the-art wired interconnection architectures such as Network-on-Chip (NoC) are specially designed to handle on-chip unicast traffic and cannot mask the high off-chip communication latency caused by the chip-to-chip I/Os in multichip systems. Moreover, with the increase in memory-intensive applications and hence one-to-many traffic in a multichip system, this scenario gets even worse as conventionally one-to-many traffic is handled as multiple unicast traffic in a multihop NoC infrastructure. Consequently, a small proportion of such one-to-many traffic increases energy consumption and message latency significantly for chip-to-chip communication. Therefore, to support such one-to-many traffic, these multichip systems with in-package memory need one-to-many traffic-aware interconnection infrastructure. To address these issues, we propose the design of one-to-many traffic-aware Wireless Network-in-Package (WiNiP) architecture by using a novel asymmetric WiNiP topology and a traffic-aware Medium Access Control (MAC) mechanism. With cycle-accurate simulations, we demonstrate that the proposed WiNiP architecture reduces the energy consumption and latency up to 46.96 and 47.08 percent respectively for multichip data transfer compared to state-of-the-art wired NiPs for application-specific traffic.

PriFi: Low-Latency Anonymity for Organizational Networks

AbstractOrganizational networks are vulnerable to trafficanalysis attacks that enable adversaries to infer sensitive information fromnetwork traffic—even if encryption is used. Typical anonymous communication networks are tailored to the Internet and are poorly suited for organizational networks.We present PriFi, an anonymous communication protocol for LANs, which protects users against eavesdroppers and provides high-performance traffic-analysis resistance. PriFi builds onDining Cryptographers networks (DC-nets), but reduces the high communication latency of prior designs via a new client/relay/server architecture, in which a client’s packets remain on their usual network path without additional hops, and in which a set of remote servers assist the anonymization process without adding latency. PriFi also solves the challenge of equivocation attacks, which are not addressed by related work, by encrypting traffic based on communication history. Our evaluation shows that PriFi introduces modest latency overhead (≈ 100ms for 100 clients) and is compatible with delay-sensitive applications such as Voice-over-IP.

EdgeKV: Decentralized, scalable, and consistent storage for the edge

Edge computing moves the computation closer to the data and the data closer to the user to overcome the high latency communication of cloud computing. Storage at the edge allows data access with high speeds that enable latency-sensitive applications in areas such as autonomous driving and smart grid. However, several distributed services are typically designed for the cloud and building an efficient edge-enabled storage system is challenging because of the distributed and heterogeneous nature of the edge and its limited resources. In this paper, we propose EdgeKV, a decentralized storage system designed for the network edge. EdgeKV offers fast and reliable storage, utilizing data replication with strong consistency guarantees. With a location-transparent and interface-based design, EdgeKV can scale with a heterogeneous system of edge nodes. We implement a prototype of the EdgeKV modules in Golang and evaluate it in both the edge and cloud settings on the Grid’5000 testbed. We utilize the Yahoo! Cloud Serving Benchmark (YCSB) to analyze the system’s performance under realistic workloads. Our evaluation results show that EdgeKV outperforms the cloud storage setting with both local and global data access with an average write response time and throughput improvements of 26% and 19% respectively under the same settings. Our evaluations also show that EdgeKV can scale with the number of clients, without sacrificing performance. Finally, we discuss the energy efficiency improvement when utilizing edge resources with EdgeKV instead of a centralized cloud.

ITLS: Lightweight Transport-Layer Security Protocol for IoT With Minimal Latency and Perfect Forward Secrecy

Enabling end-to-end secure communication is essential for many Internet-of-Things (IoT) application scenarios. Transport-layer security (TLS) and datagram TLS (DTLS) are the de-facto protocols for communication security in the IP-based IoT. However, the current authentication approaches of TLS are confronted with the heavy overhead and security issues in the resource-constrained IoT scenario. On the other hand, the identity-based cryptography (IBC) becomes an attractive cryptographic solution for the IoT. Unfortunately, the current IBC-based proposals exist the problem of either high communication latency or low level of security. In this article, we propose the first lightweight secure transport protocol called iTLS, which delivers protected data in the first flight with perfect forward secrecy, and provides implicit mutual authentication without certificates. iTLS dynamically generates identity-based early keys before receiving a server response, allowing clients to send the encrypted data without additional round trips. Furthermore, it employs the ephemeral secret ticket to obtain an ephemeral server key in the previous connection. Therefore, the early key established afterward can provide full forward secrecy. Design and implementation in the form of extension make iTLS fully compatible with TLS 1.3 and easy to be converted to a DTLS version. Our evaluation shows that iTLS reduces the network traffic overhead by at least 61.2%, and handshake latency on an ideal network by at least 60% compared to the certificate-based TLS. The results demonstrate iTLS achieves strong adaptability on the low-power and lossy IoT networks.

A degradable NoC router for the improvement of fault-tolerant routing performance

Network-on-chip (NoC) provides high computation performance for a wide range of applications including robotics and artificial intelligence. This paper deals with the issue of improving the fault-tolerant routing performance for realizing high-performance NoCs. The major drawbacks of the conventional fault-tolerant routing methods are low node utilization efficacy and high communication latency. To solve these problems, we propose a novel NoC router which enables to logically reconstruct faulty input buffers. In contrast to most conventional methods, where routers with partially faulty input buffers are regarded as faulty, the proposed method regards them as fault-free routers with degraded input buffers. Simulation results obtained by a cycle accurate custom simulator show that the proposed method reduces the number of faulty and unused nodes and improves communication latency by up to 93% and 87%, respectively, compared with the conventional methods.

Tactile-Internet-Based Telesurgery System for Healthcare 4.0: An Architecture, Research Challenges, and Future Directions

Telesurgery in the 5G era has huge potential to deliver healthcare surgical services to remote locations using high-speed data transfer with a wireless communication channel. It provides benefits to society in view of its improved precision and accuracy to diagnose patients even from remote locations. However, the existing traditional telesurgery system has high communication latency and overhead, which limits its applicability in a wide range of future applications. To mitigate these issues, in this article, we analyze and give insights on the 5G-enabled Tactile Internet (TI)-based telesurgery system for Healthcare 4.0. The URLCC service of 5G ensures ultra-low latency (<; 1 ms) and ultra-high reliability (99.999 percent) communication channel for remote surgery. We propose an architecture for telesurgery with two different aspects of communication channel: traditional network and 5G-enabled TI. Then we present a recent case study on the world's first successfully executed teleslanting heart surgery. The analysis shows that the proposed architecture with TI as a network backbone has faster response time and higher reliability in comparison to the existing system. Finally, some key open issues and research challenges of the traditional telesurgery architecture in terms of latency and reliability are highlighted.

Private mobile edge cloud for 5G network applications

Resource‐intensive real‐time IoT and 5G network applications require low latency, high data rates, and considerable computing and storage resources. Mobile cloud computing systems cannot fulfill the requirements of these applications due to high communication latency. Consequently, recently proposed edge computing implementations require adding new infrastructure or updating existing infrastructure. In addition, edge computing does not exploit the capabilities of end devices, which have become more powerful than supercomputers in the last decade. To overcome the drawbacks of mobile cloud and edge computing systems, we have proposed a private mobile edge cloud in which multiple mobile and stationary devices such as sensors, robots, home appliances, and smartphones, interconnected through infrastructure‐less and infrastructure‐based wireless local area networks are combined to create a small‐scale cloud data center at a local physical area such as a home. We have also discussed opportunities and research challenges associated with development of proposed system and a high‐level architecture based on a cross layer design for an efficient management of resources.

Tree Partitioning Reduction

Solving tridiagonal linear-equation systems is a fundamental computing kernel in a wide range of scientific and engineering applications, and its computation can be modeled with parallel algorithms. These parallel solvers are typically designed to compute problems whose data fit in a common shared-memory space where all the cores taking part in the computation have access. However, when the problem size is large, data cannot be entirely stored in the common shared-memory space, and a high number of high-latency communications are performed. One alternative is to partition the problem among different memory spaces. At this point, conventional parallel algorithms do not facilitate the partition of computation in independent tiles, since each reduction depends on equations that may be in different tiles. This article proposes an algorithm based on a tree reduction, called the Tree Partitioning Reduction (TPR) method, which partitions the problem into independent slices that can be partially computed in parallel within different common shared-memory spaces. The TPR method can be implemented for any parallel and distributed programming paradigm. Furthermore, in this work, TPR is efficiently implemented for CUDA GPUs to solve large size problems, providing highly competitive performance results with respect to existing packages, being, on average, 22.03× faster than CUSPARSE.

Peer-Assisted Computation Offloading in Wireless Networks

Computation offloading has been widely used to alleviate the performance and energy limitations of smartphones by sending computationally intensive applications to the cloud. However, mobile devices with poor cellular service quality may incur high communication latency and high energy consumption for offloading, which will reduce the benefits of computation offloading. In this paper, we propose a peer-assisted computation offloading (PACO) framework to address this problem. In PACO, a client experiencing poor service quality can choose a neighbor with better service quality to be the offloading proxy. Through peer to peer interface such as WiFi direct, the client can offload computation tasks to the proxy which further transmits them to the cloud server through cellular networks. We propose algorithms to decide which tasks should be offloaded to minimize the energy consumption. We have implemented PACO on Android and have implemented three computationally intensive applications to evaluate its performance. Experimental results and simulation results show that PACO makes it possible for users with poor cellular service quality to benefit from computation offloading and PACO significantly reduces the delay and energy consumption compared to existing schemes.

A 10-Gb/s/ch, 0.6-pJ/bit/mm Power Scalable Rapid-ON/OFF Transceiver for On-Chip Energy Proportional Interconnects

Modern multiprocessor system-on-chips employ network-on-chip (NoC) to efficiently connect different components together. NoCs need global and local interconnects to deliver high on-chip bandwidth and low communication latency to avoid being a performance bottleneck. They must also have high throughput density to reduce area occupied by wires. This paper presents techniques to implement power efficient transceivers for on-chip links that can achieve energy proportional operation. Conventional on-chip links optimized for best energy efficiency at peak data rate suffer from degraded energy efficiency under low utilization conditions. Dynamic voltage and frequency scaling and clock gating can partially alleviate this problem, but become ineffective in applications like mobile devices, where the data traffic can be very sporadic. In this paper, architecture and circuit techniques to improve energy efficiency under all utilization levels are presented. The proposed transceiver uses single-ended signaling with only 0.5 $\mu \text{m}$ width and spacing and achieves 5-Gb/ $\mu \text{m}$ throughput density. Fast locking signaling and clocking circuits greatly reduce the power-ON time. Fabricated in 65-nm CMOS technology, the proposed 10-Gb/s transceiver achieves wake-up time in less than 17 ns. More than 125 $\times $ effective data rate scaling (10 Gb/s to 80 Mb/s) is obtained with an energy efficiency degradation of only 1.6 $\times $ (627 to 997 fJ/b/mm). When the supply voltage is scaled from 1 to 0.7 V, the peak data rate scales from 10 to 6 Gb/s and the power scalable range increases to 208 $\times $ (10 Gb/s to 48 Mb/s) with the energy efficiency degradation of only 1.2 $\times $ (627 to 753 fJ/b/mm).

Cloud-based mobile service provisioning for system performance optimisation

Currently, the mobile applications require intensive computational resources, particularly CPUs, RAMs, storage and battery to successfully complete the expected computing operation. Although distant clouds feature high availability and elastic scalability, and performance gain of utilising such resources is decreased by high communication latency due to large number of intermediate hops between the mobile device and the distant public clouds. Therefore, local cloud is suitable choice for some mobile devices. In this paper, hybrid cloud-assisted mobile service optimisation model is proposed to tackle limited resources of mobile devices and enhance the overall system performance. The aim of hybrid cloud-assisted mobile service optimisation is that mobile cloud system utility is optimised while satisfying huge number of mobile requests and improving individual user's quality of service (QoS) and reducing system overheads. The hybrid cloud-assisted mobile service scheduling algorithm enables mobile applications conducted on mobile devices to complete all tasks by leveraging computing resources of public cloud and local cloud. The proposed algorithm is validated through a series of experiments.

Geo-distribution of actor-based services

Many service applications use actors as a programming model for the middle tier, to simplify synchronization, fault-tolerance, and scalability. However, efficient operation of such actors in multiple, geographically distant datacenters is challenging, due to the very high communication latency. Caching and replication are essential to hide latency and exploit locality; but it is not a priori clear how to combine these techniques with the actor programming model. We present Geo, an open-source geo-distributed actor system that improves performance by caching actor states in one or more datacenters, yet guarantees the existence of a single latest version by virtue of a distributed cache coherence protocol. Geo's programming model supports both volatile and persistent actors, and supports updates with a choice of linearizable and eventual consistency. Our evaluation on several workloads shows substantial performance benefits, and confirms the advantage of supporting both replicated and single-instance coherence protocols as configuration choices. For example, replication can provide fast, always-available reads and updates globally, while batching of linearizable storage accesses at a single location can boost the throughput of an order processing workload by 7x.

Low Polling Time TDM ONoC With Direction-Based Wavelength Assignment

Optical network-on-chip (ONoC) is a promising technology for future on-chip many-core communications. ONoC can achieve high bandwidth density and low communication latency under low network cost, which leads to an energy-efficient network. Time-division multiplexing (TDM)-based ONoC aims to solve the congestion problem faced by many optical circuit switching (OCS)-based mesh ONoCs. But TDM-based ONoC still suffers fromhigh network polling time, which causes high average network latency, especially under low network load. In this paper, we propose a novel TDMONoC using torus topology and direction-based wavelength assignment, named TTWA. TTWA ONoC combines TDM communication strategy and torus network topology to solve the congestion problem under the heavy network load faced by OCS-based ONoCs.With the utilization of direction-based wavelength assignment and torus topology, we further propose a high-efficiency communication strategy to improve the adjacent inter-cluster parallel communications and decrease the total TDM slot number, which contributes to a smaller network polling time and lower average network latency. A novel optical router is simultaneously designed to meet the communication strategy in TTWA. The analyses and simulation results indicate that TTWA ONoCimproves the polling time compared with other TDM-based ONoCs and achieves better network performance compared with the equivalent OCS-based ONoCs under various synthetic traffic patterns and realistic scientific applications. After the calculation,TTWA ONoC reduces the network insertion loss of OCS-based mesh ONoC 46.92% and that of OCS-based torus ONoC 31.75% and reduces the required laser power of OCS-based mesh ONoC 62.90% and that of OCSbased torus ONoC 40.66% under the worst-case situation.

Cloud-based Mobile Service Provisioning For System Performance Optimization

Currently, the mobile applications require intensive computational resources, particularly CPUs, RAMs, storage and battery to successfully complete the expected computing operation. Although distant clouds feature high availability and elastic scalability, and performance gain of utilising such resources is decreased by high communication latency due to large number of intermediate hops between the mobile device and the distant public clouds. Therefore, local cloud is suitable choice for some mobile devices. In this paper, hybrid cloud-assisted mobile service optimisation model is proposed to tackle limited resources of mobile devices and enhance the overall system performance. The aim of hybrid cloud-assisted mobile service optimisation is that mobile cloud system utility is optimised while satisfying huge number of mobile requests and improving individual user's quality of service (QoS) and reducing system overheads. The hybrid cloud-assisted mobile service scheduling algorithm enables mobile applications conducted on mobile devices to complete all tasks by leveraging computing resources of public cloud and local cloud. The proposed algorithm is validated through a series of experiments.

ASPIRE

Many vertex-centric graph algorithms can be expressed using asynchronous parallelism by relaxing certain read-after-write data dependences and allowing threads to compute vertex values using stale (i.e., not the most recent) values of their neighboring vertices. We observe that on distributed shared memory systems, by converting synchronous algorithms into their asynchronous counterparts, algorithms can be made tolerant to high inter-node communication latency. However, high inter-node communication latency can lead to excessive use of stale values causing an increase in the number of iterations required by the algorithms to converge. Although by using bounded staleness we can restrict the slowdown in the rate of convergence, this also restricts the ability to tolerate communication latency. In this paper we design a relaxed memory consistency model and consistency protocol that simultaneously tolerate communication latency and minimize the use of stale values. This is achieved via a coordinated use of best effort refresh policy and bounded staleness. We demonstrate that for a range of asynchronous graph algorithms and PDE solvers, on an average, our approach outperforms algorithms based upon: prior relaxed memory models that allow stale values by at least 2.27x; and Bulk Synchronous Parallel (BSP) model by 4.2x. We also show that our approach frequently outperforms GraphLab, a popular distributed graph processing framework.

Garbled Routing (GR): A generic framework towards unification of anonymous communication systems

Anonymous Communication Systems conceal the identity of the communicating parties to preserve their privacy. Various approaches exist, yet none is taking advantage of the diversity of the available solutions to offer a higher anonymity. We introduce a generic framework, a high level host architecture, that allows the mixture of various communication protocols. Our proposal relies on plugin components to offer generic message processing, and on dynamic routing schemes to offer a generic circuit establishment. The results include a potentially higher anonymity for all the networks deployed within the framework, and a pathway towards sharing user-bases and code-bases, mixing the low- and high-latency communication, and benefiting from security-by-obscurity. Blending various protocols also achieves some level of network unobservability. This paper presents the design of the generic framework, the path to its adoption, the model of two real-world systems, the analysis of various security aspects, and the experimental results.

Network-Coded Relaying in Multiuser Multicast D2D Network

D2D communication trades short-range communication for achieving high communication rate and short communication latency. Relay aided D2D communication can further tackle the problem of intermediate obstacles blocking the communication. In this work, multidevice multicast communication via a layer of parallel relay nodes is considered. Two relaying strategies, respectively, called the conventional relaying (CR) and network-coded relaying (NCR), are proposed. The throughput of these two schemes is analytically derived and evaluated through numerical study. Theoretically, NCR shows advantage over CR in twofold: one is higher throughput and the other is requiring less relay nodes and, hence, consuming less aggregate power. Numerical studies verify the analysis and show that the throughput performance gap between the two schemes increases significantly, actually exponentially with the number of devices.