Network-intensive Applications Research Articles

A New Method for using Wavelet-OFDM and Its Applications in Healthcare to Improve the Services of the 5G Mobile Network and IoT-Related Communication Devices

The orthogonal frequency division multiplex (OFDM), which is based on the Fourier transform, is at the center of the current wireless communication systems, including 5G.Due to its performance and capacity to support network-intensive applications like the Internet of Things (IoT), numerous studies have suggested that wavelet transform-based OFDM is a superior alternative to Fourier in physical layer solutions. In this paper, we compare the performance of wavelet transforms to the requirements of the future wireless application system and offer guidelines and methods for wavelet applications in the design of 5G waveforms. A comprehensive discussion of the impact on healthcare follows. Taking into consideration the following 5G key performance indicators (KPIs), a comprehensive performance comparison has been conducted using an image as the test data: energy savings, complexity of modulation and demodulation, dependability, latency, spectral efficiency, effect of transmission and reception under asynchronous transmission, and resistance to time- and frequency-selective channels are all factors to consider. The usage guidelines for the wavelet transform are then discussed. The guidelines are sufficient to act as a guide for future developments and as approaches to tradeoffs.

A New Approach for Enhancing the Services of the 5G Mobile Network and IOT-Related Communication Devices Using Wavelet-OFDM and Its Applications in Healthcare

The heart of the current wireless communication systems (including 5G) is the Fourier transform-based orthogonal frequency division multiplex (OFDM). Over time, a lot of research has proposed the wavelet transform-based OFDM as a better replacement of Fourier in the physical layer solutions because of its performance and ability to support network-intensive applications such as the Internet of Things (IoT). In this paper, we weigh the wavelet transform performances against the future wireless application system requirements and propose guidelines and approaches for wavelet applications in 5G waveform design. This is followed by a detailed impact on healthcare. Using an image as the test data, a comprehensive performance comparison between Fourier transform and various wavelet transforms has been done considering the following 5G key performance indicators (KPIs): energy efficiency, modulation and demodulation complexity, reliability, latency, spectral efficiency, effect of transmission/reception under asynchronous transmission, and robustness to time-/frequency-selective channels. Finally, the guidelines for wavelet transform use are presented. The guidelines are sufficient to serve as approaches for tradeoffs and also as the guide for further developments.

Flow-Aware Adaptive Pacing to Mitigate TCP Incast in Data Center Networks

In data center networks, many network-intensive applications leverage large fan-in and many-to-one communication to achieve high performance. However, the special traffic patterns, such as micro-burst and high concurrency, easily cause TCP Incast problem and seriously degrade the application performance. To address the TCP Incast problem, we first reveal theoretically and empirically that alleviating packet burstiness is much more effective in reducing the Incast probability than controlling the congestion window. Inspired by the findings and insights from our experimental observations, we further propose a general supporting scheme Adaptive Pacing (AP), which dynamically adjusts burstiness according to the flow concurrency without any change on switch. Additionally, a sender-based approach is proposed to estimate the flow concurrency. Another feature of AP is its broad applicability. We integrate AP transparently into different TCP protocols (i.e., DCTCP, L2DCT and D2TCP). Through a series of large-scale NS2 simulations and testbed experiments, we show that AP significantly reduces the Incast probability across different TCP protocols and the network goodput can be increased consistently by on average $7\times $ under severe congestion.

HyperCo: Optimizing Network Performance in ARM-Based Mobile Virtualization

In the ARM-based mobile virtualized environment, optimizing both the network throughput and the latency for network-intensive applications is of great importance. Our experimental studies have shown that the context switch between the host and guest triggered by a hypercall results in CPU overload and thus degrades the I/O performance under an intensive workload. To address this problem, we propose the Adaptive Hypercall Coalescing (HyperCo) algorithm, a software-only approach to optimizing the network I/O performance by reducing the number of hypercalls and achieving a trade-off between throughput and latency. We implement HyperCo by modifying the front-end driver of Virtio-net on the KVM/ARM platform, and we carry out extensive performance evaluations, which indicate that the number of hypercalls and the load on the CPU can be significantly reduced. We show that HyperCo can significantly improve the network throughput under an intensive workload while keeping a low penalty by adapting a coalescing interval dynamically at a low frequency of network I/O requests.

Power-network aware VM migration heuristics for multi-tier web applications

Cloud computing has become an attractive and promising platform, offering on-demand resources for multi-tier web applications. However, an inappropriate and inefficient resource management practices may negatively affect the service level agreement (SLA) and the response time experienced by users, essentially for high load operating conditions. Furthermore, this may result in substantial amount of energy consumption in data centers, which consequently leads to a high operational cost. This paper proposes an effective power-network aware virtual machine (VM) migration heuristics to deal with high SLA violation (SLAV) and energy consumption. Our research consists of two parts. The first part introduces a network-aware VM migration algorithm. The algorithm considers steady-state traffic condition to minimize the negative effect of migration on other flows. The network gain (NG) is calculated for candidate VMs and the VM with the maximum NG is selected. The second part, extends the network-aware algorithm with energy-awareness capabilities. In addition to NG, power gain (PG) is calculated for each candidate VM and two lists are created for each congested link: NG list and PG list. The VM with the lowest sum of the rank is selected. An extensive simulation is done in CloudSim. The results show that the power-network aware algorithm can reduce the energy consumption without significant increase in SLAV. This research enables us to take a step further towards building low latency, energy-efficient and environment-friendly data centers running network intensive applications.

Ring Optical Packet switched (OPS) network: Quality of Service (QoS) and traffic model

Optical Packet Switched (OPS) technology is under study as a promising solution for addressing the rapid growth of network-intensive applications having different Quality of Service (QoS) requirements. By employing Optical Circuit Switched (OCS) networks, an optimized Packet Loss Ratio (PLR), delay and Packet Delay Variation (PDV) performance can be obtained. It is, however, very demanding to offer a QoS comparable to OCS network through the OPS network in order to support a significant and growing number of applications. Thus, moving from OCS to OPS network, QoS is a critical issue. Hence, this paper analyzes the QoS requirements of network-intensive applications and services. Moreover, the paper derived an expression for performance metrics of a single OPS switch and ring OPS network. It is found that the International Telecommunication Union - Telecommunication (ITU-T) classification is extended with the new requirements of applications and services. For example, the upper bound delay, PDV and PLR of video streaming requires (100 ms, 400 ms), 50 ms and 1E−05 respectively. In this work, these values are improved as delay within 10 ms, PDV in micro seconds and PLR of 1E−07 at full load. It is also shown that a Fixed Delay Line (FDL) has to be considered when low PLR is required and when PDV and delay are not critical issues.

FlexPoll: adaptive event polling for network-intensive applications

In today's data centers supporting Internet-scale computing and input/output (I/O) services, increasinglymore network-intensive applications are deployed on the network as a service. To this end, it is critical for the applications to quickly retrieve requests from the network and send their responses to the network. To facilitate this network function, operating system usually provides an event notification mechanism so that the applications (or the library) know if the network is ready to supply data for them to read or to receive data for them to write. As a widely used and representative notification mechanism, epoll in Linux provides a scalable and high-performance implementation by allowing applications to specifically indicate which connections and what events on them need to be watched. As epoll has been used in some major systems, including key-value (KV) systems, such as Redis and Memcached, and web server systems such as NGINX, we have identified a substantial performance issue in its use. For the sake of efficiency, applications usually use epoll's system calls to inform the kernel exactly of what events they are interested in and always keep the information up-to-date. However, in a system with demanding network traffic, such a rigid maintenance of the information is not necessary and the excess number of system calls for this purpose can substantially degrade the system's performance. In this paper, we use Redis as an example to explore the issue. We propose a strategy of informing the kernel of the interest events in a manner adaptive to the current network load, so that the epoll system calls can be reduced and the events can be efficiently delivered. We have implemented an event-polling library, named as FlexPoll, purely in user-level without modifying any kernel code. Our evaluation on Redis shows that the query throughput can be improved by up to 46.9% on micro-benchmarks, and even up to 67.8% on workloads emulating real-world access patterns. FlexPoll is a generic mechanism thus it can be adopted by other applications in a straightforward manner, such as NGINX and Memcached.

Adaptive Remus: adaptive checkpointing for Xen-based virtual machine replication

With the ever increasing dependence on computers and networks, many systems are required to be continuously available in order to fulfil their mission. Virtualization technology enables high availability to be offered in a convenient, cost-effective manner: with the encapsulation provided by virtual machines (VMs), entire systems can be replicated transparently in software, obviating the need for expensive fault-tolerant hardware. Remus is a VM replication mechanism for the Xen hypervisor that provides high availability despite crash failures. Replication is performed by checkpointing the VM at fixed intervals. However, there is an antagonism between processing and communication regarding the optimal checkpoint interval: while longer intervals benefit processor-intensive applications, shorter intervals favour network-intensive applications. Thus, any chosen interval may not always be suitable for the hosted applications, limiting Remus usage in many scenarios. This work introduces Adaptive Remus, a proposal for adaptive checkpointing in Remus that dynamically adjusts the replication frequency according to the characteristics of running applications. Experimental results indicate that our proposal improves performance for applications that require both processing and communication, without harming applications that use only one type of resource.

Research on application classification method in cloud computing environment

Energy consumption is a very important issue that has attracted the attention of many cloud providers as it takes a large quotient of the operation cost for cloud data center. To decrease the energy consumption in cloud data center, one possible solution is to process different types of applications with different strategies. To reach this goal, it is important to know the type of application before it be dealt with. In this paper, we present an application type classification method by monitoring the usage of the resources of application. Through analysis, we find that only part of the parameters are much related to different types of applications. Using these parameters we put forward a feature model that can effectively classify the types of different applications. Extensive experiments show that the model put forward can effectively and accurately classify CPU intensive application, I/O intensive application and network intensive application. It can be used as the basis of efficient utilization of the cloud resources.

Tango

Mobile devices have less computational power and poorer Internet connections than other computers. Computation offload [1, 2, 3, 4], in which some portions of an application are migrated to a server, has been proposed as one way to remedy this deficiency. Yet, partitionbased offload is challenging because it requires applications to accurately predict whether mobile or remote computation will be faster, and it requires that the computation be large enough to overcome the cost of shipping state to and from the server. Further, offload does not currently benefit network-intensive applications.

PIASA: A power and interference aware resource management strategy for heterogeneous workloads in cloud data centers

Cloud data centers have been progressively adopted in different scenarios, as reflected in the execution of heterogeneous applications with diverse workloads and diverse quality of service (QoS) requirements. Virtual machine (VM) technology eases resource management in physical servers and helps cloud providers achieve goals such as optimization of energy consumption. However, the performance of an application running inside a VM is not guaranteed due to the interference among co-hosted workloads sharing the same physical resources. Moreover, the different types of co-hosted applications with diverse QoS requirements as well as the dynamic behavior of the cloud makes efficient provisioning of resources even more difficult and a challenging problem in cloud data centers. In this paper, we address the problem of resource allocation within a data center that runs different types of application workloads, particularly CPU- and network-intensive applications. To address these challenges, we propose an interference- and power-aware management mechanism that combines a performance deviation estimator and a scheduling algorithm to guide the resource allocation in virtualized environments. We conduct simulations by injecting synthetic workloads whose characteristics follow the last version of the Google Cloud tracelogs. The results indicate that our performance-enforcing strategy is able to fulfill contracted SLAs of real-world environments while reducing energy costs by as much as 21%.

Burst2Save: Reducing network-induced energy consumption in the home environment

This paper focuses on the energy efficiency of network-attached home devices, like PCs, tablets, and set-top boxes. These devices already include sophisticated power management schemes, which usually provide a good proportionality level between energy consumption and workload. However, they may become very inefficient in the presence of network-intensive applications and common residential access scenarios (e.g., DSL). As outlined by the experimental results obtained, the origin of this inefficiency fundamentally resides in the gap between (fast) working speeds of power management schemes, and (too slow) network packet inter-arrival times due to the limited bandwidth commonly deployed in residential access. Starting from these considerations, we designed, developed, and experimentally evaluated a simple traffic shaping mechanism, Burst2Save, aimed at mitigating this gap. The Burst2Save allows reducing the energy consumption induced by the network dynamics up to about an order of magnitude without affecting the Quality of Experience (QoE) of end-users under common realistic scenarios.

Guaranteeing QoS of media-based applications in virtualized environment

With the rapid development of web technology and smart phone, multimedia contents spread all over the Internet. The prevalence of virtualization technology enables multimedia service providers to run media servers in virtualized servers or rented virtual machines (VMs) in a cloud environment. Although server consolidation using virtualization can substantially increase the efficient use of server resources, it introduces resources competition among VMs running different applications. Recently, hypervisors do not make any Quality of Service (QoS) guarantee for media-based applications if they are consolidated with other network-intensive applications, which leads to significant performance degradation. For example, Xen only offers a static method to allocate network bandwidth. In this paper, we find that the performance of media-based applications running in VMs degrades seriously when they are consolidated with other VMs running network-intensive applications and argues that dynamic network bandwidth allocation is essential to guarantee the QoS of media-based applications. Then, we present a dynamic network bandwidth allocation system in virtualized environment, which allocates network bandwidth dynamically and effectively, and does not interrupt running services in VMs. The experiments show that our system can not only guarantee the QoS of media-based applications well but also maximize the system's the overall performance while ensuring the QoS of media-based applications.

Network Virtualization for QoS-Aware Resource Management in Cloud Data Centers: A Survey

The increasing popularity of Cloud Computing is leading to the emergence of large virtualized data centers hosting increasingly complex and dynamic IT systems and services. Over the past decade, the efficient sharing of computational resources through virtualization has been subject to intensive research, while network management in cloud data centers has received less attention. A variety of network-intensive applications require QoS (Quality-of-Service) provisioning, performance isolation and support for flexible and efficient migration of virtual machines. In this paper, we survey existing network virtualization approaches and evaluate the extent to which they can be used as a basis for realizing the mentioned requirements in a cloud data center. More specifically, we identify generic network virtualization techniques, characterize them according to their features related to QoS management and performance isolation, and show how they can be composed together and used as building blocks for complex network virtualization solutions. We then present an overview of selected representative cloud platforms and show how they leverage the generic techniques as a basis for network resource management. Finally, we outline open issues and research challenges in the area of performance modeling and proactive resource management of virtualized data center infrastructures.

Up, down and around the stack

Broadband characterization has recently attracted much attention from the research community and the general public. Given this interest and the important business and policy implications of residential Internet service characterization, recent years have brought a variety of approaches to profiling Internet services, ranging from Web-based platforms to dedicated infrastructure inside home networks. We have previously argued that network-intensive applications provide an almost ideal vantage point for broadband service characterization at sufficient scale, nearly continuously and from end users. While we have shown that the approach is indeed effective at characterization and can enable performance comparisons between service providers and geographic regions, a key unanswered question is how well the performance characteristics captured by these network-intensive applications can predict the overall user experience with other applications. In this paper, using BitTorrent as an example network-intensive application, we present initial results that demonstrate how to obtain estimates of bandwidth and latency of a network connection by leveraging passive monitoring and limited active measurements from network intensive applications. We then analyze user experienced web performance under a variety of network conditions and show how estimated metrics from this network intensive application can serve as good web performance predictors.

Modeling and simulation of distributed computing workflows in heterogeneous network environments

Next-generation computation- and network-intensive collaborative applications in various science, engineering, and e-commerce fields feature large-scale computing workflows of complex structures. Efficient algorithms are needed for task scheduling, module deployment, and service provisioning to support the execution of such distributed workflows in heterogeneous network environments and optimize their end-to-end performance for fast system response or smooth data flow. However, deploying large-scale distributed applications in real network environments is extremely challenging due to the inherent dynamics in the reliability, availability, accessibility, and capacity of massively distributed system resources, which are typically shared among a broad community of users over the Internet or dedicated connections. We propose a simulation system to study the execution dynamics of distributed computing workflows and evaluate the network performance of workflow scheduling or mapping algorithms before actual deployment and experimentation. The proposed simulation system visually illustrates the dynamic execution process of workflows in network environments by simulating module execution on computer nodes and data transfer over network links in a completely distributed and parallel manner. Furthermore, the simulation system takes background traffic and workload into consideration to achieve a high level of simulation accuracy for distributed applications deployed in shared production network environments. We implement the simulation system using multi-threaded programming and conduct extensive testings on various mapping schemes using a large number of simulated workflows and networks. The simulation-based performance measurements are quantitatively confirmed by both the experimental observations collected in real networks and the theoretical results obtained by rigorous performance analysis based on well-defined mathematical models.

A bandwidth advisor for wireless networks

Many network intensive applications can benefit substantially from reasonable estimates of future available bandwidth. As a simple example, consider a user, John, that is about to board a flight that is leaving shortly. Using his palmtop equipped with the Aura ubiquitous computing system, John has just finished editing some very large documents that he needs to email over the airport's wireless network. Just before he begins transmission, a 747 begins deplaning passengers at a gate adjacent to where John is waiting; these deplaning passengers are heavily loading the network in John's cell. John's Aura client contacts the bandwidth advisor and learns that there is insufficient bandwidth for John to complete his transmission before his plane leaves. Fortunately, Aura learns from the bandwidth advisor that a nearby cell has ample bandwidth which enables John to move into that cell briefly and complete his transmission before catching his plane.The utility of bandwidth advisors has lead to efforts such as Remos and the Network Weather Service which have investigated providing bandwidth advice in wired network settings. This research builds on this previous work by developing a bandwidth advisor that is specifically tuned for wireless networks, and is suited to the needs of mobile applications.