A Genetic Algorithm for Flow Scheduling in Data Center Networks

Data Center Networks (DCNs) suffer from the congestions caused by unbalanced traffic distributions. Nevertheless, it is difficult to address such a challenge with the existent Ethernet-based DCN architecture. Wireless networking has been considered as a viable approach with a high potential to tackle the problem due to its flexibility. This paper presents Wireless Link Scheduling for Data Center Networks (WLS-DCN) to provide a feasible wireless DCN. More specifically, we propose a wireless DCN architecture as well as a scheduling mechanism. In our wireless scheduling, two different optimization objectives are considered, with one targeting the unbalanced traffic distribution and one maximizing the total network utility, under the constraints of limited wireless resources and co-channel interference. We propose two heuristics for the two problems and carry out extensive simulation study to validate the performance of our design. Our results demonstrate that WLSDCN can significantly improve the performance of DCN.

We study the problem of flow scheduling in data center networks. Using speed scaling, our aim is to find an online scheduling algorithm that minimizes the total energy consumption of the network by determining both the transmission order and rates of the arriving flows while providing a strict flow deadline guarantee. Observing the superlinear property of link power consumption, the key challenge is in constantly determining the minimum transmission rate for “delay-tolerable” flows without any priori knowledge. To leverage the flow arrival pattern, we propose a probability-based flow prediction model to capture the uncertainty of the network flows. Based on the prediction model, we propose a tunable online flow scheduling algorithm to solve the online flow scheduling problem effectively. By introducing a scaling factor on bandwidth allocation, this algorithm allows us to conduct arbitrary trade-offs between the conservative and aggressive behaviors in terms of energy conser- vation. The effectiveness of the proposed algorithm is validated through rigorous theoretical analysis and further confirmed by extensive numerical simulations.

The advent and continued growth of large data centers has led to much interest in switch architectures that can economically meet the high capacities needed for interconnecting the thousands of servers in these data centers. Various multilayer architectures employing thousands of switches have been proposed in the literature. We make use of the observation that the traffic in a data center is a mixture of relatively static and rapidly fluctuating components, and develop a combined scheduler for both these components using a generalization of the load-balanced scheduler. The presence of the known static component introduces asymmetries in the ingress-egress capacities, which preclude the use of a load-balanced scheduler as is. We generalize the load-balanced scheduler and also incorporate an opportunistic scheduler that sends traffic on a direct path when feasible to enhance the overall switch throughput. Our evaluations show that this scheduler works very well despite avoiding the use of a central scheduler for making packet-by-packet scheduling decisions.

Numerous scheduling approaches have been proposed to improve user experiences in a data center network (DCN) by reducing flow completion time (FCT). Mimicking the shortest job first (SJF) has been proved to be the prominent way to improve FCT. To do so, some approaches require flow size or completion time information in advance, which is not possible in scenarios like HTTP chunk transfer or database query response. Some information-agnostic schemes require involving end-hosts for counting the number of bytes sent. We present Longer Stay Less Priority (LSLP), an information-agnostic flow scheduling scheme, like Multi-Level Feedback Queue (MLFQ) scheduler in operating systems, that aims to mimic SJF using P4 switches in a DCN. LSLP considers all the flows as short flows initially and assigns them to the highest priority queue, and flows get demoted to the lower priority queues over time. LSLP estimates the active time of a flow by leveraging the state-of-the-art P4 switch’s programmable nature. LSLP estimates the active time of a group of new flows that arrive during a time interval and assigns their packets to the highest priority. At the beginning of the next time interval, arriving packets of old flows are placed one priority lower except for those already in the lowest priority queue. Therefore, short flows can be completed in the few higher priority queues while long flows are demoted to lower priority queues. We have evaluated LSLP via a series of tests and shown that its performance is comparable to the existing scheduling schemes.

Online social networks (OSN) have become a prevalent communication tool with a user scale of billions. Users send messages and share photos and videos to their friends every day. As a consequence, a large amount of flows are produced by file storage and backup in the back end. Most flows are deadline intensive, and the scale of flow account is large; these characteristics create new challenges for flow scheduling in data center networks. In this study, we conclude the flow distribution of OSN based on previous studies. OSN flow can be classified into a large amount of bursty flow and a limited amount of large flow. The bursty mice flows are mostly critical real-time requirements, which support instant messages and routing information; the large flows seldom require real-time transmission, because they mainly support file transferring and updating. A mixed flow causes bottlenecks in rack switches easily, thereby resulting in deadline loss for real-time applications and lower throughput. The study divides flow based on different metrics and provides priority-based flow scheduling for OSN datacenter (PFO). PFO allocates rate for different flows on the basis of flow size and deadline information. Our objective is to decrease the average completion time for bursty flows and ensure a high throughput. The analysis and evaluation shows that the proposed scheduling increases the goodput more than DCTCP and D2TCP, and decreases the fraction of missed deadlines.

Many data center applications perform rich and complex tasks (e.g., executing a search query or generating a user's news-feed). From a network perspective, these tasks typically comprise multiple flows, which traverse different parts of the network at potentially different times. Most network resource allocation schemes, however, treat all these flows in isolation -- rather than as part of a task -- and therefore only optimize flow-level metrics. In this paper, we show that task-aware network scheduling, which groups flows of a task and schedules them together, can reduce both the average as well as tail completion time for typical data center applications. To achieve these benefits in practice, we design and implement Baraat, a decentralized task-aware scheduling system. Baraat schedules tasks in a FIFO order but avoids head-of-line blocking by dynamically changing the level of multiplexing in the network. Through experiments with Memcached on a small testbed and large-scale simulations, we show that Baraat outperforms state-of-the-art decentralized schemes (e.g., pFabric) as well as centralized schedulers (e.g., Orchestra) for a wide range of workloads (e.g., search, analytics, etc).

Hybrid ant genetic algorithm for efficient task scheduling in cloud data centers

Background:AMobileAdHocNetwork (MANET) consistsofmany independentnodes that operate over awireless topology with numerous Quality of Service (QoS) requirements. So, a robust routing protocol that also provides QoS parameters to its different range of applications is needed. Various network parameters like node density, node mobility, field size etc. also effects the QoS parameters of MANETs. Methods: The traditional reactive routing protocols do not have any backup route in case of a node failure and hence are not effective for MANETs. Ad Hoc on Demand Distance Vector Backup Routing (AODV BR) protocol performs better than traditional routing protocols but is not effective when the backup route fails. So, to counter this problem, the authors had proposed AODV nth BR that keeps on providing backup routes when multiple nodes fail and hence successfully transfers the data to the destination. The nodes for backup routing are selected based on their distance from the failed node and energy efficiency. Results: In this paper, the performance of AODV nth BR has been compared with AODV, DSR and AODV BR protocol under varying node density (nodes 20, 60,100) and node mobility conditions (5m/s, 20m/s, 50m/s). The values for Packet Delivery Fraction (PDF) for 60 nodes at speed 20m/s in case of AODV nth BR protocol after 6000 rounds is 0.3054. Comparatively, value of AODV BR is 0.2664, AODV is 0.1936 and DSR is 0.1056. QoS performance is measured in terms of end to end delay, packet delivery fraction and lifetime of devices and it has been shown through simulated results that AODV nth BR protocol gives better output than traditional routing protocols. Conclusion: From the simulated results explained in detail in the paper, it has been observed that route after link breakage is found to be best with AODV nth BR protocol and this can be clearly seen in the QoS parameters obtained.Keywords: AODV BR, AODV nth BR, MANETs, Node Density, Node Mobility

Data centers (DCs), owing to the exponential growth of Internet services, have emerged as an irreplaceable and crucial infrastructure to power this ever-growing trend. A DC typically houses a large number of computing and storage nodes, interconnected by a specially designed network, namely, DC network (DCN). The DCN serves as a communication backbone and plays a pivotal role in optimizing DC operations. However, compared to the traditional network, the unique requirements in the DCN, for example, large scale, vast application diversity, high power density, and high reliability, pose significant challenges to its infrastructure and operations. We have observed from the premium publication venues (e.g., journals and system conferences) that increasing research efforts are being devoted to optimize the design and operations of the DCN. In this paper, we aim to present a systematic taxonomy and survey of recent research efforts on the DCN. Specifically, we propose to classify these research efforts into two areas: 1) DCN infrastructure and 2) DCN operations. For the former aspect, we review and compare the list of transmission technologies and network topologies used or proposed in the DCN infrastructure. For the latter aspect, we summarize the existing traffic control techniques in the DCN operations, and survey optimization methods to achieve diverse operational objectives, including high network utilization, fair bandwidth sharing, low service latency, low energy consumption, high resiliency, and etc., for efficient DC operations. We finally conclude this survey by envisioning a few open research opportunities in DCN infrastructure and operations.

Today, there is a generalized standard usage of internet for all.The devices via multiple technologies that facilitates to provide few communication methods to scholars to work with. By forming multiple paths in the data center network, latest generation data centers offer maximum bandwidth with robustness. To utilize this bandwidth, it is necessary that different data flows take separate paths. In brief, a single-path transport seems inappropriate for such networks. By using Multipath TCP, we must reconsider data center networks, with a diverse approach as to the association between topology, transport protocols, routing. Multipath TCP allows certain topologies that single path TCP cannot use. In newer generation data centers, Multipath TCP is already deployable using extensively deployed technologies such as Equal-cost multipath routing. But, major benefits will come when data centers are specifically designed for multipath transports. Due to manifold of technologies like Cloud computing, social networking, and information networks there is a need to deploy the number of large data centers. While Transport Control Protocol is the leading Layer-3 transport protocol in data center networks, the operating conditions like high bandwidth, small-buffered switches, and traffic patterns causes poor performance of TCP. Data Center TCP algorithm has newly been anticipated as a TCP option for data centers which address these limitations. It is worth noting that traditional TCP protocol.

The number of data centers has been increased for various reasons such as cloud computing, big-data analysis, multimedia service, etc. With public interests on data center, many novel technologies for data center networks have been proposed and deployed to support data center operations more efficiently and effectively. However, the construction of data center network incurs significant costs. Moreover, various technologies interplay each other to achieve multiple objectives, and it makes difficult to validate and/or verify characteristics of data center network. In addition, it difficult to perform experiments with a number of hosts and switches. Therefore, it is necessary to observe the characteristics of target data center network before building it. A common approach to evaluate data center is to run simulations that should be similar with real-world data center environment. However, generating traffic with the characteristics of data center networks is not matured yet. People still employ a traffic generator based on the characteristics of Internet traffic. We design a traffic generator that shows more accurate characteristics of data center network traffic. Various traffic characteristics exploited explored by several studies are considered. The proposed method generates flow-level network traffic matrix based on Poisson Shot-Noise model. We implemented the traffic generator using Python programming language to create traffic matrix. To evaluate the proposed method, we compare the results with real data center network traffic. Our results show that the generated traffic owns similar characteristics with the real network traffic in terms of flow size, duration, and the mean and variance of total traffic rate.

Data center networks, using a reliable, high speed communication network, is a core component of cloud computing. Recently, optical networks using Space Division Multiplexing has been proposed as a solution to the speed bottleneck anticipated in data center networks. To our knowledge the new challenges of designing such communication systems have not been addressed yet. In this paper we have proposed an optimal approach to the problem of developing a path-protection scheme to handle communication requests in data center (DC) networks using elastic optical networking and space division multiplexing. We have formulated our problem as an Integer Linear Program (ILP). We have also proposed a heuristic that can handle problems of practical size. Our simulations explore important features of our approach.

Interference and unreliability of wireless transmissions limit the throughput of wireless sensor networks. Multi-radio multi-channel technology shows its prospect to significantly improve the network capacity by reducing co-channel interference, and opportunistic cooperative offers a great chance to cope with the unreliability of wireless transmissions. In this paper, we present a multi-radio multi-channel opportunistic cooperative routing (MMOCR) protocol to improve the network throughput. Different from the traditional routing protocols, MMOCR chooses some opportunistic forwarding nodes on the channel with the least interference. The cumulative interference strength (CIS) is proposed to quantify different channel conditions, and a receiver-oriented mechanism to avoid the deafness problem. Packets are collaborative forwarded in accordance with the priorities of forwarding candidate nodes and a slotted acknowledgement is used to guarantee packet delivery and reduce duplication. We finally compare MMOCR with traditional opportunistic routing and deterministic routing protocols, the results show the proposed routing strategy achieves a better performance, especially in terms of the network throughput and packet delivery radio.

Given the current expansion of cloud computing, the expected advent of the Internet of Things, and the requirements of future fifth‐generation network infrastructures, significantly larger pools of computational and storage resources will soon be required. This emphasizes the need for more scalable data centers that are capable of providing such an amount of resources in a cost‐effective way. A quick look into today's commercial data centers shows that they tend to rely on variations of well‐defined leaf‐spine/Clos data center network (DCN) topologies, offering low latency, ultrahigh bisectional bandwidth, and enhanced reliability against concurrent failures. However, DCNs are typically restricted by the use of the Transmission Control Protocol/Internet Protocol (TCP/IP) suite, thus suffering limited routing scalability. In this work, we study the benefits that replacing TCP/IP with the recursive internetwork architecture (RINA) can bring into commercial DCNs, focusing on forwarding and routing scalability. We quantitatively evaluate the benefits that RINA solutions can yield against those based on TCP/IP and highlight how, by deploying RINA, topological routing solutions can improve even more the efficiency of the network. To this goal, we propose a rule‐and‐exception forwarding policy tailored to the characteristics of several DCN variants, enabling fast forwarding decisions with merely neighbors' information. Upon failures, few exceptions are necessary, whose computation can also profit from the known topology. Extensive numerical results show that the proposed policy requirements depend mainly on the number of neighbors and concurrent failures in the DCN rather than its size, dramatically reducing the amount of forwarding and routing information stored at DCN nodes.

The information and communication technology nowadays more than ever depends on the Internet and cloud computing, so that the data centers (DCs) have been converted to a constitutive unit of the cloud computing. A DC is composed of two primary parts: servers and Data Center Networks (DCNs). Robustness and scalability are two major challenges of the DCNs that are expanded based on two strategies, scale-out, and scale-up. This paper is distinctive from the related studies in two aspects. The first one is to simultaneously focus on both the scalability and the robustness challenges of the DCNs. For this purpose, we will concentrate on the comparison of robustness in the scalable models of these networks. The second one is, despite the previous work that only evaluated the DCN robustness under topological changes, we evaluated the robustness and fault tolerance against three types of unexpected changes in topology, traffic, and COI (community of interest) in the present work. Hence, we have chosen the network criticality (NC) as a graph-theoretic metric for analyzing DCN robustness. Afterward, we compare some structural and spectral graph metrics with NC among some well-known DCNs, and their scale-out and scale-up. Our results are useful to select the appropriate scaling strategy with the goal of maximizing the robustness of existing DCNs and provide a guideline for designing the new robust and scalable DCN.