Joint Static and Dynamic Traffic 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.

Traffic in today’s data center networks grows rapidly. Traditional routing and forwarding protocols in data center usually rely on static hashing, and may cause avoidable bottlenecks. We present a genetic algorithm to make better use of the resources. Our goal is to minimize the maximum single link utilization of the network. And results show that our algorithm performs better than a recent greedy algorithm.

In the Software Defined Networking (SDN) based Data Center Networks (DCN), load balancing is one of the research hotspots. This paper proposes a Dynamic Traffic Scheduling (DTS) strategy for load balancing taking the advantages of the SDN central controller. Considering the realtime network status, the paths can be adjusted during the flow transmission. A flow equilibrium degree is defined as the trigger of enabling DTS. The implementation process of DTS are also described in detail. An OpenFlow based SDN simulation platform is constructed. The experimental results indicate that DTS strategy generate better performance compared with other routing strategy without dynamic rerouting mechanism under various load.

As the scale of data centers and cloud computing applications increases, data center networks play a critical role in meeting the huge communication bandwidth requirement of such applications. The scalability of conventional electronic data center networks is limited by wiring complexity and reaching distance of links under fixed power budget. To overcome this problem, in this paper we propose a nonblocking hybrid switch architecture, called NEO (Nonblocking Electronic and Optical), which is able to provide nonblocking interconnections for as many as 1,000,000 servers in a data center. NEO maintains electronic interconnections for intra-pod networks, while providing interpod interconnections by optical core switches, which not only increases the scalability of the switch architecture, but also lowers the switch cost and power consumption compared to other existing optical switch architectures. We also design a packet scheduler for NEO, which adopts a credit flow control mechanism and a parallel scheduling algorithm to avoid packet loss, and provide low communication latency. Our simulation results demonstrate that NEO achieves very low average packet delay compared to other existing optical switching architectures under various traffic patterns.

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.

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.

Bandwidth-hungry applications such as Cloud computing, video sharing and social networking drive the creation of more powerful Data Centers (DCs) to manage the large amount of packetized traffic. Data center network (DCN) topologies rely on thousands of servers that exchange data via the switching backbone. Cluster switches and routers are employed to provide interconnectivity between elements of the same DC and inter DCs and must be able to handle the continuously variable loads. Hence, robust and scalable switching modules are needed. Conventional DCNs adopt multistage switches that are crossbar or/and memory based. However, current multistage packet switch architectures, with their space-memory variants, are either too complex to implement, have poor performance, or not cost effective. In this paper, we propose a novel and highly scalable multistage packet- switch design based on Network-on-Chip (NoC) fabrics for DCNs. In particular, we describe a novel three-stage packet-switch fabric with a Round-Robin packets dispatching scheme where each central stage module is based on a multihop Unidirectional NoC (UDN), instead of a classic singlehop crossbar fabric. The proposed design, referred to as Clos-UDN, overcomes all the shortcomings of conventional multistage architectures. In particular, as we shall demonstrate, the proposed Clos-UDN architecture: (i) Obviates the need for a complex and costly input modules, by means of few, yet simple, input FIFO queues. (ii) Avoids the need for a complex and synchronized scheduling process over a high number of input- output modules and/or port pairs. (iii) Provides speedup, load balancing and path- diversity thanks to a dynamic dispatching scheme as well as the NoC based fabric nature. Extensive simulation studies are conducted to compare the proposed Clos-UDN switch to traditional multistage switches. Simulation results show that the Clos-UDN outperforms previous designs under a wide range of input traffic scenarios, making it highly appealing for ultra-high capacity DC networks.

Today's data centers tend to have tens to hundreds of thousands of servers to provide massive and sophisticated services. Statistically, data center and data center networks (DCNs) remain highly underutilized which can be exploited for energy-saving. In this paper, we have studied energy-saving problem for network and server sides of the data center. The problem was formulated as a Mixed Integer Linear Program (MILP) that is solvable by an optimization software to jointly minimize the energy consumed by the servers and DCN. To overcome the optimization software high computational time, a heuristic algorithm to provide practical and efficient solution is introduced. The heuristic algorithm has two stages: first, it uses the virtual machines (VM) and the predicted servers resource utilization to provide VM consolidation algorithm and turn-off unused servers. The second stage uses an abstract performance-aware network flow consolidation that focused the traffic on subset of the network and turn-off the unused network devices. Simulation experiments using CloudsimSDN were conducted to validate the heuristic using real traces from Wikipedia in terms of energy consumption and average response time. The results show that the heuristic can save servers and network energy while maintaining performance.

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).

Data centers are a key building block in the rapidly growing area of internet technology. A typical data center has tens of thousands of servers, and communication between them must be flexible and robust. Vortex light beams have orbital angular momentum and can provide a useful and flexible method for optical wireless communication in data centers. Vortex beams can be generated with orbital angular momentum but independent of polarization, and used in a multiplexed system. We propose a multiplexing vortex system to increase the communication capacity using optical wireless communication for data center networks. We then evaluate performance. This paper is intended for use as an engineering guideline for design of vortex multiplexing in data center applications.

Data centers often hold tens to hundreds of thousands of servers in order to offer cloud computing services at scale. Ethernet switching and IP routing have their own advantages and limitations in building data center networks. Recent research, such as PortLand and BCube, has proposed scalable data center network designs. A common feature of these designs is that their addressing and routing are customized to specific topologies. In this paper, we propose a generic addressing, routing and forwarding protocol for data center networks, which works on arbitrarily layered'' network topologies. We first form the network as a multi-rooted tree. Each network node (i.e., hosts and switches) is then assigned one or more locators, and each locator encodes a downward path from the roots to this node. Data center networks often have rich path diversity, so tracking all locators of a destination node will cause switches to have very large forwarding tables. We further use a new forwarding model to reduce the forwarding states. In addition, the multiple-locator mechanism brings built-in support for multi-path routing, load balancing and fault tolerance. Evaluations based on simulations and prototype experiments demonstrate that our proposal achieves our design goals.

Data center networks (DCNs) are at the forefront of a radical transformation, fueled by the rapid rise of artificial intelligence (AI), cloud-native adoption, and enterprise digitalization, which together place unprecedented demands on their design and performance. From healthcare AI and intelligent transportation to business analytics, modern DCNs are expected to support a diverse and evolving mix of bandwidth-demanding applications, each with distinct quality of service (QoS) requirements. Yet, most state-of-the-art DCN solutions rely on oversimplified DCN data traffic models, failing to address the required application-level differentiation in terms of end-to-end (E2E) latency demands. In response, we propose ALAS-DCN, a novel, to our knowledge, application-aware, low-latency, scalable DCN architecture, based on optical packet switching in the intra-rack and inter-rack domains that is both designed and rigorously evaluated using commercial QoS specifications and realistic traffic patterns of different applications coexisting within the same DCN. ALAS-DCN integrates three hierarchical optical sub-networks: intra-rack, inter-rack, and bridge, each governed by a custom wavelength division multiplexing (WDM) medium access control (MAC) protocol that dynamically prioritizes critical traffic packets, according to the specific application E2E latency demands. Unlike prior approaches, our evaluation leverages realistic workload traces generated by established traffic generators, capturing the application diversity within modern data center (DC) environments. Extensive experimental simulations across five representative DC categories, each hosting a different mix of modern applications, demonstrate that ALAS-DCN consistently achieves up to 95% throughput efficiency and sub-µs latency for high priority traffic across a variety of applications, consistently meeting their differentiated latency constraints. Scalable up to 20,000 servers, ALAS-DCN redefines the DCN as not just a transport layer but an intelligent enabler of next-generation application performance.

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.

Energy efficiency and low carbon enabler green IT framework for data centers considering green metrics