Decentralized task-aware scheduling for 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.

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.

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.

An algorithm for network and data-aware placement of multi-tier applications in cloud data centers

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.

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.

With the fast development of information technology (IT) industry, the demand and market volume for off-line power supplies keep increasing, especially those for telecommunication, computer servers and data centers. As the capital expenditure was measured by the square footage occupied rather than power consumption, the development of high power density converter system is of greater interesting. The rising energy prices have resulted in the cost of power and cooling exceeding the purchase cost in less than two years. Therefore, highly efficient power conversion is required for the power converter system. Generally, the power supply unit (PSU) for power distribution system (PDS) in data center and telecom are the standard two-stage approach which normally consists of power factor correction (PFC) circuit and isolated DC-DC converter. The two-stage power conversion has demonstrated excellent performance and high reliability, since the design can be optimized for each stage. However, limitations to prevent the existing two-stage PSU to fulfill future requirements for the PDS in data center and telecom applications are revealed, and a very promising and fundamentally different approach with the single-stage isolated converter is proposed in this dissertation. The development of single-stage converters with the option of placing the energy storage outside of the PSU creates new degrees of freedom regarding e.g. simplified rectifier racks in telecom and data center. This provides tangible benefits in the form of space saving, better airflow for power unit in rectifier racks and improved lifespan. The three-phase isolated buck matrix-type rectifier, capable of achieving high power density and high efficiency, is identified as an excellent candidate for the medium power level (5 kW~10 kW) single-stage power supply design. Nevertheless, the matrix-type rectifiers are known for their relatively complex modulation and commutation techniques, and lack of ride-through capability such as the stringent case of one phase loss operation. This dissertation work provides comprehensive study on the commutation method and modulation scheme design for the isolated buck matrix-type rectifier. It aims to analyze the operation principle of the rectifier and propose viable modulation and commutation schemes for this rectifier under both three-phase and single-phase operation. The method is verified by the hardware experiments of the PSUs with high efficiency (> 98%) and high power density (> 70 W/in3 ) for 54 V and 380 VDC applications. The prototypes demonstrated in the experiments show the effectiveness of the proposed modulation and commutation schemes for industry.

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.

In data center, applications which are typically multi-server, high-parallel, long-running like web-related applications are really common. They always compute over large-scale dataset and consume plenty of energy. Up until now, most researches focuses on trading a loss of performance for energy saving. However, managing the power is more important compared with reducing it. In this paper, we add energy consumption to the list of managed resources and help managers to control power profile of applications in data center. Tenants put forward the power budget and corresponding response time target of their own applications before they rent servers. We designed strategies to make every application in data center running under a global power cap and their own power budget. We first propose a Global Feedback Power Allocation Policy to periodically allocate the global power cap among the applications. We also devise a Local Efficient Power Policy to determine application-level power cap and allocate it among servers running the application. Extra budget during each period can be used in the rest of the tenancy to increase the application-level power cap to minimize the response time. We use Shell, WWW, DNS and Mail workloads to evaluate our policy.

With the proliferation of distributed applications in data centers and the increase in latency-sensitive flows, it has become increasingly important to provide deterministic latency guarantees for business flows. The main component of business flow latency in the data center is queuing latency. Existing congestion control methods work in a passive reaction working approach, which can neither determine the queuing delay nor guarantee zero data loss. In this situation, this paper proposes a mechanism to deploy asynchronous traffic shapers on the Layer 3 switches of the data center network from the network side perspective. This mechanism shapes the data flow at each hop switch, which can prevent buffer overflow and packet loss, thus improving the deterministic performance of the data center network latency guarantee. We built a large Layer 2 data center network for simulation to verify the asynchronous traffic shaping mechanism based on laboratory conditions and we found that the shaping mechanism has deterministic delay guarantee capability, which can improve the delay deterministic performance of the network.KeywordsAsynchronous Traffic ShapingData Center NetworkDeterministic Latency

Large data centers consume large amounts of electricity. Estimating the energy consumption in a data center can be of great importance to data centers administrators in order to know the energy-consuming tasks and take actions for reducing the total energy consumption. Smart workflow mechanisms can be built to reduce the energy consumption of data centers significantly. In this paper, we are investigating the factors that affect the energy consumption of scientific applications in data centers. We also use eight machine learning methods to estimate the energy consumption of multi-threaded scientific applications. Extensive computational results on a computer with 20 cores show that the CPU usage is the most important parameter in the power consumed by an application. However, better results can be obtained when the CPU utilization is combined with other parameters. We generate various regression models that predict the energy consumption of an application with an average accuracy of 99%. Simpler models with one and two parameters can achieve comparable accuracy with more complex models. We also compare various machine learning methods for their ability to obtain accurate predictions using as few parameters as possible.

With the emerging diverse applications in data centers, the demands on quality of service in data centers also become diverse, such as high throughput of elephant flows and low latency of deadline-sensitive flows. However, traditional TCPs are ill-suited to such situations and always result in the inefficiency (e.g. missing the flow deadline, inevitable throughput collapse) of data transfers. This further degrades the user-perceived quality of service (QoS) in data centers. To reduce the flow completion time of mice and deadline-sensitive flows along with promoting the throughput of elephant flows, an efficient and deadline-aware priority-driven congestion control (PCC) protocol, which grants mice and deadline-sensitive flows the highest priority, is proposed in this paper. Specifically, PCC computes the priority of different flows according to the size of transmitted data, the remaining data volume, and the flows' deadline. Then PCC adjusts the congestion window according to the flow priority and the degree of network congestion. Furthermore, switches in data centers control the input/output of packets based on the flow priority and the queue length. Different from existing TCPs, to speed up the data transfers of mice and deadline-sensitive flows, PCC provides an effective method to compute and encode the flow priority explicitly. According to the flow priority, switches can manage packets efficiently and ensure the data transfers of high priority flows through a weighted priority scheduling with minor modification. The experimental results prove that PCC can improve the data transfer performance of mice and deadline-sensitive flows while guaranting the throughput of elephant flows.

Virtualization technologies are extensively utilized in data centers, particularly cloud computing. This facilitates data center management and diminishes the number of physical machines (servers) and, subsequently, their cooling requirements, leading to cost, space, and power consumption reductions. When applications in data centers are executing independent parallel transactions, but with similar performance requirements, the appropriate level of virtual machine consolidation on a server poses a fundamental challenge for capacity planning. This article introduces a method to evaluate the performance speedup achieved through virtualization on any server and the effects of virtualization and consolidation overheads on physical or virtual machine scalability. This research formalizes the speedup and overheads, using classical computer architecture statements. but at the same time proposes a new method to analyze these overhead amounts and types, showing the scalability and efficiency of different consolidations in the same server and its comparison against no consolidation. This work also proposes a new way to determine the optimal number of physical servers and the optimal number of consolidated virtual machines for a given transaction workload. The real experimentation was performed with different workload sizes, types of virtualizations and different servers. The method presented also facilitates the representation of linear scalability against the real degree of parallelism of either physical machines or consolidated virtual machines for a given transaction workload, as well as striking the right balance between speedup and energy in virtual server consolidation.

Address auto-configuration is a key problem in data center networks, where servers and switches encode topology information into their addresses for routing. A recent work DAC [2] has been introduced to address this problem. Without malfunctions, DAC can auto-configure all the devices quickly. But in case of malfunctions, DAC requires significant human efforts to correct malfunctions and it can cause substantial operation delay of the whole data center. In this paper, we further optimize address auto-configuration process even in the presence of malfunctions. Instead of waiting for all the malfunctions to be corrected, we could first configure the devices that are not involved in malfunctions and let them work first. This idea can be translated to considerable practical benefits because in most cases malfunctions in data centers only account for a very small portion. To realize the idea, we conceptually remove the malfunctions from the physical data center topology graph and mathematically convert the address configuration problem into induced sub graph isomorphism problem, which is NP-complete. We then introduce an algorithm that can solve the induced sub graph isomorphism quickly by taking advantage of data center topology characteristics and induced sub graph properties. We extensively evaluate our design on representative data center structures with various malfunction scenarios. The evaluation results demonstrate that the proposed framework and algorithm are efficient and labor-free to deal with the mapping task in the presence of error devices.