High Efficiency High Power Density Isolated Matrix-Type Rectifier for Telecom and Data Center Applications

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

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.

As new technologies emerge data centers and servers have established themselves as one of the largest and fastest growing consumers of power. While switched capacitor converter topologies have some very attractive features, namely low reliance on magnetic components and high efficiency, several critical factors have prevented their adoption in high current data center applications. The family of converters proposed are novel intermediate bus converter that demonstrates the highest performance yet achieved for 48 V to 12 V conversion with up to 2.5 kW/in <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> power density, higher than 99% peak efficiency, and 97.2% full load efficiency for 12 V/70 A output. The reduction of voltage stress across the MOSFETs as well as extremely low reliance on magnetics are the key driving factors behind this high efficiency and power density, and are achieved without a sensitive resonant design or the usage of complex control technique.

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.

Cloud computing has been a cost-efficient paradigm for deploying various applications in datacenters in recent years. Therefore, efficient provisioning for virtual data center (VDC) requests from different service providers (SPs) over physical data centers plays a vital role in improving the quality of service (QoS) and reducing the operational cost of SPs. Therefore, a significant attention has been paid for the VDC provisioning problem. However, few approaches have been proposed for the problem of reliable VDC embedding across multiple data centers, as most of them only consider the problem of VDC mapping within a single data center. In this paper, we study the problem of QoS-aware VDC provisioning across multiple data centers, such that the total bandwidth consumption in the inter-data center backbone network is minimized while satisfying the reliability requirement of each VDC request. We formulate this problem as a mathematical optimization problem by using integer linear programming (ILP) and propose an efficient heuristic algorithm called reliable VDC embedding (RVDCE) algorithm to solve this NP-hard problem. The simulation results show that the proposed algorithm performs better in terms of blocking ratio, CPU resource consumption, and bandwidth consumption of backbone network than the existing solution. In addition, this paper has also incorporated integrated security to minimize security vulnerabilities seen in other similar approaches. Apart from demonstrating how to resolve security challenges in our VDC proposal, cost calculations have been implemented to demonstrate the robustness, resiliency, validity, and effectiveness of the VDC provisioning solution for cloud computing.

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 the era of 'dark silicon', modern data centers have to provision additional hardware resources to guarantee the Quality of Service (QoS) of applications in case of bursty workloads that typically occur in low frequency but high intensity. Fortunately, Computational Sprinting has proven to be an effective approach to boost the computing performance of many-core processor chips, which allows a chip to exceed its power and thermal limits temporarily by turning on all processor cores and absorbing the extra heat dissipation with novel phase-changing materials. Consequently, it offers a promising way to deal with these occasional workload bursts by unleashing the full potentials of hardware, avoiding deploying extra computing resources. In this work, we propose ESprint, a QoS-aware management system based on an effective feedback control mechanism for latency-critical applications in data centers. ESprint can perform computational sprinting by precisely scheduling core count, frequency levels, and sprinting duration, serving bursty workloads without QoS violation under the thermal constraint. Specifically, ESprint effectively predict load intensity in the next time interval, and further dynamically allocates appropriate computing resources to minimize actual power consumption. Our prototype-based evaluation results show that ESprint achieves up to 1.92x improvement on energy efficiency for typical workloads while ensuring QoS, over the non-sprinting strategy. We also explore the design space among energy efficiency, core count/frequency scaling techniques, workload characteristics, burst intensity, and QoS requirements, and draw several key insights to guide the effective use of computational sprinting in data centers.

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

Convergence of projected primal-dual dynamics with applications in data centers

With the increasing popularity of data-intensive applications in data centers, the switching fabric in the internode network becomes significant. Silicon-photonic switching fabrics have a bright future in data centers, which offer high bandwidth, high energy efficiency, and low latency. However, integrating a high radix multistage switching fabric in a single chip faces challenges. A large number of waveguide crossings on the silicon photonic die causes massive power loss and introduces a tremendous amount of crosstalk noise. In this article, we propose a chip partition optimization platform (POP), which can decrease the number of waveguide crossings and shorten the on-chip traversal distance of optical signals. Our algorithms can effectively reduce the power loss and crosstalk noise in silicon-photonic multistage switching fabrics, and help to improve the signal integrity. For example, compared with the common design, POP can achieve 33-dB improvement on average power loss, 42-dB improvement on the worst-case power loss, and 39-dB improvement on the worst-case signal to noise ratio, in a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1024\times1024$ </tex-math></inline-formula> butterfly based silicon-photonic switching fabric.

Experimental study on the performance of a novel data center air conditioner combining air cooling and evaporative cooling

This work presents an on-line, energy- and communication-aware scheduling strategy for SaaS applications in data centers. The applications are composed of various services and represented as workflows. Each workflow consists of tasks related to each other by precedence constraints and represented by Directed Acyclic Graphs (DAGs). The proposed scheduling strategy combines advantages of state-of-the-art workflow scheduling strategies with energy-aware independent task scheduling approaches. The process of scheduling consists of two phases. In the first phase, virtual deadlines of individual tasks are set in the central scheduler. These deadlines are determined using a novel strategy that favors tasks which are less dependent on other tasks. During the second phase, tasks are dynamically assigned to computing servers based on the current load of network links and servers in a data center. The proposed approach, called Minimum Dependencies Energy-efficient DAG (MinD+ED) scheduling, has been implemented in the GreenCloud simulator. It outperforms other approaches in terms of energy efficiency, while keeping a satisfiable level of tardiness.

Containerization technology utilizes operating system level virtualization to package applications to run with required libraries and are isolated from other processes on the same host. Lightweight and quick deployment make containers popular in many data centers. Running distributed applications in data centers usually involves multiple clusters of machines. Docker Swarm is a container orchestration tool for managing a cluster of Docker containers and their hosts. However, Docker Swarm’s scheduler does not consider resource utilization when placing containers in a cluster. This paper first investigated performance interference in container clusters. Our experimental study showed that distributed applications’ performance can be degraded when co-located with other containers which aggressively consume resources. A new scheduler is proposed to improve performance while keeping high resource utilization. The experimental results demonstrated that the proposed prototype with machine learning based clustering algorithms could effectively improve distributed applications’ performance by up to 14.5% with an average at around 12%. This work also provides theoretical bounds for the container placement problem.