Adaptive Consistency Protocol Based on Grouping Multi-Paxos

There is an important class of applications in data centre called online dataintensive (OLDI) application, such as search engine, electronic shopping, and advertising, which has low latency, high throughput, and high burstiness. For better user experience, the query response delay in the data centre is controlled under 300 milliseconds. DCTCP can better meet the requirements of OLDI, but all the switches in the data centre are required to support RED. For data centre that does not fully support RED switch, TCP protocol can only be used. TCP Incast will appear in the application, which is far from the requirements of OLDI application. This paper proposes the TCP protocol of data centre based on window control. It is in existing switches and network devices,and centralized control makes the send window match with the switch queue cache. In the case of ensuring low latency, it keeps the switch queue in a small range of fluctuation, enabling the data centre to meet low latency, high throughput and high burstiness application requirements. Simulation experiments verify that WCTCP congestion control method can effectively solve the above problems. It is 5 times lower than TCPNewReno packet loss rate, 6 times higher than the network utilization rate, and the network performance is close to the DCTCP protocol.

For addressing problems such as long delays, latency fluctuations, and frequent timeouts in conventional Transmission Control Protocol (TCP) in a data center environment, Data Center TCP (DCTCP) has been proposed as a TCP replacement to satisfy the requirements of data center networks. It is gaining more popularity in academic as well as industry areas due to its performance in terms of high throughput and low latency, and is widely deployed in data centers. However, according to the recent research about the performance of DCTCP, authors have found that most times the sender’s congestion window reduces to one segment, which results in timeouts. In addition, the authors observed that the nonlinear marking mechanism of DCTCP causes severe queue oscillation, which results in low throughput. To address the above issues of DCTCP, we propose multiple congestion points using double threshold and congestion reaction using window adjustment (DT-CWA) mechanisms for improving the performance of DCTCP by reducing the number of timeouts. The results of a series of simulations in a typical data center network topology using Qualnet network simulator, the most widely used network simulator, demonstrate that the proposed window-based solution can significantly reduce the timeouts and noticeably improves the throughput compared to DCTCP under various network conditions.

Causal consistency has emerged as an attractive middle-ground to architecting cloud storage systems, as it allows for high availability and low latency, while supporting semantics stronger than eventual consistency. However, causally-consistent cloud storage systems have seen limited deployment in practice. A key factor is these systems employ full replication of all the data in all the data centers (DCs), incurring high cost. A simple extension of current causal systems to support partial replication by clustering DCs into rings incurs availability and latency problems. We propose Karma, the first system to enable causal consistency for partitioned data stores while achieving the cost advantages of partial replication without the availability and latency problems of the simple extension. Our evaluation with 64 servers emulating 8 geo-distributed DCs shows that Karma (i) incurs much lower cost than a fully-replicated causal store (obviously due to the lower replication factor); and (ii) offers higher availability and better performance than the above partial-replication extension at similar costs.

Data replication is a well-known strategy to achieve the availability, scalability and performance improvement goals in the data management world. However, the cost of maintaining several database replicas always strongly consistent is very high. The CAP theorem shows that a shared-data system can choose at most two out of three properties: consistency, availability, and tolerance to partitions. In practice, most of the cloud-based data management systems tend to overcome the difficulties of distributed replication by relaxing the consistency guarantees of the system. In particular, they implement various forms of weaker consistency models such as eventual consistency. This solution is accepted by many new Web 2.0 applications (e.g. social networks) which could be more tolerant with a wider window of data staleness (replication delay).However, unfortunately, there are no generic application-independent and consumer-centric mechanisms by which software applications can specify and manage to what extent inconsistencies can be tolerated. We introduce an adaptive framework for database replication at the middleware layer of cloud environments. The framework provides flexible mechanisms to enable software applications of keeping several database replicas (that can be hosted in different data centers) with different levels of service level agreements (SLA) for their data freshness. The experimental evaluation demonstrates the effectiveness of our framework in providing the software applications with the required flexibility to achieve and optimize their requirements in terms of overall system throughput, data freshness and invested monetary cost.

Low latency, high throughput, and fairness are the stringent performance requirements of data center networks. For web applications continue to thrive, data center must remain effective by addressing these challenges. While most of the challenges have been handled by data center specific transport protocols (e.g. DCTCP), these transport protocols are not default options for data center users. Free choice of transport protocols demands careful consideration for an effective solution. We argue that queuing scheme at layer-2 switches hold the key to address these issues. Therefore, in this paper, we present FaLL, a queuing scheme for data center networks that meets following performance requirements: fair share of bandwidth, low queuing latency, high throughput, and ease of deployment. FaLL uses an efficiency module, a fairness module and a target delay based dropping scheme to meet these goals. FaLL requires very small amount of state information. Through rigorous experiments on real testbed, we show that FaLL outperforms various peer solutions in variety of network conditions.

Carbon footprint and service coverage tradeoffs in geo-diverse sites

Task assignment optimization in geographically distributed data centers

Chapter 12 - Disaster Recovery and Data Networking

The 2007 US EPA report to Congress (US EPA, 2007) on the state of energy consumption in data centers brought to light the true energy inefficiencies built into today’s data centers. Marquez et al. (2008) conducted an initial analysis on the productivity of a Pacific Northwest National Lab computer using The Green Grid’s Data Center Energy Productivity metric (The Green Grid, 2008). Their study highlights how the Top500 ranking of computers disguises the serious energy inefficiency of today’s High Performance Computing data centers. In the rapidly expanding Cloud Computing space, the race will be won by the providers that deliver the lowest cost of computing — such cost is heavily influenced by the operational costs incurred by data centers. As a means to address the urgent need to lower the cost of computing, solution providers have been intensely focusing on real-time monitoring, visualization, and control/management of data centers. The monitoring aspect involves the widespread use of networks of sensors that are used to monitor key data center environmental variables such as temperature, relative humidity, air flow rate, pressure, and energy consumption. Such data is then used to visualize and analyze data center problem areas (e.g., hotspots), which is then followed by control/management actions designed to alleviate such problem areas. The authors have been researching the operational benefits of a network of sensors tied in to a software package that uses the data to visualize, analyze, and control/manage the data center cooling system and IT Equipment for maximum operational efficiency. The research is being conducted in a corporate production data center that is networked in to the authors’ company’s global network of data centers. Results will be presented that highlight the operational benefits that are realizable through real-time monitoring and visualization.

As the infrastructure of cloud computing and big data, data centers have been deployed widely. Then, how to make full use of the computing and storage resources in data centers will be the focus. Data center networks are considered important solution for the problem above, which include intra-data center and inter-data center networks. Both of them will depend on the optical networking due to its advantages, such as high bandwidth, low latency, and low energy consumption. Data center interconnected by flexi-grid optical networks is a promising scenario to allocate spectral resources for applications in a dynamic, tunable and efficient control manner. Compared with inter-data center, optical interconnect in intra-data center networks is a more pressing need and promising scenario to accommodate these applications in a dynamic, flexible and efficient manner. OpenFlow based Software Defined Networking (SDN) is considered as a good technology, which is very suitable for data center networks. This paper mainly focuses on the data center optical networks based on software defined networking (SDN), which can control the heterogeneous networks with unified resource interface. Architecture and experimental demonstration of OpenFlow-based optical interconnects in intra-data center Networks and OpenFlow-based flexi-grid optical networks for inter-data center are presented in the paper respectively. Some future works are listed finally.

Eventually-consistent federated scheduling for data center workloads

This Informational RFC describes Data Center TCP (DCTCP): a TCP congestion control scheme for data-center traffic. DCTCP extends the Explicit Congestion Notification (ECN) processing to estimate the fraction of bytes that encounter congestion rather than simply detecting that some congestion has occurred. DCTCP then scales the TCP congestion window based on this estimate. This method achieves high- burst tolerance, low latency, and high throughput with shallow- buffered switches. This memo also discusses deployment issues related to the coexistence of DCTCP and conventional TCP, discusses the lack of a negotiating mechanism between sender and receiver, and presents some possible mitigations. This memo documents DCTCP as currently implemented by several major operating systems. DCTCP, as described in this specification, is applicable to deployments in controlled environments like data centers, but it must not be deployed over the public Internet without additional measures.

SummarySharing the network infrastructure, the performance of emerging distributed applications and services in data centers is directly impacted by the network. As these applications are becoming more and more demanding, it is challenging to satisfy their requirements of low latency, high throughput, and low packet loss rate simultaneously. Prior approaches typically resort to flow control or scheduling mechanisms, prioritizing flows according to their demands. However, none of the methods can solely satisfy the various demands of data center applications. Addressing this challenge, we propose tasch, a preference aware flow scheduling mechanism equipped in the software network edge (ie, end‐host networking). This mechanism utilizes multiple separate queues for flows with different preferences, which guarantees low packet delay for latency‐sensitive flows and provides bandwidth guarantees for throughput‐sensitive flows. A coordinating algorithm is presented to share the network resource among multiple queues with pareto‐optimality. tasch is implemented as a thin and plugable kernel module in Linux based hypervisors, which lies between the complicated physical network and tenants VMs. Subsequently, based on the flow traces of real‐world applications, extensive experiments were conducted to verify the effectiveness of network management mechanism.

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.

Spectrum engineering in flexible grid data center optical networks