A4: Microarchitecture-Aware LLC Management for Datacenter Servers with Emerging I/O Devices

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.

Integration of "Traditional" Data Centre Services with GIS Technology

Disseminating sensory information from sensor nodes to the gateway node towards Data Center Server has to be guaranteed its fruitfulness. However, these important information commonly are transmitted over wireless connections whereas everyone including adversaries may join freely in the network. Hence, the correctness of received information should be emphasized. To do so, such information should be ascertained from the authenticated sensor nodes, kept its secrecy, prevented from the alteration, and guaranteed its integrity, respectively without revealing sensor nodes' identity. On the other hand, data sensors in the Data Center Server also should be kept secret and only be accessed by authenticated users over common public connection such as Internet without exposing the users' privacy. Therefore, an anonymous authentication system in collecting data sensors from sensor nodes to the Data Center Server and accessing them by everyone would be strongly desirable. In this paper, we propose an implementation of anonymous authentication system in the wireless sensor network using VLR group signature.

Inter-data center interconnect with IP over elastic optical network (EON) is a promising scenario to meet the high burstiness and high-bandwidth requirements of data center services. In our previous work, we implemented multi-stratum resources integration among IP networks, optical networks and application stratums resources that allows to accommodate data center services. In view of this, this study extends to consider the service resilience in case of edge optical node failure. We propose a novel multi-stratum resources integrated resilience (MSRIR) architecture for the services in software defined inter-data center interconnect based on IP over EON. A global resources integrated resilience (GRIR) algorithm is introduced based on the proposed architecture. The MSRIR can enable cross stratum optimization and provide resilience using the multiple stratums resources, and enhance the data center service resilience responsiveness to the dynamic end-to-end service demands. The overall feasibility and efficiency of the proposed architecture is experimentally verified on the control plane of our OpenFlow-based enhanced SDN (eSDN) testbed. The performance of GRIR algorithm under heavy traffic load scenario is also quantitatively evaluated based on MSRIR architecture in terms of path blocking probability, resilience latency and resource utilization, compared with other resilience algorithms.

On the use of the genetic programming for balanced load distribution in software-defined networks

The migration of computation and storage to cloud data centers has imposed new communication requirements and a need for fundamental changes in infrastructure. The cloud paradigm means that significant network communication will occur between servers in data centers. Furthermore, the migration of virtual machines and containers across physical servers means that in addition to traditional requirements of efficiency, scalability, and easy manageability, future data center networks must also support efficient mobility. To solve the problem, the paper proposes a radical redesign for data center networks, using a new architectural approach that we call DCnet. The DCnet approach changes the addressing and routing schemes at layers 2 and 3 completely. The new addressing mechanism allows an organization to assign addresses that span multiple data centers and permits VM mobility across data centers. Despite radical changes in addressing and routing, the DCnet architecture retains basics from Ethernet and IP, allowing existing hardware building blocks to be reused. The paper uses IPv6 in examples, but the new addressing model and address mobility approach can be applied to IPv4. By assigning each addressable entity a unique ID, and using the ID as the host suffix in an IP address, DCnet allows legacy operating systems and applications to be used without changes. The paper reports a simulation study that uses mininet and SDN to demonstrate the feasibility of the DCnet approach.

With the wide application of data-driven intelligence technology, the management efficiency and quality of urban rail transit improve considerably. However, due to data privacy and security protection, a large amount of data is still scattered among operators or rail transit departments in the form of data islands. The data barriers between urban rail departments severely hinder the intelligent development of urban rail transit systems. In this study, we design a distributed data-sharing system based on blockchain for urban rail transit system. Data production node in urban rail department, such as servers in data centers, wayside equipment, and onboard equipment, can share their data in a distributed way. Blockchain provides audit and check functions to guarantee data-sharing security. We explore the data-sharing incentive mechanism which has an impact on sharing willingness of data production nodes. To maximize the individual utility of nodes, we formulate an evolutionary game model for data nodes with bounded rationality to adapt their sharing strategies. The uniqueness and stability of the equilibrium of the game are also analyzed theoretically. Extensive experiments are conducted and illustrated that data production nodes can share their data efficiently and safely in our proposed data-sharing system. Our proposed evolutionary game model can determine the most effective data-sharing incentive mechanism.

Impact of Developer Choices on Energy Consumption of Software on Servers

TCP is widely used in the communications between servers in data centers. However, TCP has scaling limit that its ability to provide fair sharing at bottleneck link is reduced as the number of competing flows increases. Such limit is common in current data center networks and causes unfairness among TCP flows on a small time scale. The unfairness would add unpredictable delay to short flows, which would affect the user experiences of soft real-time applications. In this paper, we propose a simple and cost-effective method — shrinking MTU — to improve the fairness among TCP flows. Through model analysis and extensive simulations, the fairness on a small time scale is improved and the completion time of short flows is reduced by decreasing MTU size.

Traditional disk-oriented storage systems can no longer meet the high requirements of applications in latency and bandwidth, for which RAMCloud, was proposed to replace the disk with RAM. We consider using the memory of end-users in existing Internet to achieve a similar memory storage system. However the end-users' behavior are not as controllable as the servers in data center, which would lead to a high data loss rate. So how to ensure the data's reliability is the biggest challenge. To improve the reliability, as well as the resource utilization, we present a thought of Contributor scoring, establishing a scoring model according to Contributors' behavior. This model will classify Contributors into certain levels. Moreover, we train the Model with genetic algorithms. Experimental results show that the optimized model can effectively divide the Contributor, ensure certain reliability.It has great significance to this user memory storage system for improving the reliability and resource utilization.

The cost effective deployment of applications into cloud has resulted in significant increase of cloud based services. This has in turn led to large number of data centers in delivering such services at scale, offering myriad of user experiences and minimal downtime. Such commitments of providing differentiated services at scale, invites the necessity to manage energy and performance of constituting nodes in data centers without impacting service level agreements (SLAs). Ensuring energy efficiency in these data centers is a major problem in cloud computing. Many optimization policies like workload consolidation, machine placement etc. helps in containing the energy requirement of servers in data centers. In this paper, we introduce a data-driven prognostic neural network based framework that will consider power consumed by all the components in server beyond incoming request load and effectively forecast it at any point in future.

ARM processors, with their low power consumption and heat dissipation, have been highly successful in embedded systems. In the recent past, there have been attempts to adopt these energy-efficient processors for servers in data centers. However, a fundamental question remains open with ARM-based systems on server side is whether they are capable of handling compute-intensive workloads at scale. This paper gives our answer to this question with an empirical approach. We study the performance characteristics of the Amazon Graviton Processor – an ARM64 processor with the Cortex-A72 micro-architecture – using the A1 (Graviton) product family on AWS EC2, with comparisons to the I3 and M5 product families based on Intel Xeon processors. We use a combination of micro benchmark and performance counters to identify the lack of L3 cache and the slower memory access speed limit Graviton’s capability in achieving higher performance. We confirm Graviton’s capability in handling various large-scale horizontally scalable compute-intensive workloads, including multi-tier web service, video transcoding and terabyte scale sorting. In our large-scale evaluations, the test worker fleet has up to 1600 vCPU cores, which is by far the largest ARM64 cluster that has been reported. We observe that the A1 product family achieves the same price-performance in multi-tier web service, up to 37% cost saving in video transcoding, and up to 65% cost saving in terabyte scale sorting, as compared with the I3 and M5 product families.

Cloud computing refers to internet software as a service, hardware and servers in data centers, which run the applications. Cloud computing is highly scalable and provides virtualized resources as a subscription to users. Use of clouds in education provides great opportunity for students to improve achieved learning outcomes, and learners can gain significant advantages in the new learning environment. Cloud adaption can also help to significantly reduce cost of infrastructure, software and human resources. In this article service oriented cloud computing architecture is used to transfer E-learning into the cloud. These architectures cover challenges of e-learning such as scalability, application development, efficient use of resources, saving expense, and security.

An expandable and cost-effective data center network

Innovative server simulator for data centers and critical indices of performance evaluation