Latency-driven performance in data centres

Data centre based cloud computing has revolutionised the way businesses use computing infrastructure. Instead of building their own data centres, companies rent computing resources and deploy their applications on cloud hardware. Providing customers with well-defined application performance guarantees is of paramount importance to ensure transparency and to build a lasting collaboration between users and cloud operators. A user’s application performance is subject to the constraints of the resources it has been allocated and to the impact of the network conditions in the data centre. In this dissertation, I argue that application performance in data centres can be improved through cluster scheduling of applications informed by predictions of application performance for given network latency, and measurements of current network latency in data centres between hosts. Firstly, I show how to use the Precision Time Protocol (PTP), through an open-source software implementation PTPd, to measure network latency and packet loss in data centres. I propose PTPmesh, which uses PTPd, as a cloud network monitoring tool for tenants. Furthermore, I conduct a measurement study using PTPmesh in different cloud providers, finding that network latency variability in data centres is still common. Normal latency values in data centres are in the order of tens or hundreds of microseconds, while unexpected events, such as network congestion or packet loss, can lead to latency spikes in the order of milliseconds. Secondly, I show that network latency matters for certain distributed applications even in small amounts of tens or hundreds of microseconds, significantly reducing their performance. I propose a methodology to determine the impact of network latency on distributed applications performance by injecting artificial delay into the network of an experimental setup. Based on the experimental results, I build functions that predict the performance of an application for a given network latency. Given the network latency variability observed in data centers, applications’ performance is determined by their placement within the data centre. Thirdly, I propose latency-driven, application performance-aware, cluster scheduling as a way to provide performance guarantees to applications. I introduce NoMora, a cluster scheduling architecture that leverages the predictions of application performance dependent upon network latency combined with dynamic network latency measurements taken between pairs of hosts in data centres to place applications. Moreover, I show that NoMora improves application performance by choosing better placements than other scheduling policies.MEASUREMENT FOR EUROPE: TRAINING AND RESEARCH FOR INTERNET COMMUNICATIONS SCIENCE, European Commission FP7 Marie Curie Innovative Training Networks (ITN) ENDEAVOUR, European Commission Horizon 2020 (H2020) Industrial Leadership (IL

Optimising data centre operation by removing the transport bottleneck

Data centres lie at the heart of almost every service on the Internet. Data centres are used to provide search results, to power social media, to store and index email, to host “cloud” applications, for online retail and to provide a myriad of other web services. Consequently the more efficient they can be made the better for all of us. The power of modern data centres is in combining commodity off-the-shelf server hardware and network equipment to provide what Google’s Barrosso and Ho ̈lzle describe as “warehouse scale” computers. Data centres rely on TCP, a transport protocol that was originally designed for use in the Internet. Like other such protocols, TCP has been optimised to maximise throughput, usually by filling up queues at the bottleneck. However, for most applications within a data centre network latency is more critical than throughput. Consequently the choice of transport protocol becomes a bottleneck for performance. My thesis is that the solution to this is to move away from the use of one-size-fits-all transport protocols towards ones that have been designed to reduce latency across the data centre and which can dynamically respond to the needs of the applications. This dissertation focuses on optimising the transport layer in data centre networks. In particular I address the question of whether any single transport mechanism can be flexible enough to cater to the needs of all data centre traffic. I show that one leading protocol (DCTCP) has been heavily optimised for certain network conditions. I then explore approaches that seek to minimise latency for applications that care about it while still allowing throughput-intensive applications to receive a good level of service. My key contributions to this are Silo and Trevi. Trevi is a novel transport system for storage traffic that utilises fountain coding to max- imise throughput and minimise latency while being agnostic to drop, thus allowing storage traffic to be pushed out of the way when latency sensitive traffic is present in the network. Silo is an admission control system that is designed to give tenants of a multi-tenant data centre guaranteed low latency network performance. Both of these were developed in collaboration with others

Performance-oriented service management in clouds

Cloud computing has provided the convenience for many IT-related and traditional industries to use feature-rich services to process complex requests. Various services are deployed in the cloud and they interact with each other to deliver the required results. How to effectively manage these services, the number of which is ever increasing, within the cloud has unavoidably become a critical issue for both tenants and service providers of the cloud. In this thesis, we develop the novel resource provision frameworks to determine resources provision for interactive services. Next, we propose the algorithms for mapping Virtual Machines (VMs) to Physical Machines (PMs) under different constraints, aiming to achieve the desired Quality-of-Services (QoS) while optimizing the provisions in both computing resources and communication bandwidth. Finally, job scheduling may become a performance bottleneck itself in such a large scale cloud. In order to address this issue, the distributed job scheduling framework has been proposed in the literature. However, such distributed job scheduling may cause resource conflict among distributed job schedulers due to the fact that individual job schedulers make their job scheduling decisions independently. In this thesis, we investigate the methods for reducing resource conflict. We apply the game theoretical methodology to capture the behaviour of the distributed schedulers in the cloud. The frameworks and methods developed in this thesis have been evaluated with a simulated workload, a large-scale workload trace and a real cloud testbed

Analyzing challenging aspects of IPv6 over IPv4

The exponential expansion of the Internet has exhausted the IPv4 addresses provided by IANA. The new IP edition, i.e. IPv6 introduced by IETF with new features such as a simplified packet header, a greater address space, a different address sort, improved encryption, powerful section routing, and stronger QoS. ISPs are slowly seeking to migrate from current IPv4 physical networks to new generation IPv6 networks. ‎The move from actual IPv4 to software-based IPv6 is very sluggish, since billions of computers across the globe use IPv4 addresses. The configuration and actions of IP4 and IPv6 protocols are distinct. Direct correspondence between IPv4 and IPv6 is also not feasible. In terms of the incompatibility problems, all protocols can co-exist throughout the transformation for a few years. Compatibility, interoperability, and stability are key concerns between IP4 and IPv6 protocols. After the conversion of the network through an IPv6, the move causes several issues for ISPs. The key challenges faced by ISPs are packet traversing, routing scalability, performance reliability, and protection. Within this study, we meticulously analyzed a detailed overview of all aforementioned issues during switching into ipv6 network

Network flow optimization for distributed clouds

Internet applications, which rely on large-scale networked environments such as data centers for their back-end support, are often geo-distributed and typically have stringent performance constraints. The interconnecting networks, within and across data centers, are critical in determining these applications' performance. Data centers can be viewed as composed of three layers: physical infrastructure consisting of servers, switches, and links, control platforms that manage the underlying resources, and applications that run on the infrastructure. This dissertation shows that network flow optimization can improve performance of distributed applications in the cloud by designing high-throughput schemes spanning all three layers. At the physical infrastructure layer, we devise a framework for measuring and understanding throughput of network topologies. We develop a heuristic for estimating the worst-case performance of any topology and propose a systematic methodology for comparing performance of networks built with different equipment. At the control layer, we put forward a source-routed data center fabric which can achieve near-optimal throughput performance by leveraging a large number of available paths while using limited memory in switches. At the application layer, we show that current Application Network Interfaces (ANIs), abstractions that translate an application's performance goals to actionable network objectives, fail to capture the requirements of many emerging applications. We put forward a novel ANI that can capture application intent more effectively and quantify performance gains achievable with it. We also tackle resource optimization in the inter-data center context of cellular providers. In this emerging environment, a large amount of resources are geographically fragmented across thousands of micro data centers, each with a limited share of resources, necessitating cross-application optimization to satisfy diverse performance requirements and improve network and server utilization. Our solution, Patronus, employs hierarchical optimization for handling multiple performance requirements and temporally partitioned scheduling for scalability

RETRACTED: Analyzing challenging aspects of IPv6 over IPv4

This article has been retracted by the publisher. This article has been retracted at the request of The International Arab Journal of Information Technology (IAJIT) report because of misconduct and plagiarism. The document and its content have been removed from the Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, and reasonable effort should be made to remove all references to this article

Analyzing challenging aspects of IPv6 over IPv4

The exponential expansion of the Internet has exhausted the IPv4 addresses provided by IANA. The new IP edition, i.e. IPv6 introduced by IETF with new features such as a simplified packet header, a greater address space, a different address sort, improved encryption, powerful section routing, and stronger QoS. ISPs are slowly seeking to migrate from current IPv4 physical networks to new generation IPv6 networks. ‎The move from actual IPv4 to software-based IPv6 is very sluggish, since billions of computers across the globe use IPv4 addresses. The configuration and actions of IP4 and IPv6 protocols are distinct. Direct correspondence between IPv4 and IPv6 is also not feasible. In terms of the incompatibility problems, all protocols can co-exist throughout the transformation for a few years. Compatibility, interoperability, and stability are key concerns between IP4 and IPv6 protocols. After the conversion of the network through an IPv6, the move causes several issues for ISPs. The key challenges faced by ISPs are packet traversing, routing scalability, performance reliability, and protection. Within this study, we meticulously analyzed a detailed overview of all aforementioned issues during switching into ipv6 network