Modelling low power compute clusters for cloud simulation

In order to minimise their energy use, data centre operators are constantly exploring new ways to construct computing infrastructures. As low power CPUs, exemplified by ARM-based devices, are becoming increasingly popular, there is a growing trend for the large scale deployment of low power servers in data centres. For example, recent research has shown promising results on constructing small scale data centres using Raspberry Pi (RPi) single-board computers as their building blocks. To enable larger scale experimentation and feasibility studies, cloud simulators could be utilised. Unfortunately, stateof-the-art simulators often need significant modification to include such low power devices as core data centre components. In this paper, we introduce models and extensions to estimate the behaviour of these new components in the DISSECT-CF cloud computing simulator. We show that how a RPi based cloud could be simulated with the use of the new models. We evaluate the precision and behaviour of the implemented models using a Hadoop-based application scenario executed both in real life and simulated clouds

Heterogeneous network policy enforcement in data centers

With the emergence of network function virtualization, data center start to deploy a variety of network function boxes (NFBs) in both physical and virtual form factors in order to combines inherent efficiency offered by physical NFBs with the agility and flexibility of virtual ones. However, existing schemes are limited to exclusively consider physical or virtual NFBs, which may reduce the performance efficiency of services running atop. In this paper, we propose a Heterogeneous NetwOrk Policy Enforcement scheme (HOPE) to overcome these challenges. An efficient algorithm that can closely approximate optimal latencywise NF service chaining is proposed. The experimental results have also shown that HOPE can outperform greedy algorithm by 25% in terms of network latency and is 56x more efficient than naive depth-first search algorithm

Enforcing network policy in heterogeneous network function box environment

Data center operators deploy a variety of both physical and virtual network functions boxes (NFBs) to take advantages of inherent efficiency offered by physical NFBs with the agility and flexibility of virtual ones. However, such heterogeneity faces great challenges in correct, efficient and dynamic network policy implementation because, firstly, existing schemes are limited to exclusively physical or virtual NFBs and not a mix, and secondly, NFBs can co-exist at various locations in the network as a result of emerging technologies such as Software Defined Networking (SDN) and Network Function Virtualization (NFV). In this paper, we propose a Heterogeneous netwOrk pOlicy enforCement scheme (HOOC) to overcome these challenges. We first formulate and model HOOC, which is shown be to NP-Hard by reducing from the Multiple Knapsack Problem (MKP). We then propose an efficient online algorithm that can achieve optimal latency-wise NF service chaining amongst heterogenous NFBs. In addition, we also provide a greedy algorithm when operators prefer smaller run-time than optimality. Our simulation results show that HOOC is efficient and scalable whilst testbed implementation demonstrates that HOOC can be easily deployed in the data center environments

Modest BBR: Enabling better fairness for BBR congestion control

As a vital component of TCP, congestion control defines TCP's performance characteristics. Hence, it is important for congestion control to provide high link utilization and low queuing delay. Recent BBR tries to estimate available bottleneck capacity to achieve this goal. However, its aggressiveness characteristics generate a massive amount of packet retransmission which harms loss-based congestion control protocol such as Cubic. In this paper, we first dive into this issue and reveal that the aggressiveness of BBR can degrade the performance of Cubic, as well as the overall Internet transmission. Then we present Modest BBR, a simple yet effective solution based on BBR, by responding to retransmission less aggressively. Through extensive testbed experiments and Mininet simulation, we show Modest BBR can preserve high throughput and short convergence time while improve the overall performance when coexisting with Cubic. For example, Modest BBR gets similar throughput compared to BBR, while it improves 7.1% of the overall throughput and achieves better fairness to loss-based schemes

Latency-aware joint virtual machine and policy consolidation for mobile edge computing

To guarantee an efficient and high-performance environment for mobile devices to perform offloading with low end-to-end delay, it is important to ensure no network policies are violated. In this paper, we explore the simultaneous, dynamic virtual machine (VM) and policy consolidation, and formulate the Policy-VM Latency-aware Consolidation problem for Mobile Edge Computing, which is shown to be NP-Hard. We propose the PL-Edge, an efficient scheme to jointly consolidate network policies and virtual machines for mobile edge computing to reduce communication end-to-end delays among devices and virtual machines. Our simulation results demonstrate that the proposed PL-Edge can significantly reduces policy-flows end-to-end delay by nearly 45% while adhering strictly to the requirements of network policies

Track: Tracerouting in SDN networks with arbitrary network functions

The centralization of control plane in Software defined networking (SDN) creates a paramount challenge on troubleshooting the network as packets are ultimately forwarded by distributed data planes. Existing path tracing tools largely utilize packet tags to probe network paths among SDN-enabled switches. However, network functions (NFs) or middleboxes, whose presence is ubiquitous in today's networks, can drop packets or alter their tags - an action that can collapse the probing mechanism. In addition, sending probing packets through network functions could corrupt their internal states, risking of the correctness of servicing logic (e.g., incorrect load balancing decisions). In this paper, we present a novel troubleshooting tool, Track, for SDN-enabled network with arbitrary NFs. Track can discover the forwarding path including NFs taken by any packets, without changing the forwarding rules in switches and internal states of NFs. We have implemented Track on RYU controller. Our extensive experiment results show that Track can achieve 95.08% and 100% accuracy for discovering forwarding paths with and without NFs respectively, and can efficiently generate traces within 3 milliseconds per hop

Synergistic policy and virtual machine consolidation in cloud data centers

In modern Cloud Data Centers (DC)s, correct implementation of network policies is crucial to provide secure, efficient and high performance services for tenants. It is reported that the inefficient management of network policies accounts for 78% of DC downtime, challenged by the dynamically changing network characteristics and by the effects of dynamic Virtual Machine (VM) consolidation. While there has been significant research in policy and VM management, they have so far been treated as disjoint research problems. In this paper, we explore the simultaneous, dynamic VM and policy consolidation, and formulate the Policy-VM Consolidation (PVC) problem, which is shown to be NP-Hard. We then propose Sync, an efficient and synergistic scheme to jointly consolidate network policies and virtual machines. Extensive evaluation results and a testbed implementation of our controller show that policy and VM migration under Sync significantly reduces flow end-to-end delay by nearly 40%, and network-wide communication cost by 50% within few seconds, while adhering strictly to the requirements of network policies

Scalable traffic-aware virtual machine management for cloud data centers

Virtual Machine (VM) management is a powerful mechanism for providing elastic services over Cloud Data Centers (DC)s. At the same time, the resulting network congestion has been repeatedly reported as the main bottleneck in DCs, even when the overall resource utilization of the infrastructure remains low. However, most current VM management strategies are traffic-agnostic, while the few that are traffic-aware only concern a static initial allocation, ignore bandwidth oversubscription, or do not scale. In this paper we present S-CORE, a scalable VM migration algorithm to dynamically reallocate VMs to servers while minimizing the overall communication footprint of active traffic flows. We formulate the aggregate VM communication as an optimization problem and we then define a novel distributed migration scheme that iteratively adapts to dynamic traffic changes. Through extensive simulation and implementation results, we show that S-CORE achieves significant (up to 87%) communication cost reduction while incurring minimal overhead and downtime

TCon: A transparent congestion control deployment platform for optimizing WAN transfers

Nowadays, many web services (e.g., cloud storage) are deployed inside datacenters and may trigger transfers to clients through WAN. TCP congestion control is a vital component for improving the performance (e.g., latency) of these services. Considering complex networking environment, the default congestion control algorithms on servers may not always be the most efficient, and new advanced algorithms will be proposed. However, adjusting congestion control algorithm usually requires modification of TCP stacks of servers, which is difficult if not impossible, especially considering different operating systems and configurations on servers. In this paper, we propose TCon, a light-weight, flexible and scalable platform that allows administrators (or operators) to deploy any appropriate congestion control algorithms transparently without making any changes to TCP stacks of servers. We have implemented TCon in Open vSwitch (OVS) and conducted extensive test-bed experiments by transparently deploying BBR congestion control algorithm over TCon. Test-bed results show that the BBR over TCon works effectively and the performance stays close to its native implementation on servers, reducing latency by 12.76% on average

Live migration on ARM-based micro-datacentres

Live migration, underpinned by virtualisation technologies, has enabled improved manageability and fault tolerance for servers. However, virtualised server infrastructures suffer from significant processing overheads, system inconsistencies, security issues and unpredictable performance which makes them unsuitable for low-power and resource-constraint computing devices that processing latency-sensitive, 'Big-data'-type data. Consequently, we ask: 'How do we eliminate the overhead of virtualisation whilst still retaining its benefits?' Motivated by this question, we investigate a practical approach for a bare-metal live migration scheme for ARM-based instances low-power servers and edge devices. In this paper, we position ARM-based bare-metal live migration as a technique that will underpin the efficiency on edge-computing and on Micro-datacentres. We also introduce our early work on identifying three key technical challenges and discuss their solutions