Packet switched transport technologies have been suggested to be used in 5G front-haul networks. Front-haul bandwidth and packet latency are two major challenges to be addressed in these networks. This paper focuses on capacity planning for packet-based front-haul technologies considering the latency requirements of the 5G front-haul flows. More specifically, we answer three main questions for Constant Bit Rate (CBR) and Variable Bit Rate (VBR) packet front-haul networks: First, is statistical multiplexing gain always achievable in packet front-haul networks? Second, what is the minimum bandwidth required to accommodate a set of front-haul flows on a front-haul link? Third, what is the proper bandwidth allocation and the scheduling algorithm among the packets of different antennas to guarantee the latency requirements of the flows? It is shown that for CBR traffic no statistical multiplexing gain is achievable and the Earliest Deadline First (EDF) is the optimal packet scheduler. For VBR traffic, statistical multiplexing gain is achievable only when the latency requirement of at least one flow is greater than the packet inter-arrival time. The optimal bandwidth allocation is also determined for a Weighted Fair Queueing (WFQ) scheduler. Finally, simulation is used to validate the theoretical results for both CBR and VBR front-haul traffic.

In our previous work, we described a resource allocation and scheduling technique for processing an open stream of MapReduce jobs with SLAs (characterized by an earliest start time, an execution time, and a deadline) called the Hadoop Constraint Programming based Resource Management technique (HCP-RM). Since the user-estimated job execution times are used to perform resource allocation and scheduling, error/inaccuracies in the execution times can hinder the ability of HCP-RM from making effective scheduling decisions, leading to a degradation in system performance. This paper focuses on improving the robustness of HCP-RM by introducing a mechanism to handle errors/inaccuracies in user estimates of job execution times that are submitted as part of the job's SLA. A Prescheduling Error Handling technique (PSEH) is devised to adjust the user-estimated execution times of the jobs to make them more accurate before they are used by the resource management algorithm. Results of experiments conducted on a Hadoop cluster deployed on Amazon EC2 demonstrate the effectiveness of the PSEH technique in improving system performance.

Resource allocation and scheduling on clouds are required to harness the power of the underlying resource pool such that the service provider can meet the quality of service requirements of users, which are often captured in service level agreements (SLAs). This paper focuses on resource allocation and scheduling on clouds and clusters that process MapReduce jobs with SLAs. The resource allocation and scheduling problem is modelled as an optimization problem using constraint programming, and a novel MapReduce Constraint Programming based Resource Management algorithm (MRCP-RM) is devised that can effectively process an open stream of MapReduce jobs where each job is characterized by an SLA comprising an earliest start time, a required execution time, and an end-to-end deadline. A detailed performance evaluation of MRCP-RM is conducted for an open system subjected to a stream of job arrivals using both simulation and experimentation on a real system. The experiments on a real system are performed on a Hadoop cluster (deployed on Amazon EC2) that runs our new Hadoop Constraint Programming based Resource Management algorithm (HCP-RM) that incorporates a technique for handling data locality. The results of the performance evaluation demonstrate the effectiveness of MRCP-RM/HCP-RM in generating a schedule that leads to a low proportion of jobs missing their deadlines (P) and also provide insights into system behaviour and performance. In the simulation experiments, it is observed that MRCP-RM achieves on average an 82 percent lower P compared to a technique from the existing literature when processing a synthetic workload from Facebook. Furthermore, in the experiments performed on a Hadoop cluster deployed on Amazon EC2, it is observed that HCP-RM achieved on average a 63 percent lower P compared to an EDF-Scheduler for a wide variety of workload and system parameters experimented with.

In the Cloud Radio Access Network (C-RAN)architecture, a fronthaul network carries digitized radio signals from Remote Radio Head (RRH) to centralized Baseband Unit (BBU) using the Common Public Radio Interface (CPRI), which has a one-way jitter requirement of several nanoseconds. It is widely believed that Ethernet cannot meet this stringent jitter requirement, thus today's fronthaul networks are primarily based on optical networks. In this paper, we propose ZeroJitter, an SDN based scheduling algorithm and a potential SDN application, for Ethernet to satisfy the CPRI jitter requirement. We implement ZeroJitter and test it in NS-3 with a realistic fronthaul network topology. Both our analysis and simulation results show that jitter incurred by Ethernet could be minimized or even completely removed with ZeroJitter under reasonable constraints. While technical challenges remain, our results suggest that SDN could enable Ethernet to be qualified for fronthaul networks, resulting in significant economic benefit.

There has been a debate on whether or not Ethernet, a highly cost effective technology, could meet the stringent latency and jitter requirements imposed by CPRI. To facilitate the discussion, we conducted simulations to understand how Ethernet performs when carrying CPRI traffic with the two Ethernet enhancements, currently being standardized by IEEE, namely frame preemption (802.1Qbu) and scheduled traffic (802.1Qbv). Our simulation results led to two conclusions: 1) Ethernet networks with or without frame preemption, regardless of being shared or dedicated to CPRI traffic, can not meet the CPRI jitter requirement of 8:138 ns, confirming a widely hold belief; 2) Ethernet with the enhancement of scheduled traffic in conjunction with a well defined scheduling algorithm could significantly lower or even completely remove jitter thus could meet CPRI jitter requirement. To the best of our knowledge, this paper is among the first to study the performance of Ethernet with IEEE 802.1Qbu and 802.1Qbv enhancements and to demonstrate by simulation that Ethernet with scheduled traffic could meet CPRI jitter requirement.

In this paper we propose an approach for applying traffic load balancing to multicast traffic through real-time link cost modification in a software defined network (SDN) controller. We present an SDN controller architecture supporting traffic monitoring, group management, and multicast traffic routing. An implemented prototype is described, and this prototype is used to implement shortest path multicast routing techniques which make use of the real-time state of traffic flows in the network. This prototype is evaluated through experimentation in Mininet emulated wide area networks. Evaluation is presented in terms of resulting network performance metrics focusing on the distribution of traffic flows. Our results demonstrate that real-time modification of links costs produces statistically significant improvements in traffic distribution metrics, with an average improvement of up to 52.8% in traffic concentration relative to shortest-path routing. This indicates that SDN enables the use of real-time modification of link cost functions as an effective technique for implementing traffic load balancing for multicast traffic.

A novel MapReduce constraint programming based matchmaking and scheduling algorithm (MRCP) that can handle MapReduce jobs with deadlines and achieve high system performance is devised. The MRCP algorithm is incorporated into Hadoop, which is a widely used open source implementation of the MapReduce programming model, as a new scheduler called the CP-Scheduler. This paper originates from the collaborative research with our industrial partner concerning the engineering of resource management middleware for high performance. It describes our experiences and the challenges that we encountered in designing and implementing the prototype CP-based Hadoop scheduler. A detailed performance evaluation of the CP-Scheduler is conducted on Amazon EC2 to determine the CP-Scheduler's effectiveness as well as to obtain insights into system behaviour and performance. In addition, the CP-Scheduler's performance is also compared with an earliest deadline first (EDF) Hadoop scheduler, which is implemented by extending Hadoop's default FIFO scheduler. The experimental results demonstrate the effectiveness of the CP-Scheduler's ability to handle an open stream of MapReduce jobs with deadlines in a Hadoop cluster.

Software-Defined Networking has been gaining increasing attention in both the research and the industry communities. Separating the control and data planes has brought many advantages such as greater control plane programmability, more vendor independence, possibility of network virtualization, lowered operational expenses, etc. SDN deployments are possible in a variety of contexts: Enterprise networks, WANs and networks connecting data centers. The realization and operation of an SDN requires the inclusion of a central controller. SDN however raises several concerns for WANs including performance limitations due to the larger propagation delays of control information to and from the controller, the increased work load due to a larger number of network elements and concerns as to performance impacts related to the controller placement. This paper attempts to address some of these issues by examining the effects of using source routing as an alternative to hop by hop in an Internet2 SDN-based WAN production deployment. Our analysis shows that source routing can bring significant gains in SDN convergence performance in WAN environments and reduce the sensitivity in performance due to controller placement.

Clouds that are rapidly gaining in popularity require an effective resource manager that can harness the power of the underlying resource pool, and provide resources on demand to its users. This paper focuses on resource management on clouds for workflow requests characterized by Service Level Agreements (SLAs). Specifically, we devise a novel MapReduce constraint programming based resource manager (MRCP-RM) that can effectively perform matchmaking and scheduling of MapReduce jobs, each characterized by an SLA comprising an earliest start time, execution time, and an end-to-end deadline. Using discrete event simulation a performance evaluation of MRCP-RM is conducted for an open system subjected to a stream of job arrivals. The simulation results demonstrate the effectiveness of the resource manager and provide insights into system behaviour and performance.

This paper focuses on devising efficient resource management techniques used by the resource management middleware in clouds that handle MapReduce jobs with end-to-end service level agreements (SLAs) comprising an earliest start time, execution time, and a deadline. This research and development work, performed in collaboration with our industrial partner, presents the formulation of the matchmaking and scheduling problem for MapReduce jobs as an optimization problem using: Mixed Integer Linear Programming (MILP) and Constraint Programming (CP) techniques. In addition to the formulations devised, our experience in implementing the MILP and CP models using various open source as well as commercial software packages is described. Furthermore, a performance evaluation of the different approaches used to implement the formulations is conducted using a variety of different workloads.

This paper proposed a Distributed Address Resolution Protocol (DARP) for large layer 2 networks used in a data center. As data centers continue to grow in size, there is an increased amount of overhead required to resolve network addresses using the traditional Address Resolution Protocol (ARP). The DARP attempts to reduce this overhead for large data centers with thousands of nodes and allow for the resolution of network address with minimal strain on the underlying network infrastructure. By using Distributed Hash Tables (DHTs) as the core data structure technology to maintain address records, we attempt to create a decentralized and reliable service that trades the sporadic overhead associated with current approaches with a consistent and predictable overhead. To determine the viability of the protocol, a series of simulations were developed and run via the OPNET Modeler software package. The simulation results demonstrate that DARP outperforms ARP by reducing the number of messages. Data centersaddress resolution protocoldistributed hash tables.