Guaranteed Bandwidth Services Research Articles

AmoebaNet: An SDN-enabled network service for big data science

Data transfer is now an essential function for science discoveries, particularly within big data environments. To support data transfer for big data science, there is a need for high performance, scalable, end-to-end, and programmable networks that enable science applications to use the network most efficiently. The existing network paradigm that support big data science consists of three major components: terabit networks that provide high network bandwidths, Data Transfer Nodes (DTNs) and Science DMZ architecture that bypasses the performance hotspots in typical campus networks, and on-demand secure circuits/paths reservation systems, such as ESNet OSCARS and Internet2 AL2S, which provides automated, guaranteed bandwidth service in WAN. This network paradigm has proven to be very successful. However, to reach its full potentials, we claim that existing network paradigm for big data science must address three major problems: the last mile problem, the scalability problem, and the programmability problem. To address these problems, we proposed a solution called AmoebaNet. AmoebaNet applies Software Defined Networking (SDN) technology to provide “QoS-guaranteed” network services in campus or local area networks. AmoebaNet complements existing network paradigm for big data science: it allows application to program networks at run-time for optimum performance; and, in conjunction with WAN circuits/paths reservation system such as ESNet OSCARS and Internet2 AL2S; it solves the last mile problem and the scalability problem.

Budget-Optimized Network-Aware Joint Resource Allocation in Grids/Clouds Over Optical Networks

Resource allocation is an important component of many Cloud computing and datacenter management problems. For infrastructure as a service (IaaS) in the Cloud, the Cloud service provider allocates computing resources such as processor, memory, and storage. In addition to the computing infrastructures, the Cloud service provider in the future would also allocate bandwidth for some applications that require guaranteed bandwidth service to transmit a large amount of data. This type of guaranteed bandwidth service can be provided by provisioning a distinct connection from end-to-end, e.g., by provisioning wavelength(s) in a wavelength division multiplexed wavelength routed network. In this paper, we focus on interdatacenter network-aware optimal resource allocation in the Cloud from the customer's perspective. We develop a mixed integer linear programming (MILP) optimal mathematical model and heuristics (Best-Fit and Tabu search) to solve the budget optimized joint-resource allocation problem to minimize the rental cost for each customer. The experimental results show that our heuristics can achieve an approximate optimal solution to the MILP solution and can reduce the customer's rental cost by at least 30%. The Best-Fit heuristic with shortest job execution time first and simplest job structure first (SSF) scheduling policies have a better performance in terms of the traffic blocking rate. The traffic blocking rates under both scheduling policies are 5–25% less than other policies. The Tabu search-based heuristic with SSF job scheduling policy has a better performance in terms of the traffic blocking rate than other job scheduling policies. In addition, the Tabu search-based heuristic also reduces the blocking rate by 4–30% compared with the Best-Fit heuristic under any job scheduling policy.

Enhancing TCP for networks with guaranteed bandwidth services

In this paper, we consider TCP based applications with bandwidth guarantees , but can also benefit from any additional best-effort service offered by the network. Through simulations we show that default TCP cannot offer such applications the ideal throughput – the aggregate throughput of the reserved bandwidth and the best effort bandwidth. To illustrate the reasons for its degraded performance, we study TCP’s congestion window adaptation and self-clocking mechanisms in detail. Based on the insights obtained from the study, we propose an adaptation of TCP called GTCP that employs changes to TCP’s congestion control mechanisms to provide applications the optimal aggregate throughput of best-effort and reserved bandwidth. Compared with TCP, GTCP does not involve any additional implementation overhead, and only the sender need to be changed (the receiver remains to be a default TCP implementation). Through simulations and experiments over the Internet we show that GTCP achieves significantly better performance than default TCP in the target environment.

A Two-Step Scheduling Algorithm to Support Dual Bandwidth Allocation Policies in an Ethernet Passive Optical Network

In this paper, we design a two-step scheduling algorithm to support multiple bandwidth allocation policies for upstream channel access in an Ethernet passive optical network. The proposed scheduling algorithm allows us a simultaneous approach for multiple access control policies: static bandwidth allocation for guaranteed bandwidth service and dynamic bandwidth allocation for on-demand, dynamic traffic services. In order to reduce the scheduling complexity, we separate the process of the transmission start- time decision from the process of grant generation. This technique does not require the timing information of other bandwidth allocation modules, so respective modules are free from a heavy amount of timing information or complex processing.