Computer education research highlights the benefits of peer learning in computer science undergraduate populations with diverse backgrounds. Especially among commuter students, emphasis on peer learning outside the lecture and computer labs can provide effective and scalable learning outcomes. Typically, students use computer labs to perform their assignments and group projects. However, these labs pose challenges to students due to issues with efficient scheduling, resource allocation, and limited permissions. While cloud environments can be a solution to overcome these hurdles, they can be costly in the long run, which may not be affordable for the institution and the students. In this paper, we propose a pedagogical approach that leverages peer learning through the use of personal computers and distributed computing in practice exercises, particularly in computer science courses where multiple computers are necessary or beneficial due to the complexity or scale of the computational problem. We identify specific undergraduate upper-level courses (namely, Computer Networks, Computer and Network Security, Distributed Computing, and Data Mining) as the courses to incorporate the proposed approach. Our evaluations highlight early success in terms of student performance and satisfaction.

Small-scale data centers at the edge are becoming prominent in offering various services to the end-users following the cloud model while avoiding the high latency inherent to the classic cloud environments when accessed from remote Internet regions. However, we should address several challenges to facilitate the end-users finding and consuming the relevant services from the edge at the Internet scale. First, the scale and diversity of the edge hinder seamless access. Second, a framework where researchers openly share their services and data in a secured manner among themselves and with external consumers over the Internet does not exist. Third, the lack of a unified interface and trust across the service providers hinder their interchangeability in composing workflows by chaining the services. Thus, creating a workflow from the services deployed on the various edge nodes is presently impractical. This paper designs Viseu, a latency-aware blockchain framework to provide Virtual Internet Services at the Edge. Viseu aims to solve the puzzle of network service discovery at the edge, considering the peers' reputation and latency when choosing the service instances. Viseu enables peers to share their computational resources, services, and data among each other in an untrusted environment, rather than relying on a set of trusted service providers. By composing workflows from the peers' services, rather than confining them to the pre-established service provider and consumer roles, Viseu aims to facilitate scientific collaboration across the peers natively. Furthermore, by offering services from multiple peers close to the end-users, Viseu also minimizes end-to-end latency and data loss in the service execution at the Internet scale.

Summary Sharing of computing resources and workload across different big data frameworks is challenging due to their lack of interoperable interfaces. In contrast, web services natively support an interoperable execution. Therefore, an increasing number of big data workflows are composed of data services and web service implementations that access and process big data. On the other hand, big data execution in the wide area networks needs to minimize latency and communication overheads to be able to scale seamlessly. Lack of network‐awareness of classic web service execution beyond data centers significantly challenges the scope of data services. Software‐Defined Networking (SDN) offers better control and management to the network, by unifying the control plane centrally, away from the distributed data plane devices. In this paper, we propose Software‐Defined Data Services (SDDS), an SDN‐based distributed service composition and workflow placement approach for data services in wide area networks. We first present the design of an SDDS framework that models the big data executions as composable data service workflows in multi‐domain network environments. We then evaluate the performance of a prototype SDDS framework through microbenchmarks. The benchmarks highlight the efficiency of SDDS in data service execution inside and beyond data centers.

The diversity of data management systems affords developers the luxury of building heterogeneous architectures to address the unique needs of big data. It allows one to mix-nmatch systems that can store, query, update, and process data based on specific use cases. However, this heterogeneity brings with it the burden of developing custom interfaces for each data management system. Existing big data frameworks fall short in mitigating these challenges imposed. In this paper, we present Bindaas, a secure and extensible big data middleware that offers uniform access to diverse data sources. By providing a RESTful web service interface to the data sources, Bindaas exposes query, update, store, and delete functionality of the data sources as data service APIs, while providing turn-key support for standard operations involving access control and audit-trails. The research community has deployed Bindaas in various production environments in healthcare. Our evaluations highlight the efficiency of Bindaas in serving concurrent requests to data source instances with minimal overheads.

Autonomous Systems (ASes) can reach hundreds of networks via Internet eXchange Points (IXPs), allowing improvements in traffic delivery performance and competitiveness. Despite the benefits, any pair of ASes needs first to agree on exchanging traffic. By surveying 100+ network operators, we discovered that most interconnection agreements are established through ad-hoc and lengthy processes heavily influenced by personal relationships and brand image. As such, ASes prefer long-term agreements at the expense of a potential mismatch between actual delivery performance and current traffic dynamics. ASes also miss interconnection opportunities due to trust reasons. To improve wide-area traffic delivery performance, we propose Dynam-IX, a framework that allows operators to build trust cooperatively and implement traffic engineering policies to exploit the rich interconnection opportunities at IXPs quickly. Dynam-IX offers a protocol to automate the interconnection process, an intent abstraction to express interconnection policies, a legal framework to digitally handle contracts, and a distributed tamper-proof ledger to create trust among ASes. We build and evaluate a Dynam-IX prototype and show that an AS can establish tens of agreements per minute with negligible overhead for ASes and IXPs.

Leveraging Software-Defined Networking (SDN) and middleboxes, application-level policies can be propagated to the network. SMART is an SDN middlebox architecture that differentiates network flows based on tenant inputs. By leveraging FlowTags software middlebox in addition to the OpenFlow rules, it supports a larger scope of tenant preferences and rules from the application layer to alter the network flow behaviour. It thus ensures timely delivery of priority flows by dynamically diverting them to a less congested path or even cloning the packets of higher priority flows along with the original flow.

Cloud networks are tested over simulation, emulation, and physical environments at different stages of development. Configuration management tools manage deployments and migrations across different cloud platforms, mitigating tedious system administration efforts. However, currently a cloud networking simulation cannot be migrated into an emulation, or vice versa, without rewriting and manually re-deploying the simulated application. This paper presents SENDIM, a Simulation, Emulation, aNd Deployment Integration Middleware for cloud networks. As an orchestration platform for incrementally building Software-Defined Cloud Networks (SDCN), SENDIM manages the development and deployment of algorithms and architectures the entire length from visualization, simulation, emulation, to physical deployments.

eScience consists of computation-intensive workflows executing on highly distributed networks. Service compositions aggregate web services to automate scientific and enterprise business processes. Along with the increased demand for data quality and Quality of Service (QoS) for an accurate outcome in a shorter completion time, execution of the eScience workflows and service compositions are also required to be distributed efficiently across various geo-distributed nodes. This paper presents Mayan,1a Software-Defined Networking (SDN) based approach for service composition. Mayan i) facilitates an adaptive execution of scientific workflows, ii) offers a more efficient service composition by leveraging distributed execution frameworks, in addition to the traditional web service engines, and iii) enables a very large-scale reliable service composition by finding and consuming the current best-fit among the multiple implementations or deployments of the same service.

Data centers offer computational resources with various levels of guaranteed performance to the tenants, through differentiated Service Level Agreements (SLA). Typically, data center and cloud providers do not extend these guarantees to the networking layer. Since communication is carried over a network shared by all the tenants, the performance that a tenant application can achieve is unpredictable and depends on factors often beyond the tenant's control. We propose ViTeNA, a Software-Defined Networking-based virtual network embedding algorithm and approach that aims to solve these problems by using the abstraction of virtual networks. Virtual Tenant Networks (VTN) are isolated from each other, offering virtual networks to each of the tenants, with bandwidth guarantees. Deployed along with a scalable OpenFlow controller, ViTeNA allocates virtual tenant networks in a work-conservative system. Preliminary evaluations on data centers with tree and fat-tree topologies indicate that ViTeNA achieves both high consolidation on the allocation of virtual networks and high data center resource utilization.

Software-defined networking (SDN) increases the network programmability, promoting an effective development of networked systems of cloud scale. As the scale of the networks and systems is growing larger and larger with time, programmability of the systems and networks is researched intensively. Many emulators are proposed and implemented to emulate large and complex networks inside a single computer, or a cluster of computers in the research lab. However, the emulators lack the ability to represent large systems such as data center networks or content delivery networks. Many of the network algorithms and design choices can also be tested for their functionality and efficiency in a simulator environment. While network emulators and simulators exist, a generic network flow simulator that is easy to program a variety of highly distributed and gigantic systems is still lacking. This paper presents xSDN, an expressive simulator for dynamic network flows. Adhering to the principles of software-defined networking paradigm from the design, xSDN focuses to be lean, light-weight, easy to learn and configure, and efficient, that can simulate networks of a scale of million nodes within a few seconds.

Cloud Computing researches involve a tremendous amount of entities such as users, applications, and virtual machines. Due to the limited access and often variable availability of such resources, researchers have their prototypes tested against the simulation environments, opposed to the real cloud environments. Existing cloud simulation environments such as CloudSim and EmuSim are executed sequentially, where a more advanced cloud simulation tool could be created extending them, leveraging the latest technologies as well as the availability of multi-core computers and the clusters in the research laboratories. This research seeks to develop Cloud2Sim, a concurrent and distributed cloud simulator, extending CloudSim while exploiting the features provided by Hazel cast, Infinispan and Hibernate Search to distribute the storage and execution of the simulation.