The Internet has led to the creation of a digital society, where (almost) everything is connected and is accessible from anywhere. However, despite their widespread adoption, traditional IP networks are complex and very hard to manage. It is both difficult to configure the network according to predefined policies, and to reconfigure it to respond to faults, load, and changes. To make matters even more difficult, current networks are also vertically integrated: the control and data planes are bundled together. Software-defined networking (SDN) is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network's control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. The separation of concerns, introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution. In this paper, we present a comprehensive survey on SDN. We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking, its roots, and the standardization activities regarding this novel paradigm. Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach. We provide an in-depth analysis of the hardware infrastructure, southbound and northbound application programming interfaces (APIs), network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications. We also look at cross-layer problems such as debugging and troubleshooting. In an effort to anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts and challenges of SDN. In particular, we address the design of switches and control platforms—with a focus on aspects such as resiliency, scalability, performance, security, and dependability—as well as new opportunities for carrier transport networks and cloud providers. Last but not least, we analyze the position of SDN as a key enabler of a software-defined environment.

https://publications.uni.lu/bitstream/10993/20596/1/survey_on_SDN.pdf

Software-Defined Networking: A Comprehensive Survey

Cloud computing has recently emerged as a new paradigm for hosting and delivering services over the Internet. Cloud computing is attractive to business owners as it eliminates the requirement for users to plan ahead for provisioning, and allows enterprises to start from the small and increase resources only when there is a rise in service demand. However, despite the fact that cloud computing offers huge opportunities to the IT industry, the development of cloud computing technology is currently at its infancy, with many issues still to be addressed. In this paper, we present a survey of cloud computing, highlighting its key concepts, architectural principles, state-of-the-art implementation as well as research challenges. The aim of this paper is to provide a better understanding of the design challenges of cloud computing and identify important research directions in this increasingly important area.

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Cloud computing: state-of-the-art and research challenges

Software-Defined Networking (SDN) is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network's control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. The separation of concerns introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution. In this paper we present a comprehensive survey on SDN. We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking, its roots, and the standardization activities regarding this novel paradigm. Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach. We provide an in-depth analysis of the hardware infrastructure, southbound and northbound APIs, network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications. We also look at cross-layer problems such as debugging and troubleshooting. In an effort to anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts and challenges of SDN. In particular, we address the design of switches and control platforms -- with a focus on aspects such as resiliency, scalability, performance, security and dependability -- as well as new opportunities for carrier transport networks and cloud providers. Last but not least, we analyze the position of SDN as a key enabler of a software-defined environment.

IoT security: Review, blockchain solutions, and open challenges

Many technical communities are vigorously pursuing research topics that contribute to the Internet of Things (IoT). Nowadays, as sensing, actuation, communication, and control become even more sophisticated and ubiquitous, there is a significant overlap in these communities, sometimes from slightly different perspectives. More cooperation between communities is encouraged. To provide a basis for discussing open research problems in IoT, a vision for how IoT could change the world in the distant future is first presented. Then, eight key research topics are enumerated and research problems within these topics are discussed.

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Research Directions for the Internet of Things

Network function virtualization (NFV) allows network services, such as firewalls and routing, to be deployed into a virtual environment and run on commodity hardware. Recently service providers and developers can deploy their network functions (NF) prototypes on a shared infrastructure, and all the NFs are being controlled by the manager of the platform. NFV platforms run these NFs together, and share the system resources to optimize the utilization. This means that limited resources such as CPU cores or memory have to be shared. Several recent NFV systems run network services with one shared memory region, so that they can achieve high performance with zero-copy I/O. This resource sharing brings security problems since it allows malicious NFs to easily modify data from other NFs. To enhance the security of NFV, we are designing a platform to provide stronger memory isolation between different NFs. Our approach is based on the architecture developed for our OpenNetVM platform, which supports lightweight NFs, flexible management, but assumes a single shared memory pool for all NFs.

Measuring Performance and Isolation Tradeoffs for NFV

Communication Networks are no longer just pipes that move data from place to place. They are becoming powerful, software-based infrastructures capable of transforming and inspecting data as it moves between hosts. Two trends reflect this: the use of Software Defined Networks (SDN) to dynamically manage the network control plane (i.e., routing), and Network Function Virtualization (NFV) to provide a high-performance network data plane (e.g., middleboxes - services like firewalls, proxies, etc.). NFV promises to enable a vast array of in-network software functions running efficiently in virtualized environments. This is a paradigm shift from traditional networks that relied on purpose-built, costly and inflexible hardware middleboxes. To accelerate research and development of network middleware, we have developed OpenNetVM, an open-source (BSD license) software infrastructure for high speed packet processing.

https://dl.acm.org/doi/pdf/10.1145/3155889.3155895

Active network middleware with opennetVM

The microservices architecture has become ubiquitous in the cloud environment. It simplifies application development by breaking monolithic applications into manageable micro services that can be developed and deployed independently of the whole. However, the move from a monolithic or simple multi-tier architecture to a distributed microservice "service mesh" leads to new challenges due to the more complex application topology.A particular problem when automatically managing the performance of microservices is that since each service component scales up and down independently, it can easily create load imbalance problems on shared backend services accessed by multiple components. Traditional load balancing algorithms were designed for centralized load balancers sitting between a group of clients and a server farm. These algorithms, however, do not port over well to a distributed microservice architecture where load balancers are deployed client-side. In this paper we propose a self managing load balancing system, BLOC, which provides consistent response times to users without using a centralized metadata store or explicit messaging between nodes.We show that different service layers scaling independently can create unacceptably wide response time distributions and long tails, hurting client experience. This is because popular microservice load balancing algorithms, like Least Connection, only use a single component’s view of the backend load to guide decisions. This limited perspective leads to an unevenly balanced system and the potential for incast problems where a large number of frontend components can easily overload a shared backend. BLOC uses overload control approaches like rate limiting, active queue management and backpressure to provide feedback to the load balancers. The load balancers react to this feedback with techniques like backoff and retries. We show that this performs significantly better in solving the incast problem in microservice architectures.Evaluating this framework, we found that BLOC improves the response time distribution range, between the 10th and 90th percentiles, by 2 to 4 times and the tail, 99th percentile, latency by two times.

BLOC: Balancing Load with Overload Control In the Microservices Architecture

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Virtual machine migration

By modelling how the probability distributions of individuals’ states evolve as new information flows through a network, belief propagation has broad applicability ranging from image correction to virus propagation to even social networks. Yet, its scant implementations confine themselves largely to the realm of small Bayesian networks. Applications of the algorithm to graphs of large scale are thus unfortunately out of reach. To promote its broad acceptance, we enable belief propagation for both small and large scale graphs utilizing GPU processing. We therefore explore a host of optimizations including a new simple yet extensible input format enabling belief propagation to operate at massive scale, along with significant workload processing updates and meticulous memory management to enable our implementation to outperform prior works in terms of raw execution time and input size on a single machine. Utilizing a suite of parallelization technologies and techniques against a diverse set of graphs, we demonstrate that our implementations can efficiently process even massive networks, achieving up to nearly 121x speedups versus our control yet optimized single threaded implementations while supporting graphs of over ten million nodes in size in contrast to previous works’ support for thousands of nodes using CPU-based multi-core and host solutions. To assist in choosing the optimal implementation for a given graph, we provide a promising method utilizing a random forest classifier and graph metadata with a nearly 95% F1-score from our initial benchmarking and is portable to different GPU architectures to achieve over an F1-score of over 72% accuracy and a speedup of nearly 183x versus our control running in this new environment.

Timothy Wood

Papers

Measuring Performance and Isolation Tradeoffs for NFV

Active network middleware with opennetVM

BLOC: Balancing Load with Overload Control In the Microservices Architecture

Virtual machine migration

Rumor Has It: Optimizing the Belief Propagation Algorithm for Parallel Processing