Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

An Energy-driven Network Function Virtualization for Multi-domain Software Defined Networks

Network Functions Virtualization (NFV) in Software Defined Networks (SDN) emerged as a new technology for creating virtual instances for smooth execution of multiple applications. Their amalgamation provides flexible and programmable platforms to utilize the network resources for providing Quality of Service (QoS) to various applications. In SDN-enabled NFV setups, the underlying network services can be viewed as a series of virtual network functions (VNFs) and their optimal deployment on physical/virtual nodes is considered a challenging task to perform. However, SDNs have evolved from single-domain to multi-domain setups in the recent era. Thus, the complexity of the underlying VNF deployment problem in multi-domain setups has increased manifold. Moreover, the energy utilization aspect is relatively unexplored with respect to an optimal mapping of VNFs across multiple SDN domains. Hence, in this work, the VNF deployment problem in multi-domain SDN setup has been addressed with a primary emphasis on reducing the overall energy consumption for deploying the maximum number of VNFs with guaranteed QoS. The problem in hand is initially formulated as a "Multi-objective Optimization Problem" based on Integer Linear Programming (ILP) to obtain an optimal solution. However, the formulated ILP becomes complex to solve with an increasing number of decision variables and constraints with an increase in the size of the network. Thus, we leverage the benefits of the popular evolutionary optimization algorithms to solve the problem under consideration. In order to deduce the most appropriate evolutionary optimization algorithm to solve the considered problem, it is subjected to different variants of evolutionary algorithms on the widely used MOEA framework (an open source java framework based on multi-objective evolutionary algorithms).Comment: Accepted for publication in IEEE INFOCOM 2019 Workshop on Intelligent Cloud Computing and Networking (ICCN 2019

Deep Q Learning for Self Adaptive Distributed Microservices Architecture (in press)

One desired aspect of a self-adapting microservices architecture is the ability to continuously monitor the operational environment, detect and observe anomalous behavior, and provide a reasonable policy for self-scaling, self-healing, and self-tuning the computational resources in order to dynamically respond to a sudden change in its operational environment. The behaviour of a microservices architecture is continuously changing overtime, which makes it a challenging task to use a statistical model to identify both the normal and abnormal behaviour of the services running. The performance of the microservices cluster could fluctuate around the demand to accommodate scalability, orchestration and load balancing demands. To achieve the desired high levels of self-adaptability, this research implements microservices architectures model following the MAPE-K model. Our proposed architecture employs Markov decision process (MDP) to identify the transition from one cluster state to another. Our proposed architecture employs a deep Q- learning network (DQN) for dynamically selecting the adaptation action that yield the highest reward. This paper evaluates the effectiveness of using DQN and MDP agent to achieve high level of self-adaptability of microservice architecture. We argue in this paper that such integration between DQN and MDP in MAPE-K model offers microservice architecture with self-adaptability against the contextual changes in the operational environment. The self-adaptation property is achieved by allowing the MDP agent to explore the observation space and lets the DQN to select the adaptation policy with the highest reward, then the MDP agent executes the adaptation action and observes the changes. We believe integrating DQN into the adaptation action selection process improves the effectiveness of the adaptation and reduces the adaptation risk including resources over-provisioning and thrashing. The proposed model preserves the cluster state and preventing multiple actions to taking place at the same time. Our model also guarantees that the executed adaptation action fits the current execution context and achieves the adaptation goals

Will SDN be part of 5G?

For many, this is no longer a valid question and the case is considered settled with SDN/NFV (Software Defined Networking/Network Function Virtualization) providing the inevitable innovation enablers solving many outstanding management issues regarding 5G. However, given the monumental task of softwarization of radio access network (RAN) while 5G is just around the corner and some companies have started unveiling their 5G equipment already, the concern is very realistic that we may only see some point solutions involving SDN technology instead of a fully SDN-enabled RAN. This survey paper identifies all important obstacles in the way and looks at the state of the art of the relevant solutions. This survey is different from the previous surveys on SDN-based RAN as it focuses on the salient problems and discusses solutions proposed within and outside SDN literature. Our main focus is on fronthaul, backward compatibility, supposedly disruptive nature of SDN deployment, business cases and monetization of SDN related upgrades, latency of general purpose processors (GPP), and additional security vulnerabilities, softwarization brings along to the RAN. We have also provided a summary of the architectural developments in SDN-based RAN landscape as not all work can be covered under the focused issues. This paper provides a comprehensive survey on the state of the art of SDN-based RAN and clearly points out the gaps in the technology.Comment: 33 pages, 10 figure

Distributed Security Policy Analysis

Computer networks have become an important part of modern society, and computer network security is crucial for their correct and continuous operation. The security aspects of computer networks are defined by network security policies. The term policy, in general, is defined as ``a definite goal, course or method of action to guide and determine present and future decisions''. In the context of computer networks, a policy is ``a set of rules to administer, manage, and control access to network resources''. Network security policies are enforced by special network appliances, so called security controls.Different types of security policies are enforced by different types of security controls. Network security policies are hard to manage, and errors are quite common. The problem exists because network administrators do not have a good overview of the network, the defined policies and the interaction between them. Researchers have proposed different techniques for network security policy analysis, which aim to identify errors within policies so that administrators can correct them. There are three different solution approaches: anomaly analysis, reachability analysis and policy comparison. Anomaly analysis searches for potential semantic errors within policy rules, and can also be used to identify possible policy optimizations. Reachability analysis evaluates allowed communication within a computer network and can determine if a certain host can reach a service or a set of services. Policy comparison compares two or more network security policies and represents the differences between them in an intuitive way. Although research in this field has been carried out for over a decade, there is still no clear answer on how to reduce policy errors. The different analysis techniques have their pros and cons, but none of them is a sufficient solution. More precisely, they are mainly complements to each other, as one analysis technique finds policy errors which remain unknown to another. Therefore, to be able to have a complete analysis of the computer network, multiple models must be instantiated. An analysis model that can perform all types of analysis techniques is desirable and has three main advantages. Firstly, the model can cover the greatest number of possible policy errors. Secondly, the computational overhead of instantiating the model is required only once. Thirdly, research effort is reduced because improvements and extensions to the model are applied to all three analysis types at the same time. Fourthly, new algorithms can be evaluated by comparing their performance directly to each other. This work proposes a new analysis model which is capable of performing all three analysis techniques. Security policies and the network topology are represented by the so-called Geometric-Model. The Geometric-Model is a formal model based on the set theory and geometric interpretation of policy rules. Policy rules are defined according to the condition-action format: if the condition holds then the action is applied. A security policy is expressed as a set of rules, a resolution strategy which selects the action when more than one rule applies, external data used by the resolution strategy and a default action in case no rule applies. This work also introduces the concept of Equivalent-Policy, which is calculated on the network topology and the policies involved. All analysis techniques are performed on it with a much higher performance. A precomputation phase is required for two reasons. Firstly, security policies which modify the traffic must be transformed to gain linear behaviour. Secondly, there are much fewer rules required to represent the global behaviour of a set of policies than the sum of the rules in the involved policies. The analysis model can handle the most common security policies and is designed to be extensible for future security policy types. As already mentioned the Geometric-Model can represent all types of security policies, but the calculation of the Equivalent-Policy has some small dependencies on the details of different policy types. Therefore, the computation of the Equivalent-Policy must be tweaked to support new types. Since the model and the computation of the Equivalent-Policy was designed to be extendible, the effort required to introduce a new security policy type is minimal. The anomaly analysis can be performed on computer networks containing different security policies. The policy comparison can perform an Implementation-Verification among high-level security requirements and an entire computer network containing different security policies. The policy comparison can perform a ChangeImpact-Analysis of an entire network containing different security policies. The proposed model is implemented in a working prototype, and a performance evaluation has been performed. The performance of the implementation is more than sufficient for real scenarios. Although the calculation of the Equivalent-Policy requires a significant amount of time, it is still manageable and is required only once. The execution of the different analysis techniques is fast, and generally the results are calculated in real time. The implementation also exposes an API for future integration in different frameworks or software packages. Based on the API, a complete tool was implemented, with a graphical user interface and additional features

Middleware Technologies for Cloud of Things - a survey

The next wave of communication and applications rely on the new services provided by Internet of Things which is becoming an important aspect in human and machines future. The IoT services are a key solution for providing smart environments in homes, buildings and cities. In the era of a massive number of connected things and objects with a high grow rate, several challenges have been raised such as management, aggregation and storage for big produced data. In order to tackle some of these issues, cloud computing emerged to IoT as Cloud of Things (CoT) which provides virtually unlimited cloud services to enhance the large scale IoT platforms. There are several factors to be considered in design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying suitable "Middleware". Middleware sits between things and applications that make a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next we study different architecture styles and service domains. Then we presents several middlewares that are suitable for CoT based platforms and lastly a list of current challenges and issues in design of CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268, Digital Communications and Networks, Elsevier (2017