Orchestration and management of cloud computing entities necessitate measuring and analysis of real-time monitored performance metrics. However, decision making in current management platforms are addressed separately in different cloud stack layers. These isolated active management decisions may degrade the total performance of the cloud system. Since, cloud computing platforms lack an integrated analytics and management capability, in this paper, we propose an integrated platform to detect and predict situations where corrective actions are required. First, a Dynamic Bayesian Network (DBN) is trained and updated by collected data to calculate the causal dependencies among various entities in different cloud service layers. The correlation values are then fed into a Long Short-Term Memory (LSTM) neural network to predict the future states. States that violate the Service Level Agreement(SLA) of cloud services are learned with training data, and if the forecasted states threaten the SLA of cloud services, associated events are generated to trigger management actions. Next, management actions are assigned a different set of events using a reinforcement learning approach. A set of experiments based on collected data from a real cloud service environment is conducted to validate the proposed approach. Experimental results indicate that the proposed method outperforms the current management solutions and improves web request response time by up to 7% and decreases SLA violation by 79% in the context of web application auto-scaling.

Ethernet has been proposed for the 5G fronthaul to transport the Common Public Radio Interface (CPRI) traffic between the radio equipment (RE) and the radio equipment control (REC). The advantages of adopting an Ethernet transport are threefold 1) the low cost of equipment, 2) the use of a shared infrastructure with statistical multiplexing, as well as 3) the ease of operations, administration and maintenance (OAM). In this paper, we introduce distributed timeslot scheduler for CPRI over Ethernet (DTSCoE) as a scheduling algorithm for IEEE 802.1Qbv to support CPRI traffic. DTSCoE is built upon the stream reservation protocol (SRP) IEEE 802.1Qcc to propagate timeslot information across the datapath without any centralized coordination. The simulation results demonstrate that DTSCoE reduces one-way delay to minimum and reduces the jitter to zero which satisfies the CPRI requirements.

Network operators are faced with the challenge of deploying and managing middleboxes (also called inline services) such as firewalls within their broadband access, datacenter or enterprise networks. Due to the lack of available protocols to route traffic through middleboxes, operators still rely on error-prone and complex low-level configurations to coerce traffic through the desired set of middleboxes. Built upon the recent software-defined networking (SDN) architecture and OpenFlow protocol, this paper proposes StEERING, short for SDN inlinE sERvices and forwardlNG. It is a scalable framework for dynamically routing traffic through any sequence of middleboxes. With simple centralized configuration, StEERING can explicitly steer different types of flows through the desired set of middleboxes, scaling at the level of per-subscriber and per-application policies. With its capability to support flexible routing, we further propose an algorithm to select the best locations for placing services, such that the performance is optimized. Overall, StEERING allows network operators to monetize their middlebox deployment in new ways by allowing subscribers flexibly to select available network services.