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In the current cloud business environment, the cloud provider (CP) can provide a means for offering the required quality of service (QoS) for multiple classes of clients. We consider the cloud market where various resources such as CPUs, memory, and storage in the form of Virtual Machine (VM) instances can be provisioned and then leased to clients with QoS guarantees. Unlike existing works, we propose a novel Service Level Agreement (SLA) framework for cloud computing, in which a price control parameter is used to meet QoS demands for all classes in the market. The framework uses reinforcement learning (RL) to derive a VM hiring policy that can adapt to changes in the system to guarantee the QoS for all client classes. These changes include: service cost, system capacity, and the demand for service. In exhibiting solutions, when the CP leases more VMs to a class of clients, the QoS is degraded for other classes due to an inadequate number of VMs. However, our approach integrates computing resources adaptation with service admission control based on the RL model. To the best of our knowledge, this study is the first attempt that facilitates this integration to enhance the CP's profit and avoid SLA violation. Numerical analysis stresses the ability of our approach to avoid SLA violation while maximizing the CP’s profit under varying cloud environment conditions.

The IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) has been recently standardized as the de facto solution for routing in the context of the emerging Internet of Things (IoT) paradigm. RPL, along with other standards, has provided a baseline framework for IoT that has helped advance communications in the world of embedded resource-constrained networks. However, RPL still suffers from issues that may limit its efficiency such as the absence of an efficient load-balancing primitive. In this study, we show how RPL suffers from a load-balancing problem that may harm both the reliability of the protocol and its network lifetime. To address this problem, a novel load-balancing scheme is introduced that significantly enhances the reliability of RPL and fosters the protocol’s efficiency in terms of power consumption.

Medium Access Control (MAC) protocols based on Time Division Multiple Access (TDMA) can improve the reliability and efficiency of WBAN. However, traditional static TDMA techniques adopted by IEEE 802.15.4 and IEEE 802.15.6 do not sufficiently consider the channel status or the buffer requirements of the nodes within heterogeneous contexts. Although there are some solutions that have been proposed to alleviate the effect of the deep fade in WBAN channel by adopting dynamic slot allocation, these solutions still suffer from some reliability and energy efficiency issues and they do not avoid channel deep fading. This paper presents two novel and generic TDMA based techniques to improve WBAN reliability and energy efficiency. Both techniques synchronize nodes adaptively whilst tackling their channel and buffer status in normal and emergency contexts. Extensive simulation experiments using various traffic rates and time slot lengths demonstrate that the proposed techniques improve the reliability and the energy efficiency compared to IEEE 802.15.4 and IEEE 802.15.6 in both situations, the normal and emergency contexts. This improvement has been achieved in terms of packet loss, up to 90% and energy consumption, up to 13%, confirming the significant enhancements made by the developed scheduling techniques.

While Cooja has been proven to be an ideal tool for the simulation of RPL in WSNs, there are challenges involved in its use, particularly in regard to a lack of documentation. The Contiki website provides an image of Instant Contiki which can then be used with the virtualisation tool VMware. Once Instant Contiki is successfully started the Contiki website can then be referred to for brief instructions regarding a simple network setup on Cooja. However, this is the sum total of any official documentation regarding Cooja, with the majority of support being provided within Internet discussion groups. This report, therefore, aims to provide an instruction set, to enable anyone with no previous exposure to Cooja to be able to create network layouts, compile motes, examine output using the Sensor Data Collect plugin and also utilise scripts to produce more fine-grained results.