No Arabic abstract
Network Function Virtualization (NFV) and Service Function Chaining (SFC) have been widely used to enable flexible and agile network management. To enhance reliability, some research has proposed to deploy backup function instances for prompt recovery when a primary instance fails. While most of the recent studies focus on speeding up recovery, less attention has been paid to the problem of minimizing the state update cost. In this work, we present PiggyBackup (Piggyback-based Backup), an efficient backup instance deployment and update protocol. Our key idea is to reuse the existing service chains traversing through servers in a network to help piggyback the update information. By doing this, we eliminate the header overhead and reduce the amount of update traffic significantly. To realize such a piggyback-based update more efficiently, we investigate the backup instance deployment and chain selection problems to enhance piggybacking opportunities and reduce the forwarding hop counts with explicit consideration of the distribution of service chains. Our simulation results show that PiggyBackup reduces the average overall update overhead by 47.65% and 39.56%, respectively, in a fat-tree topology as compared to random deployment and shortest path based deployment.
Previous works proved that the combination of the linear neuron network with nonlinear activation functions (e.g. ReLu) can achieve nonlinear function approximation. However, simply widening or deepening the network structure will introduce some training problems. In this work, we are aiming to build a comprehensive second-order CNN implementation framework that includes neuron/network design and system deployment optimization.
In the Internet of Things (IoT) networks, caching is a promising technique to alleviate energy consumption of sensors by responding to users data requests with the data packets cached in the edge caching node (ECN). However, without an efficient status update strategy, the information obtained by users may be stale, which in return would inevitably deteriorate the accuracy and reliability of derived decisions for real-time applications. In this paper, we focus on striking the balance between the information freshness, in terms of age of information (AoI), experienced by users and energy consumed by sensors, by appropriately activating sensors to update their current status. Particularly, we first depict the evolutions of the AoI with each sensor from different users perspective with time steps of non-uniform duration, which are determined by both the users data requests and the ECNs status update decision. Then, we formulate a non-uniform time step based dynamic status update optimization problem to minimize the long-term average cost, jointly considering the average AoI and energy consumption. To this end, a Markov Decision Process is formulated and further, a dueling deep R-network based dynamic status update algorithm is devised by combining dueling deep Q-network and tabular R-learning, with which challenges from the curse of dimensionality and unknown of the environmental dynamics can be addressed. Finally, extensive simulations are conducted to validate the effectiveness of our proposed algorithm by comparing it with five baseline deep reinforcement learning algorithms and policies.
Sensors used in applications such as agriculture, weather, etc., monitoring physical parameters like soil moisture, temperature, humidity, will have to sustain their battery power for long intervals of time. In order to accomplish this, parameter which assists in reducing the consumption of power from battery need to be attended to. One of the factors affecting the consumption of energy is transmit and receive power. This energy consumption can be reduced by avoiding unnecessary transmission and reception. Efficient routing techniques and incorporating aggregation whenever possible can save considerable amount of energy. Aggregation reduces repeated transmission of relative values and also reduces lot of computation at the base station. In this paper, the benefits of aggregation over direct transmission in saving the amount of energy consumed is discussed. Routing techniques which assist aggregation are incorporated. Aspects like transmission of average value of sensed data around an area of the network, minimum value in the whole of the network, triggering of event when there is low battery are assimilated.
Network function (NF) developers need to provide highly available solutions with diverse packet processing features at line rate. A significant challenge in developing such functions is to build flexible software that can be adapted to different operating environments, vendors, and operator use-cases. Today, refactoring NF software for specific scenarios can take months. Furthermore, network operators are increasingly adopting fast-paced development practices for continuous software delivery to gain market advantage, which imposes even shorter development cycles. A key aspect in NF design is state management, which can be optimized across deployments by carefully selecting the underlying data store. However, migrating to a data store that suits a different use-case is time consuming because it requires code refactoring while revisiting its application programming interfaces, APIs. In this paper we introduce FlexState, a state management system that decouples the NF packet processing logic from the data store that maintains its state. The objective is to reduce code refactoring significantly by incorporating an abstraction layer that exposes various data stores as configuration alternatives. Experiments show that FlexState achieves significant flexibility in optimizing the NF state management across several scenarios with negligible overhead.
Resources in a distributed system can be identified using identifiers based on random numbers. When using a distributed hash table to resolve such identifiers to network locations, the straightforward approach is to store the network location directly in the hash table entry associated with an identifier. When a mobile host contains a large number of resources, this requires that all of the associated hash table entries must be updated when its network address changes. We propose an alternative approach where we store a host identifier in the entry associated with a resource identifier and the actual network address of the host in a separate host entry. This can drastically reduce the time required for updating the distributed hash table when a mobile host changes its network address. We also investigate under which circumstances our approach should or should not be used. We evaluate and confirm the usefulness of our approach with experiments run on top of OpenDHT.