Privacy-Aware Load Balancing in Fog Networks: A Reinforcement Learning Approach

Fog Computing has emerged as a solution to support the growing demands of real-time Internet of Things (IoT) applications, which require high availability of these distributed services. Intelligent workload distribution algorithms are needed to maximize the utilization of such Fog resources while minimizing the time required to process these workloads. These load balancing algorithms are critical in dynamic environments with heterogeneous resources and workload requirements along with unpredictable traffic demands. In this paper, load balancing is provided using a Reinforcement Learning (RL) algorithm, which optimizes the system performance by minimizing the waiting delay of IoT workloads. Unlike previous studies, the proposed solution does not require load and resource information from Fog nodes, which makes the algorithm dynamically adaptable to possible environment changes over time. This also makes the algorithm aware of the privacy requirements of Fog service providers, who might like to hide such information to prevent competing providers from calculating better pricing strategies. The proposed algorithm is interactively evaluated on a Discrete-event Simulator (DES) to mimic a practical deployment of the solution in real environments. In addition, we evaluate the algorithm's generalization ability on simulations longer than what it was trained on, which, to the best of our knowledge, has never been explored before. The results provided in this paper show how our proposed approach outperforms baseline load balancing methods under different workload generation rates.Comment: 9 pages, 9 figures, 1 tabl

Energy load balancing for fixed clustering in wireless sensor networks

Clustering can be used as an effective technique to achieve both energy load balancing and an extended lifetime for a wireless sensor network (WSN). This paper presents a novel approach that first creates energy balanced fixed/static clusters, and then, to attain energy load balancing within each fixed cluster, rotates the role of cluster head through uniformly quantized energy levels based approach to prolong the overall network lifetime. The method provided herein, not only provides near-dynamic clustering performance but also reduces the complexity due to the fact that cluster formation phase is implemented once. The presented simulation results clearly show the efficacy of this proposed algorithm and thus, it can be used as a practical approach to obtain maximized network lifetime for energy balanced clusters in fixed clustering environments. © 2012 IEEE

Non-Gaussian residual based short term load forecast adjustment for distribution feeders

The evolving role for electricity network operators means that load forecasting at the distribution level has become increasingly important, presenting the need for anticipation of the behavior of highly dynamic and diversely distributed loads. The commonly held assumption of Gaussian residuals in forecasting does not always hold for distribution network loads, increasing the uncertainty in balancing a system at this network level. To reduce the operational impact of forecast errors, this paper utilizes different multivariate joint probability distributions to capture the intra-day dependency structure of forecast residuals. Transforming these to the conditional form enables forecast corrections to be made at variable horizons even in the absence of the forecast model. Improvements in accuracy are demonstrated on benchmark load forecast models at distribution level low voltage substations. A practical distribution system application on scheduling embedded energy storage shows substantial reductions in grid imports and hence costs to distribution level customers from utilizing the proposed intraday correction approach

Joint Dynamic Radio Resource Allocation and Mobility Load Balancing in 3GPP LTE Multi-Cell Network

Load imbalance, together with inefficient utilization of system resource, constitute major factors responsible for poor overall performance in Long Term Evolution (LTE) network. In this paper, a novel scheme of joint dynamic resource allocation and load balancing is proposed to achieve a balanced performance improvement in 3rd Generation Partnership Project (3GPP) LTE Self-Organizing Networks (SON). The new method which aims at maximizing network resource efficiency subject to inter-cell interference and intra-cell resource constraints is implemented in two steps. In the first step, an efficient resource allocation, including user scheduling and power assignment, is conducted in a distributed manner to serve as many users in the whole network as possible. In the second step, based on the resource allocation scheme, the optimization objective namely network resource efficiency can be calculated and load balancing is implemented by switching the user that can maximize the objective function. Lagrange Multipliers method and heuristic algorithm are used to resolve the formulated optimization problem. Simulation results show that our algorithm achieves better performance in terms of user throughput, fairness, load balancing index and unsatisfied user number compared with the traditional approach which takes resource allocation and load balancing into account, respectively

Practical Algorithms for Multicast Support in Input Queues Switches

Abstract — This paper deals with multicast flow support in N × N Input Queued switch architectures. A practical approach to support multicast traffic is presented, assuming that O(N) queues are available at each input port. The focus is on dynamic queueing policies, where, at each input port, multicast flows are assigned to one among the available queues when flows become active: flows are assigned to queues according to switch queue status and, possibly, to flow information. We discuss queueing assignments, scheduling algorithms and flow activity definition models. We explain why dynamic queueing disciplines may outperform static policies, and we show that, even in the most favorable conditions for static policies, they provide comparable performance. I

rDLB: A Novel Approach for Robust Dynamic Load Balancing of Scientific Applications with Parallel Independent Tasks

Scientific applications often contain large and computationally intensive parallel loops. Dynamic loop self scheduling (DLS) is used to achieve a balanced load execution of such applications on high performance computing (HPC) systems. Large HPC systems are vulnerable to processors or node failures and perturbations in the availability of resources. Most self-scheduling approaches do not consider fault-tolerant scheduling or depend on failure or perturbation detection and react by rescheduling failed tasks. In this work, a robust dynamic load balancing (rDLB) approach is proposed for the robust self scheduling of independent tasks. The proposed approach is proactive and does not depend on failure or perturbation detection. The theoretical analysis of the proposed approach shows that it is linearly scalable and its cost decrease quadratically by increasing the system size. rDLB is integrated into an MPI DLS library to evaluate its performance experimentally with two computationally intensive scientific applications. Results show that rDLB enables the tolerance of up to (P minus one) processor failures, where P is the number of processors executing an application. In the presence of perturbations, rDLB boosted the robustness of DLS techniques up to 30 times and decreased application execution time up to 7 times compared to their counterparts without rDLB