New Results on Communication- and Memory-Aware Load Balancing Model and Algorithms

Distribution systems (DS) are a focal connection point for existing and new renewable energy sources. DS have increasing sizes; they connect increasing numbers of renewable energy sources and have a mandate to operate as financial entities seeking to maximize their profits in an electricity market. To achieve these goals, DS require highly efficient planning tools. In this paper, we propose a successive fuzzy mixed integer linear programming (MILP) method for optimally placing and sizing capacitors in a DS that explicitly models induction run wind electric generators (WEGs). The optimization problem is linearized, transformed into a fuzzy MILP model and is solved successively while updating the solution vector. The fuzzy MILP method employs fuzzy models of voltage constraints that are often violated in a planned system that lacks reactive power support. The use of fuzzy constraints makes the proposed solution method very robust by avoiding infeasibility and reducing the need for user expertise. Further, the fuzzy MILP method represents the dual objectives of capacitor cost and annual energy loss minimization as fuzzy sets with individual satisfaction functions. The method maximizes the minimum of these satisfaction functions during optimization. Tests on 69-bus and 2002-bus DS show that the method performs well and is robust.

In this paper, we classify the dynamic, decentralized load balancing algorithms for heterogenous distributed computer systems into three policies: queue adjustment policy (QAP), rate adjustment policy (RAP) and queue and rate adjustment policy (QRAP). We propose two efficient algorithms, referred to as rate based load balancing via virtual routing (RLBVR) and queue based load balancing via virtual routing (QLBVR), which belong to the above RAP and QRAP policies, respectively. We also consider algorithms estimated load information scheduling algorithm (ELISA) that was introduced in the literature, to implement QAP policy. Our focus is to analyze and understand the behaviors of these algorithms in terms of their load balancing ability under varying load conditions (light, moderate, or high) and the minimization of mean response time of jobs. We compare the above classes of algorithms by a number of rigorous simulation experiments to elicit their behavior under some influencing parameters. From these experiments, recommendations are drawn to prescribe the suitability of the algorithms under various situations.

In this paper, we classify the dynamic, decentralized load balancing algorithms for heterogenous distributed computer systems into three policies: queue adjustment policy (QAP), rate adjustment policy (RAP) and queue and rate adjustment policy (QRAP). We propose two efficient algorithms, referred to as rate based load balancing via virtual routing (RLBVR) and queue based load balancing via virtual routing (QLBVR), which belong to the above RAP and QRAP policies, respectively. We also consider algorithms estimated load information scheduling algorithm (ELISA) that was introduced in the literature, to implement QAP policy. Our focus is to analyze and understand the behaviors of these algorithms in terms of their load balancing ability under varying load conditions (light, moderate, or high) and the minimization of mean response time of jobs. We compare the above classes of algorithms by a number of rigorous simulation experiments to elicit their behavior under some influencing parameters. From these experiments, recommendations are drawn to prescribe the suitability of the algorithms under various situations.

In recent year, alarming rate of natural disasters around the world have demanded the need for operative solution in field of power generation, to control polluted energy sources which are major cause of global warming. Microgrid facilitates penetration of renewable energy sources into the existing distribution systems to reduce the overall carbon footprint of the globe by reducing the dependency on the main grid. Efficient but linear microgrid resource scheduling algorithms are gaining interest in present time due to its simplicity and fast computation. This research paper aims to serve the purposes by designing a mixed integer linear programming based microgrid scheduling problem while various types of scenario, minimize the electricity cost for the utilities also maintain the generation and load balance. The strategy is implemented on a small microgrid to prove its efficacy .In this paper mainly optimal scheduling of microgrid has been done in various scenario, and obtained there global minimum electricity cost to the help of mixed integer linear programming (MILP) algorithm. Microgrid is a small scale type of power grid, which provides the energy locally, its offers integration between distributed energy resources and the locally connected loads. Microgrid able to be operated with the main grid and in standalone mode also ability to transitions between these two modes, the mode of operation of microgrid is depends on the system operating condition. The reliability of power grid is improving more when it’s integrated with the Microgrid and works together. Power exchange with the upper stream grid is done through the point of common coupling (PCC). Microgrid having renewable energy resources i.e. PV, Wind and non-renewable energy resources, DG, FC, and MC connected with the Battery storage type system and locally connected loads. So it is a very reliable scenario, main grid with the microgrid, it is more beneficial, economy and stable types of system. Keywords : Optimal Microgrid Scheduling, Linear Programming DOI : 10.7176/APTA/76-05 Publication date :March 31 st 2019

In the design of time-critical applications, schedulability analysis can be used to define the feasibility region of tasks so that optimization techniques can find the best design solution that satisfies the deadlines. This method has been applied to obtain the optimal task implementation, priority assignment or placement of tasks onto CPUs in previous work. The definition of the feasibility region based on response time calculation requires many integer variables and is too complex for solvers. Approximation techniques have been used to define a convex subset of the feasibility region, often used in conjunction with branch and bound to compute sub-optimal solutions. In this paper, we provide an improved and simpler feasibility analysis method that allows an exact definition of the feasibility region in Mixed Integer Linear Programming (MILP) optimization methods. The encoding of the feasibility region using our method requires a significantly smaller number of binary variables and is viable for the treatment of industrial-size problems as shown by the experiments.

The intuition of the power companies is to supply quality power to the consumers with reduced line losses. Reduction of transmission and Distribution line losses is a great challenge for most of the developing countries. This paper demonstrates the network reconfiguration in a practical low voltage Indian utility Distribution Feeder. Various configurations are suggested and the real power losses are computed using General Algebric Modeling System (GAMS), Mixed Integer Linear Programming (MILP) CPLEX solver. Load balancing is done for mitigation of voltage profile at the highly loaded nodes. Load flow analysis is performed through Forward / Backward Sweep analysis, which is more suitable for the radial system. Real power loss and voltage at nodes are compared and presented before and after network reconfiguration. This paper emphasizes the importance of network reconfiguration for developing countries like India.

Receding horizon load restoration for coupled transmission and distribution system considering load-source uncertainty

A distributed system consists of several autonomous nodes. In a distributed system some of the nodes may be overloaded due to a large number of job arrivals while other nodes may remain idle without any processing. The performance of a distributed system depends crucially on dividing up work effectively among the computing nodes. So a way is needed to share load across all the computing nodes. In centralized load balancing schemes, the load balancing decision is taken by a central server. So this scheme is not scalable. In contrast, fully distributed schemes are scalable, but they use local information. A hierarchical dynamic load balancing model is proposed in this paper where an ordinary node does not need to have a global system wide knowledge about the states of other nodes in the system. The proposed model is semi distributed as each cluster is represented by a cluster master.

Videoconferencing system is widely and successfully used on the Internet and standard/high definition videoconferencing is a new heated topic in research. However, researches in various videoconferencing systems (e.g. Access Grid, DVTS, VLC, and Ultra Grid) are faced with the problem of scalability. Bandwidth is the primary factor that limits the system capacity. In this paper, we propose a scalable and distributed standard definition videoconferencing system and design its architecture as well as forwarding model. We use several data stream transmitters to cooperatively forward video streams and design the system architecture, components' behaviors, and forwarding models on both control and data plane, with reasonable load allocation and balance. We also implement our idea based on DVTS as a multi-point standard-definition distributed DVTS Plus and test it on CERNET. Experiments on CERNET show that distributed DVTS Plus now supports higher system capacity, easier deployment and better scalability. Keywords-videoconferencing; distributed system; load balancing; scalability; large-scale conference management; DVTS

Network reconfiguration for load balancing is the determination of switching-options for a particular set of loads on a distribution system that improves load balancing the most and is performed altering the topological structure of distribution feeders by changing open/closed status of sectionalizing and tie-switches. By transferring loads from the heavily loaded feeders to the lightly loaded ones, network reconfiguration can balance feeder loads and alleviate overload conditions of a network. In this paper a novel method is proposed by formulating an algorithm to reconfigure distribution networks for load balancing. Systematic logical approach is used to determine the feasible switching combinations, select the status of the switches, and find the best combination of switches for maximum improvement in load balancing. The algorithm has been developed in two stages. In the first stage, a limited number of switching combinations is formed by trinary logic principle. A simultaneous switching approach is applied logically to find the best combination by selecting the appropriate switching options, connecting the switches accordingly, and assigning the loads for the newly formed combinations. In the next stage, the search is extended to investigate whether any other switching combination gives rise to maximum improvement in load balancing, compared to the improvement obtained in the first stage. This is carried out by considering one after another, the branches next to the open-branches of the best configuration, and the switching configuration that gives the maximum improvement in load balancing is identified. The proposed method has been tested on a 69-bus test system, and the test results indicate that the algorithm proposed is able to determine the appropriate switching-options of the optimal (or near optimal) configuration with less computation. This paper proposes a scheme to implement the simultaneous switching algorithm for real-time on-line control of three-phase distribution networks, with symmetrical loading per phase.

The AGGREGATE project team successfully developed and validated various modules for outage management. Brief summaries of each module are provided to showcase their strength for outage management and restoration for a distribution system with a high penetration of connected distribution energy resources (DERs).In recent years, inverter-based DERs have been widely deployed in distribution system. A most of behind-the-meter (BTM) solar power generation is not visible to the utility. The data-driven DER and load estimation modules are using machine learning (ML) and artificial intelligence (AI) to manage this issue, which provides an opportunity for distribution system operators (DSOs) to operate systems and make decisions in real-time for a distribution system with a high penetration of DERs deployed. Also, the estimated DER and true load can be further leveraged in network aggregation and cold-load pick up estimation for reducing the computing complexity and providing for fast restoration. After load demand and DER power generations have been estimated, the information will support topology and state estimation(SE). The topology estimation module demonstrated the viability of mixed integer linear programming (MILP) formulation to estimate the most likely operational radial topology and outage sections using power flow measurements, historical/estimated load and DERs data and smart meter ping measurements. Formulation includes continuous (power flow, load and DERs data) and binary measurements (smart meter ping measurements) in a single formulation. Errors in continuous data and binary data are modeled as normal distribution and Bernoulli distribution, respectively. In the future distribution grid, the power injection from controllable DERs will be essential for efficient and resilient grid operation. However, determining the optimal DER injections and restoration actions is dependent on knowledge of the system states. State estimation (SE), already the cornerstone of transmission energy management systems, will become commonplace in distribution management systems as more measurements become available from deployment of automated metering infrastructure (AMI). Observability analysis is the first step in SE, as it determines the sufficiency of the available measurements for accurately estimating the current system states. A new type of pseudo-measurement called a Correlational Measurement (CM) is introduced in this module, to enhance the observability of the system to enable more accurate SE. CMs encapsulate knowledge of correlation between demand patterns for similar classes of loads as well as injection patterns for same-technology renewable DERs. During grid contingency scenarios, DERs have been traditionally disconnected, without any fault ride-through capabilities. However, with new regulations and better technology, it is feasible for these resources to contribute to the grid’s restoration after an adverse event and hence enhance resilience. The controllability module proposes a two-step restoration scheme for the power system restoration process by leveraging additional degrees of freedom in power electronics interfaced DERs for mitigating voltage problems. In a resilience mode without the utility system, the distribution grid relies on DERs to serve critical load. In such a severe event with multiple faults on the distribution feeders, actuation of various protective devices (PDs) divides the distribution system into electrical islands. The undetected actuated PDs due to fault current contributions from DERs can delay the restoration process, thereby reducing the system resilience. The Advanced Outage Management (AOM) and the Advanced Feeder Restoration (AFR) modules developed in this project provide improved system resilience with multiple DERs. AOM identifies the faulted sections and actuated PDs in a distribution system with DERs by incorporating smart meter data. The most credible outage scenario including fault locations, PD actuations, and fault indicator (FI) failures is identified by a set of binary integer linear programming incorporating hypotheses. The AFR module serves to restore a distribution system with available energy resources taking into consideration the availability of utility sources and DERs. By partitioning the system into islands, critical load will be served with the available generation resources within islands based on the solution of a MILP. When the utility systems become available, the optimal path will be determined by a spanning tree search algorithm that reconnects these islands back to substations and restores the remaining load. The transmission and distribution (T&D) co-simulation module was used to validate the effect of a control action performed on the distribution side assets as it propagates to the transmission side. This ensures that the control action performed results in a feasible operating point on both the transmission and the distribution system. In addition to validation, the team used the T&D co-simulation module to demonstrate how distribution system assets can be used to mitigate issues on the transmission system. Specifically, the team demonstrated that appropriate switching operations on the distribution side can alleviate the line overload condition on the transmission side without causing new operational constraint violations.

The client-server model is widely used in many distributed systems in which proper assignment of client is an important factor for the better performance of system. A large number of clients may exist in a distributed system that establishes communication with one another through a variety of intermediary servers. The primary objective of the overall system is to allocate clients to servers to maintain the load balancing as well as communication cost. There are two criteria for server load evaluation. The first one is complete load, followed by task scheduling, with complete load and task scheduling being diametrically opposed measurements. It seems to be NP-hard to figure out the appropriate client-server allotment for a distributed system's total load and load balancing. In this research, we examine distinct server load balancing strategies for the assignment of clients in distributed systems.

i»? A. A. Helal, A. A. Heddaya, and B. B. Bhargrava Kluwer Academic Publishers, Boston, 1996, 176 pp. ISBN 0-7923-9800-9, $118.50 Computing in the 1990s has reached the state of distributed computing. A basis of this form of computing is a distributed computing system, which is built on the following three components: (a) personal computers, (b) local and fast wide area networks, and (c) system and application software. By amalgamating computers and networks into one single computing system and providing an appropriate system software, a distributed computing system has created the possibility of sharing information and peripheral resources. Furthermore, these systems improved performance of a computing system and individual users through parallel execution of programs, load balancing and sharing, and replication of programs and data. Distributed computing systems are also characterized by enhanced availability, and increased reliability. Replication is the key to providing high availability, fault tolerance, and enhanced performance in a distributed computing system. As companies move toward systems that are more open and distributed, replication is becoming increasingly important in the ability to provide data and services that are current, correct and available, which is a key factor in maintaining a competitive advantage over rivals. However, replication has also generated some serious challenges. For example, when a replica is updated, how do we propagate such an update to other replicas? If multiple replicas are updated simultaneously and some of the updates conflict one another, how do we resolve the conflict? How do we deal with the situation where a replica is down and then subsequently recovered? How do we deal with network partitions? Not surprisingly, considerable research efforts have been directed towards the solution of these challenges. However, most of the research results are scattered among many journals, conference proceedings, theses, and technical reports. The book by Helal, Heddaya and Bhargava has gathered the best material of these research results and formed a coherent collection that includes definitions, theoretical background, algorithms, annotated bibliographies of commercial and experimental prototype systems, and more than 200 references. This book classifies the entities that can be replicated on a distributed computing system into the following categories: (a) data, (b) process, (c) objects, and (d) messages. After the introduction on the goals and main approaches of replication techniques, the book uses individual chapters to describe major methods for each of the above replication categories. The main focus of this part, however, is on the replication of data. The book also contains two chapters on the replication issues in heterogeneous, mobile, and large-scale systems, and on the future of replication techniques. Apart from the normal chapters, the book contains a rich set of appendices that are very useful for further studies of replication techniques. These appendices include brief descriptions of two dozen commercial and experimental systems that use replication techniques, annotated bibliographies of selected literature on various topics of replication, and an introduction on serializability theory. Each annotated bibliography contains an overview of the specific topic and is written by an invited expert in that field. The combination of the normal chapters and appendices cover the entire spectrum of replication, from definitions, concepts, theories, algorithms, techniques, to systems. The book serves as an excellent introduction and roadmap to the issues of replication. Although it does not provide a detailed description of every replication techniques and systems, the book covers most of the fundamental work that allows the reader to understand the roots of these techniques and systems, and to find the ways to search for the details. This book is a valuable reference book for anyone studying replication techniques or implementing replication systems, specifically, it can be very useful to post-graduate students, practitioners, and beginning researchers of this exciting field. Wanlei Zhou, School of Computing and Mathematics, Deakin University

This paper describes a simplified parallelization of the near-field interaction in the multilevel fast multipole algorithms (MLFMA) based on a data decomposition strategy with an improved load balancing and the data locality. It shows good performance on distributed memory computers using the message passing interface (MPI).

A computational comparison of cargo prioritization and terminal allocation problem models