Sharif Digital Repository

Recently, due to the restructuring of power systems and the high penetration level of local renewables, distribution systems have encountered with the complexity of power management. Therefore, the modern systems would be operated in a multi-agent structure which facilitates the power management as well as privacy protections of independent entities. In this structure, the distribution system is assumed to compose of several agents who independently schedule their local resources in order to maximize their own profits. Consequently, this paper provides an efficient peer-to-peer (P2P) active power management framework in a multi-agent distribution system while considering network constraints... 

Peer-to-peer (P2P) energy trading platform is an upcoming energy generation and effective energy managing strategy that rewards proactive customers (acting as prosumers) in which individuals trade energy for products and services. On the other hand, P2P trading is expected to give multiple benefits to the grid in minimizing the peak load demand, energy consumption costs, and eliminating network losses. However, installing P2P energy trading on a broader level in electrical-based networks presents a number of modeling problems in physical and virtual network layers. As a result, this article presents a thorough examination of P2P studies of energy trade literature. An overview is given with... 

By increasing the penetration level of the combined heat and power systems and demand response programs between energy consumers in the distribution network, the interaction between electricity and the natural gas network becomes more complicated. This paper proposes a peer-to-peer (P2P) energy sharing scheme for energy trading among Smart Energy Hubs (S.E. Hubs) which can trade both electrical and thermal energy with each other to reduce their cost and, reduce their dependency to gas and electricity utility companies. A two-stage energy strategy is presented. In the first stage, the S.E. Hubs total social cost is reduced by finding the optimal energy sharing profiles. And, in the second... 

This article deals with solving distributed optimization problems with equality constraints by a class of uncertain nonlinear heterogeneous dynamic multiagent systems. It is assumed that each agent with an uncertain dynamic model has limited information about the main problem and limited access to the information of the state variables of the other agents. A distributed algorithm that guarantees cooperatively solving of the constrained optimization problem by the agents is proposed. Via applying this algorithm, the agents do not need to continuously broadcast their data. It is shown that the proposed algorithm can be useful in solving resource allocation problems. IEEE  

In this paper, a distributed model predictive control is proposed to control Lipschitz nonlinear systems. The cooperative distributed scheme is considered where a global infinite horizon objective function is optimized for each subsystem, exploiting the state and input information of other subsystems. Thus, each control law is obtained separately as a state feedback of all system's states by solving a set of linear matrix inequalities. Due to convexity of the design, convergence properties at each iteration are established. Additionally, the proposed algorithm is modified to optimize only one control input at a time, which leads to a further reduction in the computation load. Finally, two... 

These days, considering the importance of reliability in industries, utilities’ role for preparing high levels of reliability becomes more important. This can be done by adding numbers of switches and Tie switches which impose high expenses to system. These expenditures should be provided by customers. Level of reliability in different points of network is different. So customers which pay more, receive higher level of reliability. If a customer raises its payment, utility change the location of switches in order to raise level of reliability of that customer. all customers change their payment and then utility fixes the switches at a point that all customers reach their favorable... 

We consider the problem of massive matrix multiplication, which underlies many data analytic applications, in a large-scale distributed system comprising a group of worker nodes. We target the stragglers' delay performance bottleneck, which is due to the unpredictable latency in waiting for slowest nodes (or stragglers) to finish their tasks. We propose a novel coding strategy, named entangled polynomial code, for designing the intermediate computations at the worker nodes in order to minimize the recovery threshold (i.e., the number of workers that we need to wait for in order to compute the final output). We demonstrate the optimality of entangled polynomial code in several cases, and show... 

To enhance performance, common processors feature relaxed memory models that reorder instructions. However, the correctness of concurrent programs is often dependent on the preservation of the program order of certain instructions. Thus, the instruction set architectures offer memory fences. Using fences is a subtle task with performance and correctness implications: using too few can compromise correctness and using too many can hinder performance. Thus, fence insertion algorithms that given the required program orders can automatically find the optimum fencing can enhance the ease of programming, reliability, and performance of concurrent programs. In this paper, we consider the class of... 

In this paper, we propose CodedSketch, as a distributed straggler-resistant scheme to compute an approximation of the multiplication of two massive matrices. The objective is to reduce the recovery threshold, defined as the total number of worker nodes that the master node needs to wait for to be able to recover the final result. To exploit the fact that only an approximated result is required, in reducing the recovery threshold, some sorts of pre-compression are required. However, compression inherently involves some randomness that would lose the structure of the matrices. On the other hand, considering the structure of the matrices is crucial to reduce the recovery threshold. In... 

We present a constant-time randomized distributed algorithms in the congested clique model that computes an O(α)-vertex-coloring, with high probability. Here, α denotes the arboricity of the graph, which is, roughly speaking, the edge-density of the densest subgraph. Congested clique is a well-studied model of synchronous message passing for distributed computing with all-to-all communication: per round each node can send one O(log n)-bit message algorithm to each other node. Our O(1)-round algorithm settles the randomized round complexity of the O(α)-coloring problem. We also explain that a similar method can provide a constant-time randomized algorithm for decomposing the graph into O(α)... 

Background: Long reads provide valuable information regarding the sequence composition of genomes. Long reads are usually very noisy which renders their alignments on the reference genome a daunting task. It may take days to process datasets enough to sequence a human genome on a single node. Hence, it is of primary importance to have an aligner which can operate on distributed clusters of computers with high performance in accuracy and speed. Results: In this paper, we presented IMOS, an aligner for mapping noisy long reads to the reference genome. It can be used on a single node as well as on distributed nodes. In its single-node mode, IMOS is an Improved version of Meta-aligner (IM)... 

The electric power industry is the main part of Science development, and today, with the advent of technology, the demand for electric power has been expanded. On the other hand, smart grids are developing heavily. One of the notable features of these networks is the presence of a plug-in hybrid electric vehicle (PHEV). The addition of these cars to the network has its own advantages and disadvantages. One of the most important issues in smart grids is network management and control of critical system parameters. In this paper the effect of these cars on the grid is investigated. These vehicles impose an increase in production capacity in the uncontrolled charge mode. They also have the... 

The electric power industry is the main part of Science development, and today, with the advent of technology, the demand for electric power has been expanded. On the other hand, smart grids are developing heavily. One of the notable features of these networks is the presence of a plug-in hybrid electric vehicle (PHEV). The addition of these cars to the network has its own advantages and disadvantages. One of the most important issues in smart grids is network management and control of critical system parameters. In this paper the effect of these cars on the grid is investigated. These vehicles impose an increase in production capacity in the uncontrolled charge mode. They also have the... 

This paper presents a framework for expansion planning studies of distributed energy storage systems (DESSs) in high wind penetrated power systems. The main objective is to find optimal location and capacity of DESSs in the viewpoint of independent system operator (ISO) while ensuring the maximum usage of wind farms output generation. Three different criteria are introduced for expansion planning studies. Minimizing wind curtailment cost together with transmission congestion cost are considered to properly deal with the issues associated with the curtailment of wind energy and constraints of transmission network. Furthermore, the minimum normalized profit for all DESSs' owners needs to be... 

In recent years, high interest in using Virtual Machines (VMs) in data centers and cloud computing has significantly increased the demand for high-performance data storage systems. A straightforward approach to providing a high-performance storage system is using Solid-State Drives (SSDs). Inclusion of SSDs in storage systems, however, imposes significantly higher cost compared to Hard Disk Drives (HDDs). Recent studies suggest using SSDs as a caching layer for HDD-based storage subsystems in virtualized platforms. Such studies neglect to address the endurance and cost of SSDs, which can significantly affect the efficiency of I/O caching. Moreover, previous studies only configure the cache... 

The widespread use of cloud computing services raises the question of how one can delegate the processing tasks to the untrusted distributed parties without breaching the privacy of its data and algorithms. Motivated by the algorithm privacy concerns in a distributed computing system, in this paper, we introduce the private function retrieval (PFR) problem, where a user wishes to efficiently retrieve a linear function of K messages from N non-communicating replicated servers while keeping the function hidden from each individual server. The goal is to find a scheme with minimum communication cost. To characterize the fundamental limits of the communication cost, we define the capacity of PFR... 

How can we optimally trade extra computing power to reduce the communication load in distributed computing? We answer this question by characterizing a fundamental tradeoff between computation and communication in distributed computing, i.e., the two are inversely proportional to each other. More specifically, a general distributed computing framework, motivated by commonly used structures like MapReduce, is considered, where the overall computation is decomposed into computing a set of “Map” and “Reduce” functions distributedly across multiple computing nodes. A coded scheme, named “coded distributed computing” (CDC), is proposed to demonstrate that increasing the computation load of the... 

Characteristics and way of behavior of attacks and infiltrators on computer networks are usually very difficult and need an expert. In addition; the advancement of computer networks, the number of attacks and infiltrations is also increasing. In fact, the knowledge coming from expert will lose its value over time and must be updated and made available to the system and this makes the need for expert person always felt. In machine learning techniques, knowledge is extracted from the data itself which has diminished the role of the expert. Various methods used to detect intrusions, such as statistical models, safe system approach, neural networks, etc., all weaken the fact that it uses all the... 

Execution speed seriously bothers application developers and users for wearable devices such as Google Glass. Intensive applications like 3D games suffer from significant delays when CPU is busy. Energy is another concern when the devices are in low battery level but users need them for urgency use. To ease such pains, one approach is to expand the computational power by cloud offloading. This paradigm works well when the available Internet access has enough bandwidth. Another way is to leverage nearby devices for computation-offloading, which is known as device-to-device (D2D) offloading. In this paper, we present Dandelion, a unified code offloading system for wearable computing. Dandelion... 

We consider load balancing in a network of caching servers delivering contents to end users. Randomized load balancing via the so-called power of two choices is a well-known approach in parallel and distributed systems. In this framework, we investigate the tension between storage resources, communication cost, and load balancing performance. To this end, we propose a randomized load balancing scheme which simultaneously considers cache size limitation and proximity in the server redirection process. In contrast to the classical power of two choices setup, since the memory limitation and the proximity constraint cause correlation in the server selection process, we may not benefit from the...