Incentive Based Load Shedding Management in a Microgrid Using Combinatorial Auction with IoT Infrastructure

This paper presents a novel incentive-based load shedding management scheme within a microgrid environment equipped with the required IoT infrastructure. The proposed mechanism works on the principles of reverse combinatorial auction. We consider a region of multiple consumers who are willing to curtail their load in the peak hours in order to gain some incentives later. Using the properties of combinatorial auctions, the participants can bid in packages or combinations in order to maximize their and overall social welfare of the system. The winner determination problem of the proposed combinatorial auction, determined using particle swarm optimization algorithm and hybrid genetic algorithm, is also presented in this paper. The performance evaluation and stability test of the proposed scheme are simulated using MATLAB and presented in this paper. The results indicate that combinatorial auctions are an excellent choice for load shedding management where a maximum of 50 users participate

Resource Management in Large-scale Systems

The focus of this thesis is resource management in large-scale systems. Our primary concerns are energy management and practical principles for self-organization and self-management. The main contributions of our work are: 1. Models. We proposed several models for different aspects of resource management, e.g., energy-aware load balancing and application scaling for the cloud ecosystem, hierarchical architecture model for self-organizing and self-manageable systems and a new cloud delivery model based on auction-driven self-organization approach. 2. Algorithms. We also proposed several different algorithms for the models described above. Algorithms such as coalition formation, combinatorial auctions and clustering algorithm for scale-free organizations of scale-free networks. 3. Evaluation. Eventually we conducted different evaluations for the proposed models and algorithms in order to verify them. All the simulations reported in this thesis had been carried out on different instances and services of Amazon Web Services (AWS). All of these modules will be discussed in detail in the following chapters respectively

Resource management in a containerized cloud : status and challenges

Cloud computing heavily relies on virtualization, as with cloud computing virtual resources are typically leased to the consumer, for example as virtual machines. Efficient management of these virtual resources is of great importance, as it has a direct impact on both the scalability and the operational costs of the cloud environment. Recently, containers are gaining popularity as virtualization technology, due to the minimal overhead compared to traditional virtual machines and the offered portability. Traditional resource management strategies however are typically designed for the allocation and migration of virtual machines, so the question arises how these strategies can be adapted for the management of a containerized cloud. Apart from this, the cloud is also no longer limited to the centrally hosted data center infrastructure. New deployment models have gained maturity, such as fog and mobile edge computing, bringing the cloud closer to the end user. These models could also benefit from container technology, as the newly introduced devices often have limited hardware resources. In this survey, we provide an overview of the current state of the art regarding resource management within the broad sense of cloud computing, complementary to existing surveys in literature. We investigate how research is adapting to the recent evolutions within the cloud, being the adoption of container technology and the introduction of the fog computing conceptual model. Furthermore, we identify several challenges and possible opportunities for future research

A review on job scheduling technique in cloud computing and priority rule based intelligent framework

In recent years, the concept of cloud computing has been gaining traction to provide dynamically increasing access to shared computing resources (software and hardware) via the internet. It’s not secret that cloud computing’s ability to supply mission-critical services has made job scheduling a hot subject in the industry right now. Cloud resources may be wasted, or in-service performance may suffer because of under-utilization or over-utilization, respectively, due to poor scheduling. Various strategies from the literature are examined in this research in order to give procedures for the planning and performance of Job Scheduling techniques (JST) in cloud computing. To begin, we look at and tabulate the existing JST that is linked to cloud and grid computing. The present successes are then thoroughly reviewed, difficulties and flows are recognized, and intelligent solutions are devised to take advantage of the proposed taxonomy. To bridge the gaps between present investigations, this paper also seeks to provide readers with a conceptual framework, where we proposed an effective job scheduling technique in cloud computing. These findings are intended to provide academics and policymakers with information about the advantages of a more efficient cloud computing setup. In cloud computing, fair job scheduling is most important. We proposed a priority-based scheduling technique to ensure fair job scheduling. Finally, the open research questions raised in this article will create a path for the implementation of an effective job scheduling strateg

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

Resource Management In Cloud And Big Data Systems

Cloud computing is a paradigm shift in computing, where services are offered and acquired on demand in a cost-effective way. These services are often virtualized, and they can handle the computing needs of big data analytics. The ever-growing demand for cloud services arises in many areas including healthcare, transportation, energy systems, and manufacturing. However, cloud resources such as computing power, storage, energy, dollars for infrastructure, and dollars for operations, are limited. Effective use of the existing resources raises several fundamental challenges that place the cloud resource management at the heart of the cloud providers\u27 decision-making process. One of these challenges faced by the cloud providers is to provision, allocate, and price the resources such that their profit is maximized and the resources are utilized efficiently. In addition, executing large-scale applications in clouds may require resources from several cloud providers. Another challenge when processing data intensive applications is minimizing their energy costs. Electricity used in US data centers in 2010 accounted for about 2% of total electricity used nationwide. In addition, the energy consumed by the data centers is growing at over 15% annually, and the energy costs make up about 42% of the data centers\u27 operating costs. Therefore, it is critical for the data centers to minimize their energy consumption when offering services to customers. In this Ph.D. dissertation, we address these challenges by designing, developing, and analyzing mechanisms for resource management in cloud computing systems and data centers. The goal is to allocate resources efficiently while optimizing a global performance objective of the system (e.g., maximizing revenue, maximizing social welfare, or minimizing energy). We improve the state-of-the-art in both methodologies and applications. As for methodologies, we introduce novel resource management mechanisms based on mechanism design, approximation algorithms, cooperative game theory, and hedonic games. These mechanisms can be applied in cloud virtual machine (VM) allocation and pricing, cloud federation formation, and energy-efficient computing. In this dissertation, we outline our contributions and possible directions for future research in this field

Cost-effective resource management for distributed computing

Current distributed computing and resource management infrastructures (e.g., Cluster and Grid) suffer from a wide variety of problems related to resource management, which include scalability bottleneck, resource allocation delay, limited quality-of-service (QoS) support, and lack of cost-aware and service level agreement (SLA) mechanisms. This thesis addresses these issues by presenting a cost-effective resource management solution which introduces the possibility of managing geographically distributed resources in resource units that are under the control of a Virtual Authority (VA). A VA is a collection of resources controlled, but not necessarily owned, by a group of users or an authority representing a group of users. It leverages the fact that different resources in disparate locations will have varying usage levels. By creating smaller divisions of resources called VAs, users would be given the opportunity to choose between a variety of cost models, and each VA could rent resources from resource providers when necessary, or could potentially rent out its own resources when underloaded. The resource management is simplified since the user and owner of a resource recognize only the VA because all permissions and charges are associated directly with the VA. The VA is controlled by a ’rental’ policy which is supported by a pool of resources that the system may rent from external resource providers. As far as scheduling is concerned, the VA is independent from competitors and can instead concentrate on managing its own resources. As a result, the VA offers scalable resource management with minimal infrastructure and operating costs. We demonstrate the feasibility of the VA through both a practical implementation of the prototype system and an illustration of its quantitative advantages through the use of extensive simulations. First, the VA concept is demonstrated through a practical implementation of the prototype system. Further, we perform a cost-benefit analysis of current distributed resource infrastructures to demonstrate the potential cost benefit of such a VA system. We then propose a costing model for evaluating the cost effectiveness of the VA approach by using an economic approach that captures revenues generated from applications and expenses incurred from renting resources. Based on our costing methodology, we present rental policies that can potentially offer effective mechanisms for running distributed and parallel applications without a heavy upfront investment and without the cost of maintaining idle resources. By using real workload trace data, we test the effectiveness of our proposed rental approaches. Finally, we propose an extension to the VA framework that promotes long-term negotiations and rentals based on service level agreements or long-term contracts. Based on the extended framework, we present new SLA-aware policies and evaluate them using real workload traces to demonstrate their effectiveness in improving rental decisions

Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019