A Pareto-based Genetic Algorithm for Optimized Assignment of VM Requests on a Cloud Brokering Environment

International audienceIn this paper, we deal with cloud brokering for the assignment optimization of VM requests in three-tier cloud infrastructures. We investigate the Pareto-based meta-heuristic approach to take into account multiple client and brokercentric optimization criteria. We propose a new multi-objective Genetic Algorithm ( MOGA-CB ) that can be integrated in a cloud broker. Two objectives are considered in the optimization process: minimizing both the response time and the cost of the selected VM instances to satisfy the clients and to maximize the profit of the broker. The approach has been experimented using realistic data of different types of Amazon EC2 instances and their pricing history. The reported results show that MOGA-CB provides efficiently effective Pareto sets of solutions

A service broker for Intercloud computing

This thesis aims at assisting users in finding the most suitable Cloud resources taking into account their functional and non-functional SLA requirements. A key feature of the work is a Cloud service broker acting as mediator between consumers and Clouds. The research involves the implementation and evaluation of two SLA-aware match-making algorithms by use of a simulation environment. The work investigates also the optimal deployment of Multi-Cloud workflows on Intercloud environments

A Survey on Meta-Heuristic Scheduling Optimization Techniques in Cloud Computing Environment

As cloud computing is turning out to be evident that the eventual fate of the cloud industry relies on interconnected cloud systems where the resources are probably going to be provided by various cloud service suppliers. Clouds are also seen as being multifaceted; if the user requires only computing capacity and wishes to personalize it as per his requirements, the infrastructure cloud suppliers are able to provide this convenience as virtual machines.Many optimized meta-heuristic scheduling techniques are introduced for scheduling of bag-of-tasks applications in heterogeneous framework of clouds.The overall analysis demonstrates that, utilizing different meta-heuristic techniques can offer noteworthy benefits in the terms of speed and performance

A Research Perspective on Data Management Techniques for Federated Cloud Environment

Cloud computing has given a large scope of improvement in processing, storage and retrieval of data that is generated in huge amount from devices and users. Heterogenous devices and users generates the multidisciplinary data that needs to take care for easy and efficient storage and fast retrieval by maintaining quality and service level agreements. By just storing the data in cloud will not full fill the user requirements, the data management techniques has to be applied so that data adaptiveness and proactiveness characteristics are upheld. To manage the effectiveness of entire eco system a middleware must be there in between users and cloud service providers. Middleware has set of events and trigger based policies that will act on generated data to intermediate users and cloud service providers. For cloud service providers to deliver an efficient utilization of resources is one of the major issues and has scope of improvement in the federation of cloud service providers to fulfill user’s dynamic demands. Along with providing adaptiveness of data management in the middleware layer is challenging. In this paper, the policies of middleware for adaptive data management have been reviewed extensively. The main objectives of middleware are also discussed to accomplish high throughput of cloud service providers by means of federation and qualitative data management by means of adaptiveness and proactiveness. The cloud federation techniques have been studied thoroughly along with the pros and cons of it. Also, the strategies to do management of data has been exponentially explored

IoTにおけるリソースの最適化

Recently, there are more than 9 billion things that connected in the Internet of Things (IoT) and the number is exceed more than 24 billion in 2020. It means that numerous data will be generated because of increasing quickly of the number of things. An infrastructure should be developed to manage the connected things in IoT. Moreover, cloud computing will play important role in terms of data storage and analysis for IoT. Therefore, a cloud broker is considered as an intermediary in the infrastructure for managing the connected things. The cloud broker will find the best deal between clients and service providers. However, there are three problems among cloud broker, clients and service providers that are the response time of the request from clients, the energy consumption of the system and the profit of the cloud broker. The three problems are considered as multi-objective optimization problem to maximize the profit of the broker while minimizing the response time of the request and the energy consumption. A multi-objective particle swarm optimization (MOPSO) is proposed to solve the problem. MOPSO is compared with a non-dominated sorting genetic algorithm-II (NSGA-II) and a random search algorithm to show the performance. Since, there are a lot of data including social media and geographic location, generated in IoT. Coupling social media with geographic location has boosted the worth of understanding the real-world situations. Event detection aims to find more specific topic which represents real-world event. However,identification of unusual and seemingly inconsistent patterns in data, called outliers, is necessary. The problem is how to partition a spatio-temporal domain to find a meaningful local outlier pattern. A k-dimensional (KD) tree partitioning is applied to divide a spatio-temporal domain into sub-cells. The optimal partitioning problem in a spatio-temporal domain has been proven as an NP-complete problem. Therefore, a genetic algorithm is proposed to solve the problem. Moreover, the smart grid is strongly related to IoT technologies. It is enabled by IoT to handle big data and reduce the number of communication protocols. The micro-grid is studied because of micro-grids are part of a larger system that makes the smart grid to become reality. The operation management problem and pollutant emission problem are important problems for the micro-grid system. Thus, reducing the total energy expenses and pollutant emission of micro-grid and improving the renewable energy sources (battery energy storage) are considered together with the operation management of the micro-grid system. A fitness-based modified game particle swarm optimization (FMGPSO) algorithm is proposed to minimize the total costs of operation and pollutant emissions in the microgrid and multi-microgrid system. FMGPSO is compared with A non-dominated sorting genetic algorithm-III (NSGA-III), a multi-objective covariance matrix adaptation evolution strategy (MO-CMAES), and a speed-constrained multi-objective particle swarm optimization (SMPSO) to show the performance.最近では、Internet of Things（IoT）に接続されているものは90億件を超え、2020年には240億件を超えている。それは、物事の数が急速に増えるため、多くのデータが生成されることを意味する。IoTで接続されたものを管理するためのインフラストラクチャを開発する必要がある。さらに、クラウドコンピューティングは、IoTのデータストレージと分析の観点から重要な役割を果たしている。したがって、クラウドブローカーは、接続されたものを管理するためのインフラストラクチャの仲介者とみなされる。クラウドブローカーは、クライアントとサービスプロバイダーの間で最良の取引を見つけるだろう。しかし、クラウドブローカー、クライアントおよびサービスプロバイダーには、クライアントからの要求の応答時間、システムのエネルギー消費、クラウドブローカーのプロセスという3つの問題がある。この3つの問題は、要求の応答時間とエネルギー消費を最小限に抑えながら、ブローカーのプロビジョニングを最大化するための多目的最適化問題とみなされる。この問題を解決するために、多目的粒子群最適化（MOPSO）が提案されている。 MOPSOは、非優性選別遺伝的アルゴリズム-II（NSGA-II）およびランダム探索アルゴリズムと比較され、性能が示される。ソーシャルメディアや地理的な場所など、IoTで生成される多くのデータがあるためである。地理的な場所とソーシャルメディアを結び付けることで、現実の状況を理解する価値が高まっている。イベント検出は、実際のイベントを表すより特定のトピックを見つけることを目指している。しかし、異常値と呼ばれる異常なパターンや一見不整合なパターンの同定が必要である。問題は、時空間ドメインを分割して意味のある局所的な奇妙なパターンを見つける方法である。時空間領域をサブセルに分割するために、k次元（KD）ツリー分割が適用される。時空間領域における最適な分割問題は、NP完全な問題として証明されている。したがって、この問題を解決するための遺伝的アルゴリズムが提案されている。さらに、スマートグリッドはIoT技術と強く関連している。 IoTによって大きなデータを処理し、通信プロトコルの数を減らすことができる。マイクログリッドはスマートグリッドを現実化させるより大きなシステムの一部であるため、マイクログリッドが研究されている。運用管理上の問題や汚染物質排出問題は、マイクログリッドシステムにとって重要な問題である。したがって、マイクログリッドシステムの運用管理とともに、マイクログリッドの総エネルギー費用と汚染物質排出量の削減と再生可能エネルギー源の改善（バッテリエネルギー貯蔵）が考慮されている。マイクログリッドおよびマルチマイクログリッドシステムにおける操作および汚染物質排出の総コストを最小限に抑えるため、MGPSO アルゴリズムが提案されている。 FMGPSOは、非優先ソート遺伝的アルゴリズム-III（NSGA-III）、多目的共分散行列適応進化戦略（MO-CMAES）、および性能を示すために速度が制約された多目的粒子群最適化（SMPSO）と比較される。室蘭工業大学 (Muroran Institute of Technology)博士（工学

Multiobjective Optimization in Cloud Brokering Systems for Connected Internet of Things

Currently, over nine billion things are connected in the Internet of Things (IoT). This number is expected to exceed 20 billion in the near future, and the number of things is quickly increasing, indicating that numerous data will be generated. It is necessary to build an infrastructure to manage the connected things. Cloud computing (CC) has become important in terms of analysis and data storage for IoT. In this paper, we consider a cloud broker, which is an intermediary in the infrastructure that manages the connected things in CC. We study an optimization problem for maximizing the profit of the broker while minimizing the response time of the request and the energy consumption. A multiobjective particle swarm optimization (MOPSO) is proposed to solve the problem. The performance of the proposed MOPSO is compared with that of a genetic algorithm and a random search algorithm. The results show that the MOPSO outperforms a well-known genetic algorithm for multiobjective optimization