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

Improving shared access to Cloud of Things resources.

Cloud of Things (CoT) is an emerging paradigm that integrates Cloud Computing and Internet of Things (IoT) to support a wide range of real-world applications. Resource allocation plays a vital role in CoT, especially when allocating IoT physical resources to Cloud-based applications to ensure seamless application execution. Due to the heterogeneity and the constrained capacities of IoT resources, resource allocation is a challenge. This complexity leads to missing/limiting shared access to the IoT physical resources and consequently lessen the reusability of the resources across multiple applications. This issue results in, 1) replicating IoT deployments making them expensive and not feasible for many prospective users, 2) existing IoT infrastructures are over-provisioned to meet the unpredictable application requirements in which resources may be significantly underutilised, and 3) the adoption of CoT is slowed. Improving shared access to CoT resources can provide efficient resource allocation, improve resource utilisation and likely to reduce the cost of IoT deployments. Existing solutions include small-scale, hardware and platform-dependent mechanisms to enable or improve shared access to IoT resources. The research presented in this thesis considers trading CoT resources in a marketplace as an approach to improve shared access to CoT resources. It proposes a solution to Cot resource allocation that re-imagines CoT resources as commodities that can be provided and consumed by the marketplace participants. The novel contributions of the research presented in this thesis are summarised as follows: 1) a model to describe and quantify the value of CoT resources, 2) a resource sharing and allocation strategy called Exclusive Shared Access (ESA) to CoT resources, 3) a QoS-aware optimisation model for trading CoT resources as a single and multipleobjective optimisation problem, and 4) a marketplace architecture and experimental evaluation to verify its performance and scalability

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

AMACoT: a marketplace architecture for trading Cloud of Things resources

Cloud of Things (CoT) is increasingly viewed as a paradigm that can satisfy the diverse requirements of emerging IoT applications. The potential of CoT is not yet realised due to challenges in sharing and reusing IoT physical resources across multiple applications. Existing approaches provide small-scale and hardware-dependent shared access to IoT resources. This paper considers using market mechanisms to commoditise CoT resources as the approach to enable shared access to CoT resources and to improve their reusability. In order to achieve this, the requirements for trading CoT resources are discussed to conceptualise the proposed approach. A generic description model for CoT resource is introduced to quantify the value of CoT resources. In this paper, a marketplace architecture for trading CoT resources referred to as AMACoT is proposed. By formulating the trading of CoT resources as an optimisation problem, the proposed approach is experimentally validated. The evaluation measures the system performance and verifies the optimisation problem using three evolutionary algorithms. The evaluation of the optimisation algorithms demonstrates the optimality of trading CoT resources solutions in terms of resource cost, resource utilisation, provider lock-in and provider profit

A multiobjective QoS model for trading Cloud of Things resources

The emerging Cloud of Things (CoT) paradigm promises to meet the diverse requirements of many real-world applications, which previously could not be fulfilled by either Cloud Computing or Internet of Things (IoT). Trading CoT resources is a challenging aspect particularly when managing Quality of Service (QoS) as resource providers and application developers have different priorities. This paper focuses on the challenge of supporting QoS when trading CoT resources and performing resource allocation. The contributions of this paper are 1) the problem of managing QoS while trading CoT resources is investigated as an optimisation problem 2) a QoS model is proposed to solve the problem by optimising five different QoS objectives 3) evaluation which confirms the feasibility of the proposed model in optimising diverse QoS requirements

Enabling exclusive shared access to Cloud of Things resources

Cloud of Things (CoT) is an emerging paradigm that integrates Cloud Computing and Internet of Things (IoT). CoT is constrained by the limited computing capabilities of IoT resources and the costly investment required to deploy IoT infrastructure. Despite the support of existing CoT implementations to various applications, IoT physical resources are still computationally limited and cannot to be shared as other Cloud resources yet. This paper proposes a new approach to improve shared access to IoT resources. The new approach relies on optimising resource trading of IoT resources to enable exclusive access to allocated resources at a given time. A generic architecture is proposed to support the proposed approach along with notations required to commoditise IoT resources. A case study of multiple application uses is presented. Simulations are carried out to evaluate the feasibility of the approach using three optimisation techniques. The evaluation of the proposed approach includes optimising the cost of resource allocation, different QoS metrics and the coverage of IoT resources

An evaluation of optimisation approaches in Cloud of Things resource trading

Cloud Computing and Internet of Things (IoT) have evolved to meet the requirements of many real-world applications. Many of these requirements cannot be fulfilled by using either technology separately. In order to fulfil such diverse requirements, the integration of Cloud Computing and IoT is emerging as a new paradigm called Cloud of Things (CoT). CoT is expected to host heterogeneous resources and fulfil complex requirements of resource providers and consumers. This complexity poses a real challenge for resource allocation in CoT. To tackle this challenge, resource allocation is described as a trading optimisation problem and utility functions are used to rank candidate resource allocation assignments. The contributions of this paper are 1) introducing vocabularies needed for trading CoT resources 2) proposing a marketplace system architecture to enable efficient trading of CoT resources 3) examining the use of different utility functions to rank candidate resource assignments 4) performing simulations to validate the effectiveness of the proposed approach using three optimisation algorithms

Optimising operation management for multi-micro-grids control

Nowadays, renewable energy sources in a micro-grid (MG) system have increased challenges in terms of the irregularly and fluctuation of the photovoltaic and wind turbine units. It is necessary to develop battery energy storage. The MG central controller is helping to develop it in the MG system for improving the time of availability. Thus, reducing the total energy expenses of MG and improving the renewable energy sources (battery energy storage) are considered together with the operation management of the MG system. This study proposes fitness-based modified game particle swarm optimisation (FMGPSO) algorithm to optimise the total costs of operation and pollutant emissions in the MG and multi-MG system. The optimal size of battery energy storage is also considered. A non-dominated sorting genetic algorithm-III, a multi-objective covariance matrix adaptation evolution strategy, and a speed-constrained multi-objective particle swarm optimisation are compared with the proposed FMGPSO to show the performance. The results of the simulation show that the FMGPSO outperforms both the comparison algorithms for the minimisation operation management problem of the MG and the multi-MG system

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