A Survey of Resource Management Challenges in Multi-cloud Environment: Taxonomy and Empirical Analysis

Cloud computing has seen a great deal of interest by researchers and industrial firms since its first coined. Different perspectives and research problems, such as energy efficiency, security and threats, to name but a few, have been dealt with and addressed from cloud computing perspective. However, cloud computing environment still encounters a major challenge of how to allocate and manage computational resources efficiently. Furthermore, due to the different architectures and cloud computing networks and models used (i.e., federated clouds, VM migrations, cloud brokerage), the complexity of resource management in the cloud has been increased dramatically. Cloud providers and service consumers have the cloud brokers working as the intermediaries between them, and the confusion among the cloud computing parties (consumers, brokers, data centres and service providers) on who is responsible for managing the request of cloud resources is a key issue. In a traditional scenario, upon renting the various cloud resources from the providers, the cloud brokers engage in subletting and managing these resources to the service consumers. However, providers’ usually deal with many brokers, and vice versa, and any dispute of any kind between the providers and the brokers will lead to service unavailability, in which the consumer is the only victim. Therefore, managing cloud resources and services still needs a lot of attention and effort. This paper expresses the survey on the systems of the cloud brokerage resource management issues in multi-cloud environments

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

A Game-Theoretic Based QoS-Aware Capacity Management for Real-Time EdgeIoT Applications

More and more real-time IoT applications such as smart cities or autonomous vehicles require big data analytics with reduced latencies. However, data streams produced from distributed sensing devices may not suffice to be processed traditionally in the remote cloud due to: (i) longer Wide Area Network (WAN) latencies and (ii) limited resources held by a single Cloud. To solve this problem, a novel Software-Defined Network (SDN) based InterCloud architecture is presented for mobile edge computing environments, known as EdgeIoT. An adaptive resource capacity management approach is proposed to employ a policy-based QoS control framework using principles in coalition games with externalities. To optimise resource capacity policy, the proposed QoS management technique solves, adaptively, a lexicographic ordering bi-criteria Coalition Structure Generation (CSG) problem. It is an onerous task to guarantee in a deterministic way that a real-time EdgeIoT application satisfies low latency requirement specified in Service Level Agreements (SLA). CloudSim 4.0 toolkit is used to simulate an SDN-based InterCloud scenario, and the empirical results suggest that the proposed approach can adapt, from an operational perspective, to ensure low latency QoS for real-time EdgeIoT application instances

A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data Center

As cloud computing usage grows, cloud data centers play an increasingly important role. To maximize resource utilization, ensure service quality, and enhance system performance, it is crucial to allocate tasks and manage performance effectively. The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers. The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies, categories, and gaps. A literature review was conducted, which included the analysis of 463 task allocations and 480 performance management papers. The review revealed three task allocation research topics and seven performance management methods. Task allocation research areas are resource allocation, load-Balancing, and scheduling. Performance management includes monitoring and control, power and energy management, resource utilization optimization, quality of service management, fault management, virtual machine management, and network management. The study proposes new techniques to enhance cloud computing work allocation and performance management. Short-comings in each approach can guide future research. The research's findings on cloud data center task allocation and performance management can assist academics, practitioners, and cloud service providers in optimizing their systems for dependability, cost-effectiveness, and scalability. Innovative methodologies can steer future research to fill gaps in the literature

A Game-Theoretic Approach to Strategic Resource Allocation Mechanisms in Edge and Fog Computing

With the rapid growth of Internet of Things (IoT), cloud-centric application management raises questions related to quality of service for real-time applications. Fog and edge computing (FEC) provide a complement to the cloud by filling the gap between cloud and IoT. Resource management on multiple resources from distributed and administrative FEC nodes is a key challenge to ensure the quality of end-user’s experience. To improve resource utilisation and system performance, researchers have been proposed many fair allocation mechanisms for resource management. Dominant Resource Fairness (DRF), a resource allocation policy for multiple resource types, meets most of the required fair allocation characteristics. However, DRF is suitable for centralised resource allocation without considering the effects (or feedbacks) of large-scale distributed environments like multi-controller software defined networking (SDN). Nash bargaining from micro-economic theory or competitive equilibrium equal incomes (CEEI) are well suited to solving dynamic optimisation problems proposing to ‘proportionately’ share resources among distributed participants. Although CEEI’s decentralised policy guarantees load balancing for performance isolation, they are not faultproof for computation offloading. The thesis aims to propose a hybrid and fair allocation mechanism for rejuvenation of decentralised SDN controller deployment. We apply multi-agent reinforcement learning (MARL) with robustness against adversarial controllers to enable efficient priority scheduling for FEC. Motivated by software cybernetics and homeostasis, weighted DRF is generalised by applying the principles of feedback (positive or/and negative network effects) in reverse game theory (GT) to design hybrid scheduling schemes for joint multi-resource and multitask offloading/forwarding in FEC environments. In the first piece of study, monotonic scheduling for joint offloading at the federated edge is addressed by proposing truthful mechanism (algorithmic) to neutralise harmful negative and positive distributive bargain externalities respectively. The IP-DRF scheme is a MARL approach applying partition form game (PFG) to guarantee second-best Pareto optimality viii | P a g e (SBPO) in allocation of multi-resources from deterministic policy in both population and resource non-monotonicity settings. In the second study, we propose DFog-DRF scheme to address truthful fog scheduling with bottleneck fairness in fault-probable wireless hierarchical networks by applying constrained coalition formation (CCF) games to implement MARL. The multi-objective optimisation problem for fog throughput maximisation is solved via a constraint dimensionality reduction methodology using fairness constraints for efficient gateway and low-level controller’s placement. For evaluation, we develop an agent-based framework to implement fair allocation policies in distributed data centre environments. In empirical results, the deterministic policy of IP-DRF scheme provides SBPO and reduces the average execution and turnaround time by 19% and 11.52% as compared to the Nash bargaining or CEEI deterministic policy for 57,445 cloudlets in population non-monotonic settings. The processing cost of tasks shows significant improvement (6.89% and 9.03% for fixed and variable pricing) for the resource non-monotonic setting - using 38,000 cloudlets. The DFog-DRF scheme when benchmarked against asset fair (MIP) policy shows superior performance (less than 1% in time complexity) for up to 30 FEC nodes. Furthermore, empirical results using 210 mobiles and 420 applications prove the efficacy of our hybrid scheduling scheme for hierarchical clustering considering latency and network usage for throughput maximisation.Abubakar Tafawa Balewa University, Bauchi (Tetfund, Nigeria

Service level agreement specification for IoT application workflow activity deployment, configuration and monitoring

PhD ThesisCurrently, we see the use of the Internet of Things (IoT) within various domains such as healthcare, smart homes, smart cars, smart-x applications, and smart cities. The number of applications based on IoT and cloud computing is projected to increase rapidly over the next few years. IoT-based services must meet the guaranteed levels of quality of service (QoS) to match users’ expectations. Ensuring QoS through specifying the QoS constraints using service level agreements (SLAs) is crucial. Also because of the potentially highly complex nature of multi-layered IoT applications, lifecycle management (deployment, dynamic reconfiguration, and monitoring) needs to be automated. To achieve this it is essential to be able to specify SLAs in a machine-readable format. currently available SLA specification languages are unable to accommodate the unique characteristics (interdependency of its multi-layers) of the IoT domain. Therefore, in this research, we propose a grammar for a syntactical structure of an SLA specification for IoT. The grammar is based on a proposed conceptual model that considers the main concepts that can be used to express the requirements for most common hardware and software components of an IoT application on an end-to-end basis. We follow the Goal Question Metric (GQM) approach to evaluate the generality and expressiveness of the proposed grammar by reviewing its concepts and their predefined lists of vocabularies against two use-cases with a number of participants whose research interests are mainly related to IoT. The results of the analysis show that the proposed grammar achieved 91.70% of its generality goal and 93.43% of its expressiveness goal. To enhance the process of specifying SLA terms, We then developed a toolkit for creating SLA specifications for IoT applications. The toolkit is used to simplify the process of capturing the requirements of IoT applications. We demonstrate the effectiveness of the toolkit using a remote health monitoring service (RHMS) use-case as well as applying a user experience measure to evaluate the tool by applying a questionnaire-oriented approach. We discussed the applicability of our tool by including it as a core component of two different applications: 1) a contextaware recommender system for IoT configuration across layers; and 2) a tool for automatically translating an SLA from JSON to a smart contract, deploying it on different peer nodes that represent the contractual parties. The smart contract is able to monitor the created SLA using Blockchain technology. These two applications are utilized within our proposed SLA management framework for IoT. Furthermore, we propose a greedy heuristic algorithm to decentralize workflow activities of an IoT application across Edge and Cloud resources to enhance response time, cost, energy consumption and network usage. We evaluated the efficiency of our proposed approach using iFogSim simulator. The performance analysis shows that the proposed algorithm minimized cost, execution time, networking, and Cloud energy consumption compared to Cloud-only and edge-ward placement approaches