Biblio

The rapid growth of power Internet of Things devices has led to traditional data security sharing mechanisms that are no longer suitable for attribute and permission management of massive devices. In response to this problem, this article proposes a blockchain-based data security sharing mechanism for the power Internet of Things, which reduces the risk of data leakage through decentralization in the architecture and promotes the integration of multiple information and methods.

Resource scheduling in a computing system addresses the problem of packing tasks with multi-dimensional resource requirements and non-functional constraints. The exhibited heterogeneity of workload and server characteristics in Cloud-scale or Internet-scale systems is adding further complexity and new challenges to the problem. Compared with,,,, existing solutions based on ad-hoc heuristics, Machine Learning (ML) has the potential to improve further the efficiency of resource management in large-scale systems. In this paper we,,,, will describe and discuss how ML could be used to understand automatically both workloads and environments, and to help to cope with scheduling-related challenges such as consolidating co-located workloads, handling resource requests, guaranteeing application's QoSs, and mitigating tailed stragglers. We will introduce a generalized ML-based solution to large-scale resource scheduling and demonstrate its effectiveness through a case study that deals with performance-centric node classification and straggler mitigation. We believe that an MLbased method will help to achieve architectural optimization and efficiency improvement.

In the multi-cloud tenancy environments, Web Service offers an standard approach for discovering and using capabilities in an environment that transcends ownership domains. This brings into concern the ownership and security related to Web Service governance. Our approach for this issue involves an ESB-integrated middleware for security criteria regulation on Clouds. It uses an attribute-based security policy model for the exhibition of assets consumers' security profiles and deducing service accessing decision. Assets represent computing power/functionality and information/data provided by entities. Experiments show the middleware to bring minor governance burdens on the hardware aspect, as well as better performance with colosum scaling property, dealing well with cumbersome policy files, which is probably the situation of complex composite service scenarios.