Analysis of Security Based Accessibility Control Models for Cloud Computing

Access control has become the most necessary requirement to limit unauthorized and privileged access to information systems in cloud computing. Access control models counter the additional security challenges like rules, domain names, job allocation, multi hosting and separation of tasks. This paper classifies the conventional and modern access control models which has been utilized to restrain these access flaws by employing a variety of practices and methodologies. It examine the frequent security threats to information confidentiality, integrity, data accessibility and their approach used for cloud solutions. This paper proposed a priority based task scheduling access control (PbTAC) model to secure and scheduled access of resources & services rendered to cloud user. PbTAC model will ensure the job allocation, tasks scheduling and security of information through its rule policies during transmission between parties. It also help in reducing system overhead by minimize the computation and less storage cost.

Energy-Efficient Load Balancing Algorithm for Workflow Scheduling in Cloud Data Centers Using Queuing and Thresholds

Cloud computing is a rapidly growing technology that has been implemented in various fields in recent years, such as business, research, industry, and computing. Cloud computing provides different services over the internet, thus eliminating the need for personalized hardware and other resources. Cloud computing environments face some challenges in terms of resource utilization, energy efficiency, heterogeneous resources, etc. Tasks scheduling and virtual machines (VMs) are used as consolidation techniques in order to tackle these issues. Tasks scheduling has been extensively studied in the literature. The problem has been studied with different parameters and objectives. In this article, we address the problem of energy consumption and efficient resource utilization in virtualized cloud data centers. The proposed algorithm is based on task classification and thresholds for efficient scheduling and better resource utilization. In the first phase, workflow tasks are pre-processed to avoid bottlenecks by placing tasks with more dependencies and long execution times in separate queues. In the next step, tasks are classified based on the intensities of the required resources. Finally, Particle Swarm Optimization (PSO) is used to select the best schedules. Experiments were performed to validate the proposed technique. Comparative results obtained on benchmark datasets are presented. The results show the effectiveness of the proposed algorithm over that of the other algorithms to which it was compared in terms of energy consumption, makespan, and load balancing.

Cable tree wiring - benchmarking solvers on a real-world scheduling problem with a variety of precedence constraints

Cable trees are used in industrial products to transmit energy and information between different product parts. To this date, they are mostly assembled by humans and only few automated manufacturing solutions exist using complex robotic machines. For these machines, the wiring plan has to be translated into a wiring sequence of cable plugging operations to be followed by the machine. In this paper, we study and formalize the problem of deriving the optimal wiring sequence for a given layout of a cable tree. We summarize our investigations to model this cable tree wiring problem (CTW). as a traveling salesman problem with atomic, soft atomic, and disjunctive precedence constraints as well as tour-dependent edge costs such that it can be solved by state-of-the-art constraint programming (CP), Optimization Modulo Theories (OMT), and mixed-integer programming (MIP). solvers. It is further shown, how the CTW problem can be viewed as a soft version of the coupled tasks scheduling problem. We discuss various modeling variants for the problem, prove its NP-hardness, and empirically compare CP, OMT, and MIP solvers on a benchmark set of 278 instances. The complete benchmark set with all models and instance data is available on github and was included in the MiniZinc challenge 2020.