Towards payment-bound analysis in cloud systems with task-prediction errors

Conference Theme: Change we are leadingIn modern cloud systems, how to optimize user service level based on virtual resources customized on demand is a critical issue. In this paper, we comprehensively analyze the payment bound under a cloud model with virtual machines (VMs), by taking into account that task’s workload may be predicted with errors. The analysis is based on an optimized resource allocation algorithm with polynomial time complexity. We theoretically derive the upper bound of task payment based on a particular margin of workload prediction-error. We also extend the payment-minimization algorithm to adapt to the dynamic changes of host availability over time, and perform the evaluation by a real-cluster environment with 56 VMs deployed. Experiments confirm the correctness of our theoretical inference, and show that our payment-minimization solution can keep 95% of user payments below 1.15 times as large as the theoretical values of the ideal payment with hypothetically accurate information. The ratio for the rest user payments can be limited to about 1.5 at the worst case.postprin

Resource Management In Cloud And Big Data Systems

Cloud computing is a paradigm shift in computing, where services are offered and acquired on demand in a cost-effective way. These services are often virtualized, and they can handle the computing needs of big data analytics. The ever-growing demand for cloud services arises in many areas including healthcare, transportation, energy systems, and manufacturing. However, cloud resources such as computing power, storage, energy, dollars for infrastructure, and dollars for operations, are limited. Effective use of the existing resources raises several fundamental challenges that place the cloud resource management at the heart of the cloud providers\u27 decision-making process. One of these challenges faced by the cloud providers is to provision, allocate, and price the resources such that their profit is maximized and the resources are utilized efficiently. In addition, executing large-scale applications in clouds may require resources from several cloud providers. Another challenge when processing data intensive applications is minimizing their energy costs. Electricity used in US data centers in 2010 accounted for about 2% of total electricity used nationwide. In addition, the energy consumed by the data centers is growing at over 15% annually, and the energy costs make up about 42% of the data centers\u27 operating costs. Therefore, it is critical for the data centers to minimize their energy consumption when offering services to customers. In this Ph.D. dissertation, we address these challenges by designing, developing, and analyzing mechanisms for resource management in cloud computing systems and data centers. The goal is to allocate resources efficiently while optimizing a global performance objective of the system (e.g., maximizing revenue, maximizing social welfare, or minimizing energy). We improve the state-of-the-art in both methodologies and applications. As for methodologies, we introduce novel resource management mechanisms based on mechanism design, approximation algorithms, cooperative game theory, and hedonic games. These mechanisms can be applied in cloud virtual machine (VM) allocation and pricing, cloud federation formation, and energy-efficient computing. In this dissertation, we outline our contributions and possible directions for future research in this field

Design of a low carbon economy model by carbon cycle optimization in supply chain

IntroductionConcerning economic globalization, enterprises must work with the cooperative partner to obtain more profits and overall planning of the supply chain has become a new focus for enterprise development. This paper studies the joint emission reduction of the supply chain in green low-carbon economy development and achieve joint emission and economic cost reduction through the optimization of carbon emission and economic dispatch.MethodsThe paper firstly uses the multi-agent model to complete the fullcycle modeling of carbon emission and economic cost; Secondly, the simulated annealing-adaptive chaos-particle swarm optimization (SAACPSO) method is used to optimize various parameters in the model to achieve emission and cost reductionResultsThe results show that after the optimization, the economic cost is reduced by 0.07 and the carbon emission is also reduced by 0.16; Finally, the practical test of the model is conducted with the collected data from the local company. The results show that the multi-objective optimization model of a joint enterprise supply chain is significantly better than single optimization in terms of emission reduction.DiscussionIt provides new ideas for a green economy and technical support for the global planning of supply chain integration