Negotiation-based resource provisioning and task scheduling algorithm for cloud systems

Cloud computing has drawn significant attention from both academia and industry as an emerging computing paradigm where data, applications, or processing power are provided as services through the Internet. Cloud computing extends the existing computing infrastructure owned by the cloud service providers (CSPs) to achieve the economies of scale through virtualization and aggregated computing resources. End users, on the other hand, can reach these services through an elastic utility computing environment with minimal upfront investment. Nevertheless, pervasive use of cloud computing and the resulting rise in the number of data centers have brought forth concerns about energy consumption and carbon emission. Therefore, this paper addresses the problem of resource provisioning and task scheduling on a cloud platform under given service level agreements, in order to minimize the electric bills and maximize the profitability for the CSP. User task graphs and dependencies are randomly generated, whereas user requests for CPU and memory resources are extracted from the Google cluster trace. A general type of dynamic pricing scenario is assumed where the energy price is both time-of-use and total power consumption-dependent. A negotiation-based iterative approach has been proposed for the resource provisioning and task scheduling that is inspired by a routing algorithm. More specifically, in each iteration, decisions made in the previous iteration are ripped-up and re-decided, while a congestion model is introduced to dynamically adjust the resource provisioning decisions and the schedule of each task based on the historical results as well as the current state of affairs. Experimental results demonstrate that the proposed algorithm achieves up to 63% improvement in the total electrical energy bill of an exemplary data center compared to the baseline.