With rapid availability of renewable energy sources and growing interest in their use in the datacenter industry presents opportunities for service providers to reduce their energy related costs, as well as, minimize the ecological impact of their infrastructure. However, renewables are largely intermittent and can, negatively affect users’ applications and their performance, therefore, the profit of the service providers. Furthermore, services could be offered from those geographical locations where electricity is relatively cheaper than other locations; which may degrade the applications’ performance and potentially increase users’ costs. To ensure larger providers’ profits and lower users’ costs, certain non-interactive workloads could be either: moved and executed in geographical locations offering the lowest energy prices; or could be queued and delayed to execute later (in day or night time) when renewables, such as solar and wind energies, are at peak. However, these may have negative impacts on the energy consumption, workloads performance, and users’ costs. Therefore, to ensure energy, performance and cost efficiencies, appropriate workload scheduling, placement, migration, and resource management techniques are required to mange the infrastructure resources, workloads, and energy sources. In this paper, we propose a workload placement and three different migration policies that maximize the providers’ revenues, ensure the workload performance, reduce energy consumption, along with reducing ecological impacts and users’ costs. Using real workload traces and electricity prices for several geographical locations and distributed, heterogeneous, datacenters, our experimental evaluation suggest that the proposed approaches could save significant amount of energy ($\sim$15.26%), reduces service monetary costs ($\sim$0.53% - $\sim$19.66%), improves ($\sim$1.58%) or, at least, maintains the expected level of applications’ performance, and increases providers’ revenue along with environmental sustainability, against the well-known first fit (FF), best fit (BF) heuristic algorithms, and other closest rivals.

随着可再生能源的快速可用性以及对它们在数据中心行业中的使用的日益增长的兴趣，为服务提供商提供了减少其与能源相关的成本以及将其基础架构的生态影响降至最低的机会。但是，可再生能源在很大程度上是断断续续的，并且可能会对用户的应用及其性能产生负面影响，从而影响服务提供商的利润。此外，可以从电费比其他地方便宜的那些地理位置提供服务；这可能会降低应用程序的性能，并可能增加用户的成本。为了确保更大的供应商利润和更低的用户成本，某些非交互式工作负载可以：在提供最低能源价格的地理位置移动和执行；或者当太阳能和风能等可再生能源达到峰值时，可以排队等待并推迟执行（晚间或白天）。但是，这些可能会对能耗，工作负载性能和用户成本产生负面影响。因此，为了确保能源，性能和成本效率，需要适当的工作负荷调度，放置，迁移和资源管理技术来管理基础结构资源，工作负荷和能源。在本文中，我们提出了工作负载布置和三种不同的迁移策略，这些策略可以最大程度地提高提供商的收入，确保工作负载性能，降低能耗以及减少生态影响和用户成本。使用几个地理位置和分布式异构数据中心的实际工作量跟踪和电价，$〜$15.26％），降低服务货币成本（$〜$0.53％- $〜$19.66％），改善了（$〜$1.58％），或者至少与著名的首次拟合（FF），最佳拟合（BF）启发式算法以及其他最接近的竞争对手相比，可以维持应用程序性能的预期水平，并增加提供商的收入以及环境的可持续性。