Guest Editors' Introduction: Special Issue on Green and Energy Efficient Cloud Computing Part II
CLOUD Computinghas had a huge commercial impact and has attracted the interest of the research community. Public clouds allow their customers to outsource the management of physical resources, and rent a variable amount of resources in accordance to their specific needs. Private clouds allow companies to manage on premises resources, exploiting the capabilities offered by the cloud technologies, such as using virtualization to improve resource utilization and cloud software for resource management automation. Hybrid clouds, where private infrastructures are integrated and complemented by external resources, are becoming a common scenario aswell, for example tomanage load peaks. Cloud applications are hosted by data centers whose size ranges from tens to tens of thousands of servers, which raises significant challenges related to energy and cost management. It has been estimated that the Information and Communication Technology (ICT) industry alone is responsible for 2 3 percent of the global greenhouse gas emissions. Therefore, we must find innovative methods and tools to manage the energy efficiency and carbon footprint of data centers, so that they can operate and scale in a cost effective and environmentally sustainable manner. These methods and tools are often categorized as Data Center Infrastructure Management (DCIM) to monitor, control, and optimize data centers with extensive automation. DCIM must also effectively manage the quality of service provided by the data center, since cloud customers require high reliability, availability, usability, and low response times. While significant advancements have been made to increase the physical efficiency of power supplies and cooling components that improve the Power Usage Effectiveness (PUE) index, such improvements are often circumscribed to the huge data centers run by large cloud companies. Even stronger effort is needed to improve the data center computational efficiency, as servers are today highly underutilized, with typical operating range between 10 and 30 percent. In this respect, advancements are needed both to improve the energy efficiency of servers and to dynamically consolidate the workload on fewer, and better utilized, servers. This special issue has offered the scientific and industrial communities a forum to present new research, development, and deployment efforts in the field of green and energyefficient Cloud Computing. Indeed, the special issue attracted a large number of good quality papers. After two or, in some cases, three rounds of reviews each involving at least three expert reviewers 18 papers have been selected for publication among the 44 initially submitted. The accepted papers have been split into two issues of this journal. The first issue, published as Vol 4, No. 2, included nine papers that focus on the opportunities offered by the modern virtualization technology for reducing energy consumption and carbon emissions, through techniques and methods that aim to achieve optimal allocation and scheduling of virtual machines (VMs) both in single platforms and in geographically distributed scenarios involving multiple data centers. This issue includes nine papers that are more specifically devoted to the energyefficient management of the physical infrastructure of data centers and cloud facilities, renewable energy, and networking and storage systems. The first two papers of this issue explore the opportunities that network virtualization offers for performance improvement and energy efficiency in cloud data centers. In [1] Ghazisaeedi andHuang illustrate how network virtualization technology helps consolidate and optimize the infrastructure by allowing the coexistence of multiple virtual networks over a single physical network. The work presents a methodology and a set of algorithms that are able to reduce the power consumption in physical links during off peak time. Specifically, the authors combine coarse grained/centralized optimization, where the granularity is at the virtual link level, with fine grained/local optimization, where the granularity is the traffic capacity that must be allocated to a virtual link. They formulate the problem as an Integer Linear Program (ILP) and devise a novel heuristic algorithm to increase scalability in large network sizes. Yang and colleagues address virtual data center embedding in a virtual network environment [2] The problem R. Bianchini is with Microsoft Research, Redmond, WA 98052. S.U. Khan is with the Department of Electrical and Computer Engineering, North Dakota State University, Fargo, ND 58108 6050. E mail: samee.khan@ndsu.edu. C. Mastroianni is with the Institute for High Performance Computing and Networking, ICAR CNR, via P Bucci 41C, Rende (CS) 87036, Italy. E mail: mastroianni@icar.cnr.it.
IEEE Trans. Cloud Comput.