Green Data Centers: A Survey, Perspectives, and Future Directions <s> E. Future Research Directions <s> As much of the world's computing continues to move into the cloud, the overprovisioning of computing resources to ensure the performance isolation of latency-sensitive tasks, such as web search, in modern datacenters is a major contributor to low machine utilization. Being unable to accurately predict performance degradation due to contention for shared resources on multicore systems has led to the heavy handed approach of simply disallowing the co-location of high-priority, latency-sensitive tasks with other tasks. Performing this precise prediction has been a challenging and unsolved problem. In this paper, we present Bubble-Up, a characterization methodology that enables the accurate prediction of the performance degradation that results from contention for shared resources in the memory subsystem. By using a bubble to apply a tunable amount of "pressure" to the memory subsystem on processors in production datacenters, our methodology can predict the performance interference between co-locate applications with an accuracy within 1% to 2% of the actual performance degradation. Using this methodology to arrive at "sensible" co-locations in Google's production datacenters with real-world large-scale applications, we can improve the utilization of a 500-machine cluster by 50% to 90% while guaranteeing a high quality of service of latency-sensitive applications. <s> BIB001 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> E. Future Research Directions <s> Enterprises today face several challenges when hosting line-of-business applications in the cloud. Central to many of these challenges is the limited support for control over cloud network functions, such as, the ability to ensure security, performance guarantees or isolation, and to flexibly interpose middleboxes in application deployments. In this paper, we present the design and implementation of a novel cloud networking system called CloudNaaS. Customers can leverage CloudNaaS to deploy applications augmented with a rich and extensible set of network functions such as virtual network isolation, custom addressing, service differentiation, and flexible interposition of various middleboxes. CloudNaaS primitives are directly implemented within the cloud infrastructure itself using high-speed programmable network elements, making CloudNaaS highly efficient. We evaluate an OpenFlow-based prototype of CloudNaaS and find that it can be used to instantiate a variety of network functions in the cloud, and that its performance is robust even in the face of large numbers of provisioned services and link/device failures. <s> BIB002 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> E. Future Research Directions <s> Ensuring the quality of service (QoS) for latency-sensitive applications while allowing co-locations of multiple applications on servers is critical for improving server utilization and reducing cost in modern warehouse-scale computers (WSCs). Recent work relies on static profiling to precisely predict the QoS degradation that results from performance interference among co-running applications to increase the number of "safe" co-locations. However, these static profiling techniques have several critical limitations: 1) a priori knowledge of all workloads is required for profiling, 2) it is difficult for the prediction to capture or adapt to phase or load changes of applications, and 3) the prediction technique is limited to only two co-running applications. To address all of these limitations, we present Bubble-Flux, an integrated dynamic interference measurement and online QoS management mechanism to provide accurate QoS control and maximize server utilization. Bubble-Flux uses a Dynamic Bubble to probe servers in real time to measure the instantaneous pressure on the shared hardware resources and precisely predict how the QoS of a latency-sensitive job will be affected by potential co-runners. Once "safe" batch jobs are selected and mapped to a server, Bubble-Flux uses an Online Flux Engine to continuously monitor the QoS of the latency-sensitive application and control the execution of batch jobs to adapt to dynamic input, phase, and load changes to deliver satisfactory QoS. Batch applications remain in a state of flux throughout execution. Our results show that the utilization improvement achieved by Bubble-Flux is up to 2.2x better than the prior static approach. <s> BIB003 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> E. Future Research Directions <s> Energy expenditure has become a significant fraction of data center operating costs. Recently, "geographical load balancing" has been proposed to reduce energy cost by exploiting the electricity price differences across regions. However, this reduction of cost can paradoxically increase total energy use. We explore whether the geographical diversity of Internet-scale systems can also provide environmental gains. Specifically, we explore whether geographical load balancing can encourage use of "green" renewable energy and reduce use of "brown" fossil fuel energy. We make two contributions. First, we derive three distributed algorithms for achieving optimal geographical load balancing. Second, we show that if the price of electricity is proportional to the instantaneous fraction of the total energy that is brown, then geographical load balancing significantly reduces brown energy use. However, the benefits depend strongly on dynamic energy pricing and the form of pricing used. <s> BIB004
To reduce the energy consumption of data centers through efficient utilization of resources, many proposals have been put forward for the green implementation data centers. Because of their exponential growth and underutilization, data centers still pose several challenges, and several inadequately addressed research directions remain with regard to resource management: • A major reason why the resource utilization of data centers remains low is that operators are concerned about potential QoS violations. Recently, several studies have suggested the existence of interference between collocated applications BIB001 , BIB003 when servers are virtualized and shared to improve utilization. The challenge of virtual machine management and data center right-sizing is to improve resource utilization while preserving QoS guarantees, which benefits both data center operators and users. • In a multi-tenant data center environment, different tenants may desire different levels of application performance. This characteristic requires heterogeneous resource management mechanisms, which will introduce additional complexity and overhead. Moreover, tenants possess their own resources, which may not be controlled by the data center operators in a centralized manner. Thus far, few studies have focused on energy-aware resource management in multi-tenant data centers, and the task of providing efficient polices for such scenarios requires future exploration. • Although traffic consolidation technologies offer considerable energy savings, they can also have severe effects on network performance. The virtualization of network functions is a promising approach to providing efficient support for the deployment and management of network services. However, current implementations such as CloudNaaS BIB002 do not account for energy consumption. Finding a good balance between energy consumption and network performance remains a challenging problem for current data center networks. • Most existing studies concerning power distribution are focused on intra-data-center networking. However, geo-graphically distributed data centers can also offer opportunities for power distribution. For example, a workload can be preferentially scheduled to data centers with high power capping or a greater amount of stored energy by virtue of being located nearer to power stations. The question of how to reduce energy consumption by combining power distribution, application performance and workload scheduling remains an open research problem. • A demand-response mechanism is an energy-efficient method of overcoming the instability that is characteristic of renewable energy BIB004 . In SaaS data centers, such a mechanism also causes service providers to lose users in response to supply conditions. A promising solution is to design beneficial pricing schemes to incentivize service providers to reduce brown energy consumption.