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Apache Cassandra is an highly scalable and available NoSql datastore, largely used by enterprises of each size and for application areas that range from entertainment to big data analytics. Managed Cassandra service providers are emerging to hide the complexity of the installation, fine tuning and operation of Cassandra virtual data centers (VDCs). This paper address the problem of energy efficient auto-scaling of Cassandra VDC in managed Cassandra data centers. We propose three energy-aware autoscaling algorithms: Opt, LocalOpt and LocalOpt-H. The first provides the optimal scaling decision orchestrating horizontal and vertical scaling and optimal placement. The other two are heuristics and provide sub-optimal solutions. Both orchestrate horizontal scaling and optimal placement. LocalOpt consider also vertical scaling. In this paper: we provide an analysis of the computational complexity of the optimal and of the heuristic auto-scaling algorithms; we discuss the issues in auto-scaling Cassandra VDC and we provide best practice for using auto-scaling algorithms; we evaluate the performance of the proposed algorithms under programmed SLA variation, surge of throughput (unexpected) and failures of physical nodes. We also compare the performance of energy-aware auto-scaling algorithms with the performance of two energy-blind auto-scaling algorithms, namely BestFit and BestFit-H. The main findings are: VDC allocation aiming at reducing the energy consumption or resource usage in general can heavily reduce the reliability of Cassandra in term of the consistency level offered. Horizontal scaling of Cassandra is very slow and make hard to manage surge of throughput. Vertical scaling is a valid alternative, but it is not supported by all the cloud infrastructures. [ABSTRACT FROM AUTHOR]

Cloud computing can leverage over-provisioned resources that are wasted in traditional data centers hosting production applications by consolidating tasks with lower QoS and SLA requirements. However, the dramatic fluctuation of workloads with lower QoS and SLA requirements may impact the performance of production applications. Frequent task eviction, killing and rescheduling operations also waste CPU cycles and create overhead. This paper aims to schedule hybrid workloads in the cloud data center to reduce task failures and increase resource utilization. The multi-prediction model, including the ARMA model and the feedback based online AR model, is used to predict the current and the future resource availability. Decision to accept or reject a new task is based on the available resources and task properties. Evaluations show that the scheduler can reduce the host overload and failed tasks by nearly 70%, and increase effective resource utilization by more than 65%. The task delay performance degradation is also acceptable. [ABSTRACT FROM AUTHOR]

Data centres are quickly evolving to support new demands for Cloud-Computing services. Extreme workload peaks represent a challenge for the maintenance of the performance and service level agreements, even more when operation costs need to be minimised. In this paper, we first present an extensive analysis of the impact of extreme workloads in large-scale realistic Cloud-Computing data centres, including a comparison between the most relevant centralised resource-managing models. Moreover, we extend our previous works by proposing a new energy-efficiency policy called Bullfighter which is able to keep performance key performance indicators while reducing energy consumption in extreme scenarios. This policy employs queue-theory distributions to foresee workload demands and adapt automatically to workload fluctuations even in extreme environments, while avoiding the fine-tuning required for other energy policies. Finally, it is shown through extensive simulation that Bullfighter can save more than 40% of energy in the aforementioned scenarios without exerting any noticeable impact on data-centre performance. [ABSTRACT FROM AUTHOR]

Recently scientists, politicians, students, associations and actors are sounded the alarm to save our planet. The slogan of Greta Thunberg "Our house is on fire" urges any person to act on the climate. As researchers in the field of databases, one of the most active research communities, we are compelled to propose little and big steps to save our planet. It should be noticed that DBMSs are one of the main energy consumers, as responsible to store and efficiently process data. In data stores, research on energy consumption has been mainly focused on some specific types of stores: data centers, database clusters, known as big infrastructures. These stores are computer warehouses dedicated to store and process in a parallel manner a large amount of data. They include different servers and network infrastructures. Energy consumption in traditional DBMSs got less attention compared to data centers, and at the same time, they are widely used in the actual applications. In DB-Engine (https://db-engines.com/en/ranking) ranking DBMSs according to their popularity, traditional DBMSs (Oracle, MySQL, SQL Server, PostgreSQL, DB2) are the top 5 of the most popular systems. This motivates us to integrate energy consumption in the components of these DBMSs. Query optimizers are one of the energy consumer's components. The actual studies were focused on integrating energy in query optimization in the mono-core processor architecture. Recently, thanks to multi-core, these studies have to be revisited. In this paper, we propose a new approach to integrate the energy dimension into query optimizers in the multi-core processor architecture. Firstly, we present a rich state of the art on energy consumption in the context of traditional databases. Secondly, a crossing from sequential query processing mode to parallel mode is given. Thirdly, we propose a cost model capturing energy in a multicore architecture. Its parameter values are obtained by using non-linear regression and neural network techniques. Finally, our cost model is integrated into the query optimizer in PostgreSQL on which several experiments were conducted showing the efficiency and effectiveness of our proposal. [ABSTRACT FROM AUTHOR]

The proliferation of cloud data center applications and network function virtualization (NFV) boosts dynamic and QoS dependent traffic into the data centers network. Currently, lots of network routing protocols are requirement agnostic, while other QoS-aware protocols are computationally complex and inefficient for small flows. In this paper, a computationally efficient congestion avoidance scheme, called CECT, for software-defined cloud data centers is proposed. The proposed algorithm, CECT, not only minimizes network congestion but also reallocates the resources based on the flow requirements. To this end, we use a routing architecture to reconfigure the network resources triggered by two events: (1) the elapsing of a predefined time interval, or, (2) the occurrence of congestion. Moreover, a forwarding table entries compression technique is used to reduce the computational complexity of CECT. In this way, we mathematically formulate an optimization problem and define a genetic algorithm to solve the proposed optimization problem. We test the proposed algorithm on real-world network traffic. Our results show that CECT is computationally fast and the solution is feasible in all cases. In order to evaluate our algorithm in term of throughput, CECT is compared with ECMP (where the shortest path algorithm is used as the cost function). Simulation results confirm that the throughput obtained by running CECT is improved up to 3× compared to ECMP while packet loss is decreased up to 2×. [ABSTRACT FROM AUTHOR]

Energy efficient virtual machine (VM) consolidation in modern data centers is typically optimized using methods such as Mixed Integer Programming, which typically require precise input to the model. Unfortunately, many parameters are uncertain or very difficult to predict precisely in the real world. As a consequence, a once calculated solution may be highly infeasible in practice. In this paper, we use methods from robust optimization theory in order to quantify the impact of uncertainty in modern data centers. We study the impact of different parameter uncertainties on the energy efficiency and overbooking ratios such as e.g. VM resource demands, migration related overhead or the power consumption model of the servers used. We also show that setting aside additional resource to cope with uncertainty of workload influences the overbooking ration of the servers and the energy consumption. We show that, by using our model, Cloud operators can calculate a more robust migration schedule leading to higher total energy consumption. A more risky operator may well choose a more opportunistic schedule leading to lower energy consumption but also higher risk of SLA violation. [ABSTRACT FROM AUTHOR]

Cloud computing and web emerging applications have created the need for more powerful data centers. These data centers need high bandwidth interconnects that can sustain the high interaction between the web-, application- and database-servers. Data center networks based on electronic packet switches will have to consume excessive power in order to satisfy the required communication bandwidth of future data centers. Optical interconnects have gained attention recently as a promising energy efficient solution offering high throughput, low latency and reduced energy consumption compared to current networks based on commodity switches. This paper presents a comparison on the power consumption of several optical interconnection schemes based on AWGRs, Wavelength Selective Switches (WSS) or Semiconductor Optical Amplifiers (SOAs). Based on a thorough analysis of each architecture, it is shown that optical interconnects can achieve at least an order of magnitude higher energy efficiency compared to current data center networks based on electrical packet based switches and they could contribute to greener IT network infrastructures. [ABSTRACT FROM AUTHOR]

In modern data centers, energy consumption accounts for a considerably large slice of operational expenses. The existing work in data center energy optimization is focusing only on job distribution between computing servers based on workload or thermal profiles. This paper underlines the role of communication fabric in data center energy consumption and presents a scheduling approach that combines energy efficiency and network awareness, named DENS. The DENS methodology balances the energy consumption of a data center, individual job performance, and traffic demands. The proposed approach optimizes the tradeoff between job consolidation (to minimize the amount of computing servers) and distribution of traffic patterns (to avoid hotspots in the data center network). [ABSTRACT FROM AUTHOR]

Cloud data centers often schedule heterogeneous workloads without considering energy consumption and carbon emission aspects. Tremendous amount of energy consumption leads to high operational costs and reduces return on investment and contributes towards carbon footprints to the environment. Therefore, there is need of energy-aware cloud based system which schedules computing resources automatically by considering energy consumption as an important parameter. In this paper, energy efficient autonomic cloud system [Self-Optimization of Cloud Computing Energy-efficient Resources (SOCCER)] is proposed for energy efficient scheduling of cloud resources in data centers. The proposed work considers energy as a Quality of Service (QoS) parameter and automatically optimizes the efficiency of cloud resources by reducing energy consumption. The performance of the proposed system has been evaluated in real cloud environment and the experimental results show that the proposed system performs better in terms of energy consumption of cloud resources and utilizes these resources optimally. [ABSTRACT FROM AUTHOR]

Data volume in today's world has been tremendously increased. Large-scaled and diverse data sets are raising new big challenges of storage, process, and query. Particularly, real-time data analysis becomes more and more frequently. Multi-tiered, hybrid storage architectures, which provide a solid way to combine solid-state drives with hard disk drives (HDDs), therefore become attractive in enterprise data centers for achieving high performance and large capacity simultaneously. However, from service provider's perspective, how to efficiently manage all the data hosted in data center in order to provide high quality of service (QoS) is still a core and difficult problem. The modern enterprise data centers often provide the shared storage resources to a large variety of applications which might demand for different service level agreements (SLAs). Furthermore, any user query from a data-intensive application could easily trigger a scan of a gigantic data set and inject a burst of disk I/Os to the back-end storage system, which will eventually cause disastrous performance degradation. Therefore, in the paper, we present a new approach for automated data movement in multi-tiered, hybrid storage clusters, which lively migrates the data among different storage media devices, aiming to support multiple SLAs for applications with dynamic workloads at the minimal cost. Detailed trace-driven simulations show that this new approach significantly improves the overall performance, providing higher QoS for applications and reducing the occurrence of SLA violations. Sensitivity analysis under different system environments further validates the effectiveness and robustness of the approach. [ABSTRACT FROM AUTHOR]

Energy efficiency is a major concern to datacenter operators, because the large amounts of energy used by parallel computing infrastructures increases costs and affects the electricity grid. Datacenter power consumption can be reduced by applying intelligent control techniques to dynamically adjust power demand, but this is hampered by conflicting objectives. For instance, the workload can be controlled to adjust power, but at the expense of service quality. Or, the cooling infrastructure demand can be manipulated without affecting workloads, but at the risk of shifting the datacenter temperature outside the acceptable limits. This paper proposes a multiobjective, evolutionary approach to solving the problem of energy-aware task scheduling in datacenters. Our approach takes into account three problem objectives (power consumption, temperature, and quality of service) when both computing and cooling infrastructures are holistically controlled. We report the two best solutions to each of these problem objectives, as well as the selected trade-off solutions between them. [ABSTRACT FROM AUTHOR]

Data deduplication has become a commodity in large-scale storage systems, especially in data backup and archival systems. However, due to the removal of redundant data, data deduplication de-linearizes data placement and forces the data chunks of the same data object to be divided into multiple separate units. In our preliminary study, we found that the de-linearization of data placement compromises the data spatial locality that is used to improve data read performance, deduplication throughput and deduplication efficiency in some deduplication approaches, which significantly affects deduplication performance and makes some deduplication approaches become less effective. In this paper, we first analyze the negative effect of data placement de-linearization to deduplication performance, and then propose an effective approach called De-Frag to reduce the de-linearization of data placement. The key idea of De-Frag is to choose some redundant data to be written to the disks rather than be removed. It quantifies the spatial locality of each chunk group by spatial locality level (SPL for short) and writes the redundant chunks to disks when SPL value is smaller than a preset value, thus to reduce the de-linearization of data placement and enhance the spatial locality. As shown in our experimental results driven by real world datasets, De-Frag effectively enhances data spatial locality and improves deduplication throughput, deduplication efficiency, and data read performance, at the cost of slightly lower compression ratios. [ABSTRACT FROM AUTHOR]

There are typically multiple heterogeneous servers providing various services in cloud computing. High power consumption of these servers increases the cost of running a data center. Thus, there is a problem of reducing the power cost with tolerable performance degradation. In this paper, we optimize the performance and power consumption tradeoff for multiple heterogeneous servers. We consider the following problems: (1) optimal job scheduling with fixed service rates; (2) joint optimal service speed scaling and job scheduling. For problem (1), we present the Karush-Kuhn-Tucker (KKT) conditions and provide a closed-form solution. For problem (2), both continuous speed scaling and discrete speed scaling are considered. In discrete speed scaling, the feasible service rates are discrete and bounded. We formulate the problem as an MINLP problem and propose a distributed algorithm by online value iteration, which has lower complexity than a centralized algorithm. Our approach provides an analytical way to manage the tradeoff between performance and power consumption. The simulation results show the gain of using speed scaling, and also prove the effectiveness and efficiency of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, an autonomic performance management approach is introduced that can be applied to a general class of web services deployed in large scale distributed environment. The proposed approach utilizes traditional large scale control-based algorithms by using interaction balance approach in web service environment for managing the response time and the system level power consumption. This approach is developed in a generic fashion that makes it suitable for web service deployments, where web service performance can be adjusted by using a finite set of control inputs. This approach maintains the service level agreements, maximizes the revenue, and minimizes the infrastructure operating cost. Additionally, the proposed approach is fault-tolerant with respect to the failures of the computing nodes inside the distributed deployment. Moreover, the computational overhead of the proposed approach can also be managed by using appropriate value of configuration parameters during its deployment. [ABSTRACT FROM AUTHOR]

Emerging high-performance distributed computing environments are enabling new end-to-end formulations in science and engineering that involve multiple interacting processes and data-intensive application workflows. For example, current fusion simulation efforts are exploring coupled models and codes that simultaneously simulate separate application processes, such as the core and the edge turbulence. These components run on different high performance computing resources, need to interact at runtime with each other and with services for data monitoring, data analysis and visualization, and data archiving. As a result, they require efficient and scalable support for dynamic and flexible couplings and interactions, which remains a challenge. This paper presents DataSpaces a flexible interaction and coordination substrate that addresses this challenge. DataSpaces essentially implements a semantically specialized virtual shared space abstraction that can be associatively accessed by all components and services in the application workflow. It enables live data to be extracted from running simulation components, indexes this data online, and then allows it to be monitored, queried and accessed by other components and services via the space using semantically meaningful operators. The underlying data transport is asynchronous, low-overhead and largely memory-to-memory. The design, implementation, and experimental evaluation of DataSpaces using a coupled fusion simulation workflow is presented. [ABSTRACT FROM AUTHOR]

Management is an important challenge for future enterprises. Previous work has addressed platform management (e.g., power and thermal management) separately from virtualization management (e.g., virtual machine (VM) provisioning, application performance). Coordinating the actions taken by these different management layers is important and beneficial, for reasons of performance, stability, and efficiency. Such coordination, in addition to working well with existing multi-vendor solutions, also needs to be extensible to support future management solutions potentially operating on different sensors and actuators. In response to these requirements, this paper proposes vManage, a solution to loosely couple platform and virtualization management and facilitate coordination between them in data centers. Our solution is comprised of registry and proxy mechanisms that provide unified monitoring and actuation across platform and virtualization domains, and coordinators that provide policy execution for better VM placement and runtime management, including a formal approach to ensure system stability from inefficient management actions. The solution is instantiated in a Xen environment through a platform-aware virtualization manager at a cluster management node, and a virtualization-aware platform manager on each server. Experimental evaluations using enterprise benchmarks show that compared to traditional solutions, vManage can achieve additional power savings (10% lower power) with significantly improved service-level guarantees (71% less violations) and stability (54% fewer VM migrations), at low overhead. [ABSTRACT FROM AUTHOR]

Ever increasing data center complexity poses a significant burden on IT administrators. This burden can become unbearable without the help of self-managed systems that monitor themselves and automatically modify their state in order to carry out business processes according to high level objectives set by service level agreements (SLA) and policies. Among the key IT management tasks that must be automated and enhanced to realize the idea of an autonomic and highly dynamic data center, are discovery, configuration, and provisioning of new servers. In this direction, this paper describes pre-boot capabilities endowing the bare metal server with the ability to be discovered, queried, configured, and provisioned at time zero using industry standards like Common Information Model (CIM), CIM-XML, and Service Location Protocol (SLP). The capabilities are implemented as a payload of an Intel® Extensible Firmware Interface (EFI)-compliant BIOS, the Intel® Rapid Boot Toolkit (IRBT), allowing a resource manager to discover a new server during pre-boot, possibly in a bare-metal state, and then perform an asset inventory, configure the server including CPU-specific settings, and provision it with the most appropriate image. All these tasks may be carried out based on decisions taken by the resource manager according to server capabilities, application requirements, SLAs, and high-level policies. Additionally, this system uses reliable protocols, thus minimizing error possibilities. Future work is proposed, including the integration of a persistent hypervisor for enhanced management capabilities. [ABSTRACT FROM AUTHOR]

Abstract  Power consumption and cooling overheads are becoming increasingly significant for enterprise datacenters, affecting overall costs and the ability to extend resource capacities. To help mitigate these issues, active power management technologies are being deployed aggressively, including power budgeting, which enables improved power provisioning and can address critical periods when power delivery or cooling capabilities are temporarily reduced. Given the use of virtualization to encapsulate application components into virtual machines (VMs), however, such power management capabilities must address the interplay between budgeting physical resources and the performance of the virtual machines used to run these applications. This paper proposes a set of management components and abstractions for use by software power budgeting policies. The key idea is to manage power from a VM-centric point of view, where the goal is to be aware of global utility tradeoffs between different virtual machines (and their applications) when maintaining power constraints for the physical hardware on which they run. Our approach to VM-aware power budgeting uses multiple distributed managers integrated into the VirtualPower Management (VPM) framework whose actions are coordinated via a new abstraction, termed VPM tokens. An implementation with the Xen hypervisor illustrates technical benefits of VPM tokens that include up to 43% improvements in global utility, highlighting the ability to dynamically improve cluster performance while still meeting power budgets. We also demonstrate how VirtualPower based budgeting technologies can be leveraged to improve datacenter efficiency in the context of cooling infrastructure management. [ABSTRACT FROM AUTHOR]

Abstract  Data centers, as resource providers, are expected to deliver on performance guarantees while optimizing resource utilization to reduce cost. Virtualization techniques provide the opportunity of consolidating multiple separately managed containers of virtual resources on underutilized physical servers. A key challenge that comes with virtualization is the simultaneous on-demand provisioning of shared physical resources to virtual containers and the management of their capacities to meet service-quality targets at the least cost. This paper proposes a two-level resource management system to dynamically allocate resources to individual virtual containers. It uses local controllers at the virtual-container level and a global controller at the resource-pool level. An important advantage of this two-level control architecture is that it allows independent controller designs for separately optimizing the performance of applications and the use of resources. Autonomic resource allocation is realized through the interaction of the local and global controllers. A novelty of the local controller designs is their use of fuzzy logic-based approaches to efficiently and robustly deal with the complexity and uncertainties of dynamically changing workloads and resource usage. The global controller determines the resource allocation based on a proposed profit model, with the goal of maximizing the total profit of the data center. Experimental results obtained through a prototype implementation demonstrate that, for the scenarios under consideration, the proposed resource management system can significantly reduce resource consumption while still achieving application performance targets. [ABSTRACT FROM AUTHOR]

Abstract   With the increased complexity of platforms, the growing demand of applications and data centers’ servers sprawl, power consumption is reaching unsustainable limits. The need to improved power management is becoming essential for many reasons including reduced power consumption & cooling, improved density, reliability & compliance with environmental standards. This paper presents a theoretical framework and methodology for autonomic power and performance management in e-business data centers. We optimize for power and performance (performance-per-watt) at each level of the hierarchy while maintaining scalability. We adopt mathematically-rigorous optimization approach to minimize power while meeting performance constraints. Our experimental results show around 72% savings in power while maintaining performance as compared to static power management techniques and 69.8% additional savings with both global and local optimizations. [ABSTRACT FROM AUTHOR]

Abstract  Previous studies have revealed that paravirtualization imposes minimal performance overhead on High Performance Computing (HPC) workloads, while exposing numerous benefits for this field. In this study, we are investigating the impact of paravirtualization on the performance of automatically-tuned software systems. We compare peak performance, performance degradation in constrained memory situations, performance degradation in multi-threaded applications, and inter-VM shared memory performance. For comparison purposes, we examine the proficiency of ATLAS, a quintessential example of an autotuning software system, in tuning the BLAS library routines for paravirtualized systems. Our results show that the combination of ATLAS and Xen paravirtualization delivers native execution performance and nearly identical memory hierarchy performance profiles in both single and multi-threaded scenarios. Furthermore, we show that it is possible to achieve memory sharing among OS instances at native speeds. These results expose new benefits to memory-intensive applications arising from the ability to slim down the guest OS without influencing the system performance. In addition, our findings support a novel and very attractive deployment scenario for computational science and engineering codes on virtual clusters and computational clouds. [ABSTRACT FROM AUTHOR]

Abstract  As outsourcing data centers emerge to host applications and services from many different organizations, it is critical for data center owners to isolate different applications while dynamically and optimally allocate sharable resources among them. To address this issue, we propose a virtual-appliance-based autonomic resource provisioning framework for large virtualized data centers. We present the architecture of the data center with enriched autonomic features. We define a non-linear constrained optimization model for dynamic resource provisioning and present a novel analytic solution. Key factors, including virtualization overhead and reconfiguration delay, are incorporated into the model. Experimental results based on a prototype demonstrate that the system-level performance has been greatly improved by taking advantage of fine-grained server consolidation, and the whole system exhibits flexible adaptation in failure scenarios. Experiments with the impact of switching delay also show the efficiency of the framework due to significantly reduced provisioning time. [ABSTRACT FROM AUTHOR]

Cloud computing model offers various platforms services and provides a scalable, on-demand service at any time-anywhere manner. However, in the outsourcing strategy, users no longer have direct control and ownership of data, which causes serious major concerns related to proper secure auditing. Previously, many schemes have been drafted to admit proper public verification through third-party authorities. On the other hand, these schemes may leak identity or data value to third party auditor (TPA) by making improbable expectations in a manner that the TPA has the adequate computation capability and can only handle static archive data blocks. So an efficient, secure, and dynamic auditing model is craved to prompt users that their sensitive data are accurately stored in the cloud data center. To accommodate this, we introduce a secure novel public auditing scheme named Dynamic Large Branching Hash Tree (DLBHT) with Homomorphic Verifiable Authenticator (HVA) based aggregate signature scheme, which performs auditing with less communication and computational overhead. This model supports batch auditing and batch dynamic updating operations by maintaining a provably secure dynamic large branching tree (DLBHT) at the cloud server-side. The security and performance analysis of our model shows that it is efficient and secure for practical arrangements, which specifically diminishes communication and computational cost of auditing procedure. [ABSTRACT FROM AUTHOR]

The cloud computing is established as the technology that provides all the need oriented and the use dependent IT resources that has been used very frequently for the business information systems. In relation to the terms of integration of all the decentralized information systems, the cloud systems have been providing an approach for stable solution. But the data security has been found to be a major challenge in the using of cloud systems and being a main reason as to why the companies avoid cloud service usage. The main question that is faced is the manner in which the cloud systems are used for the integration of such decentralized information systems that are to be designed and based on the technology and the organization to ensure privacy of law of that of the cloud user that may be guaranteed. Today there is a lot of attention that is being received from the academic as well as the perspective of the industry by means of an in depth cloud computing resource. There are several issues of security like availability, confidentiality and integrity. The experts have been studying the anonymous authentication for the data archiving to the clouds. In the conditions in which there is a communication that is achieved without penalty or fear, an anonymous authentication will be important. This has been needed by the organizations for protecting all their vital data from that of their industrial espionage. From among the techniques of authentication that has been attributed to a decentralized mechanism it may appear to be efficient. For this work, a trust-based secure and anonymous authentication for multiple attributes is make use of. The Recursive Best First Search (RBFS) algorithm will be used for finding the optimal weights from among the trust partners. [ABSTRACT FROM AUTHOR]

Power management is becoming very important in data centers. To apply power management in cloud computing, Green Computing has been proposed and considered. Cloud computing is one of the new promising techniques, that are appealing to many big companies. In fact, due to its dynamic structure and property in online services, cloud computing differs from current data centers in terms of power management. To better manage the power consumption of web services in cloud computing with dynamic user locations and behaviors, we propose a power budgeting design based on the logical level, using distribution trees. By setting multiple trees or forest, we can differentiate and analyze the effect of workload types and Service Level Agreements (SLAs, e.g. response time) in terms of power characteristics. Based on these, we introduce classified power capping for different services as the control reference to maximize power saving when there are mixed workloads. [ABSTRACT FROM AUTHOR]

Abstract  Power management is becoming an increasingly critical component of modern enterprise computing environments. The traditional drive for higher performance has influenced trends towards consolidation and higher densities, artifacts enabled by virtualization and new small form factor server blades. The resulting effect has been increased power and cooling requirements in data centers which elevate ownership costs and put more pressure on rack and enclosure densities. To address these issues, we exploit a fundamental characteristic of data centers: “platform heterogeneity”. This heterogeneity stems from the architectural and management-capability variations of the underlying platforms. We define an intelligent heterogeneity-aware load management (HALM) system that leverages heterogeneity characteristics to provide two data center level benefits: (i) power efficient allocations of workloads to the best fitting platforms and (ii) improved overall performance in a power constrained environment. Our infrastructure relies upon platform and workload descriptors as well as a novel analytical prediction layer that accurately predicts workload power/performance across different platform architectures and power management capabilities. Our allocation scheme achieves on average 20% improvements in power efficiency for representative heterogeneous data center configurations, and up to 18% improvements in performance degradation when power budgeting must be performed. These results highlight the significant potential of heterogeneity-aware management. [ABSTRACT FROM AUTHOR]