GSAN: Green Cloud-Simulation for Storage Area Networks

Cloud computing data centers are becoming increasingly popular for the provisioning of computing resources. The cost and operating expenses of data centers have skyrocketed with the increase in computing capacity. Several governmental, industrial, and academic surveys indicate that the energy utilized by computing and communication units within a data center contributes to a considerable slice of the data center operational costs.In this paper, we present a simulation environment for energy-aware cloud computing data centers. Along with the workload distribution, the simulator is designed to capture details of the energy consumed by data center components (servers, switches, and links) as well as packet-level communication patterns in realistic setups.The simulation results obtained for two-tier, three-tier, and three-tier high-speed data center architectures demonstrate the effectiveness of the simulator in utilizing power management schema, such as voltage scaling, frequency scaling, and dynamic shutdown that are applied to the computing and networking components.

Cloud computing data centers are becoming increasingly popular for the provisioning of computing resources. The cost and operating expenses of data centers have skyrocketed with the increase in computing capacity. Several governmental, industrial, and academic surveys indicate that the energy utilized by computing and communication units within a data center contributes to a considerable slice of the data center operational costs. In this paper, we present a simulation environment for energy-aware cloud computing data centers. Along with the workload distribution, the simulator is designed to capture details of the energy consumed by data center components (servers, switches, and links) as well as packet-level communication patterns in realistic setups. The simulation results obtained for two-tier, three- tier, and three-tier high-speed data center architectures demonstrate the effectiveness of the simulator in utilizing power management schema, such as voltage scaling, frequency scaling, and dynamic shutdown that are applied to the computing and networking components.

Information storage is a central pillar of information technology. To enhance student awareness about Information Storage Technology, Storage Area Network (SAN) course was introduced for undergraduate students at VII semester level in Information Science & Engineering curricula. SAN deals with storing, managing, and protecting Digital Information in Classic, Virtualized, and Cloud Environments. EMC's open curriculum, aimed at addressing the knowledge gap that exists in the IT industry in the area of information storage and emerging technologies. The Storage Area Network (SAN)/Information Storage and Management (ISM) course proposed by EMC Academic Alliance is designed and developed to enable students to achieve a comprehensive understanding of all the segments of storage technology. Here SAN course was designed by considering EMC Academic Alliance course contents and framed Course Learning Objectives. Course activity was dedicated for the study and presentation of white papers on EMC products related to the concepts which they have studied. Industry visit to EMC Corporation was organized to gain knowledge about current storage technologies and how storage concepts are implemented in Data Center. At the end of the course, an EMC Proven Professional Certification online exam was conducted in our college. Through this course we have mapped some of the program outcomes with ABET criteria.

Modern data centers must provide performance assurance for complex system software such as multi-tier web applications. In addition, the power consumption of data centers needs to be minimized to reduce operating costs and avoid system overheating. Various power-efficient performance management strategies have been proposed based on dynamic voltage and frequency scaling (DVFS). Virtualization technologies have also made it possible to consolidate multiple virtual machines (VMs) onto a smaller number of active physical servers for even greater power savings, but at the cost of a higher overhead. This paper proposes a performance-controlled power optimization solution for virtualized data centers with multi-tier applications. While existing work relies on either DVFS or server consolidation in a separate manner, our solution utilizes both strategies for maximized power savings by integrating feedback control with optimization strategies. At the application level, a multi-input-multi-output controller is designed to achieve the desired performance for applications spanning multiple VMs, on a short time scale, by reallocating the CPU resources and DVFS. At the data center level, a power optimizer is proposed to incrementally consolidate VMs onto the most power-efficient servers on a longer time scale. Empirical results on a hardware testbed demonstrate that our solution can effectively achieve performance-assured power savings. Extensive simulation results, based on a trace file of 5,415 real servers, demonstrate the efficacy of our solution in large-scale data centers.

Performance-controlled server consolidation for virtualized data centers with multi-tier applications

Modern information and communication technology along with the broad adaptation of computing paradigms, namely cloud computing has significant impact on environment due to huge amount of CO 2 emission. In the recent decade, a significant growth in the research domain of “green” and low power consumption network has been observed. With the recent advancements of compute intensive machine learning frameworks like deep learning, the energy consumption by GPUs is a challenging issue. In all of these cases, Dynamic Voltage Frequency Scaling (DVFS) has gained significant research interests and shown encouraging outcome. Dynamic Voltage and Frequency Scaling (DVFS) is utilized to reduce the energy consumption in data centers without impacting the Quality of Service. On the other side, spatio-temporal dataset is huge in size and requires compute and time intensive algorithms and techniques for different spatio-temporal query processing. In all aspects, the correlations among energy, DVFS, consolidation, and performance are the key-enablers of energy-efficient management. In this chapter, we explore different existing DVFS techniques and challenges to analyse and process spatio-temporal query in cloud based paradigm. We have considered movement datasets for demonstrating energy consumption and utility of dynamic voltage and frequency scaling.KeywordsDynamic voltage and frequency scalingSpatio-temporal QueryCloud datacenterScheduling algorithmPowerEnergy

Servers in a data center are underutilized due to over-provisioning, which contributes heavily toward the high-power consumption of the data centers. Recent research in optimizing the energy consumption of High Performance Computing (HPC) data centers mostly focuses on consolidation of Virtual Machines (VMs) and using dynamic voltage and frequency scaling (DVFS). These approaches are inherently hardware-based, are frequently unique to individual systems, and often use simulation due to lack of access to HPC data centers. Other approaches require profiling information on the jobs in the HPC system to be available before run-time. In this paper, we propose a reinforcement learning based approach, which jointly optimizes profit and energy in the allocation of jobs to available resources, without the need for such prior information. The approach is implemented in a software scheduler used to allocate real applications from the Princeton Application Repository for Shared-Memory Computers (PARSEC) benchmark suite to a number of hardware nodes realized with Odroid-XU3 boards. Experiments show that the proposed approach increases the profit earned by 40% while simultaneously reducing energy consumption by 20% when compared to a heuristic-based approach. We also present a network-aware server consolidation algorithm called Bandwidth-Constrained Consolidation (BCC), for HPC data centers which can address the under-utilization problem of the servers. Our experiments show that the BCC consolidation technique can reduce the power consumption of a data center by up-to 37%.

Cloud computing is widely associated with major capital investment in mega data centres, housing expensive blade servers and storage area networks.In this paper we argue that a modular approach to building local or regional data centres using commodity hardware and open source hardware can produce a cost effective solution that better addresses the goals of cloud computing, and provides a scalable architecture that meets the service requirements of a high quality data centre.In support of this goal, we provide data that supports three research hypotheses:1. that central processor unit (CPU) resources are not normally limiting; 2. that disk I/O transactions (TPS) are more often limiting, but this can be mitigated by maximizing the TPS-CPU ratio; 3. that customer CPU loads are generally static and small.Our results indicate that the modular, commodity hardware based architecture is near optimal.This is a very significant result, as it opens the door to alternative business models for the provision of data centres that significantly reduce the need for major up-front capital investment.

Energy consumption has become a major constraint in providing increased functionality for devices with small form factors. Dynamic voltage and frequency scaling has been identified as an effective approach for reducing the energy consumption of embedded systems. Earlier works on dynamic voltage scaling focused mainly on performing voltage scaling when the CPU is waiting for memory subsystem or concentrated chiefly on loop nests and/or subroutine calls having sufficient number of dynamic instructions. This paper concentrates on coarser program regions and for the first time uses program phase behavior for performing dynamic voltage scaling. Program phases are annotated at compile time with mode switch instructions. Further, we relate the Dynamic Voltage Scaling Problem to the Multiple Choice Knapsack Problem, and use well known heuristics to solve it efficiently. Also, we develop a simple integer linear program formulation for this problem. Experimental evaluation on a set of media applications reveal that our heuristic method obtains a 38% reduction in energy consumption on an average, with a performance degradation of 1% and upto 45% reduction in energy with a performance degradation of 5%. Further, the energy consumed by the heuristic solution is within 1% of the optimal solution obtained from the ILP approach.KeywordsExecution TimeSupply VoltageKnapsack ProblemBasic BlockProgram RegionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Resource Virtualization and Load Balancing are main objectives to reduce the power consumption and to improve the performance of large data centers (DC). The management of Cloud Data Centers (CDC) requires an accurate planning and an efficient use of system resources in order to save energy consumption (greening), to provide Quality of Service (QoS), and to meet negotiated Service Level Agreements (SLA). This contribution addresses the question of modeling and the development of generic queuing models for energy-efficient use of resources for dynamic load balancing in virtualized CDCs. Performance models are developed for energy efficiency through automatic Server Consolidation, Dynamic Voltage and Frequency Scaling (DVFS) under Static Load Balancing; Dynamic Load Balancing can be achieved through Virtual Machine (VM) migrations. The analysis of such models provides quantitative performance figures upon which the system operation can be optimized with respect to guaranteed real-time performance and energy efficiency under prescribed SLAs.

Energy conservation of large data centers for high performance computing workloads, such as deep learning with Big Data, is of critical significance, where cutting down a few percent of electricity translates into million-dollar savings. This work studies energy conservation on emerging CPU-GPU hybrid clusters through dynamic voltage and frequency scaling (DVFS). We aim at minimizing the total energy consumption of processing a batch of offline tasks or a sequence of real-time tasks under deadline constraints. We derive a fast and accurate analytical model to compute the appropriate voltage/frequency setting for each task, and assign multiple tasks to the cluster with heuristic scheduling algorithms. In particular, our model stresses the nonlinear relationship between task execution time and processor speed for GPU-accelerated applications, for more accurately capturing real-world GPU energy consumption. In performance evaluation driven by real-world power measurement traces, our scheduling algorithm shows comparable energy savings to the theoretical upper bound. With a GPU scaling interval where analytically at most 36% of energy can be saved, we record 33-35% of energy savings. Our results are applicable to energy management on modern heterogeneous clusters.

Our focus here is on data communications within IT equipment and in IT data centers. Optical communications is not new. Thus the obvious question is likely; why a paper entitled, “Why optical data communications and why now?”. The reasons are twofold. First, optical data communications is more necessary now than it has ever been in the past. It is not excessive to even consider that it will be required in the not too distant future. Second, the advances in the broad field of photonics and optics have brought optical communications nearly to the point that it can finally cross over the threshold to be less expensive than electronic signaling. In this paper we make the case why we must aggressively pursue optics for data communications at all length scales within the data center. The summarization of this paper is that optical communications is inevitable, and we offer reasons why we believe this is true.

Modern real-time embedded systems have increasingly penetrated our daily life and are also often constrained in terms of temperature and energy. In this paper, a thesis is defended that from a real-time systems perspective, thermally constrained dynamic power management approaches behave very similar to idealised dynamic voltage and frequency scaling. Hence, existing dynamic voltage and frequency scaling solutions proposed for periodic/sporadic task models can be applied to thermally constrained dynamic power management systems with moderate effort. This work presents the similarities along with the distinctive elements between two approaches. Within the case study, the porting of a dynamic voltage and frequency scaling algorithm of the literature to thermally constrained dynamic power management system is demonstrated.

This research project, which was conducted during the Summer and Fall of 2011, investigated some commercially available assessment tools with a focus on IT equipment to see if such tools could round out the DC Pro tool suite. In this research, the assessment capabilities of the various tools were compiled to help make “non-biased” information available to the public. This research should not be considered to be exhaustive on all existing vendor tools although a number of vendors were contacted. Large IT equipment OEM’s like IBM and Dell provide their proprietary internal automated software which does not work on any other IT equipment. However, found two companies with products that showed promise in performing automated assessments for IT equipment from different OEM vendors. This report documents the research and provides a list of software products reviewed, contacts and websites, product details, discussions with specific companies, a set of recommendations, and next steps. As a result of this research, a simple 3-level approach to an IT assessment tool is proposed along with an example of an assessment using a simple IT equipment data collection tool (Level 1, spreadsheet). The tool has been reviewed with the Green Grid and LBNL staff. The initial feedback has been positive although further refinement to the tool will be necessary. Proposed next steps include a field trial of at least two vendors’ software in two different data centers with an objective to prove the concept, ascertain the extent of energy and computational assessment, ease of installation and opportunities for continuous improvement. Based on the discussions, field trials (or case studies) are proposed with two vendors – JouleX (expected to be completed in 2012) and Sentilla.

Modern data centers are complex distributed environments with application workloads requiring multiple resources like processing (CPU), storage and network. Allocation of these resources to workloads needs to be handled in an integrated manner to adequately capture the relationships between different resource nodes like connectivity between an application server and storage controller in the storage area network (SAN). As data centers grow over time, heterogeneous resources coexist at the same time and this heterogeneity adds further complexity to manual resource allocation.In this work, we describe various challenges and key insights in performing fast, automatic integrated resource allocation. We briefly introduce our novel framework called SPARK (Stable-Proposals-And-Resource Knapsacks) that uses server virtualization to address combined placement of application data and CPU in SAN data centers. SPARK is based on two well-studied problems -- Stable Marriage and Knapsacks -- and is simple, fast, versatile and highly scalable. Our initial experiments show promise of our approach, consistently outperforming natural candidate algorithms by 30-40% and being within 4% of the LP-based optimal values for a wide range of experiments.