Data Centers

Abstrak – Tren teknologi saat ini, data center menjadi tulang punggung pada komputasi awan. Pusat Data (Data center) merupakan fasilitas yang digunakan untuk menempatkan sistem komputer dan komponen-komponen terkaitnya, seperti sistem telekomunikasi dan penyimpanan data (Storage). Pembangunan data center harus memenuhi standar tertentu untuk menjamin data yang tersimpan aman dan dapat diakses kapan saja (Availability). Salah satu standar data center yang telah diakui internasional adalah TIA – 942, standar ini dikeluarkan oleh Telecommunications Industry Association (TIA). Dalam melakukan perancangannya, terdapat beberapa komponen yang harus dianalisa, mulai dari penentuan lokasi, desain raised floor, desain sistem listrik, desain pencahayaan, Environment Monitoring System, dan Fire Supression System. Metode yang dilakukan dalam perancangan ini adalah dengan menganalisa persyaratan yang yang tercantum pada standar TIA-942, dan dilanjutkan dengan pembuatan desain sesuai dengan kriteria yang disyaratkan. Studi ini bertujuan untuk menemukan rancangan desain untuk data center yang ideal dan memenuhi standar TIA-942 dan dapat diterapkan pada Revitalisasi Sekolah Tinggi Sandi Negara.
Keywords: Data Center, Revitalisasi, Standar TIA – 942

Cloud computing means storing and accessing data and programs over the Internet instead of your computer's hard drive. The cloud is just a metaphor for the Internet. The elements involved in cloud computing are clients, data center and distributed server. One of the main problems in cloud computing is load balancing. Balancing the load means to distribute the workload among several nodes evenly so that no single node will be overloaded. Load can be of any type that is it can be CPU load, memory capacity or network load. In this paper we presented an architecture of load balancing and algorithm which will further improve the load balancing problem by minimizing the response time. In this paper, we have proposed the enhanced version of existing regulated load balancing approach for cloud computing by comping the Randomization and greedy load balancing algorithm. To check the performance of proposed approach, we have used the cloud analyst simulator (Cloud Analyst). Through simulation analysis, it has been found that proposed improved version of regulated load balancing approach has shown better performance in terms of cost, response time and data processing time.

Plug-and-play information technology (IT) infrastructure has been expanding very rapidly in recent years. With the advent of cloud computing, many ecosystem and business paradigms are encountering potential changes and may be able to eliminate their IT infrastructure maintenance processes. Real-time performance and high availability requirements have induced telecom networks to adopt the new concepts of the cloud model: software-defined networking (SDN) and network function virtualization (NFV). NFV introduces and deploys new network functions in an open and standardized IT environment, while SDN aims to transform the way networks function. SDN and NFV are complementary technologies; they do not depend on each other. However, both concepts can be merged and have the potential to mitigate the challenges of legacy networks. In this paper, our aim is to describe the benefits of using SDN in a multitude of environments such as in data centers, data center networks, and Network as Service offerings. We also present the various challenges facing SDN, from scalability to reliability and security concerns, and discuss existing solutions to these challenges.

Cloud computing systems promise to offer subscription-oriented, enterprise-quality computing services to users worldwide. With the increased demand for delivering services to a large number of users, they need to offer differentiated services to users and meet their quality expectations. Existing resource management systems in data centers are yet to support Service Level Agreement (SLA)-oriented resource allocation, and thus need to be enhanced to realize cloud computing and utility computing. In addition, no work has been done to collectively incorporate customer-driven service management, computational risk management, and autonomic resource management into a market-based resource management system to target the rapidly changing enterprise requirements of Cloud computing. This paper presents vision, challenges, and architectural elements of SLA-oriented resource management. The proposed architecture supports integration of marketbased provisioning policies and virtualisation technologies for flexible allocation of resources to applications. The performance results obtained from our working prototype system shows the feasibility and effectiveness of SLA-based resource provisioning in Clouds.

With the rapid growth of cloud computing developers are being concern about the massive power consumption by the data centers. The huge amount of power consume by the data centers does not only results in high operational cost but also the production of heat and carbon footprints on the environment. Researchers are making efforts to maximize the power efficiency and minimizing energy consumption of data centers. This paper presents a study of the researches and methods that have been proposed to increase the power efficiency and minimize power consumption of the data centers.

Cloud Computing is a recent paradigm that is creating high expectations about benefits such as the payper-use model and elasticity of resources. However, with this optimism come also concerns about security. In a public cloud, the user's data storage and processing is no longer done inside its premises, but in data centers owned and administrated by the cloud provider. This may be a concern for organizations that deal with critical data, such as medical records.

CFD technology enables designers to predict operating characteristics, validate performance, and visualize flow and/or thermal phenomena which is difficult to capture in the real world. This is critical for AEC applications as prototyping buildings are not practical and retrofitting existing designs is expensive. The impact of CFD on AEC applications is directly related to the interpretation of simulation results; the ability to assess performance, identifies opportunities for improvement, and quantifies the impact of modification. Results visualization provides the opportunity to optimize design performance characteristics such as energy consumption, amount of contaminant entrained, thermal comfort, solar influence, hot air recirculation, and various other metrics of AEC application design performance.

Cooling systems play a vital role in the design and operation of Data Centers (DCs), as they consume a considerable amount of energy. Hot air infiltration from the servers' outlets into their inlets, which creates hot spots and flow short-circuiting, is one of the main sources of thermal inefficiency inside air-cooled DCs. This inefficiency increases the total energy consumption of DCs especially when the allocated workload and heat dissipation of the servers are increased. Therefore, efficient thermal management of DCs is considered as one of the main challenges of the DC industry. In order to highlight the importance of this inefficiency, an operational DC has been considered in this study. The thermal behaviour of the DC has been evaluated by conducting a numerical analysis of the flow and temperature fields based on the experimental measurements in a real DC. Numerical simulation highlighted a number of undesirable hot spots near the racks. Cold aisle containment, which is one of the efficient and industrially affordable methods of thermal inefficiency reduction, has been considered. The effectiveness of this method has been evaluated using three non-dimensional metrics known as Supply Heat Index (SHI), Rack Cooling Index (RCI) and the Coefficient of Performance (COP) of the cooling system. In this case study, by applying the proposed method, SHI, RCI and COP of the cooling system have been improved by more than 0.45, 17% and 19.5%, respectively. These results demonstrate the effectiveness of cold aisle containment for energy efficiency enhancement of DCs.

New broadband applications are causing the datacenters to proliferate, raising the bar for higher interconnection speeds. So far, optical board-to-board and rack-to-rack interconnects relied primarily on low-cost commodity optical components assembled in a single package. Although this concept proved successful in the first generations of opticalinterconnect modules, scalability is a daunting issue as signaling rates extend beyond 25 Gb/s. In this paper we present our work towards the development of two technology platforms for migration beyond Infiniband enhanced data rate (EDR), introducing new concepts in board-to-board and rack-to-rack interconnects.

Cooling is a major component in the enormous energy consumption in data centers. Accurate evaluation of cooling inside a data center forms the backbone of all the attempts for improving cooling efficiency. Models based on computational fluid dynamics (CFD) are typically used for accurate evaluation, but have a drawback of high computation time. This paper presents a novel thermal predictor to evaluate data center cooling in seconds. The key idea is to extract information from a single instance of CFD simulation using metrics called as influence indices to build the fast thermal predictor. Then, this predictor can evaluate the cooling for altered operation of data center with comparable accuracy in seconds without the need for repetitive CFD simulations. This paper demonstrates the accuracy of the thermal predictor by comparing with CFD simulations for a sample, but realistic data center. The fast thermal predictor then successfully passed more challenging tests in a real production data center and proved its practical utility. The results of the thermal predictor compared with measurements carried out in the production data center are also presented. This fast thermal predictor is an important milestone in the development of a method for model-based real time control of data center cooling.

Green computing is a new term which is defined to
make computing systems such as data centers more
energy efficient. Using various methods to prepare a
green networks and help CO2 reduction has been a
major issue for climate change in recent years. This
research work is designed to help admins to make a
green data centers by using an analytical survey and
new tool. The results in this research shows making
data centers green is a major concern for data
admins, however some of them have limited
information about the energy efficiency methods.
Finally, Green Data Center (GDC), use case design
and graphical user interface recommended for
future works in order to assist network
administrators to make their data centers green.
This research expected to provide analytical survey
towards green computing application for measuring
the sustainability of data centers.

Utility Computing has facilitated the creation of new markets that has made it possible to realize the longheld dream of delivering IT as a Utility. Even though utility computing is in its nascent stage today, the proponents of utility computing envisage that it will become a commodity business in the upcoming time and utility service providers will meet all the IT requests of the companies. This paper takes a crosssectional
view at the emergence of utility computing along with different requirements needed to realize utility model. It also surveys the current trends in utility computing highlighting diverse architecture models aligned towards delivering IT as a utility. Different resource management systems for proficient
allocation of resources have been listed together with various resource scheduling and pricing strategies used by them. Further, a review of generic key perspectives closely related to the concept of delivering IT as a Utility has been taken citing the contenders for the future enhancements in this technology in the form of Grid and Cloud Computing.

Green Computing is a recent trend towards designing, building, and operating computer systems to be energy efficient. While programs such as Energy Star have been around since the early 1990s, recent concerns regarding global climate change and the energy crisis have led to renewed interest in Green Computing. Data centers are a significant consumers of energy -both to power the computers as well as to provide the necessary cooling. This paper proposes a new approach to reduce energy utilization in data centers. In particular, our approach relies on consolidating services dynamically onto a subset of the available servers and temporarily shutting down servers in order to conserve energy. We present initial work on a probabilistic service dispatch algorithm that aims at minimizing the number of running servers such that they suffice for meeting the quality of service required by service-level agreements. Given the estimated energy consumption and projected growth in data centers, the proposed effort has the potential to positively impact energy consumption.

As the place that stores the brain in an organization, a Data Center centralizes the operations of the company, its equipment and stores, maintains, propagate the data that runs through the company. Data Centers may have a physical or virtual infrastructure and data which are stored in them provides the base for the successiveness of an organization since they are much valuable when making decisions in the business processes. In simple terms, the past, present and the future of an organization relies on the outcome of the analyzed data. Due to these reasons, it is one of the top priorities of a company to ensure the security and reliability of data centers and their information. With the advancement of the technology, securing the data centers to its best level has become a complicated task, due to the various possible attacks. Making an attack on a data center becomes easier when there are loopholes in the security mechanisms that are used in data centers. In this paper the authors describe about the existing security mechanisms which are specific to the physical data centers. Furthermore, this paper has discussed the possible solutions that can be used to ensure the security of data centers as a perspective to the current Information Technology (IT) industry.

High-performance computing (HPC) clusters suffer from an overall low memory utilization that is caused by the node-centric memory allocation combined with the variable memory requirements of HPC workloads. The recent provisioning of nodes with terabytes of memory to accommodate workloads with extreme peak memory requirements further exacerbates the problem. Memory disaggregation is viewed as a promising remedy to increase overall resource utilization and enable cost-effective up-scaling and efficient operation of HPC clusters, however, the overhead of demand paging in virtual memory management has so far hindered performant implementations.
To overcome these limitations, this work presents RackMem, an efficient implementation of disaggregated memory for rack scale computing. RackMem addresses the shortcomings of Linux’s demand paging algorithm and automatically adapts to the memory access patterns of individual processes to minimize the inherent overhead of remote memory accesses.
Evaluated on a cluster with an Infiniband interconnect, RackMem outperforms the state-of-the-art RDMA implementation and Linux’s virtual memory paging by a significant margin. RackMem’s custom demand paging implementation achieves a tail latency that is two orders of magnitude better than that of the Linux kernel. Compared to the state-of-the-art remote paging solution, RackMem achieves a 28% higher throughput and a 44% lower tail latency for a wide variety of real-world workloads.

As data centers are increasingly focused on energy efficiency, it becomes important to develop low power implementations of the various applications that run on them. Data compression plays a critical role in data centers to mitigate storage and communication costs. This work focuses on building a low power, high performance implementation for canonical Huffman encoding. We develop a number of different hardware and software implementations targeting Xilinx Zynq FPGA, ARM Cortex-A9, and Intel Core i7. Despite its sequential nature, we show that our hardware accelerated implementation is substantially more energy efficient than both the ARM and Intel Core i7 implementations. When compared to highly optimized software running on the ARM processor, our hardware accelerated implementation has approximately 15 times more throughput with 10% higher power usage, resulting in an 8X benefit in energy efficiency (measured in encodings/Watt). Additionally, our hardware accelerated implementation is up to 80% faster and over 230 times more energy efficient than a highly optimized Core i7 implementation.

Live migration of virtual machines (VMs) is an important tool for data center operators to achieve maintenance, power management, and load balancing. The relatively high cost of live migration makes it difficult to employ live migration for rapid load balancing or power management operations, leaving much of its promised benefits unused. The advance of fast network interconnects has led to the development of distributed shared memory (DSM) that allows a cluster of nodes to utilize remote memory with relatively low overhead. In this paper, we explore VM live migration over DSM. We present and evaluate a novel live migration algorithm on our own DSM implementation. The evaluation of live migrating various VMs executing real-life workloads shows that live migration over DSM can reduce the total migration time by 70% and the total amount of transferred data by 65% on average. The absolute average total migration time of only 1.1 seconds demonstrates the potential of live migration over DSM to lead to better load balancing, energy management, and total cost of ownership.

Hardware performance counters (HPCs) are increasingly being used to analyze performance and identify the causes of performance bottlenecks. However, HPCs are difficult to use for several reasons. Microprocessors do not provide enough counters to simultaneously monitor the many different types of events needed to form an overall understanding of performance. Moreover, HPCs primarily count low-level micro-architectural events from which it is difficult to extract high-level insight required for identifying causes of performance problems.

Motivation: Data centers are a critical component of modern IT infrastructure but are also among the worst environmental offenders through their increasing energy usage and the resulting large carbon footprints. Efficient management of data centers, including power management, networking, and cooling infrastructure, is hence crucial to sustainability. In the absence of a "first-principles" approach to manage these complex components and their interactions, datadriven approaches have become attractive and tenable. Results: We present a temporal data mining solution to model and optimize performance of data center chillers, a key component of the cooling infrastructure. It helps bridge raw, numeric, time-series information from sensor streams toward higher level characterizations of chiller behavior, suitable for a data center engineer. To aid in this transduction, temporal data streams are first encoded into a symbolic representation, next run-length encoded segments are mined to form frequent motifs in time series, and finally these metrics are evaluated by their contributions to sustainability. A key innovation in our application is the ability to intersperse "don't care" transitions (e.g., transients) in continuous-valued time series data, an advantage we inherit by the application of frequent episode mining to symbolized representations of numeric time series. Our approach provides both qualitative and quantitative characterizations of the sensor streams to the data center engineer, to aid him in tuning chiller operating characteristics. This system is currently being prototyped for a data center managed by HP and experimental results from this application reveal the promise of our approach.

Cloud computing is a  highly adaptable and efficient infrastructure for running endeavor and web applications.  Energy consumption expenditure and concurrent effects on environment are the dynamic challenges regarding to cloud computing.  We investigated previous researches based on energy efficiency approaches and retrieved the conditions to promote green cloud computing architectures.  Data centers are considered as the backbone of cloud infrastructure.  Numerous companies are investing in establishing large data centers to accommodate different cloud computing services. These data centers absorb huge amount of energy. They are also very complex in the infrastructure.  Over the years,  power consumption has emerged as an important factor for measuring computing resources expenditure. In this paper we will scrutinize all feasible spheres in an ideal cloud infrastructure which are responsible for massive amount of energy consumption.  Here,  we will also analyze the methodologies by which energy utilization can be reduced without compromising Quality of Services and overall performance. In order to make these data centers more energy efficient, many studies have been proposed.  These researches are based on technologies such as consolidation and virtualization.  Reducing emissions of carbon dioxide and energy consumption set up new challenges. The research works for green data centers are derived from the challenges. There is a current demand of integrated energy efficient cloud framework for establishing data centers that minimizes the impacts on environment and lessens CO2 emissions. The framework should combine a green IT architecture with different procedures and activities.

Energy consumption accounts for a large percentage of the operational expenses in data centers that are used as backend computing infrastructure for cloud computing. Existing solutions for energy efficiency and job scheduling are focusing on job distribution between servers based on the computational demands, while the communication demands are ignored. This work emphases the role of communication fabric and presents a scheduling solution, named e-STAB, which takes into account traffic requirements of cloud applications providing energy efficient job allocation and traffic load balancing in data center networks. Effective distribution of network traffic improves quality of service of running cloud applications by reducing the communication-related delays and congestion-related packet losses. The validation results, obtained from the GreenCloud simulator, underline benefits and efficiency of the proposed scheduling methodology.

Cloud Computing is a recent paradigm that is creating high expectations about benefits such as the payper-use model and elasticity of resources. However, with this optimism come also concerns about security. In a public cloud, the user's data storage and processing is no longer done inside its premises, but in data centers owned and administrated by the cloud provider. This may be a concern for organizations that deal with critical data, such as medical records.

Data center switches need to satisfy stringent lowdelay and high-capacity requirements. To do so, they rely on small switch buffers. However, in case of congestion, data center switches can incur throughput collapse for short TCP flows as well as temporary starvation for long TCP flows. In this paper, we introduce a lightweight hash-based algorithm called HCF (Hashed Credits Fair) to solve these problems at the switch level while being transparent to the end users. We show that it can be readily implemented in data center switches with O(1) complexity and negligible overhead. We illustrate using simulations how HCF mitigates the throughput collapse of short flows. We also show how HCF reduces unfairness and starvation for long-lived TCP flows as well as for short TCP flows, yet maximizes the utilization on the congested link. Last, even though HCF can store packets of a same flow in different queues, we also prove that it prevents packet reordering.

Live migration of virtual machines (VMs) enables maintenance, load balancing, and power management in data centers. The cost of live migration on several key metrics combined with strict service-level objectives (SLOs), however, typically limits its practical application to situations where the underlying physical host has to undergo maintenance. As a consequence, the potential benefits of live migration with respect to increased resource usage and lower power consumption remain largely untouched. In this paper, we argue that live migration-aware SLOs combined with smart live migration algorithm selection provides an economically viable model for live migration in data centers. Based on a model predicting key parameters of VM live migration, an optimization algorithm selects the live migration technique that is expected to meet client SLOs while at the same time to optimize target metrics given by the data center operator. A comparison with the state-of-the-art shows that the presented guided live migration technique selection achieves significantly fewer SLO violations while, at the same time, minimizing the effect of live migration on the infrastructure.

With energy shortages and global climate change leading our concerns these days, the power consumption of datacenters has become a key issue. Obviously, a substantial reduction in energy consumption can be made by powering down servers when they are not in use. This paper aims at designing, implementing and evaluating a Green Scheduling Algorithm integrating a neural network predictor for optimizing server power consumption in Cloud computing. We employ the predictor to predict future load demand based on historical demand. According to the prediction, the algorithm turns off unused servers and restarts them to minimize the number of running servers, thus minimizing the energy use at the points of consumption to benefit all other levels. For evaluation, we perform simulations with two load traces. The results show that the PP20 mode can save up to 46.3% of power consumption with a drop rate of 0.03% on one load trace, and a drop rate of 0.12% with a power reduction rate of 46.7% on the other.

Challenges and security concerns are preventing businesses from consolidating a data center or joining a cloud service. They contribute to the slow progress in shifting workloads from physical servers to virtual machines. There is a big shift in infrastructure which is a challenge for IT professionals. New encryption technologies are needed to support service providers and dynamic data centers. Today data is stored on premises, in the cloud on mobile devices and across a hybrid IT landscape which has compelled a move from traditional approach to adopting and managing encrypted data [1].

It has recently been proposed that Internet energy costs, both monetary and environmental, can be reduced by exploiting temporal variations and shifting processing to data centers located in regions where energy currently has low cost. Lightly loaded data centers can then turn off surplus servers. This paper studies online algorithms for determining the number of servers to leave on in each data center, and then uses these algorithms to study the environmental potential of geographical load balancing (GLB). A commonly suggested algorithm for this setting is "receding horizon control" (RHC), which computes the provisioning for the current time by optimizing over a window of predicted future loads. We show that RHC performs well in a homogeneous setting, in which all servers can serve all jobs equally well; however, we also prove that differences in propagation delays, servers, and electricity prices can cause RHC perform badly, So, we introduce variants of RHC that are guaranteed to perform as well in the face of such heterogeneity. These algorithms are then used to study the feasibility of powering a continent-wide set of data centers mostly by renewable sources, and to understand what portfolio of renewable energy is most effective.

In order to meet the insatiable demands for cloud-based services, today’s data centers may have hundreds of thousands of servers. During the past decade, many topologies have been proposed to interconnect a large number of servers in a data center network. However, existing topologies face several challenges including the rigid structure, high network cost, and packet relay overheads on servers. In this paper, we propose a novel switch-centric network topology, called DCCube, which offers many desirable properties for data center networking. DCCube is a flexible structure because, given the switches degree and the network diameter, it is possible to construct different structures with different number of connected servers, bisection bandwidth and network cost. Furthermore, DCCube can accommodate a large number of servers with high bisection bandwidth, low cost, small diameter and desirable path diversity. By comparing with existing data center network topologies, we show that DCCube achieves both high performance and low network cost. We also design both minimal and non-minimal routing algorithms for the proposed topology. Simulation results reveal that non-minimal routing is preferred for unbalanced traffic patterns. Keywords: Data center network, Network topology, Flexibility, High performance, Routing

The continuous use of Internet services and applications triggers a sustainable growth of data transfer, therefore generating a necessity to develop an efficient and scalable transport platform. A novel-networking concept -Elastic Optical Networks (EONs) -can support bandwidth-demanding traffic from an advent of new services including cloud computing and content delivery networks. In this paper, we focus on dynamic routing of anycast and unicast traffic; anycast refers to cloud computing or content traffic served by data centers. We assume that EONs support various distance-adaptive modulation formats, namely BPSK, QPSK, and m-QAM, where m belongs to {8, 16, 32 and 64}. A higher modulation format consumes less spectrum, which can lead to a smaller blocking probability (BP) in dynamic routing. However, at the same time, a higher modulation format provides a smaller transmission distance, which triggers the need to use regenerators and increases CAPEX/OPEX network costs as well as power consumption. Therefore, the main goal of our research is to find a trade-off between the BP and using extra regenerators in the context of dynamic routing of anycast and unicast traffic in EONs.

We introduce a multi-layer silicon photonic microring resonator filter, fabricated using deposited materials, and transmit up to 12.5-Gb/s error-free data, establishing a novel class of high-performance silicon photonics for advanced photonic NoCs. Furthermore, by leveraging deposited materials, we propose a novel fully-integrated scalable photonic switch architecture for data center networks, sustaining nonblocking 256×256 port size with nanosecond-scale switching times, interconnecting 2,560 server racks with 51.2-Tb/s bisection bandwidth.

Choosing data centers and infrastructures as the site for investigation of the relationship between digital capitalism and nature is strategic. It reveals a complex relationship between urbanization as a planetary scale process linking urban and rural communities, facilitating flows of nature (energy, water, food, waste, etc.) in circulations and metabolisms that reproduce digital capitalism. In the first section, we showed how nature is constructed as both a resource and a greenwashing strategy for data centers. Then, we turned towards the metaphor of layering to understand how data infrastructures are a specific expression of digital capitalism’s secondary circuit at this historical-geographical moment. Growth in data infrastructures, as an expression of digital capitalism’s secondary circuit of fixed capital, is driven by the logic of exploitation of social production through digital means. Everyday life, subjectivity, and social knowledge become reduced to data resources for extraction by digital capitalists. This is the basis of third-wave urbanization, which we discussed in the third section, highlighting how this mode of capital accumulation is leading to new fractures and inequalities in cities like Seattle.

With the explosion of data in applications all around us, erasure coded storage has emerged as an attractive alternative to replication because even with significantly lower storage overhead, they provide better reliability against data loss. Reed-Solomon code is the most widely used erasure code because it provides maximum reliability for a given storage overhead and is flexible in the choice of coding parameters that determine the achievable reliability. However, reconstruction time for unavailable data becomes prohibitively long mainly because of network bottlenecks. Some proposed solutions either use additional storage or limit the coding parameters that can be used. In this paper, we propose a novel distributed reconstruction technique, called Partial Parallel Repair (PPR), which divides the reconstruction operation to small partial operations and schedules them on multiple nodes already involved in the data reconstruction. Then a distributed protocol progressively combines these partial results to reconstruct the unavailable data blocks and this technique reduces the network pressure. Theoretically, our technique can complete the network transfer in ⌈(log2(k + 1))⌉ time, compared to k time needed for a (k, m) Reed-Solomon code. Our experiments show that PPR reduces repair time and degraded read time significantly. Moreover, our technique is compatible with existing erasure codes and does not require any additional storage overhead. We demonstrate this by overlaying PPR on top of two prior schemes, Local Reconstruction Code and Rotated Reed-Solomon code, to gain additional savings in reconstruction time.

ABSTRACT Several studies have proposed job migration over the wide area network (WAN) to reduce the energy of networks of datacenters by taking advantage of different electricity prices and load demands. Each study focuses on only a small subset of network parameters and thus their results may have large errors. For example, datacenters usually have long-term power contracts instead of paying market prices. However, previous work neglects these contracts, thus overestimating the energy savings by 2.3x. We present a comprehensive approach to minimize the energy cost of networks of datacenters by modeling performance of the workloads, power contracts, local renewable energy sources, different routing options for WAN and future router technologies. Our method can reduce the energy cost of datacenters by up to 28%, while reducing the error in the energy cost estimation by 2.6x.

This paper presents a study on the energy efficiency of adaptive optical OFDM in data center's networks. The DSP architecture of the proposed scheme is targeted to an FPGA and achieves more than 70% lower energy consumption compared to conventional OFDM modulation.

Increasing number of cores and clock speeds on a smaller chip area implies more heat dissipation and an ever increasing heat density. This increased heat, in turn, leads to higher cooling cost and occurrence of hot spots. Effective use of dynamic voltage and frequency scaling (DVFS) can help us alleviate this problem. But there is an associated execution time penalty which can get amplified in parallel applications.

Cloud computing means storing and accessing data and programs over the Internet instead of your computer's hard drive. The cloud is just a metaphor for the Internet. The elements involved in cloud computing are clients, data center and distributed server. One of the main problems in cloud computing is load balancing. Balancing the load means to distribute the workload among several nodes evenly so that no single node will be overloaded. Load can be of any type that is it can be CPU load, memory capacity or network load. In this paper we presented an architecture of load balancing and algorithm which will further improve the load balancing problem by minimizing the response time. In this paper, we have proposed the enhanced version of existing regulated load balancing approach for cloud computing by comping the Randomization and greedy load balancing algorithm. To check the performance of proposed approach, we have used the cloud analyst simulator (Cloud Analyst). Through simulat...

Energy costs for data centers continue to rise, already exceeding $15 billion yearly. Sadly much of this power is wasted. Servers are only busy 10–30% of the time on average, but they are often left on, while idle, utilizing 60% or more of peak power when in the idle state. We introduce a dynamic capacity management policy, AutoScale, that greatly reduces the number of servers needed in data centers driven by unpredictable, time-varying load, while meeting response time SLAs. AutoScale scales the data center capacity, adding or removing servers as needed. AutoScale has two key features: (i) it autonomically maintains just the right amount of spare capacity to handle bursts in the request rate; and (ii) it is robust not just to changes in the request rate of real-world traces, but also request size and server efficiency. We evaluate our dynamic capacity management approach via implementation on a 38-server multi-tier data center, serving a web site of the type seen in Facebook or Amazon, with a key-value store workload. We demonstrate that AutoScale vastly improves upon existing dynamic capacity management policies with respect to meeting SLAs and robustness.

In this paper, we present the design and implementation of G-Route (Green Route), an autonomic service routing protocol for constructing energy-efficient service provider paths in collaborative cloud computing architectures. The chief contribution of this work resides in autonomously selecting the optimal set of composite service components sustaining the most efficient energy consumption characteristics among a set of providers for executing a particular consumer service request. The routing protocol processes accountable service energy measurements extracted securely from within the provider sites or data centers using trusted computing technologies and cryptographic mechanisms. By pushing green computing constraints into the service routing protocol decision engine, we can leverage the collaborative cloud computing service model to maximize the energy savings achieved by focusing on a path of providers that executes the service requests instead of directing the green computing efforts towards a single provider site. The major goal of G-Route is to enhance the energy savings in the overall cloud computing infrastructure. The protocol design is deployed in a real cloud computing environment using the Amazon EC2 cloud platform. The analyses of the protocol convergence characteristics, traffic overhead, and resilience demonstrate the capability of the proposed system to significantly reduce the overall energy requirements of collaborative cloud services.

100,000 to 250,000 square feet brings high-density electric loads on the order of tens of megawatts, coming on line in just 12 to 18 months. Of even greater challenge to the utility are the extraordinary measures that such cus-tomers take to ensure that their elec-tric ...

Motivated by increased concern over energy consumption in modern data centers, we propose a new, distributed computing platform called Nano Data Centers (NaDa). NaDa uses ISP-controlled home gateways to provide computing and storage services and adopts a managed peer-to-peer model to form a distributed data center infrastructure. To evaluate the potential for energy savings in NaDa platform we pick Video-on-Demand (VoD) services. We develop an energy consumption model for VoD in traditional and in NaDa data centers and evaluate this model using a large set of empirical VoD access data. We find that even under the most pessimistic scenarios, NaDa saves at least 20% to 30% of the energy compared to traditional data centers. These savings stem from energypreserving properties inherent to NaDa such as the reuse of already committed baseline power on underutilized gateways, the avoidance of cooling costs, and the reduction of network energy consumption as a result of demand and service co-localization in NaDa.

Motivated by increased concern over energy consumption in modern data centers, we propose a new, distributed computing platform called Nano Data Centers (NaDa). NaDa uses ISP-controlled home gateways to provide computing and storage services and adopts a managed peer-to-peer model to form a distributed data center infrastructure. To evaluate the potential for energy savings in NaDa platform we pick Video-on-Demand (VoD) services. We develop an energy consumption model for VoD in traditional and in NaDa data centers and evaluate this model using a large set of empirical VoD access data. We find that even under the most pessimistic scenarios, NaDa saves at least 20% to 30% of the energy compared to traditional data centers. These savings stem from energypreserving properties inherent to NaDa such as the reuse of already committed baseline power on underutilized gateways, the avoidance of cooling costs, and the reduction of network energy consumption as a result of demand and service co-localization in NaDa.

Energy expenditure has become a significant fraction of data center operating costs. Recently, "geographical load balancing" has been suggested to reduce energy cost by exploiting the electricity price differences across regions. However, this reduction of cost can paradoxically increase total energy use.