The EPOC project: Energy Proportional and Opportunistic Computing system

15397International audienceWith the emergence of the Future Internet and the dawning of new IT models such as cloud computing, the usage of data centers (DC), and consequently their power consumption, increase dramatically. Besides the ecological impact, the energy consumption is a predominant criteria for DC providers since it determines the daily cost of their infrastructure. As a consequence, power management becomes one of the main challenges for DC infrastructures and more generally for large-scale distributed systems. In this paper, we present the EPOC project which focuses on optimizing the energy consumption of mono-site DCs connected to the regular electrical grid and to renewable energy sources

Data center resilience assessment : storage, networking and security.

Data centers (DC) are the core of the national cyber infrastructure. With the incredible growth of critical data volumes in financial institutions, government organizations, and global companies, data centers are becoming larger and more distributed posing more challenges for operational continuity in the presence of experienced cyber attackers and occasional natural disasters. The main objective of this research work is to present a new methodology for data center resilience assessment, this methodology consists of: • Define Data center resilience requirements. • Devise a high level metric for data center resilience. • Design and develop a tool to validate and the metric. Since computer networks are an important component in the data center architecture, this research work was extended to investigate computer network resilience enhancement opportunities within the area of routing protocols, redundancy, and server load to minimize the network down time and increase the time period of resisting attacks. Data center resilience assessment is a complex process as it involves several aspects such as: policies for emergencies, recovery plans, variation in data center operational roles, hosted/processed data types and data center architectures. However, in this dissertation, storage, networking and security are emphasized. The need for resilience assessment emerged due to the gap in existing reliability, availability, and serviceability (RAS) measures. Resilience as an evaluation metric leads to better proactive perspective in system design and management. The proposed Data center resilience assessment portal (DC-RAP) is designed to easily integrate various operational scenarios. DC-RAP features a user friendly interface to assess the resilience in terms of performance analysis and speed recovery by collecting the following information: time to detect attacks, time to resist, time to fail and recovery time. Several set of experiments were performed, results obtained from investigating the impact of routing protocols, server load balancing algorithms on network resilience, showed that using particular routing protocol or server load balancing algorithm can enhance network resilience level in terms of minimizing the downtime and ensure speed recovery. Also experimental results for investigating the use social network analysis (SNA) for identifying important router in computer network showed that the SNA was successful in identifying important routers. This important router list can be used to redundant those routers to ensure high level of resilience. Finally, experimental results for testing and validating the data center resilience assessment methodology using the DC-RAP showed the ability of the methodology quantify data center resilience in terms of providing steady performance, minimal recovery time and maximum resistance-attacks time. The main contributions of this work can be summarized as follows: • A methodology for evaluation data center resilience has been developed. • Implemented a Data Center Resilience Assessment Portal (D$-RAP) for resilience evaluations. • Investigated the usage of Social Network Analysis to Improve the computer network resilience

Virtualization services: scalable methods for virtualizing multicore systems

Multi-core technology is bringing parallel processing capabilities from servers to laptops and even handheld devices. At the same time, platform support for system virtualization is making it easier to consolidate server and client resources, when and as needed by applications. This consolidation is achieved by dynamically mapping the virtual machines on which applications run to underlying physical machines and their processing cores. Low cost processor and I/O virtualization methods efficiently scaled to different numbers of processing cores and I/O devices are key enablers of such consolidation. This dissertation develops and evaluates new methods for scaling virtualization functionality to multi-core and future many-core systems. Specifically, it re-architects virtualization functionality to improve scalability and better exploit multi-core system resources. Results from this work include a self-virtualized I/O abstraction, which virtualizes I/O so as to flexibly use different platforms' processing and I/O resources. Flexibility affords improved performance and resource usage and most importantly, better scalability than that offered by current I/O virtualization solutions. Further, by describing system virtualization as a service provided to virtual machines and the underlying computing platform, this service can be enhanced to provide new and innovative functionality. For example, a virtual device may provide obfuscated data to guest operating systems to maintain data privacy; it could mask differences in device APIs or properties to deal with heterogeneous underlying resources; or it could control access to data based on the ``trust' properties of the guest VM. This thesis demonstrates that extended virtualization services are superior to existing operating system or user-level implementations of such functionality, for multiple reasons. First, this solution technique makes more efficient use of key performance-limiting resource in multi-core systems, which are memory and I/O bandwidth. Second, this solution technique better exploits the parallelism inherent in multi-core architectures and exhibits good scalability properties, in part because at the hypervisor level, there is greater control in precisely which and how resources are used to realize extended virtualization services. Improved control over resource usage makes it possible to provide value-added functionalities for both guest VMs and the platform. Specific instances of virtualization services described in this thesis are the network virtualization service that exploits heterogeneous processing cores, a storage virtualization service that provides location transparent access to block devices by extending the functionality provided by network virtualization service, a multimedia virtualization service that allows efficient media device sharing based on semantic information, and an object-based storage service with enhanced access control.Ph.D.Committee Chair: Schwan, Karsten; Committee Member: Ahamad, Mustaq; Committee Member: Fujimoto, Richard; Committee Member: Gavrilovska, Ada; Committee Member: Owen, Henry; Committee Member: Xenidis, Jim

Shingled Magnetic Recording disks for Mass Storage Systems

Disk drives have seen a dramatic increase in storage density over the last five decades, but to continue the growth seems difficult if not impossible because of physical limitations. One way to increase storage density is using a shingled magnetic recording (SMR) disk. Shingled writing is a promising technique that trades off the inability to update in-place for narrower tracks and thus a much higher data density. It is particularly appealing as it can be adopted while utilizing essentially the same physical recording mechanisms currently in use. Because of its manner of writing, an SMR disk would be unable to update a written track without overwriting neighboring tracks, potentially requiring the rewrite of all the tracks to the end of a band where the end of a band is an area left unwritten to allow for a non-overlapped final track. Random reads are still possible on such devices, but the handling of writes becomes particularly critical. In this manuscript, we first look at a variety of potential workloads, drawn from real-world traces, and evaluate their impact on SMR disk models. Later, we evaluate the behavior of SMR disks when used in an array configuration or when faced with heavily interleaved workloads. Specifically, we demonstrate the dramatically different effects that different workloads can have upon the opposing approaches of remapping and restoring blocks, and how write-heavy workloads can (under the right conditions, and contrary to intuition) result in a performance advantage for an SMR disk

Reliable and randomized data distribution strategies for large scale storage systems

The ever-growing amount of data requires highly scalable storage solutions. The most flexible approach is to use storage pools that can be expanded and scaled down by adding or removing storage devices. To make this approach usable, it is necessary to provide a solution to locate data items in such a dynamic environment. This paper presents and evaluates the Random Slicing strategy, which incorporates lessons learned from table-based, rule-based, and pseudo-randomized hashing strategies and is able to provide a simple and efficient strategy that scales up to handle exascale data. Random Slicing keeps a small table with information about previous storage system insert and remove operations, drastically reducing the required amount of randomness while delivering a perfect load distribution.Peer ReviewedPostprint (author’s final draft

Network Optimizations for Distributed Storage Networks

Distributed file systems enable the reliable storage of exabytes of information on thousands of servers distributed throughout a network. These systems achieve reliability and performance by storing three or more copies of data in different locations across the network. The management of these copies of data is commonly handled by intermediate servers that track and coordinate the placement of data in the network. This introduces potential network bottlenecks, as multiple transfers to fast storage nodes can saturate the network links connecting intermediate servers to the storage. The advent of open Network Operating Systems presents an opportunity to alleviate this bottleneck, as it is now possible to treat network elements as intermediate nodes in this distributed file system and have them perform the task of replicating data across storage nodes. In this thesis, we propose a new design paradigm for distributed file systems, driven by a new fundamental component of the system which runs on network elements such as switches or routers. We describe the component’s architecture and how it can be integrated into existing distributed file systems to increase their performance. To measure this performance increase over current approaches, we emulate a distributed file system by creating a block-level storage array distributed across multiple iSCSI targets presented in a network. Furthermore we emulate more complicated redundancy schemes likely to be used in distributed file systems in the future to determine what effect this approach may have on those systems and what benefits it offers. We find that this new component offers a decrease in request latency proportional to the number of storage nodes involved in the request. We also find that the benefits of this approach are limited by the ability of switch hardware to process incoming data from the request, but that these limitations can be surmounted through the proposed design paradigm

Software Defined Application Delivery Networking

In this thesis we present the architecture, design, and prototype implementation details of AppFabric. AppFabric is a next generation application delivery platform for easily creating, managing and controlling massively distributed and very dynamic application deployments that may span multiple datacenters. Over the last few years, the need for more flexibility, finer control, and automatic management of large (and messy) datacenters has stimulated technologies for virtualizing the infrastructure components and placing them under software-based management and control; generically called Software-defined Infrastructure (SDI). However, current applications are not designed to leverage this dynamism and flexibility offered by SDI and they mostly depend on a mix of different techniques including manual configuration, specialized appliances (middleboxes), and (mostly) proprietary middleware solutions together with a team of extremely conscientious and talented system engineers to get their applications deployed and running. AppFabric, 1) automates the whole control and management stack of application deployment and delivery, 2) allows application architects to define logical workflows consisting of application servers, message-level middleboxes, packet-level middleboxes and network services (both, local and wide-area) composed over application-level routing policies, and 3) provides the abstraction of an application cloud that allows the application to dynamically (and automatically) expand and shrink its distributed footprint across multiple geographically distributed datacenters operated by different cloud providers. The architecture consists of a hierarchical control plane system called Lighthouse and a fully distributed data plane design (with no special hardware components such as service orchestrators, load balancers, message brokers, etc.) called OpenADN . The current implementation (under active development) consists of ~10000 lines of python and C code. AppFabric will allow applications to fully leverage the opportunities provided by modern virtualized Software-Defined Infrastructures. It will serve as the platform for deploying massively distributed, and extremely dynamic next generation application use-cases, including: Internet-of-Things/Cyber-Physical Systems: Through support for managing distributed gather-aggregate topologies common to most Internet-of-Things(IoT) and Cyber-Physical Systems(CPS) use-cases. By their very nature, IoT and CPS use cases are massively distributed and have different levels of computation and storage requirements at different locations. Also, they have variable latency requirements for their different distributed sites. Some services, such as device controllers, in an Iot/CPS application workflow may need to gather, process and forward data under near-real time constraints and hence need to be as close to the device as possible. Other services may need more computation to process aggregated data to drive long term business intelligence functions. AppFabric has been designed to provide support for such very dynamic, highly diversified and massively distributed application use-cases. Network Function Virtualization: Through support for heterogeneous workflows, application-aware networking, and network-aware application deployments, AppFabric will enable new partnerships between Application Service Providers (ASPs) and Network Service Providers (NSPs). An application workflow in AppFabric may comprise of application services, packet and message-level middleboxes, and network transport services chained together over an application-level routing substrate. The Application-level routing substrate allows policy-based service chaining where the application may specify policies for routing their application traffic over different services based on application-level content or context. Virtual worlds/multiplayer games: Through support for creating, managing and controlling dynamic and distributed application clouds needed by these applications. AppFabric allows the application to easily specify policies to dynamically grow and shrink the application\u27s footprint over different geographical sites, on-demand. Mobile Apps: Through support for extremely diversified and very dynamic application contexts typical of such applications. Also, AppFabric provides support for automatically managing massively distributed service deployment and controlling application traffic based on application-level policies. This allows mobile applications to provide the best Quality-of-Experience to its users without This thesis is the first to handle and provide a complete solution for such a complex and relevant architectural problem that is expected to touch each of our lives by enabling exciting new application use-cases that are not possible today. Also, AppFabric is a non-proprietary platform that is expected to spawn lots of innovations both in the design of the platform itself and the features it provides to applications. AppFabric still needs many iterations, both in terms of design and implementation maturity. This thesis is not the end of journey for AppFabric but rather just the beginning