20,603 research outputs found
Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks with Mobile Users
In this paper, the problem of proactive caching is studied for cloud radio
access networks (CRANs). In the studied model, the baseband units (BBUs) can
predict the content request distribution and mobility pattern of each user,
determine which content to cache at remote radio heads and BBUs. This problem
is formulated as an optimization problem which jointly incorporates backhaul
and fronthaul loads and content caching. To solve this problem, an algorithm
that combines the machine learning framework of echo state networks with
sublinear algorithms is proposed. Using echo state networks (ESNs), the BBUs
can predict each user's content request distribution and mobility pattern while
having only limited information on the network's and user's state. In order to
predict each user's periodic mobility pattern with minimal complexity, the
memory capacity of the corresponding ESN is derived for a periodic input. This
memory capacity is shown to be able to record the maximum amount of user
information for the proposed ESN model. Then, a sublinear algorithm is proposed
to determine which content to cache while using limited content request
distribution samples. Simulation results using real data from Youku and the
Beijing University of Posts and Telecommunications show that the proposed
approach yields significant gains, in terms of sum effective capacity, that
reach up to 27.8% and 30.7%, respectively, compared to random caching with
clustering and random caching without clustering algorithm.Comment: Accepted in the IEEE Transactions on Wireless Communication
Uncovering Bugs in Distributed Storage Systems during Testing (not in Production!)
Testing distributed systems is challenging due to multiple sources of nondeterminism. Conventional testing techniques, such as unit, integration and stress testing, are ineffective in preventing serious but subtle bugs from reaching production. Formal techniques, such as TLA+, can only verify high-level specifications of systems at the level of logic-based models, and fall short of checking the actual executable code. In this paper, we present a new methodology for testing distributed systems. Our approach applies advanced systematic testing techniques to thoroughly check that the executable code adheres to its high-level specifications, which significantly improves coverage of important system behaviors. Our methodology has been applied to three distributed storage systems in the Microsoft Azure cloud computing platform. In the process, numerous bugs were identified, reproduced, confirmed and fixed. These bugs required a subtle combination of concurrency and failures, making them extremely difficult to find with conventional testing techniques. An important advantage of our approach is that a bug is uncovered in a small setting and witnessed by a full system trace, which dramatically increases the productivity of debugging
Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges
Cloud computing is offering utility-oriented IT services to users worldwide.
Based on a pay-as-you-go model, it enables hosting of pervasive applications
from consumer, scientific, and business domains. However, data centers hosting
Cloud applications consume huge amounts of energy, contributing to high
operational costs and carbon footprints to the environment. Therefore, we need
Green Cloud computing solutions that can not only save energy for the
environment but also reduce operational costs. This paper presents vision,
challenges, and architectural elements for energy-efficient management of Cloud
computing environments. We focus on the development of dynamic resource
provisioning and allocation algorithms that consider the synergy between
various data center infrastructures (i.e., the hardware, power units, cooling
and software), and holistically work to boost data center energy efficiency and
performance. In particular, this paper proposes (a) architectural principles
for energy-efficient management of Clouds; (b) energy-efficient resource
allocation policies and scheduling algorithms considering quality-of-service
expectations, and devices power usage characteristics; and (c) a novel software
technology for energy-efficient management of Clouds. We have validated our
approach by conducting a set of rigorous performance evaluation study using the
CloudSim toolkit. The results demonstrate that Cloud computing model has
immense potential as it offers significant performance gains as regards to
response time and cost saving under dynamic workload scenarios.Comment: 12 pages, 5 figures,Proceedings of the 2010 International Conference
on Parallel and Distributed Processing Techniques and Applications (PDPTA
2010), Las Vegas, USA, July 12-15, 201
Extending Demand Response to Tenants in Cloud Data Centers via Non-intrusive Workload Flexibility Pricing
Participating in demand response programs is a promising tool for reducing
energy costs in data centers by modulating energy consumption. Towards this
end, data centers can employ a rich set of resource management knobs, such as
workload shifting and dynamic server provisioning. Nonetheless, these knobs may
not be readily available in a cloud data center (CDC) that serves cloud
tenants/users, because workloads in CDCs are managed by tenants themselves who
are typically charged based on a usage-based or flat-rate pricing and often
have no incentive to cooperate with the CDC operator for demand response and
cost saving. Towards breaking such "split incentive" hurdle, a few recent
studies have tried market-based mechanisms, such as dynamic pricing, inside
CDCs. However, such mechanisms often rely on complex designs that are hard to
implement and difficult to cope with by tenants. To address this limitation, we
propose a novel incentive mechanism that is not dynamic, i.e., it keeps pricing
for cloud resources unchanged for a long period. While it charges tenants based
on a Usage-based Pricing (UP) as used by today's major cloud operators, it
rewards tenants proportionally based on the time length that tenants set as
deadlines for completing their workloads. This new mechanism is called
Usage-based Pricing with Monetary Reward (UPMR). We demonstrate the
effectiveness of UPMR both analytically and empirically. We show that UPMR can
reduce the CDC operator's energy cost by 12.9% while increasing its profit by
4.9%, compared to the state-of-the-art approaches used by today's CDC operators
to charge their tenants
Dynamic, Latency-Optimal vNF Placement at the Network Edge
Future networks are expected to support low-latency, context-aware and user-specific services in a highly flexible and efficient manner. One approach to support emerging use cases such as, e.g., virtual reality and in-network image processing is to introduce virtualized network functions (vNF)s at the edge of the network, placed in close proximity to the end users to reduce end-to-end latency, time-to-response, and unnecessary utilisation in the core network. While placement of vNFs has been studied before, it has so far mostly focused on reducing the utilisation of server resources (i.e., minimising the number of servers required in the network to run a specific set of vNFs), and not taking network conditions into consideration such as, e.g., end-to-end latency, the constantly changing network dynamics, or user mobility patterns. In this paper, we formulate the Edge vNF placement problem to allocate vNFs to a distributed edge infrastructure, minimising end-to-end latency from all users to their associated vNFs. We present a way to dynamically re-schedule the optimal placement of vNFs based on temporal network-wide latency fluctuations using optimal stopping theory. We then evaluate our dynamic scheduler over a simulated nation-wide backbone network using real-world ISP latency characteristics. We show that our proposed dynamic placement scheduler minimises vNF migrations compared to other schedulers (e.g., periodic and always-on scheduling of a new placement), and offers Quality of Service guarantees by not exceeding a maximum number of latency violations that can be tolerated by certain applications
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