https://www.cse.wustl.edu/~lu/papers/jsa14-virtualization.pdf

Challenges in real-time virtualization and predictable cloud computing

Distributed data aggregation is an important task, allowing the decentralized determination of meaningful global properties, which can then be used to direct the execution of other applications. The resulting values are derived by the distributed computation of functions like Count , Sum , and Average . Some application examples deal with the determination of the network size, total storage capacity, average load, majorities and many others. In the last decade, many different approaches have been proposed, with different trade-offs in terms of accuracy, reliability, message and time complexity. Due to the considerable amount and variety of aggregation algorithms, it can be difficult and time consuming to determine which techniques will be more appropriate to use in specific settings, justifying the existence of a survey to aid in this task. This work reviews the state of the art on distributed data aggregation algorithms, providing three main contributions. First, it formally defines the concept of aggregation, characterizing the different types of aggregation functions. Second, it succinctly describes the main aggregation techniques, organizing them in a taxonomy. Finally, it provides some guidelines toward the selection and use of the most relevant techniques, summarizing their principal characteristics.

/pdf/a-survey-of-distributed-data-aggregation-algorithms-16eiyu3imz.pdf

A Survey of Distributed Data Aggregation Algorithms

Given the continually increasing amount of commercial Cloud services in the market, evaluation of different services plays a significant role in cost-benefit analysis or decision making for choosing Cloud Computing In particular, employing suitable metrics is essential in evaluation implementations However, to the best of our knowledge, there is not any systematic discussion about metrics for evaluating Cloud services By using the method of Systematic Literature Review (SLR), we have collected the de facto metrics adopted in the existing Cloud services evaluation work The collected metrics were arranged following different Cloud service features to be evaluated, which essentially constructed an evaluation metrics catalogue, as shown in this paper This metrics catalogue can be used to facilitate the future practice and research in the area of Cloud services evaluation Moreover, considering metrics selection is a prerequisite of benchmark selection in evaluation implementations, this work also supplements the existing research in benchmarking the commercial Cloud services

pdf/on-a-catalogue-of-metrics-for-evaluating-commercial-cloud-6h25jzosgl.pdf

On a Catalogue of Metrics for Evaluating Commercial Cloud Services

Peer-to-peer (P2P) video streaming is an emerging technology that reduces the barrier to stream live events over the Internet. Unfortunately, satisfying soft real-time constraints on the delay between the generation of the stream and its actual delivery to users is still a challenging problem. Bottlenecks in the available upload bandwidth, both at the media source and inside the overlay network, may limit the quality of service (QoS) experienced by users. A potential solution for this problem is assisting the P2P streaming network by a cloud computing infrastructure to guarantee a minimum level of QoS. In such approach, rented cloud resources (helpers) are added on demand to the overlay, to increase the amount of total available bandwidth and the probability of receiving the video on time. Hence, the problem to be solved becomes minimizing the economical cost, provided that a set of constraints on QoS is satisfied. The main contribution of this paper is CLIVE, a cloud-assisted P2P live streaming system that demonstrates the feasibility of these ideas. CLIVE estimates the available capacity in the system through a gossip-based aggregation protocol and provisions the required resources from the cloud to guarantee a given level of QoS at low cost. We perform extensive simulations and evaluate CLIVE using large-scale experiments under dynamic realistic settings.

/pdf/clive-cloud-assisted-p2p-live-streaming-ueiblc86yj.pdf

CLive: Cloud-assisted P2P live streaming

http://romisatriawahono.net/lecture/rm/survey/network%20security/Li%20-%20Commercial%20Cloud%20Services%20-%202013.pdf

On evaluating commercial Cloud services

To enable decentralised actions in very large distributed systems, it is often important to provide the nodes with global knowledge about the values of attributes across all nodes. This paper shows how, given an attribute whose values are distributed across a large decentralised system, each node can efficiently estimate the statistical distribution of these values. Simulations using heavily skewed real-world node attribute distributions show that our estimation methods outperform the state-of-the-art heuristics by an order of magnitude with an average error of 0.05% and a maximum error of 2%. To obtain this accuracy, each node sends on average just 120 kB of data independent of the system size. Our algorithms also achieve this accuracy in the presence of heavy churn of system membership. Furthermore, our algorithm enables self-tuning by continuously estimating the accuracy of its own distribution approximation.

/pdf/adam2-reliable-distribution-estimation-in-decentralised-28xnitwynj.pdf

Adam2: Reliable Distribution Estimation in Decentralised Environments

Many large-scale utility computing infrastructures comprise heterogeneous hardware and software resources. This raises the need for scalable resource selection services, which identify resources that match application requirements, and can potentially be assigned to these applications. We present a fully decentralized resource selection algorithm by which resources autonomously select themselves when their attributes match a query. An application specifies what it expects from a resource by means of a conjunction of (attribute, value-range) pairs, which are matched against the attribute values of resources. We show that our solution scales in the number of resources as well as in the number of attributes, while being relatively insensitive to churn and other membership changes such as node failures.

/pdf/autonomous-resource-selection-for-decentralized-utility-pdv8laeo6f.pdf

Autonomous Resource Selection for Decentralized Utility Computing

We investigate the effects of shared resources for high-performance computing in a commercial cloud environment where multiple virtual machines share a single hardware node. Although good performance is occasionally obtained, contention degrades the expected performance and introduces significant variance. Using the DGEMM kernel and the HPL benchmark, we show that the underutilization of resources considerably improves expected performance by reducing contention for the CPU and cache space. For instance, for some cluster configurations, the solution is reached almost an order of magnitude earlier on average when the available resources are underutilized. The performance benefits for single node computations are even more impressive: Underutilization improves the expected execution time by two orders of magnitude. Finally, in contrast to unshared clusters, extending underutilized clusters by adding more nodes often improves the execution time due to an increased parallelism even with a slow interconnect. In the best case, by underutilizing the nodes performance was improved enough to entirely offset the cost of an extra node in the cluster.

Improving high-performance computations on clouds through resource underutilization

Cloud computing is currently based on hardware virtualization wherein a host operating system provides a virtual machine interface nearly identical to that of physical hardware to guest operating systems. Full transparency allows backward compatibility with legacy software but introduces unpredictability at the guest operating system (OS) level. The time perceived by the guest OS is non-linear. As a consequence, it is difficult to run real-time or latency-sensitive applications in the cloud. In this paper we describe an alternative approach to cloud computing where we run all user applications on top of a single cloud operating system called Osprey. Osprey allows dependable, predictable, and real-time computing by consistently managing all system resources and exporting relevant information to the applications. Osprey ensures compatibility with legacy software through OS emulation provided by libraries and by porting runtime environments. Osprey's resource containers fully specify constraints between applications to enforce full application isolation for real-time execution guarantees. Osprey pushes much of the state out of the kernel into user applications for several benefits: full application accounting, mobility support, and efficient networking. Using a kernel-based packet filter, Osprey dispatches incoming packets to the user application as quickly as possible, eliminating the kernel from the critical path. A real-time scheduler then decides on the priority and order in which applications process their incoming packets while maintaining the limits set forth in the resource container. We have implemented a mostly complete Osprey prototype for the x86 architecture and we plan to port it to ARM and PowerPC and to develop a Linux library OS.

Osprey: Operating system for predictable clouds

The growing end-user demand for video services with superior quality on laptops, tablets, and smartphones spurs the deployment of telco content distribution networks (CDNs). Such CDNs provide scalable and bandwidth-efficient video delivery thanks to disk-packed cache servers deployed in the telco's data centers near the clients. However, a sustainable growth of these CDNs may be hindered by their lack of energy proportionality. In this paper we propose to apply dynamic power management (DPM) to the CDN's cache servers and their disks to increase the CDN's energy efficiency. We evaluate DPM using a CDN energy simulator driven by HTTP adaptive-streaming workload traces recorded by an operational CDN delivering IPTV to mobile devices. Even for a minimally-provisioned CDN, we observe a reduction of the energy dissipation by approximately 30% thanks to large cyclic load fluctuations characteristic of IPTV delivery.

Minimizing Energy Dissipation in Content Distribution Networks Using Dynamic Power Management

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